Modeling, Simulation and Visualization Methods [1 ed.] 9781683921974

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PROCEEDINGS OF THE 2017 INTERNATIONAL CONFERENCE ON MODELING, SIMULATION & VISUALIZATION METHODS

Editors Hamid R. Arabnia Leonidas Deligiannidis, Fernando G. Tinetti Associate Editors Ashu M. G. Solo, Jane You

CSCE’17 July 17-20, 2017 Las Vegas Nevada, USA americancse.org ©

CSREA Press

This volume contains papers presented at The 2017 International Conference on Modeling, Simulation & Visualization Methods (MSV'17). Their inclusion in this publication does not necessarily constitute endorsements by editors or by the publisher.

Copyright and Reprint Permission Copying without a fee is permitted provided that the copies are not made or distributed for direct commercial advantage, and credit to source is given. Abstracting is permitted with credit to the source. Please contact the publisher for other copying, reprint, or republication permission.

© Copyright 2017 CSREA Press ISBN: 1-60132-465-0 Printed in the United States of America

Foreword It gives us great pleasure to introduce this collection of papers to be presented at the 2017 International Conference on Modeling, Simulation and Visualization Methods (MSV’17), July 17-20, 2017, at Monte Carlo Resort, Las Vegas, USA. An important mission of the World Congress in Computer Science, Computer Engineering, and Applied Computing, CSCE (a federated congress to which this conference is affiliated with) includes "Providing a unique platform for a diverse community of constituents composed of scholars, researchers, developers, educators, and practitioners. The Congress makes concerted effort to reach out to participants affiliated with diverse entities (such as: universities, institutions, corporations, government agencies, and research centers/labs) from all over the world. The congress also attempts to connect participants from institutions that have teaching as their main mission with those who are affiliated with institutions that have research as their main mission. The congress uses a quota system to achieve its institution and geography diversity objectives." By any definition of diversity, this congress is among the most diverse scientific meeting in USA. We are proud to report that this federated congress has authors and participants from 64 different nations representing variety of personal and scientific experiences that arise from differences in culture and values. As can be seen (see below), the program committee of this conference as well as the program committee of all other tracks of the federated congress are as diverse as its authors and participants. The program committee would like to thank all those who submitted papers for consideration. About 65% of the submissions were from outside the United States. Each submitted paper was peer-reviewed by two experts in the field for originality, significance, clarity, impact, and soundness. In cases of contradictory recommendations, a member of the conference program committee was charged to make the final decision; often, this involved seeking help from additional referees. In addition, papers whose authors included a member of the conference program committee were evaluated using the double-blinded review process. One exception to the above evaluation process was for papers that were submitted directly to chairs/organizers of pre-approved sessions/workshops; in these cases, the chairs/organizers were responsible for the evaluation of such submissions. The overall paper acceptance rate for regular papers was 25%; 19% of the remaining papers were accepted as poster papers (at the time of this writing, we had not yet received the acceptance rate for a couple of individual tracks.) We are very grateful to the many colleagues who offered their services in organizing the conference. In particular, we would like to thank the members of Program Committee of MSV’17, members of the congress Steering Committee, and members of the committees of federated congress tracks that have topics within the scope of MSV. Many individuals listed below, will be requested after the conference to provide their expertise and services for selecting papers for publication (extended versions) in journal special issues as well as for publication in a set of research books (to be prepared for publishers including: Springer, Elsevier, BMC journals, and others). • • • • • •

Prof. Nizar Al-Holou (Congress Steering Committee); Professor and Chair, Electrical and Computer Engineering Department; Vice Chair, IEEE/SEM-Computer Chapter; University of Detroit Mercy, Detroit, Michigan, USA Prof. Hamid R. Arabnia (Congress Steering Committee); Graduate Program Director (PhD, MS, MAMS); The University of Georgia, USA; Editor-in-Chief, Journal of Supercomputing (Springer);Fellow, Center of Excellence in Terrorism, Resilience, Intelligence & Organized Crime Research (CENTRIC). Prof. Dr. Juan-Vicente Capella-Hernandez; Universitat Politecnica de Valencia (UPV), Department of Computer Engineering (DISCA), Valencia, Spain Prof. Juan Jose Martinez Castillo; Director, The Acantelys Alan Turing Nikola Tesla Research Group and GIPEB, Universidad Nacional Abierta, Venezuela Prof. Kevin Daimi (Congress Steering Committee); Director, Computer Science and Software Engineering Programs, Department of Mathematics, Computer Science and Software Engineering, University of Detroit Mercy, Detroit, Michigan, USA Prof. Zhangisina Gulnur Davletzhanovna (IPCV); Vice-rector of the Science, Central-Asian University, Kazakhstan, Almaty, Republic of Kazakhstan; Vice President of International Academy of Informatization, Kazskhstan, Almaty, Republic of Kazakhstan

• • • • • • • • • • • • • • • • • • •

• •

Prof. Leonidas Deligiannidis (Congress Steering Committee); Department of Computer Information Systems, Wentworth Institute of Technology, Boston, Massachusetts, USA; Visiting Professor, MIT, USA Prof. Mary Mehrnoosh Eshaghian-Wilner (Congress Steering Committee); Professor of Engineering Practice, University of Southern California, California, USA; Adjunct Professor, Electrical Engineering, University of California Los Angeles, Los Angeles (UCLA), California, USA Prof. Byung-Gyu Kim (Congress Steering Committee); Multimedia Processing Communications Lab.(MPCL), Department of Computer Science and Engineering, College of Engineering, SunMoon University, South Korea Prof. Dr. Guoming Lai; Computer Science and Technology, Sun Yat-Sen University, Guangzhou, P. R. China Prof. Hyo Jong Lee (IPCV); Director, Center for Advanced Image and Information Technology, Division of Computer Science and Engineering, Chonbuk National University, South Korea Dr. Muhammad Naufal Bin Mansor; Faculty of Engineering Technology, Department of Electrical, Universiti Malaysia Perlis (UniMAP), Perlis, Malaysia Dr. Andrew Marsh (Congress Steering Committee); CEO, HoIP Telecom Ltd (Healthcare over Internet Protocol), UK; Secretary General of World Academy of BioMedical Sciences and Technologies (WABT) a UNESCO NGO, The United Nations Prof. Aree Ali Mohammed; Head, Computer Science Department, University of Sulaimani, Kurdistan Region, Iraq Dr. Ali Mostafaeipour; Industrial Engineering Department, Yazd University, Yazd, Iran Prof. Dr., Eng. Robert Ehimen Okonigene (Congress Steering Committee); Department of Electrical & Electronics Engineering, Faculty of Engineering and Technology, Ambrose Alli University, Edo State, Nigeria Prof. James J. (Jong Hyuk) Park (Congress Steering Committee); Department of Computer Science and Engineering (DCSE), SeoulTech, Korea; President, FTRA, EiC, HCIS Springer, JoC, IJITCC; Head of DCSE, SeoulTech, Korea Prof. Dr. R. Ponalagusamy; Department of Mathematics, National Institute of Technology, India Dr. Xuewei Qi; Research Faculty & PI, Center for Environmental Research and Technology, University of California, Riverside, California, USA Dr. Akash Singh (Congress Steering Committee); IBM Corporation, Sacramento, California, USA; Chartered Scientist, Science Council, UK; Fellow, British Computer Society; Member, Senior IEEE, AACR, AAAS, and AAAI; IBM Corporation, USA Ashu M. G. Solo (Publicity), Fellow of British Computer Society, Principal/R&D Engineer, Maverick Technologies America Inc. Prof. Dr. Ir. Sim Kok Swee; Fellow, IEM; Senior Member, IEEE; Faculty of Engineering and Technology, Multimedia University, Melaka, Malaysia Prof. Fernando G. Tinetti (Congress Steering Committee); School of CS, Universidad Nacional de La Plata, La Plata, Argentina; Co-editor, Journal of Computer Science and Technology (JCS&T). Dr. Haoxiang Harry Wang (CSCE); Cornell University, Ithaca, New York, USA; Founder and Director, GoPerception Laboratory, New York, USA Prof. Shiuh-Jeng Wang (Congress Steering Committee); Director of Information Cryptology and Construction Laboratory (ICCL) and Director of Chinese Cryptology and Information Security Association (CCISA); Department of Information Management, Central Police University, Taoyuan, Taiwan; Guest Ed., IEEE Journal on Selected Areas in Communications. Prof. Layne T. Watson (Congress Steering Committee); Fellow of IEEE; Fellow of The National Institute of Aerospace; Professor of Computer Science, Mathematics, and Aerospace and Ocean Engineering, Virginia Polytechnic Institute & State University, Blacksburg, Virginia, USA Prof. Jane You (Congress Steering Committee); Associate Head, Department of Computing, The Hong Kong Polytechnic University, Kowloon, Hong Kong

We would like to extend our appreciation to the referees, the members of the program committees of individual sessions, tracks, and workshops; their names do not appear in this document; they are listed on the web sites of individual tracks. As Sponsors-at-large, partners, and/or organizers each of the followings (separated by semicolons) provided help for at least one track of the Congress: Computer Science Research, Education, and Applications Press (CSREA); US Chapter of World Academy of Science; American Council on Science & Education & Federated Research Council (http://www.americancse.org/); HoIP, Health Without Boundaries, Healthcare over Internet Protocol, UK (http://www.hoip.eu); HoIP Telecom, UK (http://www.hoip-telecom.co.uk); and WABT, Human Health Medicine, UNESCO NGOs, Paris, France

(http://www.thewabt.com/ ). In addition, a number of university faculty members and their staff (names appear on the cover of the set of proceedings), several publishers of computer science and computer engineering books and journals, chapters and/or task forces of computer science associations/organizations from 3 regions, and developers of high-performance machines and systems provided significant help in organizing the conference as well as providing some resources. We are grateful to them all. We express our gratitude to keynote, invited, and individual conference/tracks and tutorial speakers - the list of speakers appears on the conference web site. We would also like to thank the followings: UCMSS (Universal Conference Management Systems & Support, California, USA) for managing all aspects of the conference; Dr. Tim Field of APC for coordinating and managing the printing of the proceedings; and the staff of Monte Carlo Resort (Convention department) at Las Vegas for the professional service they provided. Last but not least, we would like to thank the Co-Editors of MSV’17: Prof. Hamid R. Arabnia, Prof. Leonidas Deligiannidis, and Prof. Fernando G. Tinetti. We present the proceedings of MSV’17.

Steering Committee, 2017 http://americancse.org/

Contents SESSION: SIMULATION, TOOLS AND APPLICATIONS Generating Strongly Connected Random Graphs Peter Maurer

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Simulating Virtual Memory Allocations using SPEC Tools in Microsoft Hyper-V Clouds John M. Medellin, Lokesh Budhi

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Autonomously Battery Charging Tires For EVs Using Piezoelectric Phenomenon Muhammad Kamran, Raziq Yaqub, Azzam ul Asar

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Traffic Re-Direction Simulation During a Road Disaster/Collapse on Toll Road 408 in Florida Craig Tidwell

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SESSION: MODELING, VISUALIZATION AND NOVEL APPLICATIONS Performance Enhancement and Prediction Model of Concurrent Thread Execution in JVM Khondker Shajadul Hasan

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A Stochastic Method for Structural Degradation Modeling Peter Sawka, Sara Boyle, Jeremy Mange

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Increased Realism in Modeling and Simulation for Virtual Reality, Augmented Reality, and Immersive Environments Jeffrey Wallace, Sara Kambouris

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A Toolbox versus a Tool - A Design Approach Hans-Peter Bischof

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Generating Shapes and Colors using Cell Developmental Mechanism Sezin Hwang, Moon-Ryul Jun

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Modeling Business Processes: Events and Compliance Rules Sabah Al-Fedaghi

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Constructing a Takagi-Sugeno Fuzzy Model by a Fuzzy Data Shifter Horng-Lin Shieh, Ying-Kuei Yang

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SESSION: NOVEL ALGORITHMS AND APPLICATIONS + IMAGING SCIENCE + SIGNAL ENHANCEMENT AND WIRELESS INFRASTRUCTURES Estimating Cost of Smart Community Wireless Platforms Sakir Yucel

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Detection of Ultra High Frequency Narrow Band Signal Using Nonuniform Sampling Sung-won Park, Raksha Kestur

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Smart Community Wireless Platforms Sakir Yucel

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Sketch Based Image Retrieval System Based on Block Histogram Matching Kathy Khaing, Sai Maung Maung Zaw, Nyein Aye

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Smart City Wireless Platforms for Smart Cities Sakir Yucel

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Measuring Benefits, Drawbacks and Risks of Smart Community Wireless Platforms Sakir Yucel

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Magnetic Resonance Image Applied to 3-Dimensional Printing Utilizing Various Oils Tyler Hartwig, Zeyu Huang, Sara Martinson, Ritchie Cai, Jeffrey Olafsen, Keith Schubert

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SESSION: POSTER PAPERS The Lithium-ion Battery Cell Analysis using Electrochemical-Thermal Coupled Model Dongchan Lee, Keon-uk Kim, Chang-wan Kim

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Renfred-Okonigene Children Protection System Network: Where Is My Baby? Dorcas Okonigene, Robert Okonigene, Clement Ojieabu

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Generating Strongly Connected Random Graphs Peter M. Maurer Dept. of Computer Science Baylor University Waco, Texas 76798 Abstract – The algorithm presented here is capable of generating strongly connected graphs in a single pass that requires O(n) time. The method is to create a spanning tree that is a directed acyclic graph, and adding a minimal number of edges to make the spanning tree strongly connected. This is done in a way that is completely general. Once the strongly connected spanning tree is created, additional edges can be added to the tree to create an arbitrary strongly connected graph. 1

Introduction One important problem in many types of simulation is creating random data for input to the simulation. Over the years, our need for such data in the simulation of gate-level circuits has led us to create a package for creating many different types of random data [1,2]. Despite its utility, this package has become dated. It was originally intended to generate random data files for input to a simulation program. Recently, we have begun a project to upgrade this package with new features to make it more useful for modern types of programs that do not depend heavily on file-based input, and to generate types of data that are more suitable to modern programs than character strings and simple binary values. One major focus of this activity (there are many) is the generation of random graphs. Graphs can be used to model many real-world phenomena. There are too many applications to mention individually, but see [3] for an example. One new feature of our package is the creation of graph-generation subroutines that can be incorporated into existing software. The graphs are generated internally as adjacency lists and passed, as pointers, to the simulation software. Graph specifications are simple, typically one line, but permit the specification of many different types of random graphs. The most common models are the edged-oriented models, the Gilbert model [4] and the Erdős–Rényi model [5]. The vertex-oriented models, powerlaw [6] and degree-sequence [7] models are also fairly common. This paper will focus on the edge-oriented models. The Gilbert model assigns a probability of p to the existence of any edge, and the Erdős– Rényi model assigns equal probability to all graphs with M edges. For the Gilbert model we generate k vertices and add each edge from the complete graph with probability p . For the Erdős–Rényi model, we sort all edges into random order and choose the first M edges from the sorted list. (The parameters k, i, and M are specified by the user.) The power-law and degree-sequence models are also available in our package, but these are beyond the scope of this paper. Parameters can be used to specify that the graph is directed, or that the graph must be connected, or both. Creating a connected nondirected graph is relatively simple. We generate a spanning tree using Algorithm 1, and then apply either the Gilbert or the Erdős–Rényi model to the remaining edges. The purpose of Algorithm 1 is to create a spanning tree for the graph. A non-directed graph is connected if and

only if a spanning tree exists. Algorithm 1 adds Vertex 0 to the spanning tree, and then adds the remaining vertices by selecting a random vertex from the partial spanning tree as the parent of the new vertex. 1. 2.

Add Vertex 0 to the tree. For each vertex, i, 1 through k  1 a. select a vertex j at random from the existing tree vertices b. Add an edge between i and j. c. Add vertex i to the tree. Algorithm 1. Creating the Spanning Tree. When the graph is directed, simply creating a spanning tree is insufficient because the resultant graph must be strongly connected. The spanning tree is still necessary, because there must be a path from Vertex 0 to every other vertex. When creating a spanning tree for a directed graph, the first step is to modify step 2 of Algorithm 1 so that the new edge proceeds from the tree vertex to the new vertex. This insures that there is a path from Vertex 0 to every other vertex. The resultant tree is a directed acyclic graph with the root of the tree as the only source. The sinks are the leaves of the tree. There are several straightforward methods for making the spanning tree strongly connected. When adding a tree vertex, we could add two edges, one from the tree vertex to the new vertex, and another in the opposite direction. This would make the tree strongly connected, but there would be no way to generate certain types of graphs such as the simple cycle of Figure 1. Another method is to add an edge from each leaf vertex to the root vertex. This is perhaps more general, but graphs such as that pictured in Figure 2 would be impossible to generate.

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Figure 1. A Simple Cycle.

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1. 2. 3. 4.

Function call DFS(i) Set StartTime[i] to GlobalStartTime. // vertex i is first seen Increment GlobalStartTime by 1. j adjacent to vertex i For each vertex a.

Figure 2. Impossible for Leaf-to-Root. It is necessary to be able to generate any strongly connected graph on k vertices. Some attempts have been made to do so using the rejection method [8]. (The rejection method repeatedly generates directed graphs at random, and rejects those that are not strongly connected.) However, the rejection method works well only if the probability of rejection is small. When a sparse strongly connected graph is required, the rejection method is not suitable. Creation of the spanning tree is clearly the starting point for such a procedure, since it must exist. Furthermore, Algorithm 1 is able to generate any spanning tree, up to isomorphism. The next step must be to add additional edges to the graph to make it strongly connected. Furthermore, this should be done in a way that is certain to succeed and adds only a minimal number of edges to the tree. (The Leaf-toRoot method generates the absolute minimum number of edges, but is not suitable because it is not able to generate all strongly connected graphs.) To meet the needs of our generation software, we use a modified version of the Tarjan strongly connected component algorithm [9]. The Tarjan algorithm is capable of identifying any strongly connected graph by performing a single depth first search. The forward edges of the depth first search define a tree which is equivalent to our initial spanning tree. To detect a strongly connected graph, the Tarjan algorithm identifies a minimal set of back and cross edges to insure that the initial spanning tree strongly connected. Rather than detecting such edges (which do not exist in the initial spanning tree) we modify the algorithm to insert such edges where required. We do this in such a way that any suitable set of back or cross edge can be generated. Once the tree has been made strongly connected the Gilbert or Erdős– Rényi model can be applied to the remaining edges. 2

The Tarjan Algorithm To understand our method of inserting edges into the tree it is necessary to understand the principles of the Tarjan algorithm. The mechanism is based on depth first search with computed start times. Algorithm 2 shows the basic depth first search algorithm with the modification points that are used to detect strongly connected components. Start time is an integer in the range [0, n  1] where n is the number of vertices in the graph. The start time of vertex i is a sequential number indicating the order in which vertex i was first seen by the depth first search algorithm. In Algorithm 2, each element of the StartTime array (which is of size n) is initialized to 1 , and GlobalStartTime is initialized to zero. Both are global variables.

If StartTime[j] is equal to up to i 5. // backing up from i Algorithm 2, basic DFS.

1 Call DFS(j) // back

To detect strongly components, the basic DFS algorithm must be modified in three places. First, we add an array of size n named Low. After a vertex has been completely processed, it Low[i] will contain the smallest start time of any vertex that can be reached by following zero or more tree edges from Vertex i, followed by one back edge or cross edge. The following is added after step 2. 2.5. Set Low[i] equal to StartTime[i]. This step indicates that, initially, the lowest reachable start time is the start time of the current vertex. To step 4a we add Low[i] = min(Low[i],Low[j]). If it is possible to get further back than the current value of Low[i] by using tree edges, the new minimal start time recorded. The following step is added after step 4a: b. else if j is not min(Low[i],StartTime[j])

already

in

a

SCC,

Low[i]

=

If the edge (i,j) is a cross edge or a back edge, and it is further back than we have been able to get previously, its start time is recorded. The only problem that arises is when (i,j) is a cross edge to a strongly connected component that has already been identified. Such edges must be ignored. A status-array is normally used to keep track of such vertices. Finally, Step 5 is added to identify strongly connected components. If Low[i] is equal to StartTime[i], then there is no back edge or cross edge that provides a path from Vertex i to its parent. Therefore, Vertex i is in a different strongly connected component than its parent. 5. If Low[i] is equal to StartTime[i] Identify a new SCC. Tarjan’s algorithm also includes a stacking mechanism to identify the vertices belonging to a particular strongly connected component, but because our aim is to create a single strongly connected component, we will not consider this mechanism further. The same is true for the mechanism that tags vertices that have already been assigned to a strongly connected component. The full Tarjan algorithm is given in Algorithm 3.

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Int'l Conf. Modeling, Sim. and Vis. Methods | MSV'17 | 1. 2. 3. 4. 5.

Function call DFS(i) Set StartTime[i] to GlobalStartTime. // vertex i is first seen Set Low[i] equal to StartTime[i] Increment GlobalStartTime by 1. j adjacent to vertex i For each vertex a. b.

If StartTime[j] is equal to 1 Call DFS(j), Low[i] = min(Low[i],Low[j]) j is not already in a SCC, Low[i] = Else if

min(Low[i],StartTime[j]) 6. If Low[i] equals StartTime[i] Identify a new SCC Algorithm 3. The Full Tarjan Algorithm. 3

The modified Tarjan algorithm Our primary modification to the Tarjan algorithm is in Step 6, which identifies new strongly connected components. We wish to prevent this detection from taking place. In Step 6, the algorithm is backing up from Vertex i . All vertices with start times greater than or equal to StartTime[ i ] are in the subtree rooted at Vertex i . All vertices, j , that have start times less than i must meet one of the following three conditions.

5

2.5 Set IST[StartTime[i]] to i In Step 6c, v and w correspond to IST[x] and IST[y], respectively. Step 6e negates the Low[i] equals StartTime[i] condition, since Vertex i is now in the same strongly connected component as its parent. The addition of the new edge may make the Low value for some of the other vertices in the tree rooted at Vertex i incorrect, but since these values will not be accessed after backing up from Vertex i, they do not need to be changed. Once Algorithm 4 has been run, the Gilbert or Erdős–Rényi models can be applied to add additional edges to the graph. The graphs created by Algorithms 1 and 4 are strongly connected and contain no more than 2n  2 edges. Algorithm 1 adds n  1 edges. Algorithm 4 can add at most one edge per vertex, and cannot add an edge to Vertex 0. Algorithm 4 can add edges in any possible way to make the tree strongly connected. When combined with the Gilbert or Erdős–Rényi models any strongly connected graph can be generated. Both Algorithm 1 and Algorithm 4 are O(n). The Gilbert or Erdős–Rényi n2 models n 2  n are both worst-case O( ) because they must consider all 2

1. 2. 3.

Vertex j is an ancestor of Vertex i in the DFS tree. Vertex j has already been assigned to a strongly connected component. Vertex j and Vertex i have a common ancestor k , and k is reachable from j .

Ignoring condition 2, it is clear that in Step 6, that Vertex i will be in the same strongly connected component as its parent if and only if there is an edge from a vertex v with a start time greater than or equal to StartTime[ i ] to a vertex w with a start time less than StartTime[ i ]. We modify Step 6 to insert such an edge when Low[i] is equal to StartTime[i]. Doing this also insures that condition 2 can never occur. Our modified algorithm is given in Algorithm 4. Function call DFS(i) Set StartTime[i] to GlobalStartTime. // vertex i is first seen Set Low[i] equal to StartTime[i] Increment GlobalStartTime by 1. j adjacent to vertex i For each vertex a. If StartTime[j] is equal to 1 Call DFS(j), Low[i] = min(Low[i],Low[j]) b. Low[i] = min(Low[i],StartTime[j]) 6. If Low[i] equals StartTime[i] a. Set x to a random integer in the range [StartTime[i],GlobalStartTime] b. Set y to a random integer in the range [0,StartTime[i]-1] c. Identify the vertices v and w that correspond to the start times x and y. d. Add an edge from v to w. e. Set Low[i] equal to y. Algorithm 4. The Modified Tarjan Algorithm.

potential edges.

Although Algorithms 1 and 4 can generate any strongly connected tree up to isomorphism, there are many isomorphs that will never be generated. For example, Algorithm 1 always adds an edge from Vertex 0 to Vertex 1. If this is a problem, it can be solved by a random relabeling of the vertices after the graph is generated. 4

Experimental Results We ran several experiments to verify the effectiveness of our algorithm. The first experiment was to generate 10,000 5-vertex graphs, using the Gilbert model with p  0 , to verify that the examples of Figures 1 and 2 could be generated. The algorithm generated both these examples, along with many others. Figure 3 contains a sample of the generated graphs.

1. 2. 3. 4. 5.

Step 6c requires the inversion of the function that assigns start times to vertices. This is done in constant time by using an Inverse Start Time (IST) array and the following step following Step 2.

Figure 3. Some Sample Graphs. Figure 3 demonstrates the essentially random nature of the generation process. Despite the fact that there is always an edge between Vertex 0 and Vertex 1, the structure of the graphs is obviously quite random, and they are obviously all strongly connected. This experiment was run using the Gilbert model with edge probability set to zero. This was done to show the structure of the strongly connected tree. Four other experiments were run to determine the performance of the algorithm. The hardware was an Intel 3.40 Ghz core I7-2600 with 4 cores and 8GB of memory, running Linux Red Hat version 3.10. A

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Int'l Conf. Modeling, Sim. and Vis. Methods | MSV'17 | single core was used to run the experiments. The Linux time command was used to obtain the timings. Algorithm 4 was implemented iteratively rather than recursively for increased performance. The results of the experiments are given in Figure 4. The first experiment generated one million five-vertex graphs to show the performance of the algorithm with small graphs. Because the time to generate a small graph is tiny, it was necessary to generate a large number of graphs so that the execution time would be measurable. The other three experiments were designed to show performance with large graphs. A single graph was generated because the time to generate such a graph is measurable. The Gilbert model was used for all four experiments (each edge having the same probability.) For the first experiment, we set the edge probability to .5, but it was necessary to set the edge probability to zero for the other three experiments due to memory requirements. If the edge probability were set to .5 for the one million vertex graph, half a terabyte of RAM (at least) would be required to store the graph. Experiment 1,000,000 5-vertex graphs Gilbert p  .5 One 1,000,000-vertex graph, Gilbert p  0

User time 3.8 seconds .577 seconds

One 10,000,000-vertex graph Gilbert p  0 One 100,000,000-vertex graph Gilbert p  0

We have not yet addressed the vertex-oriented models, powerlaw and degree-sequence. Because each vertex has both an in-degree and an out-degree, it is not clear how to apply these models to directed graphs. It is necessary that the total of the in-degrees equal the total of the out-degrees. One model is to insist that the two degrees be identical for each vertex. Another model is to use the same set of degrees, but randomly distribute them over the vertices. It is also not clear whether the degree distributions should include the strongly connected tree edges, or whether these edges should be considered separately. For some degree distributions, it is not clear that a strongly connected graph even exists. We are currently working on these problems. 6 1. 2.

3.

4.

7.3 seconds 59.3 seconds

Figure 4. Experimental Results. We speculate that it would take about 10 minutes to generate a one billion vertex graph, but 8GB of memory is insufficient to generate a graph of this size. Conclusion The algorithm presented here is simple, easy to implement, and very fast. It can generate any strongly connected graph when used in conjunction with the Gilbert or Erdős–Rényi models, and possible node-relabeling. The algorithm should prove to be a useful tool for the generation of strongly connected graphs in most contexts.

5. 6.

7.

5

8. 9.

References Maurer, P., “Generating Test Data with Enhanced Context-Free Grammars,” IEEE Software, Vol. 7, No. 4, July 1990, pp. 50-55. Maurer, P., “The Design and Implementation of a GrammarBased Data Generator,” Software Practice and Experience, Vol. 22, No. 3, March 1992, pp. 223-244. Calvert, K., Doar, M., Zegura, E., “Modeling Internet topology,” IEEE Communications Magazine, Vol. 35, No. 6, June 1997, pp. 160-163. Gilbert, E., (1959). “Random Graphs” Annals of Mathematical Statistics. Vol. 30, No. 4 1959, pp. 1141–1144. Erdős, P.; Rényi, A., “On Random Graphs. I,” Publicationes Mathematicae, Vol. 6, 1959, pp. 290–297. Aiello, W., Chung, F., Lu, L., “A Random Graph Model for Power Law Graphs,” Experimental Mathematics Vol. 10, No. 1, 2001, pp. 53-66. Chatterjee, S., Diaconis, P., Sly, A., “Random Graphs with a Given Degree Sequence,” The Annals of Applied Probability, Vol. 21, No. 4, 2011, pp. 1400–1435. Devroye, L, Non-Uniform Random Variate Generation, Springer-Verlag, New York, 1986. Tarjan, R., “Depth-first search and linear graph algorithms,” SIAM Journal on Computing, Vol. 1, No. 2, 1972, pp. 146–160,

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Simulating Virtual Memory Allocations Using SPEC Tools in Microsoft Hyper-V Clouds John M. Medellin*, Lokesh Budhi Associate Professor and Graduate Assistant at the Master of Science in Information Systems University of Mary-Hardin Baylor Belton, TX 76513-2599, USA Abstract— Private Clouds are gaining in popularity with small to medium sized businesses. By implementing a virtualized architecture, a company can gain strategic advantage through higher utilization of their technology assets. One of the first steps in determining if the virtualized architecture will make sense is to estimate the amount of resources required to actually create a private cloud. A risky approach is to take the applications that are executing and try to run them on a cloud. This option could turn out to be costly since key legacy applications are tightly coupled and in order to run the experiment one might need to move the entire system over (build the entire Cloud and port them). A second approach could be to model key parts of the system and test them with empirical models. This could also be costly and risky if key characteristics are erroneously estimated or omitted. Perhaps a better approach could be to use an industry simulation that can predict the usage patterns of similar systems and be configured to resemble workloads in production today. This paper executes simulations both on bare metal and within the Microsoft Cloud Stack (Windows 10, Windows Server 2012 R2 and Windows Hyper-V 2016) using the industry standard SPECjbb2015 simulation environment. We focus on measurement of incremental memory allocation and report throughput differences from two bare metal architectures (Windows 10 and Windows Server 2012 R2) to the target private cloud architecture. Our work begins by allocating 8GB to each environment and increases that variable to 10GB and 12GB. Significant performance gains are gained by increasing memory allocation in the virtual machine. We believe the contribution of this work is to demonstrate how industrial strength simulation tools can be applied to real world scenarios without having to completely build-out the architectures considered. This should be particularly useful to small companies that are contemplating private cloud implementations. Keywords— Hypervisors, Workload Simulation, Retail Applications, SPEC Corporation, Microsoft Windows 10, Microsoft Windows Server 2012 R2, Microsoft Hyper-V 2016

I.

INTRODUCTION

Clouds are used by many people and organizations today to gain a variety of advantages. There are many vendors and open sources for cloud software and an equal number of techniques for evaluating them. We can mix and match products that take advantage of our particular situation and the application workload profiles we are targeting. Each candidate architecture performs and enhances certain types of applications (referred to as “workloads”). The final selection will probably be based on the types of projected applications and their workload profiles [10]. Once we select the target Cloud tools they will also have to be tuned as far as memory allocations to deliver the expected results. Many studies have been published on the impact of resource virtualization and workload characteristics on Cloud architectures. Clouds essentially contain Virtual Machines and are managed by a central authority called a Virtual Machine Manager or a “Hypervisor”. Hypervisors can be secured from traditional vendors (e.g., Microsoft Hyper-V) or on open source like the OpenStack project [9]. Hypervisors are configured to either interact with the hardware directly (Type 1) or through a Host Operating system (Type 2) [3]. A typical implementation in smaller installations is the Microsoft Hyper-V, a type 2 architecture that runs on top of Windows Server 2012 R2 and manages virtual machines that can have Windows 10 or other guest operating systems. The applications themselves execute inside the virtual machines on the guest operating systems. A key strategy for measuring the performance of certain architecture attributes is the selection of an industry-standard simulation tool that will lend credibility to the results (e.g. it resembles what is to be measured). Simulation suites for measurement of a variety of attributes are provided by the Standards Performance Evaluation Corporation “SPEC”; www.spec.org. SPEC is a non-profit organization that was created by a consortia of major technology providers who have agreed on a set of principles to be used in building benchmarking tools. SPECjbb2015 is a simulated transaction generator that can provide for very complex scenarios in a retail grocery store. The system provides a set of simulation tools that can be applied to build a scalable model to resemble reality. When used to simulate Cloud performance, the tool will deliver compute transaction workloads (impacts on the

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Int'l Conf. Modeling, Sim. and Vis. Methods | MSV'17 |

CPU/memory) that are executed in bare metal or on Virtual Machines (VMs). The functionality injects a series of standard transactions into a set of application processes that drive simulation through java applications provided. These transactions are progressively injected into the applications until the system gets saturated and can no longer provide sufficient throughput to keep up with the load being input (inputs exceed outputs). The fundamental objectives of this research are to first simulate the incremental overhead added by virtualization from bare metal to the Hyper-V environment and second to add more memory to determine the effects on overcoming the virtualization penalty. We use the SPECjbb2015 to simulate on bare metal using the Windows 10 operating system and the Windows Server 2012 R2 allocating 8GB in each case. Next, we virtualize that environment in a VM on Hyper-V, allocate the same amount of memory and execute the simulation. In a second experiment, we increase the allocated memory to 10GB and 12 GB and report the throughput statistics. The business applicability of the approach is discussed at the end of the document. In our analysis, we first present related work that has been done and how we have adapted some of those methods/results in our research. Next, we create a series of experiments which simulate the impact of virtualization as follows: •

The SPECjbb2015 is executed on bare metal under the Windows 10 and the Windows Server 2012 R2 Operating Systems. Next, we virtualize the simulation under Windows Server 2012 R2 Network OS with the Hyper-V 2016 Hypervisor and Windows 10 guest operating system. All three of these have 8GB of Memory allocated. The results are reported in SPECjbb2015 throughput transaction totals.

•

The simulations above are repeated except under varied memory allocation at 10GB and 12GB. The corresponding increase throughput totals is reported.

This research aims to demonstrate the usage of standard simulation tools in order to determine potential alternatives in Cloud resources without having to build the specific environments. The approach used could be scaled to other Cloud architectures than the one presented. II.

RELATED WORK

Virtualized environments date back a few decades. A key objective of virtualization was to keep the CPU busy while memory variables were being fetched from slower components in the computer [12]. With the advent of fully logically defined architectures in software (“softwaredefined systems”) we are now able to abstract the physical components into specifications resident in configuration files. The key software agent that manages and provisions the resources in a modern cloud is the Virtual Machine Monitor

also referred to as the “hypervisor” [3]. All policies regarding allocation and usage of physical infrastructure resources are controlled by the hypervisor. Hypervisors are assisted by other tools and agents in order to deliver a fully functional Cloud Management Platform (CMP) [5]. A. Hypervisor Architecture Throughput In their review of open source hypervisors; Freet, Agrawal, Walker and Badr [5] detail out the general characteristics that give advantages of some over others. For example, their study includes adoption reviews on Eucalyptus, OpenStack, CloudStack, OpenNebula, Nimbus and Proxmox and presents a conclusion that OpenStack and CloudStack have over 30 times more messages in discussion forums that some of their other competitors (meaning they are more top of mind in the development community). They proceed to review the architecture fit within three commercial offerings (Xen, KVM, Virtual Box and ESX/VMware) in relation to the requirements for data center virtualization and infrastructure provision. In that study, various types of workloads are simulated through each candidate hypervisor and the throughput for each is reported. We have adopted a similar throughput reporting in our methodology. Vardhan Reddy and Rajamani [15] further study the incremental overhead added by 4 different hypervisors. Their work includes measurement of the residual CPU, memory, I/O (read/write) and network (send/receive) with focused workloads for Citrix XenServer, VMWare ESXi, Linux (Ubuntu) KVM and Microsoft Hyper-V. They conclude that the Hyper-V overall performance is very close to the winning VMWare. Their results are useful as another data point for our work (the work was done on a slightly older version than ours). In our opinion, the Microsoft architecture has continued to evolve in areas such as swap-file performance and such stack would perform at least as well as their findings indicate in similar tests. Their calculations on a 32GB cloud indicate that there is a 30% overhead on RAM at that level. Our experiments begin at 8GB memory allocation and they increment by 2 GB in successive trials until the system performance can be linearly approximated based on the increments. In yet a further diagnostic approach, Ye et. al. [16] propose a very innovative method and system for measuring usage of resources along the stack. They segment their findings into impacts on hardware (indicating cache optimization should be attempted), hypervisor (the overhead from the hypervisor itself) and finally from the virtual machines themselves (the workload profile). The Virt-B system reports the results from these layers as various workloads are being processed. This work not only quantifies the impacts on performance but further diagnoses the parts of the stack that might have significant bearing on the issue. B. Virtualization Overhead Optimization Virtualization of a platform’s resources can result in significant incremental requirements compared to bare metal

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Int'l Conf. Modeling, Sim. and Vis. Methods | MSV'17 |

architectures. There are however, a set of tools and techniques that can help optimize those results in a virtualized environment. Oi and Nakajima [8] explored the effects of performance loads on the Xen. They determined that the performance of Xen could be enhanced in a virtualized environment by adjusting cache sizes in some applications in addition to incremental memory. Virtualized-Xen and baremetal Linux were compared for throughput performance in different cache and memory optimization techniques. In most circumstances, it is a combination of both that will drive throughput gains in a virtual environment. In their work, they conclude that by varying configuration elements, a more effective use of resources can be achieved. The benchmarking system used was SPECjbb2001 and the effects of Network Interface Cards (NIC) were isolated so the workload could be measured in memory usage and throughput. Our team has adopted the SPEC performance suite as a workload simulator to determine the effects on memory allocations (another attribute) rather than in network throughput. Another relevant study is Jabry, Liu, Zhu and Panneerselvam [1]; hypervisor overhead impact is studied on the following resources: disk i/o, CPU, memory and VMM (hypervisor memory usage). The study predicts the usage of resources by the hypervisor in taking total resource usage and subtracting individual component loads and until only a residual is left (presumably the hypervisor load). Those tests were conducted with VMware, Virtual Box (Oracle Corporation) and Windows Virtual PC (Microsoft). Their work benchmarked a standard load in each hypervisor environment and used IOzone to quantify load on disk i/o, RAMSpeed to quantify the impact on memory and UnixBench, to indicate the effect on CPU. Their work concludes that the hypervisor is considerably higher on CPU rather than the other components of the architecture. Each suite of simulations focused on impacting a separate part of the architecture and demonstrated how different workloads impact the choice of hypervisor. It points to the Microsoft stack being more balanced due to its integration with the other components included in that specific Cloud architecture (MS Windows). We selected the Microsoft stack in our simulation so as to provide for greater integration between the components and being able to evaluate the environment as a “whole offering” from a single vendor. Further, tightness of coupling between the units would allow for study of the simulation as a whole without the need to study the effects of separate vendor “noise”. Chen, Patel, Shen and Zhou [2] studied virtualization overhead across multiple VMs running under Xen in cloud environments. They also found that the larger resource usage was attributable to the CPU. They also propose a series of equations that are remarkably accurate in predicting the lateral scaling of workloads on all components based on the observed results of the application under study. We provide a graphical analysis of throughput under several memory

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parameters (one of the parameters for optimization of CPU performance). C. Application Workload Research Based on the research referenced, there is a significant impact on utilization of CPU from the overhead generated by the hypervisor. Further the impact is based on the type of application that is operating in the virtualized environment. NasiriGerdeh, Hosseini, RahimiZadeh and AnaLoui [7] measured throughput degradation on Web applications using the Faban suite (a web-based workload generator). They simulated the behavior of heavy transactional Web applications that tend to be very network intensive. Their work also measured the effect on memory, disk i/o and CPU. They concluded that a disproportionate difference exists in CPU resources due to the translation of domain addresses. This work further confirms that the principal resource difference is the CPU utilization even when workloads may be more i/o bound (the penalties associated where in finding addresses; a CPU task, not access to the actual addresses in the Web environment; an i/o task). We incorporate this research by focusing on actual compute resource utilization rather than network or disk access. The SPECjbb2015 suite is focused on exhausting the compute resources rather than the disk (i/o) or network resources. San Wariya, Nair and Shiwani [11] focused their research on benchmarking three hypervisors; Windows Hyper-V, VMWare/ESXi and Citrix Xen in three cloud games; 3D Mark 11, Unigine Heaven and Halo. The objectives of their study are to identify which hypervisor was better from a cloud gaming workload perspective. The three performed differently in each category but were mostly lead by the VMWare product. For our purposes however, the HALO benchmark (number of frames per second) is probably the most predictive of workloads that are CPU bound. In this category, Hyper-V performed 7% ahead of VMWare and 57% ahead of Citrix Xen. This was another reason for selection of Hyper-V as the hypervisor for our test suite. D. The SPEC Benchmarking Suite The SPECjbb2015 constitutes a workload benchmarking simulation for a Supermarket Chain. The model can be extended to include several supermarkets and several central offices in a variety of virtual machine settings. The tool set can be configured in a variety of business transaction settings so that different business patterns can be simulated (e.g., web sales versus physical store sales). The system is owned and licensed by spec.org Error! Reference source not found. which is a consortium of major IT companies that have agreed on a set of principles to guide the performance benchmarking process. The system progressively injects transaction loads into the environment until saturation is reached. A sample output of these results is seen in Figure 3. In that graphic the system begins to stress at around the 5,200 java Operations Per Second (jOPS) with a range of 5K (median tolerance) to 50K

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(max tolerance). The system reaches saturation (min tolerance) at around 6,700 jOPS and 60K. We report our results using the total transactions up to saturation. Figure 4 is a graphic representation of the architecture of the system. Figure 3: Sample SPECjbb2015 Benchmark Output www.spec.org

Figure 4: SPECjbb2015 Architecture www.spec.org

The SPECjbb products have been in existence since the late 1990s and are useful because of their industry acceptance. For example, Karlsson, Moore, Hagersten and Wood [6] used an earlier version (SPECjbb2001) and another application benchmark (ECPerf) to differentiate effects of cache misses between different types of applications. III.

EXPERIMENT DESIGN

As discussed above, experiments were designed where the same application (SPECjbb2015) was installed on:

a) Bare metal with Windows 10 b) Bare metal with Windows Server 2012 R2 c) Virtual Machine: Windows Server 2012 R2 NOS / Hyper-V/ Windows 10 Guest OS The simulation was run for a typical store sales only company with 90% store sales and 10% online sales. This is typical of smaller stores that have not adapted to the online grocery demands of consumers and are experimenting with their own private clouds. B. Application Architecture Patterns The application patterns were analyzed by deriving use cases and preparing activity diagrams from the code for the application workload being simulated. i. Use Case Analysis Use cases are a functional decomposition tool that illustrate the process interactions between actors in applications [13]. The processes that we have selected in the SPECjbb2015 suite are fairly standard and follow similar patterns. The use case diagram for the store sales architecture is similar to this one (www.UML-diagrams.org), the “adornments” in the graphic describe the usage of artifacts. Figure 5: Store Use Case Diagram

The inventory on-hand function at the physical store is susceptible to over-booked demand and out-of-stock conditions (where demand for an article exceeds supply). If the system detects an out-of-stock condition, it will proceed to cancel and back out the transaction. This process is memory intensive since it has to place the order items back into inventory and invalidate the order itself (see the error exception in the UML activity diagram below). ii. UML Activity Diagrams UML activity diagrams are a useful tool for analyzing the flow of logic through processes [13]. The following diagram was created from the code in the application.

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Int'l Conf. Modeling, Sim. and Vis. Methods | MSV'17 |

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• • • • •

Figure 6: Store UML Activity Diagram Instore Activity start Txi Sends SM an InStorePurchaseRequest

Select a Random Customer

Hewlett-Packard Envy 15t Intel i6700 quad-core processor 16G RAM 1TB Hybrid SSD 4GB NVIDIA GTX 950M chip

Retrieve Customers Previous Purchase History

IV. Reserve Specific Quantity Of Each Product

Calculate Total price

Max Products Available

Many Products to be Replenished Customer Basket Validation

Proceed to Check out

Throw an Exception STOP

Figure 8: Throughput in the Experimental Systems

Generate Reciept

Customer has enough Credit

Check Customer's Credit

Customer doesn't have enough Credit

100% 90% 80% 70% 60% 50% 40% 30% 20% 10% 0%

Transaction Fails

Move the Purchased Items from Store Inventory

Debit the cost of Each Item from Customer's Account

EXPERIMENT RESULTS

Figure 8 reports the total transaction throughput achieved under the different memory allocations in each environment. Figure 9 reports the percentage increase, using the 8GB results as the base for each environment. This is done to stress the incremental impact of each increase on the original base measurements.

Add the Available Discounts and Coupouns

Send Suppliers a Request, If any item runs out from store

Send Receipt back to HQ

10GB

12GB

Win 10

2%

2%

Win 2012

20%

7%

Win Hyp-V

95%

79%

Percent Throughput Improvement, 8GB Constant Base

Stop



C. Bare Metal Implementations Two bare metal SPECjbb2015 implementations were used in the experiments; one on Windows 10 and one on Windows Server 2012 R2. The allocations were 8GB, 10GB and 12GB of memory (total of 6 bare metal simulations). D. Virtualization Hypervisor Architecture The third environment used MS Windows 10, Server 2012 R2 and Hyper-V [4]. The SPECjbb2015 software was compiled inside the virtual machine (VM). A full physical CPU, Network Interface Card (NIC) and all storage available was allocated to the VM. RAM of 8GB, 10GB and 12GB was allocated to the Virtual Machine. A diagram for this architecture is shown below, (ours has one VM). Figure 7: Virtualized Environment Architecture (www.microsoft.com)

The above indicates there were marginal increases in throughput on bare metal versus significant increases with increase in memory on the virtual machine. Figure 9: Percentage Gain in Throughput for the Experimental Systems 250% 200% 150% 100% 50% 0% Win 10 Win 2012 Win Hyp-V

10GB

12GB

2%

2%

20%

7%

195%

37%

Percent Throughput Improvement, 8GB Constant Base

The above emphasizes the gains throughput when memory is increased in the virtual machine. Throughput keeps increasing at significant rates (although begins to curb with the second increase in memory to 12GB). V.

DISCUSSION & FUTURE PLANS

The objectives of this study are to isolate the impact of additional memory allocation on a static workload. The hypothesis that additional memory increases throughput in virtualized environments. Part of this benefit is slowed as the allocation progresses. E. Infrastructure (Machine) Specifications The infrastructure environment that the experiments were executed on had the following specifications:

A. Results Discussion As systems are virtualized, they consume greater resources due to “virtualization overhead”; they require translation of the logical to the physical and back to the

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Int'l Conf. Modeling, Sim. and Vis. Methods | MSV'17 |

logical. Our study has illustrated how using a process of simulation, a small company may avoid the costly and risky process of making the decision to virtualize in a private cloud without knowing how much incremental resource will be needed. We used Windows 10 and Server 2012 R2 to test on bare metal. It is important to understand that these are two fundamentally different Operating Systems. Windows 10 is focused on managing the desktop and execution of localized workloads. It provides rich functionality in areas such as graphics and gaming which are not simulated by the SPECjbb2015 suite but are nevertheless instantiated in it’s services. The Server 2012 R2 is a Distributed OS whose focus is to manage standard workloads associated with raw compute and storage power. Under these circumstances, the Server 2012 R2 performs with better results in traditional business process simulations like SPECjbb2015. The Hyper-V extension of the Windows Server 2012 R2 is a tool for managing the life cycle of virtual machines. It is an extension of the Network OS that communicates decisions to the virtualization layer for translation of operating parameters back and forth. This additional load constitutes overhead (more resources). Some of these resources are “fixed”; they are there by virtue of instantiation and some are “variable”; by usage of the workspace through applications. As memory is increased, there exists more workspace for applications and the overall impact of the hypervisor usage of memory is reduced. The first incremental memory allocation (from 8GB to 10GB) has a higher yield because a greater percentage of that “boost” goes to the application. The successive increment (from 10GB to 12GB) is still significant but not as high. According to hypervisor vendors and Reddy and Rajamani [15] this reduction will continue until the bare metal and virtualized environment will start to resemble each other in throughput given volume/memory mix. The simulation process allows a company to make plans of how to deploy in the future. Using a standard simulation may lead to answering some key questions as: a) Should we virtualize or keep on bare metal? b) When should we revisit our decision? A company could continue the simulation by contracting additional capacity with one of the major Cloud vendors and determine where the VM/bare metal results ultimately blur. B. Future Plans The team is busy executing additional work in running additional simulations that can implement optimization techniques. The ultimate objective is to have a “cookbook” of simulation/optimization techniques that can be used in private or hybrid cloud evaluators.

[1]

[2]

[3]

[4]

[5]

[6]

[7]

[8]

[9]

[10]

[11]

[12]

[13]

[14] [15]

[16]

L. Chen; S. Patel; H. Shen; Z. Zhou: “Profiling and Understaning Virtualization Overhead in Cloud”. 2015 44th International Conference on Parallel Processing. p. 31-40. T. Erl; Z. Mahmood; R. Puttini: “Cloud Computing Concepts, Technology & Architecture”. c. 2013 Arcitura Education, Inc./Pearson Education, Upper Saddle River, NJ. USA. A. Finn; M. Luescher; P. Lownds; D. Flynn: “Windows Server 2012 Hyper-V; Installation and Configuration Guide”. c. 2013 Wiley and Sons, Indianapolis, IN. USA. D. Freet; R. Agrawal; J. Walker; Y. Badr: “Open source cloud management platforms and hypervisor technologies: A review and comparison”. SoutheastCon 2016. p. 1-8. M. Karlsson; K.E. Moore; E. Hagersten; D.A. Wood: “Memory system Behavior of Java-Based Middleware”. The Ninth International Symposium on HighPerformance Computer Architecture, 2003. HPCA-9 2003 p. 217-228. R. NasiriGerdeh; N. Hosseini; K. RahimiZadeh; M. AnaLoui: “Performance Analysis of Web Application in Xen-based Virtualized Environment”. 2015 5th International Conference on Computer and Knowledge Engineering (ICCKE). p.258-261. H. Oi; F. Nakajima: “Performance Analysis of Large Receive Offload in a Xen Virtualized System”. 2009 International Conference on Computer Engineering and Technology. p. 475-480 J. Rhoton; J. De Clercq; F. Novak: “OpenStack Cloud Computing Architecture Guide 2014 Edition”. c. 2014 Recursive Press, USA & UK. A. Salam; Z. Gilani; S. Ul Haq: “Deploying and Managing a Cloud Infrastructure”. c. 2014 Sybex, a Wiley Brand, Indianapolis, IN. USA A. SanWariya; R. Nair; S. Shiwani: “Analyzing Processing Overhead of Type-0 Hypervisor for Cloud Gaming”. 2016 International Conference on Advances in Computing, Communication, & Automation (ICACCA) (Spring). p. 1-5. W. Stallings:th “Operating Systems: Internals and Design Principles 7 ed”. c. 2012 Pearson/Prentice Hall, Upper Saddle River, NJ. USA C. Larman; “Applying UML and Patterns: An Introduction to Object-Oriented Analysis and Design and Iterative Development (3rd Edition)” c. Pearson Education 2005, Upper River, NJ. www.spec.org P.V. Vardhan Reddy; L. Rajamani: “Virtualization Overhead Findings of Four Hypervisors in the CloudStack with SIGAR”. 2014 World Congress on Information and Communication Technologies (WICT 2014) p. 140-145. K. Ye; Z. Wu; B. Zhou; X. Jiang; C. Wang; A. Zomaya: “Virt-B: Toward Performance Benchmarking of Virtual Machine Systems”. IEEE Internet Computing, V. 18, Issue 3 (2014). p. 64-72

REFERENCES H. Al Jabry; L. Liu, Y. Zhu, J. Panneerselvam: “A Critical Evaluation thof the Performance of Virtualization Technologies”. 9 International Conference on Communications and Networking in China (2014). p. 606-611.

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Autonomously Battery Charging Tires For EVs Using Piezoelectric Phenomenon Muhammad Kamran1, Dr. Raziq Yaqub2, Dr. Azzam ul Asar1 CECOS University, Peshawar, Pakistan, 2University of Tennessee, USA

1

Abstract:-This paper illustrates the use of piezoelectric material to generate electricity in the Electric Vehicle (EV) tire. According to the proposed mechanism, the vehicle tires are embedded with layers of the Piezo-electric material along the periphery. Thus, when the EV is in motion, electricity can be generated due to mechanical stress in that part of Piezo-electric material that is in contact with the road surface. The results show that, with peripheral arrangement of Piezo-material inside the automobile tire, we can generate electricity that can be stored in a battery to run the EV for some extra miles of the total miles the EV is capable of on a single charge. The use of Polyvinylidene fluoride (PVDF); a polymer based piezoelectric material is considered due to its robust and favorable properties. Keywords— Piezoelectricity, Polyvinylidene fluoride (PVDF), Electrical power, Mechanical stress-to-electricity conversion, Automobile tire.

1 Introduction Due to the rising demand for generating energy in the most efficient way; smart, intelligent and adaptive materials are being used and one such smart substance is the piezoelectric material. Piezoelectric substances produce electric charge when mechanical stress is applied on its surface. Piezoelectric materials are composed of various materials namely crystals, ceramics, polymers etc. Polymerbased piezoelectric materials have served as the most efficient material compared to ceramics and crystals for applications where elasticity is preferred. The most commonly used polymer based piezoelectric material is Polyvinylidene Fluoride (PVDF). PVDF is a transparent, semi-crystalline, thermoplastic fluoroplastic. We have employed PVDF as the piezo electric material in our work based on the merits of PVDF as listed below [13]: (i) Piezoelectricity obtained from PVDF is several times greater than that obtained from quartz or ceramics. (ii) PVDF materials are insoluble in water, resistant to solvents, acids, bases, heat, and generate low smoke in case of any fire accidents. (iii) Has low weight and low thermal conductivity. (iv) Highly resistant to chemical corrosion and heat variations, thus withstands exposure to harsh chemical and thermal conditions. (v) Very good mechanical strength and toughness and has high abrasion resistance. (vi ) Low permeability to most gases and liquids. (vii) Unaffected by long-term exposure to ultraviolet radiation.

(viii) Less expensive compared to its counterpart. These features make them most suited to be employed in EV tires. However, proof of concept needs to be done, that requires collaboration with tires manufacturer. The rest of the paper is divided into the following sections. Section-2 describes the composition and structure of the tire, section-3 explians our proposal on embedding piezo-electric material in tire and harvesting energy from it. Section-4 calculates cost efficiency of the proposed mechanism, Section-5 suggest future work, Section-6 concludes the paper, Section-7 lists some of the key references, and finally Section-8 titled as Annex, presents the detailed mathematical analysis. Due to the rising demand for generating energy in the most efficient way; smart, intelligent and adaptive materials are being used and one such smart substance is the piezoelectric material. Piezoelectric substances produce electric charge when mechanical stress is applied on its surface. Piezoelectric materials are composed of various materials namely crystals, ceramics, polymers etc. Polymerbased piezoelectric materials have served as the most efficient material compared to ceramics and crystals for applications where elasticity is preferred. The most commonly used polymer based piezoelectric material is Polyvinylidene Fluoride (PVDF). PVDF is a transparent, semi-crystalline, thermoplastic fluoroplastic. We have employed PVDF as the piezo electric material in our work based on the merits of PVDF as listed below [13]

2 Composition and Structure of the Tire The most basic component in the tire is “rubber” which may be ‘synthetic rubber’ or ‘natural rubber’. Other components that are present in the tire are fabric wire, polymers, fillers, softeners, anti-degradents and curatives. As polymers are the backbone of rubber compounds, it is more appropriate to embed polymer piezoelectric material within the structure of the tire as done in [1]. Since the objective of using Piezoelectric material in [1] is sensing, it simply employs pallets of PVDF materials. However, we embed PVDF material as a circular ring along the entire periphery of the tire to maximize electricity generation. There are 3 main categories of tires such as Diagonal (bias) tire, belted bias tire and Radial tire. Radial tires are most commonly used in the automobile industry; therefore, this paper considers radial tire for mathematical analysis. However, it does not preclude other types.

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3 Proposal on Embedding Piezoelectric Material in Tires to Harvest Energy

the PVDF will be coming in contact with the road, and thus will ensure constant generation of electricity as shown in figure 1.

Figure 1 shows the overall concept of piezoelectric generation phenomenon in context to the proposed tire scenario.

In this section, pressure exerted by the road on the automobile tire is modeled and the amount of energy harvested in this process is calculated. Since the section involves mathematical variables, therefore for the convenience of the readers, the terminology, abbreviations and units are provided in the form of a table below:

Tire With Piezoelectric Material

Piezoelectric Phenomenon

TABLE I.

Pa rameter

Road Surface

Fig. 1.

Parameter definition

Unit

A

Area

meter2

c

Circumference

centimeter

C

Charge

Coulomb

d

Overall Concept of Piezoelectric Generation in Proposed Tire.

Figure 2 shows the cross-sectional area of the original radial tire, where we proposed to have one layer of piezoelectric material, along the periphery of the tire, below the rubber layer or any suitable place, the tire manufacturers deem suitable. Having the PVDF material as a circular ring along the entire periphery of the tire is considered to be more efficient compared to having pallets of PVDF material embed within the tire. It is because when the tires will rotate, most portions of the PVDF will be coming in contact with the road, and thus will ensure constant generation of electricity. Electricity is generated in piezoelectric materials due to mechanical stress in that part of piezoelectric material that is in contact with the road surface. Piezoelectricity is the direct result of the piezoelectric effect. The electricity so produced is fed to the car battery.

strain or

Diameter

centimeter

F

Force

Newton

g

Gravity

meter/sec2

I

Current

Ampere

k

Distance

centimeter

m

Mass of the car

kilogram

Pp

Charge surface density

Coulomb/m2

p

Power

Watts

P

Pressure

Newton/m2

t1

Time

seconds

T

Pressure exerted on the PVDF material

Newton/met er2

v

Velocity/speed

Miles/hour

V

Voltage

volts

W

Width

Centimeter

t

Having the PVDF material as a circular ring along the entire periphery of the tire is more efficient compared to having pallets of PVDF material embed within the tire. When the tires will rotate at a high speed, most portions of

Piezoelectric coefficient

D

g

Fig. 2 Piezoelectric Material as a Circular Ring in a radial tire

NOMENCLATURE, SYMBOLS AND UNITS

Appropriate piezoelectric coefficient for the axis of applied stress or strain

Thickness of a ring

or

µm

Modeling the System The experiments performed by the Curie brothers demonstrated that the Charge Surface Density is proportional to the pressure exerted, and is given by [2]

Pressure exerted by a car on the road can be given by,

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Where F is the force exerted on the tire and is equal to the weight (F = mg, where m is the mass of the car and g is the force of gravity and is 9.8 m/s2). And A is the tire surface area in contact with the road. The tire surface area A in contact with the road can be calculated as A = π x D x W x 0.1 Where 0.1 is due to the fact that 10% of the tire area is in contact with the surface of the road [3]. Output voltage for the given stress or strain is given by V0 = g3n x Xn x t Where n = 1, 2 or 3 and Xn = T [7]. As we are considering n=1 i.e. (in the piezoelectric material the electrical axis is always fixed as it is three in this case and the mechanical axis is either one, two or three), the value of g31 is specified in table 2. Moreover we are considering force on tire due to weight along Y direction as shown in figure. The component of weight acting in this way is constant and 20% of total weight [14] [15]. Now if the force is assumed to be acting axially then the area should also be taken in the specified direction. So the V 0 will be modified as [7].

Fig. 4 [15]

V0 = g31 × (Force/width × thickness) × thickness = g31 × (Force/width)

Fig. 5.

[15]

Result Calculation If the mass of the car is 1500Kg (Because electric vehicles have more weight due to their battery) then the calculated force is 14700Newtons (3675N for each tire). When the average diameter of the PVDF ring is considered to be D = 0.5588 m (22 inch), width of a ring is 0.1651 m (6.5 inch) and thickness of the ring is 110 micrometer [7]. Therefore, the Area (A) for the PVDF ring is 0.289 m2 and the surface area when 10% of the tire is in contact with the road surface is 28.9 × 10-3 m2.

Fig 3. [15]

From this value of force (F) and area (A) the pressure (T) exerted on the PVDF material is 127162.63 N/m2.

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Int'l Conf. Modeling, Sim. and Vis. Methods | MSV'17 |

TABLE II.

Parameter

TYPICAL PROPERTIES OF PVDF [7]

Sy mbol

Thickness

t

Piezoelectric Strain Constant

Valu

We plan to do the following work in future: Followings tasks would be carried throughout the length of the project.

e 55

d31

5 Future Work

Unit µm (micron, 10-6) 10-12

23

or

Piezoelectric Stress Constant

g31

10-3

216

or

According to above PVDF properties and equation the charge surface density is 2.92 × 10-6 C/m2. We know that the, Charge = Current x Time, and Power = Current x Voltage

(i) Produce simulations considering the tire industry standards such as tread, the body with sidewalls, and the beads (the rubber-covered, metal-wire that hold the tire on the wheel) (ii) Use Autodesk (It is already licensed to UT) sofware to simulate the performance of design parameters such as distribution of PVDF around the periphery of the tire. (iii) Simulate the effects of different typs/concentrations of PVDF compounds and different types of distribution of PVDF around the periphery of the tire. And also using different concentrations of PVDF in different parts of the tire. (iv) Simulate the effects of different typs/concentrations of rubber compounds and different types of distribution of rubber around the periphery of the tire. And also using different concentrations/ratios of rubber and PVDF in different parts of the tire. (v) Analyze the effects of different stresses on the proosed tire design (emulating different weights, road roughness, etc.), and discover design limitations. (vi) Analyze the effect of different temperatures (eulating different hot/cold weathers). Also simulate the effects of different sizes the tires come in.

Assumption has been made in order to make calculations easier, that the amount of time taken to generate the electricity is one second, allowing the charge to be equal to the current [3].

It has to be ensured that simulations as well as timelines for simulations meet the expectations, through validation, and comparison with the specifications of standard tires (non PVDF tires).

From all of the above equations and assumptions the power generated is 2.81mW for 1 tire. We consider only 10% of the tire touches the road surface, so in one rotation 10 times electricity is generated. In one rotation 28.1mW power is generated.

6 Conclusion

The US environment protection agency official range is 117 km (73 mile) with an energy consumption of 765 kilojoules per kilometer or 34 KWh/100mile [6] [12]. According to this energy consumption our designed car can run extra 39 km because the power generated by four tires of the car with any specified speed.

4 Cost Efficiency Cost of the tire after incorporating the PVDF ring inside the tire depends mainly on two factors, cost of the PVDF and the cost for implementing the PVDF ring inside the tire. The PVDF ring cost is from $50 - $100 per Cubic Meter, which depends on length, width and thickness of the ring [4]. If we consider the cost of the PVDF to be used in four tires to be $50 including embedding process of PVDF material inside the tire. The average life of all-season radial tire advertised by the manufacturer is 50000 miles. Using proposed technology, the EV can bring a cost saving worth 17500 additional miles. If the cost of electrical energy is $0.04/mile, a saving of $525 can be achieved.

Our work demonstrates a method of generating electricity using the PVDF material. Mathematical analysis proves that the EV can run extra 37 miles on a single charge with a speed of the car is 60mph. Since the cost of PVDF and its implementation is not so expensive, a saving of about $500 is expected over the life of the tire. Overall, the proposed method is an excellent choice to generate power when the car is on move.

Acknowledgment The 1st and 3rd authors would like to acknowledge the technical support of Dr. Raziq Yaqub for his valuable contribution extended during the course of this project and allowing to improve the mathematical model.

References [1] [2]

[3] [4] [5]

Jingang Yi, "A Piezo-Sensor-Based 'Smart Tire' System for Mobile Robots and Vehicles",March 2007. Arnau, Antonio. “Fundamentals on Piezoelectricity.” Piezoelectric Tranducers and Applications. New York, 2008. Print, pp.4. http://cosmos.ucdavis.edu/archives/2011/cluster2/Yau_ Derek.pdf. http://www.alibaba.com/productgs/322181211/PVDF_Intalox_Saddle_Ring.html http://www.carfolio.com/specifications/models/car/car= 107844&GM.

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Int'l Conf. Modeling, Sim. and Vis. Methods | MSV'17 |

[6]

[7]

17

= ∏ × 0.5588 × 0.1651

http://www.autoblog.com/2009/08/01/2010-nissan-leafelectric-car-in-person-in-depth-and-u-s-b/ Measurement Specialities Inc. April 1999 “Piezo Film Sensor Technical Manual”, P/N 1005663-1, Rev. B, pp. 3-4,28

= 0.289 m2 We assume that only 10% of the area is in contact with the road surface. Therefore, the Area (A) is given by, A = 28.9 × 10-3 m2

[8]

[9]

West Coast Green Highway. Electric Highways Project, 2010. Retrieved March 09, 2012, from http://west coastgreenhighway.com/electrichighways.htm Kris De Decker, May 3, 2010. “The status quo of electric cars: better batteries, same range.” Low-tech magazine. Retrieved March 13, 2012 from http://www.energybulletin.net/node/52736.

To determine the Pressure

If we consider the mass of the car to be 1500 kg, force can be calculated as follows: Force = m × g = 1500 × 9.8 = 14700 N

[10]

http://www.tirerack.com/tires/tiretech/techpage.jsp?tec hid=46

[11]

http://www.tirerack.com/tires/tiretech/techpage.jsp?tec hid=7.

[12]

http://teacher.pas.rochester.edu/phy121/lecturenotes/Ch apter06/Chapter6.html.

And Force on one tire will be = 14700/4 =3675N

[13]

http://www.openmusiclabs.com/wp/wpcontent/uploads/2011/11/piezo.pdf

[14]

http://en.wikipedia.org/wiki/Nissan_Leaf

[15]

http://en.wikipedia.org/wiki/Polyvinylidene_fluoride

[16]

http://www.mate.tue.nl/mate/pdfs/8351.pdf

[17]

http://road-transporttechnology.org/Proceedings/2%20%20ISHVWD/Vol%201/TRUCK%20TIRE%20TYPE S%20AND%20ROAD%20CONTACT%20PRESSUR ES%20-%20Yap%20.pdf

Therefore Pressure = 14700 / 28.9 × 10-3 = 127162.63 N / m2 To determine the charge surface density The charge surface density is given by,

Where d is the piezoelectric strain coefficient and from table 2, its value is given to be 23 × 10-12. Therefore,

= 23 × 10-12 × 127162.63 = 2.92 × 10-6 C/m2

[18]

http://www.tzlee.com/blog/?m=201103

Annex: Detailed Mathematical Analysis Calculating Patch Area of PVDF Ring Contact patch (also called footprint) is the area in which the tire is in contact with the road surface). Different vehicles have different contact patch depending on tire’s diameter and width. Tires diameter ranging from 8 to 26 which are given in detail in [8] [9]. For the sake of analysis we considered a tire with the diameter of 22 inches and width of 6.5. (I.e. the tire size of 185/55R15 commonly used for Passenger Electric Vehicles (EV)). We consider incorporating PVDF ring inside the whole width of tire, so that the tire continue to adhere with its original texture without scarifying its original purpose or violating its specifications in terms of road resistance, air pressure, etc. D = 0.5588 meters W = 0.1651 meters Therefore, Area (A) is given by, ATotal = ∏ × D × W

To determine the output voltage In the previous section, we have briefly discussed the equations for Output voltage which is given by, V0 = g3n x Xn x t In our work we consider n = 1 and the value of g31 and t is specified in table 2. Xn = (Force/width × thickness) Therefore, output voltage is, V0 = g31 × (Force/width × thickness) × thickness = g31 × (Force/width) = 216 × 10-3 × (0.2×3675/0.1651) = 961.6 V And 0.2 is the component of force that acts axially shown in figure-2c, as we are considering g31 mode so the force should also be considered along the specified direction. To determine the Total Power Total Power = Charge Surface Density x Output voltage = 2.92 × 10-6 × 961.6 = 2.81 mW We consider only 10% of the tire touches the road surface, so in one rotation 10 times electricity is generated. In one rotation of one tire 28.1mW power is produced.

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Int'l Conf. Modeling, Sim. and Vis. Methods | MSV'17 |

TABLE III. ENERGY PRODUCED AND CONSUMED BY TRAVELING 100KM AND EFFICIENCY

And, In one rotation of car energy produced is, 28.1 × 4 = 0.1124W The energy produced will be proportional to the distance traveled and will be irrespective of velocity with which it travels. Extra miles the car can travel As the extra miles are irrespective of velocity so it will be same for any speed the electric vehicle is travelling at, while the consumption can be varied depending on the road surface, terrain and traffic situation. But for simplicity we are taking the consumption to be also same because these factors are nonlinear and probabilistic and cannot be calculated. So we are taking the consumption on per mileage criteria. Energy consumption of electrical vehicle is 756 kilojoules per kilometer [12]. 765 kilojoules = 0.2125 KWh/Km

S. No:

Km/h

Energy consumed

Energy Produced

(KVH)

(KVH)

Efficiency (ƞ)

1.

50

21.25

6.07

28%

2.

60

21.25

6.07

28%

3.

70

21.25

6.07

28%

4.

80

21.25

6.07

28%

If the car travel certain distance while moving with certain velocity then according to the table 3, the energy produced and energy consumed will be same irrespective of velocity of at which it travels, therefore the efficiency is constant. Let us say an electrical vehicle is running at 50km/h for two hours (i.e. traveling 100km as energy produced is irrespective of speed) then according to the proposed modal total energy produced will be 6.07 KVh while the consumed will be 21.25 KVh. So the efficiency becomes 28%. From the above energy consumption “An electric car” with this design can run extra 39 kilometers with any specified speed.

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Int'l Conf. Modeling, Sim. and Vis. Methods | MSV'17 |

Traffic Re-Direction Simulation during a Road Disaster/Collapse on Toll Road 408 in Florida Craig L. Tidwell, PhD Seminole State College 100 Weldon Blvd., Sanford, FL 32773 [email protected]

Regular Research Paper Abstract Society takes for granted the ability to get from place to place on the existing transportation systems. The major method of transportation in the U.S. is the many surface roads, freeways, and corresponding overpasses and bridges. What happens when these paths are not available or when they are obstructed by a natural disaster or other event? We can observe the results of such events first hand by the many bridges and overpasses that have collapsed over the past 20 years. Cities and metropolitan areas must be sufficiently prepared for such events and need to carefully plan. For example, after the devastation of the bridge collapse over the Mississippi River in Minneapolis, MN, and ensuing confusion, simulations to redirect traffic in the event of another tragedy could be very beneficial. The aftermath of the collapse of the bridge impacted railways, river, road, and air transit. Even small businesses surrounding the bridge were affected by the blocked roadway. So a collapse cannot be seen as an isolated event, but as a cascading event that not only impacts traffic and safety but even the livelihood of surrounding businesses. Key Words Simulation Transportation Collapse Redirection

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complex, large-scale systems is critical. Responding to natural disasters, evaluating terrorist attack scenarios, modeling breaches in national transportation networks, or analyzing impacts of rare events are essential to maintaining security of all sectors of society” (Bandini, 2004). In the future, effective simulations for the routing of traffic to bypass a collapsed bridge or overpass could help minimize the traffic confusion if another tragedy were to happen. Also, these simulations could help in the case of construction down time due to the repairs of existing structures. Since, more and more bridges are getting a poor rating, being able to reroute traffic efficiently away from the bridges will become very beneficial. Project Focus The focus of the project is a traffic re-direction simulation in case of an overpass collapse along Toll Road 408 in Orlando, FL. Specifically, evaluating the results if the overpass at South Conway Road were to collapse and block Toll Road 408 (see Figure 1). We will examine the impact of the collapsed overpass and which are the possible reroutes in the existing roadway system around the defined traffic network. In this effort, we will have to take into consideration the potential alternate routes, traffic loads, and other relevant data elements related to the traffic network surroundings. In addition, the availability of roads around our defined traffic network will have to be reviewed due to the fact that they might be compromised due to the current road re-construction projects. Currently the FHP has nothing specifically in writing for bridge or overpass collapses (Miller, 2008). They do have standard procedures that they follow, but they have not looked at the impact of such a disaster on the surrounding road systems and traffic load efficiency.

Introduction According to the Federal Highway Administration (FHA) the loads at several points on the bridge in Minneapolis were overly stressed, and the construction materials used were insufficient to handle the weight (Roy, 2008). All over the country bridges and overpasses are now being inspected to determine their structural integrity and more bridges and overpasses are getting unsatisfactory ratings. Officials with Florida Department of Transportation's District 2 office -- which covers all of north Florida -- said that while the 1,100 bridges it inspects are safe, they listed five that were structurally deficient (News4Jax, 2007). There are many causes of bridge and road collapses. According to Wardhana (2003) the primary causes of bridge collapse can be attributed to floods and collisions. Other causes also include age and poor construction, lack of proper inspection frequency, construction (repair and new construction), weight limitation exceeded, soil erosion, weather (snow, ice, etc.), and acts of terrorism (truck bomb, etc.). No matter what the cause of a collapse, it is very obvious that proper preparation is necessary to handle the rerouting of traffic and prompt notification of the disaster to the public. Modeling and simulation is an effective and inexpensive way to deal with roadway collapse scenarios. Many software programs have been created to simulate traffic conditions for improving flow throughout a defined road network. A few of the most common software packages are TSISCORSIM, AIMSUN, SimTraffic, and TransModeler. “In today’s information technology-driven environment, the need for modeling

Figure 1 – Conway Road Overpass at 408 What makes this particular overpass one of Orlando’s most critical traffic locations is its position directly after a major toll collection site (408 and 436 an exit used for the Orlando International Airport) and its proximity to I-4 and Toll Road 417. If this particular overpass were to collapse traffic would be gridlocked for miles in either direction on Toll Road 408 and the surrounding road systems would quickly become severely congested. The ongoing expansion of Toll Road 408, and current construction at this overpass, would make the traffic problem even worse. Currently the 436 off-ramp is a single lane off-ramp and is limited to the number of vehicles that could be routed off Toll Road 408. Also, the 436 off-ramp is approximately 7,513 feet. If a collapse were to occur cars would be backed up from the 436 exit to the collapse site. At present this is a 3-lane road and could hold up to 1,127 cars (assuming an average of

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Int'l Conf. Modeling, Sim. and Vis. Methods | MSV'17 |

20 feet per car). These vehicles would then have to be directed to the eastbound lanes of 408 and sent back the other way to exit at 436 or another location. In addition, the distance between the collapse point and the closest egress route is significant. Figure 2 is an image of our network prior to the collapse over 408 at Conway Road. Each circle in the network represents a point on the network where either the roads intersect or where a traffic signal is located. The grey lines are the 408 links eastbound and westbound, and all of the black lines are the surface roads that make up our suggested network path to reroute traffic around the collapse.

During our traffic re-direction simulation we will be using CORSIM (Corridor Simulation), a microscopic traffic simulation software package. In a collaboration effort with the Department of Civil Engineering at UCF we were able to utilize a licensed copy of CORSIM to implement the models for the 408 collapse scenario. The CORSIM simulation package provides a graphical interface to define the traffic networks that is relatively user friendly. It can handle freeway systems, surface road systems, and a host of other standard traffic events (signals, stop/yield signs, different types of vehicles, turn ratios, etc.)

evacuation scenarios in the real world to test the efficiency of the plans is unrealistic. It would cost too much in time and money. Computer traffic simulations can therefore be used to simulate events that require a mass evacuation or traffic reroute, and find the best traffic management strategies (AHB20, 2006). There are primarily three types of traffic simulations models used today: microscopic, macroscopic, and mesoscopic. Microscopic simulations are the most popular type of simulation model (Burghout, 2005). Such a model can be used in many different traffic situations because of the high number of variables that are produced that can be adapted to a variety of different events (AHB20, 2006). These simulations can also “model the temporal and spatial evolution of specified non-recurrent traffic conditions” (Hawas, 2007). Microscopic traffic simulations track the movements of individual cars throughout the system and produce simulated results for each one. Every car, truck, bus, etc., is moved according to the characteristics of its vehicle type. These characteristics can include acceleration, weight, length, braking distance, and typical vehicle driver characteristics (DOT, 2007). Including these parameters makes it ideal for modeling traffic in a more realistic manner, because in the real world every vehicle is not driven the same way and people have different driving habits (Ehlert, 2001). Problems with the microscopic model are their prolonged runtime and probabilistic nature. These problems are due to the high number of tracked variables, and the fact that they are usually calibrated the same for every simulation run. Microscopic models are also the most costly due to the complexity of its algorithms and training of users. Examples of microscopic tools include AIMSUN, CORSIM, PARAMICS, SimTraffic and VISSIM (AHB20, 2006).

408 East and Westbound Lanes

Figure 2 – CORSIM Simulation Image CORSIM can be configured to accurately simulate a wide range of traffic conditions, from moderate to very congested demand. It can also effectively simulate traffic flow during an incident, from queue buildup to recovery to normalcy. The ability to simulate over-congested traffic flow conditions gives CORSIM a unique advantage over traditional empirical/analytical methods. Literature Review Traffic congestion has become more of a problem in recent years due to the increased number of cars on the roadways. To alleviate some of the traffic issues, governments are spending more money building new roads, but this is a costly solution. Computer traffic simulation is a more efficient method of solving some of the traffic problems because it looks at finding new strategies to control the flow of cars on the roadways instead of just building new infrastructures. Therefore, it maximizes the efficiency of the current traffic system (Elhert, 2001). Hurricanes, earthquakes, terrorist attacks, bridge collapses, etc., are all either natural or human-caused disasters that may require a massive evacuation from an area. However, most traffic systems are not able to handle such an influx of vehicles onto the roadways at once. As a result, special evacuation routes need to be planned in advance. The problem associated with creating evacuation routes though, is that recreating these

Macroscopic simulations are the second most popular traffic simulation model. Similar to the microscopic models, they are also used for a wide array of applications. However, they do not track individual vehicle movements throughout the system. Unlike a microscopic model, a macroscopic mode does not account for individual vehicle interactions and variables in the system but rather focuses on the collective flow of vehicles through the system (Burghout, 2005). It uses “fluid dynamics” (Ehlert, 2001) with “cumulative traffic stream characteristics” (DOT, 2007) that can include flow, speed, and density to produce simulations (DOT, 2007). Macroscopic models concentrate on the timing of traffic from one section of the simulation to the other. It was originally designed to track the collective flow of traffic on major highways and corridors. Therefore, it may not be as flexible to be used for different types of simulations as the other two models (DOT, 2007). Examples of macroscopic tools include FREQ, FREELO, SATURN, TRANSYT-7F, and KRONOS (AHB20, 2006). Mesoscopic models are third most popular type of model used in traffic simulations. Mesoscopic models are also the newest approach (AHB20, 2006). These models are used predominately with simulating dynamic traffic situations such as in traveler information systems (DOT, 2007). Mesoscopic models are unique because they simulate individual vehicles using a “macroscopic speed-density relationship” (AHB20, 2006). Therefore, by combining the “detail of micro and scalability of macro” (AHB20, 2006) these models tend to be less probabilistic then microscopic models because they do not track as many parameters per vehicle and rely more on grouping vehicle interactions into macroscopic relationship (DOT, 2007). Examples of mesoscopic tools include TRANSMODELER, DYNAMIT, CONTRAM, and DYNAMEQ (AHB20, 2006). The state of the art of traffic simulations has advanced dramatically in recent years. Microscopic models continue to add different vehicle-to-

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Int'l Conf. Modeling, Sim. and Vis. Methods | MSV'17 |

vehicle interaction logic and more unique driver personalities (AHB20, 2006). Computer traffic simulations are being successfully applied to a wide array of different applications and many researchers are experimenting with mixed-scaled simulations that are advantageous because they can be more flexible by adapting the benefits of different approaches with fewer of their downfalls (AHB20, 2006). These new applications include incorporation of simulators in real-time traffic centers to help control traffic, and increased research into traffic management in times of mass evacuation such as a natural disaster. Some of the recent problems in the area of traffic simulations include performance measurements. Since most models use different criteria to measure performance, it is often difficult to compare the results of different approaches. Also, with the increased number of simulators, it is often hard to find the right application for a specific problem. The training, calibration, and software license costs have also had a major impact. These costs have resulted in users having to pick only one product to get specialized in (AHB20, 2006). The recent Minneapolis bridge collapse caused a major traffic problem in the surrounding area and the Minnesota Department of Transportation (MnDOT) and the Federal Highway Administration (FHA) asked the University of Arizona (UA) for help in providing a solution to reroute traffic around the disaster. Professor Chiu at UA was working on a mesoscopic urban traffic simulation model that was being developed to solve the problems of large scale traffic management (Stiles, 2007). The research focused on handling cases of unexpected mass evacuations in the event of a disaster. Chiu says that because of poor planning people trying to evacuate will often get trapped in traffic jams and face the possibility of losing valuable time, property, and possibly human life. The research that Chiu does at the University of Arizona is mainly focused on trying to develop an effective algorithm to dynamically direct mass evacuations and traffic rerouting for large networks or entire cities (Chiu, 2004). Previous research tended to focus on one specific highway or small network, but Chiu’s research focuses on simulating an entire region in a relatively short time frame. Since it is impossible to predict what drivers are going to do, it is difficult to pick one static route in the case of major disaster. Therefore, UA created a dynamic traffic management software package for the entire area around the bridge collapse to help manage the flow of traffic. Chiu’s software uses real time traffic information along with census data to simulate how drivers will react to different conditions and information received through the media. It then simulates the entire city’s traffic system, either in real time or offline to allow the operators at the FHWA to determine the best traffic management strategy. These strategies can include re-timing stop lights and producing efficient detours according to current traffic information (Stiles, 2007). Modeling Approach The modeling approach consisted of first creating a baseline scenario that described the current freeway and surface roadway system surrounding the area of interest (Toll Road 408 and the Conway Rd overpass). Subsequently, all the egress routes from Toll Road 408 were identified and the surface roadway system surrounding these exit points were noted and evaluated. During the evaluation of these exit points, we had to take into consideration the possible surface roads that could be utilized for the re-route paths in order to more clearly define our traffic network to be studied. Once the re-route paths were selected, traffic count data had to be gathered from public service entities and from our own data collection efforts. In general, our modeling approach can be defined by the following steps: 1. Gather the overall traffic system network characteristics

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2. Define our traffic simulation model type 3. Traffic system network assessment of re-routing options 4. Traffic system network model creation 5. Run simulations and organize output data and perform analysis First, as much data traffic was count data that we could find from public service entities to serve as inputs for our baseline scenario. Traffic flow data during the off-peak and peak periods were collected from the expressway authority, and the Department of Transportation. In addition to our initial effort to gather information we consulted the Florida Highway Patrol to find out about current traffic re-routing procedures and to gather any recommendations. We contacted the Orange County Graphical Information System (GIS) Department to obtain GIS shape files that depict roads and highways that will help us define our traffic system network domain to be modeled but our tool did not need the shapes nor was it readily available. Secondly, we needed to define our type of traffic simulation model. The simulation model type chosen was microscopic. Microscopic simulations can model individual driver reactions at the point of detours and different conditions during re-routing due to traffic control devices, lane changing, intersections, mergers, and more (Hawas, 2007). These microscopic model characteristics are useful when comparing different traffic system network scenarios. Also, we were interested in traffic flow characteristics such as the level of aggregation of flow, speed and density in the defined traffic system network prior to vehicles reaching the re-routing point. During our traffic system network assessment of the re-routing process, we studied the network links (particular stretches of streets or highways) that will be modeled in our microscopic model. With this simulation model type we were able to define detailed traffic performance measures to be evaluated during different network representations (re-routes). These performance measures include delays, average times, number of stops per vehicles and other parameter values like individual and overall vehicle flow, speed, and density. The microscopic modeling package we are using for our traffic system network model creation is TSIS-CORSIM. CORSIM incorporates in its modeling package two industry proven simulation tools; NETSIM (for surface street simulation) and FRESIM (for freeway simulation). The CORSIM tool allowed us to place surface streets and freeways in one simulation. In addition, CORSIM permits analysis of individual vehicles and summative statistics of overall flow (link volume), gap reduction, acceleration, turning movements, and network wide average statistics. Also, as part of the model creation efforts we defined the network links characteristic of the reroute paths (length, turning lanes, speed limits, etc.) through the graphical interface provided by CORSIM called Trafed. Figure 2 illustrates the defined traffic network model. Finally, as part of our model output and analysis efforts we had to specify the input parameters such as number of vehicles, stop light configurations, speed limits, etc. Output data provided by CORSIM included individual vehicle results such as emissions, delays, and average speed (MNDOT, 2004). The data was obtained from the tool to represent the simulation results at the network links of interest for analysis. Network Definition and Data Collection We have identified the following links for our network re-routing around the Conway Overpass collapse point. Westbound Traffic: 408 west to 436 off-ramp, 436 north to Colonial (SR 50), Colonial (SR 50) to North Bumby Ave., North Bumby Ave. to South Street , and South Street to 408 on-ramp west. Eastbound Traffic: 408 east to North Conway Road off-ramp; North Conway Road off-ramp to North Conway Road; North Conway Road to Curry Ford Road; Curry Ford Road to 436; 436 to Lake Underhill; and Lake Underhill to 408 on-ramp east (see Figure 3 below).

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We selected these paths based on the number of lanes available, lowresidential impact (we wanted to minimize rerouting traffic through neighborhoods), speed limits, and connections to other alternate links in the area.

case scenarios for planning provide for adequate load planning. More specifically, data was collected at the following intersections for the westbound section of our defined network: 436 and 408 west off-ramp; Colonial (50) and the 436 intersection; 50 and North Bumby; and South Street to the 408 on-ramp. For the eastbound section of our network we collected data at the following points: 408 off-ramp at South Conway and Lake Underhill roads; South Conway Rd at Curry Ford Rd; Curry Ford Rd. and 436; 436 and Lake Underhill; and Lake Underhill at the 408 eastbound on-ramp. Vehicles were counted and recorded in 5-to-10 minute intervals and averaged to give vehicle per minute and vehicle per hour counts. Table 2 shows the exact numbers collected, the time period, and the vehicles per minute and per hour for westbound traffic, and Table 3 shows the data for eastbound traffic. Table 4 is the collected data at different peak times for Toll Road 408. Data sets were collected at different times and vantage points to ensure a higher degree of accuracy in tallying vehicles and vehicle types traversing the network.

Figure 3 – Arial view of defined network. Historical Data According to the Expressway Authority, approximately 124,790 vehicles travel past the collapse point on a weekday in a 24-hour period (OOCEA, 2008). This is a significant increase from 2001 when the count was 107,940 vehicles (OOCEA, 2008). In 6 years the number of vehicles has increased by about 20,000 (see Table 1). If this trend continues, by the year 2016 there will be another 40,000 vehicles added to the network. The 2007 numbers equate to an average of 86.6 vehicles per minute. This was in line with our observed manual traffic counts. They ranged from approximately 82 – 94 vehicles per minute (averaged over observed time period) which falls within the historical data collection number. The time of day will determine the density of vehicles traveling on the network and the immediate impact of a collapse. Based on an average vehicle size of 16 ft. (typical full-size vehicle length) and assuming an average distance between vehicles of no more than 5 ft. (assuming cars are static), the specified network could have up to 6,880 vehicles locked in congestion throughout the network (in a twoway configuration). These numbers were used to determine the efficiency of the CORSIM data, and will be used as baseline parameters in network confirmation and to determine if one-way routing of traffic is more efficient. Location

Year 2001

Year 2007

Conway Road to SR 436 / Yucatan Drive

107,940

124,790

Table 2 – Westbound Traffic Counts – Peek Time Period Table 2 shows the traffic counts that were taken during the observation of westbound traffic. These traffic counts included those links that directly impacted the network. Some of the traffic was not counted that had no real impact on the network (such as minimal offloading of vehicles on side streets not part of the defined network).

Table 1 – Toll Road 408 Average Annual Weekday Traffic (OOCEA, 2008) Observed Data Collection Data was collected at several points over a two-week period at the end of March. We collected data on all of main traffic links including eastbound and westbound traffic along 408 at multiple time periods. We made sure that our data collection took place during peak travel times where traffic load was highest. Our purpose for collecting data during this time was to make sure that we provided the model with a traffic count for a collapse during the commute time windows of 7:30-9am and 4:30-6pm. Worst-

Table 3 – Eastbound Traffic Counts – Peek Time Period Table 3 shows the data collected for the eastbound traffic on the defined network. As one can see, the number of links is similar to Table 2.

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However, the impact on the network was greater due to the different perspectives for data collection and the greater number of entrance and exit points impacting our defined traffic network system. Table 4 contains observed vehicle counts for the 408 East-West Expressway during rush hour and non-rush hour times. Even though there is some difference in quantities, the toll road is still heavily used in non-rush hours.

Table 4 – 408 Toll Road Traffic Counts – Peek/Off Peek Time Periods Data Analysis The main purpose of our analysis is to illustrate the impact of the overpass collapse on Toll Road 408 exit ramps in terms of vehicle flow, travel time of through traffic and delay time characteristics at the different exit ramps and intersections along the few possible re-routing paths. The results will be shown by a series of comparison tables that will demonstrate the difference between the vehicle flow, travel time, and time delays at different locations in the re-route paths in the simulated traffic network (Figure 4).

23

referred to as the two-way model. Lastly, we decided to make the FRESIM portion of the network one-way, limiting traffic entering the network from other streets and keeping all signals green except at major street intersections. Table 5 shows the data that was provided to CORSIM for the traffic model and is labeled with the entry link numbers, link name, and vehicles per hour. Entry Link volumes (Link numbers) 8001, 37 8004, 43 8005, 44

Link Name

Colonial west of N. Bumby Colonial east of 436 Curry Ford Rd west of Conway Rd. 8006, 45 Curry Ford Rd east of 436 8007, 46 Conway Rd. south of Curry Ford Rd. 8008,47 436 south of Curry Ford Rd. 8009, 12 N. Bumby north of Colonial 8010, 5 436 north of Colonial 8013, 48 N. Bumby south of 408 8014, 49 South St. east of N. Bumby 8015, 50 Lake Underhill east of Conway Road. Table 5 – Entry Link Volumes for Network

Vehicles per Hour 972 1224 1452 876 376 1404 612 2726 25 792 700

Table 6 contains data from the pre-collapse network, collapsed network with normal traffic flow (two-way), and collapsed network with one-way traffic flow. The data is for a sample of the street links in the network only using the NETSIM package embedded in TSIS-CORSIM. For link (2, 3), which is located on 436 at the point directly after the 408 off-ramp, the baseline model has a total delay time of all vehicles traversing the link of 116.6 minutes, where the two-way model has a delay time of 21,910 minutes, and the one-way model is 31,735 minutes. This value represents the total number of cars entering the exiting that part of the network over the simulation run.

Figure 4 – Simulated Traffic Network. Westbound Traffic: 408 west to 436 off-ramp, 436 north to Colonial (SR 50), Colonial (SR 50) to North Bumby Ave., North Bumby Ave. to South Street , and South Street to 408 on-ramp west. Eastbound Traffic: 408 east to North Conway Road off-ramp, North Conway Road off-ramp to North Conway Road., North Conway Road to Curry Ford Road., Curry Ford Road to 436, 436 to Lake Underhill, and Lake Underhill to 408 on-ramp east. The simulations were run for 10,800 seconds, or 180 minutes per each model. This was the maximum time that the simulation could be run. CORSIM uses vehicles per hour as its base metric and then creates a random seed for each simulation run. Three different scenarios were run in CORSIM to get comparative data. We defined the network model with no collapse, our baseline model, and ran that scenario multiple times to make sure that the results were similar. We found that the results did not vary significantly between runs, and in several cases were almost identical. We then altered this model to simulate the effects of a collapse at the specified overpass location. This meant redirecting the vehicles that would be on 408 to the specified off-ramps to then be placed on the street network (FRESIM) part of the model. We did not change the traffic signals or any other variables in this initial modified system

Table 6 – Sample Data from NetSim Output File Table 7 is the average from all of the data collected for one time period of a simulation. From the table, it is clear that the one-way model places more vehicles in the network (64%) than the two-way model, but at the cost of a higher delay time, slower overall speed, and more minutes/mile total time and delay time. A key indicator in the table is the minutes/mile which shows the one-way network taking over 4 minutes more per mile over the two-way network. There may be some minimal advantage to the one-way configuration, but we think that the trade-offs do not justify advocating this method.

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Table 7 – Averages for Models across Multiple MOE’s Table 8 contains the network-wide average statistics for all three models; pre-collapse network (normal), collapse network (two-way traffic), and collapse network with one-way traffic and signal changes and entry point modifications. The one-way and two-way model summative data is similar except that the one-way network had more total traffic miles (over 16,000). Also, the delay time for the one-way network is larger (worse) than the two-way model. This appears to be caused by bottlenecks at intersection points where a road with more lanes attempts to turn into a road with fewer lanes. Even with reduced signal interruption the network is slower. This was the opposite of what we had expected. Between the two options modeled (one-way or two-way) the model is more efficient to run with the two-way configuration. This is obviously much easier to implement and will also permit other side traffic (residents of the area) to still use the network normally with obvious delay times due to congestion. One will notice a substantial difference between the precollapse and post-collapse network data values. The collapse will cause significant traffic congestion, but having a plan in place would allow for the authorities to quickly respond to the event and reroute vehicles more efficiently. Table 8 below shows the differences between normal traffic, and then traffic if the overpass were to collapse for both one-way and two-way traffic. Normal Network Statistics Values Total Vehicle Miles (miles) 16,0694.44 Vehicle Hours of Move Time (veh/min) 3,378.87 Vehicle Hours of Delay Time (veh/min) 2,811.71 Vehicle Hours of Total Time (veh/min) 6,190.58 Average Speed (mph) 25.96 Minutes/Mile of Delay Time 1.05 Minutes/Mile of Total Time 2.31 Collapse Network Statistics (two-way) Total Vehicle Miles (miles) Vehicle Hours of Move Time (veh/min) Vehicle Hours of Delay Time (veh/min) Vehicle Hours of Total Time (veh/min) Average Speed (mph) Minutes/Mile of Delay Time Minutes/Mile of Total Time

Values 40,290.41 1,002.73 11,741.62 12,744.34 3.16 17.49 18.98

Collapse Network Statistics (one-way) Values Total Vehicle Miles (miles) 41,100.75 Vehicle Hours of Move Time (veh/min) 1,107.38 Vehicle Hours of Delay Time (veh/min) 14,772.46 Vehicle Hours of Total Time (veh/min) 15,789.85 Average Speed (mph) 2.60 Minutes/Mile of Delay Time 21.57 Minutes/Mile of Total Time 23.05 Table 8 – Network-Wide Average Statistics for all Models Findings and Recommendations The initial modeling efforts were conducted to recreate as accurately as possible the existing conditions of the defined traffic network. Particularly, we needed to realistically model vehicle flows and delay time around the possible overpass collapse. Analysis of our defined traffic network provided useful information in case the Conway Rd overpass were to collapse over the 408 East-West Expressway. Even though our model was confined to this location, it could be applied to any overpass or bridge collapse for planning purposes. One of the primary questions we asked about the networks was “how long would it take someone to traverse the network using the three

models”? If Joe was driving on 408, and had an E-PASS, it would take him approximately 3 minutes and 28 seconds to travel from one end of the network to the other. This is based on average travel data collected from the model of 50.96mph, over 2.95 miles. This also assumes that the travel time takes place during rush hour. If Joe had to exit at 436 and travel the street network without a collapse, he would travel approximately 5.14 miles at an average speed of 25.96mph. At this rate it would take him about 11 minutes and 52 seconds. If he were traveling eastbound on 408 and exiting at North Conway and then entering at Yukon/Lake Underhill he would travel 3.89 miles at an average speed of 25.96mph. At this rate it would take him about 9 minutes to complete the trip. The model shows that, obviously, once the collapse takes place, his travel time changes significantly. He can no longer take 408 and if he is fortunate to be on 408 before the eastbound or westbound exit links, he can expect the following times. If traffic is routed off 408 at 436 and the streets are functioning normally (no changes – two way traffic), then it will take him 97 minutes and 3 seconds at the average rate of 3.16 mph. It gets worse if the one way model is used as the speed drops to 2.60 mph. At this speed it will take him 118 minutes and 3 seconds to travel the same network. This is primarily due to bottlenecks at key sections of the network. What would do Joe the most good would be to avoid the network and try to exit at an earlier point or take an alternate path such as 417 to 528. However, everyone else is probably doing the same thing so maybe he should just turn around and take the day off. Table 9 shows these data numbers. Network Model

Avg Speed (mph) (computed by model) 50.96

Distance (miles) 2.95

Time (minutes: seconds) 3:28

Baseline – 436N route

25.96

5.14

11:52

Baseline – N.Conway route

25.96

3.89

9:00

Two-Way – 436N route

3.16

5.14

97:03

One-Way – 436N route

2.6

5.14

118:03

Baseline – 408 only

Table 9 – Traffic Length, Speed and Time to Traverse the Network We recommend that the two-way network be utilized in case of a collapse scenario. The data reveals that the one-way network does have some advantages but the data from the model shows that it is not efficient and would contribute to other problems related to side streets, requiring more emergency personnel to man the streets and extensive planning. Critical to the success of re-routing is the preparedness of emergency personnel to respond to such a disaster (Chiu, 2004). It will be important for the Expressway Authority, Department of Transportation, and Florida Highway Patrol to all agree upon a course of action in advance to effectively respond to such an event quickly. Communication with the public through radio, television, and signage also needs to be up and running as soon as possible. Conclusion Modeling and simulation is an effective and inexpensive tool to model county and state freeway and surface road system traffic scenarios. Organizing, planning and testing rerouting paths in real life scenarios will be too expensive and it will severely affect drivers, businesses and industries surrounding the particular traffic network being modeled. The simulated traffic network studied in this project is very close to one of Orlando’s most critical traffic locations. This is due to the fact that it is positioned directly after a major toll collection site (408 and 436 an exit used for the Orlando International Airport) and its proximity to I-4 and Toll Road 417. If the overpass collapse scenario actually happens the

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traffic would be gridlocked for miles in either direction on 408 and the surrounding road systems would quickly become severely congested.

Calibration and Numerical Analysis”. Journal: Transportation Research Part B

The simulated rerouted traffic network illustrated the impact of the overpass collapse on the 408 East-West Expressway in terms of vehicle flow, travel time of through traffic and delay time characteristics at the different freeway exit ramps and the surface road system available along the possible re-routing paths. Public service entities like The Expressway Authority, Department of Transportation and the Orange County Traffic Engineering Department are actively collecting traffic count data throughout the Orange County freeway and surface road system. The data collected is available to the public, private and academic entities providing services to the county or state.

Dedman, Bill. (Jan 31, 2008) “Feds let states delay inspections of bad bridges”. Retrieved February 11, 2008 from the World Wide Web: http://www.msnbc.com/.

In the past 6 years the number of vehicles traveling along the 408 stretch at the defined collapse point has increased by about 20,000 vehicles. If the observed count continues to grow then it is conceivable that another 20,000 vehicles could be added to the network. The need to continuously examine the current expressway and surface road systems is evident. Thus, the use of simulation to model traffic scenarios proves to be an effective and inexpensive tool to provide public service entities useful information for future road system planning and expansions due to the impact of traffic flow increase in a network.

Ehlert, Patrick. (Aug 25, 2001). “Microscopic traffic simulation with reactive driving agents”. 2001 IEEE Intelligent Transportation Systems Conference Proceedings. Hawas, Yaser. (April-June, 2007) “A Microscopic Simulation Model for Incident Modeling in Urban Networks”. Transportation Planning and Technology, April-June 2007. Vol. 30, Nos. 23, pp. 289-309. Lerner, Hochstaedter, Kates, Demir, Meier, and Poschinger (November 2000) “The Interplay of Multiple Scales in Traffic Flow Coupling of Microscopic, Mesoscopic, and Macroscopic Simulation”. 7th World Congress on Intelligent Transport Systems. Meng, Thu. (2004) “A Microscopic Simulation Study of Two-Way Street Network versus One-Way Street Network.” Journal of The Institution of Engineers, Singapore Vol. 44 Issue 2

In the future, effective simulations for the routing of traffic to bypass a collapsed bridge or overpass could help minimize traffic confusion if another tragedy were to occur. It could also help in the case of construction down time due to the repairs of the structures with poor ratings or new construction (which is prevalent throughout the United States). Governments need to be prepared to handle such events and modeling and simulation is an effective and inexpensive solution that can save time, money, and possibly human life.

Miller, Kim. (Feb, 2008). “FHP Contingency Plans”. Email response.

References AHB20. (Feb 3, 2006) “Freeway Operations” Retrieved from the World Wide Web: http://www.trbfreewayops.org/sim_model/AnalysisToolsResearch.pdf

Orlando-Orange County Expressway Authority (February, 2008) “System’s Traffic Data and Statistics Manual” Retrieved from the World Wide Web: http://www.expresswayauthority.com/trafficstatistics/historicaltraffic/ind ex.shtml

Associated Press. (Aug 2, 2007), “Recent bridge and highway collapses”. Retrieved February 11, 2008 from the World Wide Web: http://www.ap.org/.

Owen, Zhang, Rao, and McHale (2000) “Traffic Flow Simulation Using CORSIM”. Proceedings of the 2000 Winter Simulation Conference (pgs. 1143-1147).

Bandini, P., Cook, J., Mitchell, M.C., & Riley, L. A., (2004) “A New Paradigm for Optimizing Hybrid Simulations of Rare Event Modeling for Complex Systems”. ASTC 2004 Conference Proceedings.

Rose, Martin. (2006) “Modeling of Freeway Traffic“. Retrieved from the World Wide Web: http://www.bauinf.unihannover.de/publikationen/ICCCEBPaperRose.pdf

Bulwa, Fimrite. (April 29, 2007). “Tanker fire destroys part of MacArthur Maze 2 freeways closed near Bay Bridge”. Retrieved from the World Wide Web: http://www.sfgate.com

Roy, Jennifer. (March 18, 2008) “NTSB Reports: Construction Materials Contributed to I-35W Bridge Collapse”. Design News. Retrieved from the World Wide Web: http://www.designnews.com

Burghout, Wilco. (March, 2005). “Hybrid Mesoscopic-Microscopic Traffic Simulation”. Transportation Research Record. Caliper. (2007) “Traffic Simulation Models“. Retrieved from the World Wide Web: http://www.caliper.com/transmodeler/Simulation.htm Chiu, Yi-Chang. (3-6 Oct 2004) “Traffic scheduling simulation and assignment for area-wide evacuation”. Intelligent Transportation Systems, 2004. Proceedings. The 7th International IEEE Conference 3-6 Oct. 2004 Page(s): 537 – 542 Chiu, Y.-C. and Zhou, L. (2005) “An Anisotropic Mesoscopic Traffic Simulation Model for Dynamic Network Modeling: Part II:

Minnesota Department of Transportation (MNDOT). (May 27, 2004) “Advanced CORSIM Training Manual” SHE No. A-MNDOT0318.00. News4Jax.com (Aug 3, 2007) “DOT Rates 5 N.E. Florida Bridges ‘Deficient’.” Retrieved from the World Wide Web: http://www.news4jax.com

Stiles, Ed. (June 11, 2007). “Evacuation Software Finds Best Way to Route Millions of Vehicles“. Retrieved from the World Wide Web: http://uanews.org/node/13406 Saturn. (2007) “SATURN UGM 2007“ Retrieved from the World Wide Web: http://www.saturnsoftware.co.uk/ugm2007.html USGS. (1995) “Southern Californians Cope With Earthquakes”. Retrieved from the World Wide Web: http://pubs.usgs.gov/ Wardhana and Hadipriono (August 2003) “Analysis of Recent Bridge Failures in the United States”. J. Perf. Constr. Fac., Volume 17, Issue 3, pp. 144-150.

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SESSION MODELING, VISUALIZATION AND NOVEL APPLICATIONS Chair(s) TBA

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Performance Enhancement and Prediction Model of Concurrent Thread Execution in JVM Khondker Shajadul Hasan1 1

Department of Computing Sciences, University of Houston - Clear Lake, 2700 Bay Area Blvd., Houston TX 77058, USA, [email protected]

Abstract - Performance of a Java Virtual Machine (JVM) is quantified in terms of the JVM’s relative CPU availability at executing concurrent Java threads. The total CPU loading of a JVM is defined by the sum of the CPU utilization factors of all threads executing on the JVM. Sharp performance degradation has been observed while JVM executes concurrent threads with exactly same CPU load. An analytical model has been proposed and implemented to improve the scenario. Extensive experimental studies and statistical analysis are performed to validate the performance enhancement of concurrent thread execution and provide a basis for an empirical model for improving CPU performance. To facilitate scientific and controlled empirical evaluation, synthetically generated threads are employed that are parameterized by their CPU utilization factor, which is defined as the fraction of time a thread spends utilizing CPU resources.

Keywords: Concurrent threads; CPU efficiency; Execution Efficiency, Java Virtual Machine.

In this analytical model, a thread in the work portion of a phase will remain in the work portion until it has consumed enough CPU cycles to complete the allotted work. After completing the work portion of the phase, the thread then enters in the sleep portion where it sleeps (does not consume CPU cycles) for an amount of time defined by the CPU utilization factor. When multiple threads are spawned concurrently, the JVM runs those threads in a time sharing scheduling technique [2]. The performance (and availability) will be degraded when the work phases of all threads overlap each other in time. Figure 2 depicts a scenario where 3 threads with identical work and CPU loads are executed in a single-core execution environment. Here, each thread gets a maximum of 1⁄3 of the available CPU during their work phase, resulting in a work phase length 3 times wider than a single thread scenario. Thread-1 Work1

Sleep1

Work2

Sleep2

Work3

Sleep3

Work1

Sleep1

Work2

Sleep2

Work3

Sleep3

Work1

Sleep1

Work2

Sleep2

Work3

Sleep3

1. Introduction The primary contribution of this section is to introduce the thread execution behavior of this empirical model. Threads are considered as independent tasks, meaning there are no interdependencies among threads such as message passing. A thread is modeled by a series of alternating work and sleep phases. For the purposes of this study, the work portion of a phase is CPU-bound and requires a fixed amount of computational work (i.e., CPU cycles). The sleep portion of a phase does not consume CPU cycles, and its length relative to that of the work portion is used to define the CPU load usage factor for a thread. Figure 1 shows three work-sleep phases of a thread.

W-1

S-1

W-2

S-2

W-3

S-3

Thread-2

Thread-3

Figure 2: Three concurrent threads consist of identical work load and CPU utilization factors in a single-core environment. Alternatively, if the work phases of the three threads are staggered to where there is no overlap, then there is no contention for the CPU resource and the CPU efficiency is essentially perfect. That is, all the work phases of concurrent threads are separated so that each thread can get the full attention of the CPU.

2. Motivation of Work

Figure 1: Three work and sleep phases of a thread.

This section of the paper focuses more on the motivation behind the empirical studies to demonstrate the concurrent thread performance enhancement.

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Threads are given 2.0 × 108 units of work load to accomplish in 50 quantums. A quantum consists of a work phase and a sleep phase. Each thread needs to accomplish 4. 0 × 106 units of work in each work phase so that it can complete total work in 50 phases. Sleep phase lengths of the threads are calculated depending on CPU load and remains constant during its life time, but the work time can vary depending on CPU availability. When threads have completed their work, the report of thread execution containing start time, work time, sleep time, number of phases, and end time are stored into multiple files for statistical analysis.

120,000 110,000 100,000 Time (ms)

Besides motivation, this section also discusses the surroundings of the problem associated with executing concurrent threads with the same CPU load on JVM. The CPU performance of JVMs for executing synthetically generated concurrent threads is evaluated through experimental studies. The important objective of this empirical study is to determine the CPU performance of JVM handling concurrent threads. The system that is used for handling the single core test cases is an Intel Xeon CPU E5540 @ 2.53GHz clock speed, 1,333 MHz bus speed and 4 GB of RAM. All benchmark and experimental programs are implemented in Java language and JDK 1.6 has been used for execution.

90,000 80,000 70,000 60,000 50,000 40,000

1

2

3

4

5

6

Thread-1 98714 104615 102688 98589 95606 100042 Thread-2 96706 103598 103676 101581 100557 101224

Test Run

Figure 3: Processing time of 2 threads with equal CPU load of 0.01 units. As the work phases of both threads are staggered, the available CPU is shared among the threads which results in a larger work time which is around 72.4% more compared with the benchmark work time. Figure 4 shows test results involving 2, 3, 4, and 5 concurrent threads containing the same CPU and work load. 180,000

162,754

Figure 4: Increase of processing time as the number of thread increases.

Benchmark programs have been used to measure the ideal processing time for synthetic threads with different CPU loads. The benchmark processing time of one thread in a single core machine with a CPU load of 0.1, which is equal to 10% CPU usage, and work load of 2.0 × 108 units is 58,661 ms. For next test case, when two concurrent threads are spawned in JVM containing same CPU and work loads of 0.1 and 2.0 × 108 units, the average processing time has sharply increased to 99,842 ms for thread 1 and 100,824 ms for thread 2. Figure 3 shows the performance of those two concurrent threads.

As the CPU loads are the same, work phases of all concurrent threads were staggered on top of each other. The available CPU has been allocated among all threads which have resulted in deprived performance. Figure 4 shows the increase of execution time as the number of thread increases though the cumulative CPU load is much lower than 100% CPU load. For each scenario, several test runs have been conducted and the average work time has been taken to plot the graph. Though the aggregate CPU load is 50% for 5 threads, the work time has risen to 162,754 ms which is around 275% higher than the benchmark work time.

Time (ms)

When two concurrent threads are spawned in a single core machine with the same amount of work, the thread execution time depends on the CPU load. It has been experimentally found that if those two threads have exact same CPU load, the performance is dreadfully poor. As the work phases of both threads lined up on top of each other, each thread gets a maximum 1⁄2 of the available CPU which eventually doubles the work time. Moreover, both threads are working at the same time and sleeping at the same time which wastes the CPU resources as well. To validate this statement, several empirical studies have been conducted by spawning two threads concurrently in a single core machine with the exact same CPU load. Figure 3 shows execution times of thread 1 and 2 for multiple test runs.

160,000 151,288

140,000 128,838

120,000 100,000

100,333

80,000 60,000 40,000

58,661 1 Thread 2 Threads 3 Threads 4 Threads 5 Threads

Concurrent Threads

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6 0.5

Thread-1 127880

73029

50154

22831

18260

13221

Thread-2

34753

49757

34004

36810

31824

30932

Figure 6: A similar unexpected jump in work time of both threads when they had equal CPU load of 0.02 (20%) unit. approximately 70.91% increase in execution time. Similar case studies have been conducted considering all possibilities by employing different CPU load for thread 2. It has been found for all the cases that when both threads have the same CPU load, the work time increases noticeably which degrades the thread execution efficiency.

140,000 Time (ms)

160,000

Time (ms)

A different empirical study has been conducted to verify the above findings involving two concurrent threads. In these tests, the CPU utilization of thread 1 has been varied from 0.05 to 0.5 (5% to 50%) but thread 2 remains a constant 0.1 unit. In Figure 5, the horizontal axis represents CPU load and the vertical axis represents execution time in milliseconds. The test results show that the execution time of thread 2 is expectedly always closer to the benchmark work time except in the case when thread 1 and thread 2 has exactly the same CPU load of 0.1. That is, when the CPU load of thread 1 reaches the same 0.1 like thread 2, the work time of thread 2 increases sharply from benchmark work time of 58,661 ms to 100,824 ms. For all other cases, work times of thread 2 are acceptably closer to the benchmark work time confirming that when work phases of both threads are staggered, the performance of both threads degrades significantly.

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120,000 100,000 80,000 60,000 40,000

3. Empirical Studies for the Model

20,000 0

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2 0.1

3 0.2

4 0.3

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6 0.5

Thread-1 146793

99842

35569

22208

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Thread-2

100824

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69736

Figure 5: An unexpected jump in processing time of both threads when they had equal CPU load of 0.01 (10%) unit. Next, to verify the dependability of this finding, another similar test case has been conducted with several runs by assigning different CPU load values for the first thread. In this test case, CPU load of the first thread has been varied from 0.05 to 0.5 units but CPU load of the second thread remains a constant like previous but with a value of 0.2 unit. Figure 6 also shows a similar finding where the work time of the second thread is very close to benchmark work time except the case where CPU load of both threads are 0.2. Benchmark work time for CPU load of 0.2 is 29,642 ms but when thread 1 and thread 2 have 0.2 CPU load, the processing time for thread 1 increases to 50,154 ms and thread 2 reaches to 49,757 ms. In Figure 5, it can be seen that when the CPU load of thread 1 was 0.2, the processing time was 35,569 ms but in Figure 6, it has increased to 50,154 ms which is

In this section, an analytical model has been developed for enhancing CPU availability associated with executing concurrent threads containing the same CPU load on JVM. Several empirical studies have been conducted to determine the breach among the concurrent threads’ CPU loads to avoid possible work phase overlap. It has been found from test results that instead of having the same CPU load in all threads, having a slight variation (0.005 ≤ ℰ ≤ 0.1) can noticeably increase the performance of thread execution. Reduced gap value has been assigned iteratively to find out the minimum value which can separate the work phases. Several test results suggests that even when the thread utilization percentage is separated by 0.005 (0.5%), appreciable performance can be achieved. Multiple test cases have been conducted by applying this value to avoid work phase overlap. In this test case, CPU load of thread 1 is varied from 0.09 to 0.11 but the CPU load of thread 2 is always 0.1. That is, CPU load value of thread 1 is varied 0.005 in each cases but thread 2 always remains 0.1 unit. Data from several test runs have been used to plot the graph of Figure 7 showing the execution time for both threads. In Figure 7, it can be seen that except for the case where thread 1 and thread 2 has exactly the same

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Time (ms)

CPU load of 0.1, it provides expected performance. That is, several test case results suggests that a variation of as small as 0.5% of CPU load can result in improved performance close to benchmark time. 110,000 100,000 90,000 80,000 70,000 60,000 50,000 40,000 30,000 20,000 10,000 0

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Figure 7: Processing times of 2 concurrent threads for various CPU load.

time. That is, with a slight variation in CPU load, the execution time is much less compared to the previous case even though the cumulative CPU load is less. These test cases results indicate that the approach of variation in work load has resulted in improved work time though the combined CPU load is less. Similar case studies have been conducted by spawning 3, 4, and 5 concurrent threads containing the same 0.1 CPU load in a single core machine which depicts a similar pattern compared with the 2 thread scenario in Figure 3. Figure 9 shows a four concurrent threads scenario with a CPU load of 0.1 each. The test results found that the average processing time of threads are 151,288 ms which is approximately three times higher than the benchmark processing time. 180,000

Time (ms)

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This empirical study has been conducted to verify whether the above findings hold for two concurrent threads with the same work and CPU loads. Here thread 1 has been assigned 0.095 CPU load and thread 2 has been assigned 0.1 CPU load. That is, the CPU load gap between these two threads is 0.005 or 0.5%. The aggregate load in this case is 0.195 which is below the aggregate load of the previous case in which it was 0.2. Several test cases have been conducted for accuracy and the test result data have been used to plot the graph of Figure 8. In Figure 8, it can be seen that there are no sudden increases in work time for these threads. Moreover, their work time is close to benchmark work 80,000 75,000 70,000 65,000 60,000 55,000 50,000 45,000 40,000

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Series1 65437 64441 64414 64430 65417 64828 Series2 61670 61448 61247 61357 61240 61392

Figure 8: Processing time comparison of two concurrent threads with a slight CPU load variation of 0.005 units. Thread 1 has 0.095 and thread 2 has 0.1 CPU load.

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Thread-1 160905 154604 158615 135566 150170 151972 Thread-2 159820 150460 159546 141545 144216 151117 Thread-3 159852 158476 155450 136511 145130 151084 Thread-4 157504 151451 156557 145595 143781 150978

Figure 9: Processing time comparison of 4 concurrent threads with same CPU load of 0.1 units. The next case study has been conducted for four concurrent threads to verify whether is it possible to achieve closer to benchmark execution time performance by separating 0.5% CPU load. So, in this test case, 4 concurrent threads are spawned but their CPU loads are separated by 0.005 unit. Concurrent threads are assigned 0.09, 0.095, 0.1, and 0.105 units of CPU loads respectively. Here, the total load is 0.39 which is less than the previous test cases’ aggregate load. Test results of several run shows that the average work time is 68,583 ms which is much lesser compared with the previous case where it was 151,288 ms. So, with a slight variation in CPU load, the processing time is very close to the benchmark program processing time. That is, with a small variation in CPU load among threads, the scheduling of thread execution helps to reduce thread work phase overlap.

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180,000

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Benchmark Same CPU Load Slight Variable CPU Load

Thread-4 63561 63780 64465 63904 62490 63640

Figure 10: Work time comparison of 4 concurrent threads with a slight CPU load variation of 0.005 units from 0.09 to 0.105. As a large number of empirical studies have been conducted to validate the findings of performance enhancement, the test results of multiple scenarios of concurrent threads are presented in the following table. It shows an outstanding improvement in execution time when the CPU load is varied by 0.005 units which is exactly 0.5% of CPU load. When the CPU load for concurrent threads are varied as little as 0.5%, the execution time is very close to the benchmark execution time. Whereas, when all concurrent threads have the exact same CPU load, their work phases are staggered on top of each other which results in a very large execution time, 2 to 3 times more than the benchmark execution time depending on the number of concurrent threads. Figure 11 shows a complete scenario of concurrent threads processing time comparison among the benchmark, same CPU load, and slight variation of 0.5% in CPU load. This finding clearly suggests that the exactly same CPU loads for concurrent threads are not a good choice. Table 1: Execution time for concurrent threads with exactly same CPU load and slightly different CPU load of 0.005 units. Thread Count

Same CPU Load Time (ms)

Slightly different CPU Load (0.5%) Time (ms)

1 Thread

58,661

Not Applicable

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100,338

63,110

3 Threads

128,838

63,931

4 Threads

151,288

68,583

5 Threads

162,754

69,464

Threads

Figure 11: Performance of thread execution with a slight variation of CPU load and with exactly the same CPU load. Another empirical study for 2 concurrent threads has been conducted in which both the work and sleep times are constant throughout their life cycle. The work and sleep phase lengths are calculated during runtime depending on CPU load. The amount of work that can be accomplished in each phase will vary depending on the CPU availability. When the work stages of threads are completely out of phase, threads can accomplish the highest amount of work in that stage. Figure 12 shows a case where work phases are completely out of phase. Thread-1 W-1

S-1

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Figure 12: Work phases of three concurrent threads are separated so that each thread can get the full attention of the CPU. If the work phases of the three threads are staggered to where there is no overlap, then there is no contention for the CPU resource and the CPU efficiency is essentially perfect. That is, all the work phases of concurrent threads are separated so that each thread can get the full attention of the CPU. On the other hand, when the work phase of threads are perfectly staggered on top of each other, shown in Figure 2, the available

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CPU will be shared among the active threads which will result in less amount of work accomplished in those phases. Here, two threads are spawned concurrently with the same amount of CPU and work load. As these threads are CPU bound and starting with the work phase, the available CPU will be shared among the concurrent threads. Figure 13 show that when work phases of the threads are overlapped, the amount of work that has been accomplished is approximately 50K units. The work accomplishment varies based on the degree of work phase overlap. If threads are perfectly overlapped, then the work accomplishment can drop as low as 18K unit. When they are not overlapped, it can grow as large as 160K unit. 180,000

-3,500 -3,000 -2,500 -2,000 -1,500 -1,000 -500

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Time (ms) Figure 14: Start time difference between work phases of threads grows from cycle to cycle. and thread 2 work phase crosses 1000ms, the thread 2 work phase starts overlapping with the thread 1’s work phase of the next quantum. By calculating the start time difference, 1000-MOD (Start time difference, 1000), the work phase overlap can be seen. Figure 15 shows the work phase overlap among phases.

160,000

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Phases Figure 13: Variable work accomplished by concurrent threads with the same CPU load. To improve the scenario, a slight variation in the CPU load has been applied and several empirical studies have been conducted to analyze the outcome. Here, two concurrent threads have been spawned with the exact same amount of work but the CPU load has been varied slightly. Thread 1 has a CPU load of 0.1 whereas the CPU load of thread 2 has slightly been changed to 0.095 to conduct the test. The test results show that this slight variation (0.005 ≤ ℰ ≤ 0.1) in CPU load has resulted in improved processing time. The start time difference between the work phases of threads has increased constantly which separated the work phases and has allowed threads to acquire full CPU attention to process its allocated job quickly. Figure 14 shows increase of time difference between threads’ start times. A further analysis of start time difference between the threads shows that there are few cases where the thread work phase has overlapped as well. Here, the quantum is 1000 ms. When the time difference between thread 1

61 55 49 43 37 31 25 19 13 7 1 0.0

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Time (ms) Figure 15: Threads work phase start time difference in a scale of 100ms. The positive outcome in this model is that the numbers of work phase overlaps are very small in number compared with the previous scenario. This small number of work phase overlap enables the threads to accomplish the highest amount of work. The work accomplishment in each work phase has increased to around 161 K unit for each thread except the overlapped phases. Figure 16 shows the work accomplishment of the concurrent threads. The combined work accomplishment by both threads in each work phase is around 317K units on average compared with the previous scenario where the average work accomplishment per phase was only 183K units

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Work units

(shown in Figure 13). This improved model has taken 63 phases to accomplish the total work instead of 110 phases taken by the previous setup. The context switching overhead has resulted in poor work accomplishment for the previous scenario. 180,000 160,000 140,000 120,000 100,000 80,000 60,000 40,000 20,000 0

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has been plotted to compare measured performances among the processing time of benchmark program, threads with equal CPU load, and threads with slightly different CPU load (0.005 ≤ ℰ ≤ 0.1). Two different approaches have been adapted and executed to prove the validation of this empirical model. These empirically measured efficiency values are the indication of applying slight variation for CPU bound threads. An empirical model for CPU performance presented in this paper can play a vital role for making critical CPU load decisions for most CPU bound commercial applications.

5. References 1 5 9 13 17 21 25 29 33 37 41 45 49 53 57 61

Work phases Figure 16: Work accomplishment of threads with slight variation in CPU load. Figure 16 also depicts that as the work phase overlaps in 1 to 2, 21 to 22, 40 to 41, 59 to 61 cycles, the work accomplishment drops to around 210K unit. For all other cases, work phases have not overlapped which enables them to get full attention of the CPU to achieve maximum amount of work.

4. Conclusion This paper has presented an analytical model (and conducted empirical studies) for predicting (and measuring) CPU performance of JVM for handling synthetically generated concurrent threads. As observed, degradation in CPU performance occurs when concurrent threads has exactly same CPU load. It is more effective when the total CPU loading is less than the total capacity of all CPU cores. In addition to total CPU loading, the total number of concurrent threads is a factor in predicting CPU efficiency; more threads generally incur more context switching overhead, which results in degraded efficiency. When the total load is less than the total capacity of all cores, the relative alignment of the working and sleeping phases of the threads can have a significant impact on CPU performance. Specifically, increased overlap of the work phases implies lower performance. It was demonstrated that shifting the relative phasing of the threads, using a slight variation in CPU load, to reduce possible work phase overlap can improve the performance (i.e., CPU efficiency). Random aggregate load values for concurrent threads were assigned for each for an extensive number of experimental measurements. A thread performance efficiency chart

[1] Khondker S. Hasan, Nicolas G. Grounds, John K. Antonio, “Predicting CPU Availability of a Multicore Processor Executing Concurrent Java Threads”, 17th International Conference on Parallel and Distributed Processing Techniques and Applications (PDPTA-11), Pages: 551-557, Las Vegas, Nevada, July 2011. [2] Martha Beltrán, Antonio Guzmán and Jose Luis Bosque, “A new CPU Availability Prediction Model for Time-Shared Systems”, IEEE Computer, Vol 57, No. 7, July 2008. [3] Bill Venners, “Inside the Java 2 Virtual Machine”, Thread Synchronization, URL: http://www.artima.com/insidejvm/ed2/index.html [4] Y. Zhang, W. Sun, and Y. Inoguchi, “Predicting running time of grid tasks on cpu load predictions”, Proceedings of the 7th IEEE/ACM International Conference on Grid Computing, pp. 286–292, September 2006. [5] Heather Kreger, Ward Harold, Leigh Williamson, Java and JMX, Building Manageable Systems, Addison-Wesley 2003. [6] Khondker S. Hasan, “A Distributed Chess Playing Software System Model Using Dynamic CPU Availability Prediction”, International Conference on Software Engineering Research and Practice (SERP-11), pages: 126-132, Las Vegas, Nevada, July 2011. [7] Vitaly Mikheev, “Switching JVMs May Help Reveal Issues in Multi-Threaded Apps”, http://java.dzone.com/articles/case-studyswitching-jvms-may, May 2010. [8] R. Wolski, N. Spring, and J. Hayes, “Predicting the CPU Availability of Time-Shared Unix Systems on the Computational Grid,” Proc. Eighth International Symposium on High Performance Distributed Computing, pp. 105-112, ISBN: 07803-5681-0, August 2002. [9] P.A. Dinda, “Online Prediction of the Running Time of Tasks,” Proc. 10th IEEE Int’l Symp. High Performance Distributed Computing, pp.336-7, 2001.

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A Stochastic Method for Structural Degradation Modeling Peter Sawka1 , Sara Boyle2 , and Jeremy Mange2 1 US Army - TARDEC - Dynamics and Durability 2 US Army - TARDEC - Computational Methods and System Behavior Abstract— Degradation analysis is used for computationally modeling the effect material degradation has on various aspects of the strength of a given material. If the material in a structure is too heavily degraded it could result in a catastrophic failure when that material is placed under a stress condition. Therefore, having the ability to predict the amount of material degradation that would result in catastrophic failure of the material would be useful. In this paper we present an ongoing research project to define and implement a baseline method for randomly degrading a percentage of the material in a structure and to assess the effects of the degradation on the strength of a test coupon. We also demonstrate the effectiveness of this method for further uses, and discuss applications of the expanded concept to a variety of domains.

saturations of degradation at a specified degradation strength. This method can be utilized for a variety of degradation analyses, but for our initial application we focus on a finite element model of a steel test coupon that is undergoing a tensile test. We evaluate the resultant stresses in the baseline and in the degraded models, drawing a comparison between the samples as a metric of changing strength, or modified performance. The degraded states in the model have a modified participation factor for randomly selected elements. In our example, the knockdown factor contributes to a loss of strength and can be roughly analogous to rust or porosity, but this method is not limited to decreased performance. Enhanced participation factors can mimic increased strength in the steel specimen if, for example, it was impregnated with randomly orientated short-cut carbon nanotubes.

Keywords: Degradation analysis, modeling and simulation, CAE,

2.1 Tensile Test Coupon

FEA

To demonstrate this random generation technique, we have constructed a relatively simple finite element model of a tensile test. The test coupon is a strip of steel, clamped stationary on one end and pulled under a static uniaxial load. Figure 1 illustrates the model setup. The maximum stress, displacement, and other relevant metrics are measured and recorded for this baseline model as evaluation criteria and cross-referenced against future samples. For more complex models, multiple output metrics could be recorded and combined into a more elaborate, weighted evaluation function, considering a variety of factors and potentially across multiple zones. For example evaluating the stress levels within various materials with respect to their yield strengths or endurance limits, if a model contained multiple materials. For complex geometries, potentially multiple zones would be of interest to investigate, and these zones are not required to be located adjacently.

1. Introduction Analytical modeling and simulation is a useful tool because it provides cost effective results and a reduced timeline compared to running physical tests of the same phenomena. The current modeling and simulation toolkits that are being utilized, including finite element analysis for structures, durability, and dynamics analysis, are extremely useful for modeling well-defined problem sets, but there is not an adequate method for handling unstructured datasets, such as random degradation analysis. The current process for dealing with unstructured datasets utilizes engineering judgment to approximate the results and this narrow approach seldomly captures the entire potential scope of the data. In this project, we explore a method for modeling random degradation that captures trends for degraded materials by utilizing High Performance Computing resources and a stochastic approach for degraded material modeling. The initial results from our analysis of this random degradation modeling effort have been extremely promising and we plan to expand and improve upon the model for future work and analysis, as well as explore additional applications.

2. Process Our approach for degradation analysis utilizes a method for stochastically modifying a finite element model to create many sets of randomly degraded models for prescribed

2.2 Randomly Generated Degraded Model Using this test coupon, we model the random degradation effects previously described by defining subsets of the finite elements to represent our target zones for applying degraded status effects. Our model utilizes a single zone central to the test coupon to avoid near-field effects from the clamps. In practice, multiple zones of varying sizes may be desirable from larger or more complex structures. Next, we change pre-determined ranges of the structure’s material properties based on the desired participation factors. We specifically increase saturation up to 70% in increments of 10% by

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Fig. 1: Model Setup.

editing element properties to different degraded states. We choose degraded properties representing 95%, 90%, and 80% strengths of baseline material to demonstrate the impact of the scopes. In practical terms, this involves defining both the fullstrength material and various degraded-strength materials ahead of time, then selecting appropriate element sets within the input file and moving them to the section of the file corresponding to the appropriate new material. The new material definition reduces the elastic modulus of random element sets which adjusts their participation factor into the overall solution. This specific approach is chosen because it conveniently maintains the elements’ nodal connectivity. If we had completely deleted elements, this could potentially create unconnected regions in the model, as well as stress concentrations surrounding sharp corners. By randomly selecting from the original test coupon a set of elements of pre-defined size to then degrade, we mimic many of the interesting properties of unstructured data. This preprocessing step can be done in batch form to create a large number of input files for parallel execution of the finite element solver on a high-performance computer (HPC), since that is the computationally expensive portion of the larger process.

of trials is outside the scope of this paper (see [1]), but for a 95% level of confidence with a maximum error of E = 0.1, at least 97 runs are required. Together, these considerations give rise to a large number of required finite element solver processes, each of which is computationally expensive. For this reason, we chose to run these experiments on an HPC. At the outset of the design space exploration process, each run is completely independent and requires no process communication aside from conveying results. Thus, we took advantage of the massive parallelization that is possible with this sort of setup, by splitting the processing among as many nodes as available given the licensing constraints imposed by the finite element software. We were mainly interested in specific material measurements (see Results section), so post-processing work was also necessary. Each output data file was parsed for the information relevant to our experiments, and the results for trials with the same settings were averaged to obtain statistically significant results. These results were then correlated and examined across the design variables. Identification information for each run was also recorded to quickly reference outliers later manually, to study for example how a best or worst case scenario is structured.

3. Experimental Design

Having applied the outlined procedures to our models, a large number of datasets were executed on an HPC. Stress analysis was performed on all models. This is a common metric to roughly gauge the overall strength of the structure after the loading conditions, since the areas of maximum stress are often the weakest and most likely to ultimately initiate cracks under fatigue loading or otherwise ultimately fail from those points of origin. The expectation was that increasing material degradation would result in increasing maximum stress. All of these expectations were confirmed by our experiments, as seen in Figures 2, 3, and 4, which depict increasing stress as degradation increases. For each plot, the maximum stress increases as saturation of degradation increases. Additionally, the maximum stress at any given level of saturation increases proportionally as the amount of degradation to material properties increase across plots. The combined effect on final strength is best illustrated in Figure 5, where all the datasets are overlaid together.

In order to clearly explore the effects of this random degradation upon the modeled material, we analyzed stress results for a variety of degradation levels dl (that is, the percentage of the overall material that is degraded) as well as a variety of degraded material properties mp (that is, the strength of the degraded material compared to the original full-strength material). Based on past experience and preliminary studies, the chosen values for this set of experiments were: dl = {0.1, 0.2, ..., 0.9} mp = {0.95, 0.9, 0.8} In addition to the size of this design space, a number of independent trials with the same degradation level and material properties are necessary to generate a statistically significant result. A full derivation of the required number

4. Results

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Int'l Conf. Modeling, Sim. and Vis. Methods | MSV'17 |

Fig. 2: Normalized Max Stress versus Saturation of Degradation for 5% Degraded Material Models.

Fig. 4: Normalized Max Stress versus Saturation of Degradation for 20% Degraded Material Models.

Fig. 3: Normalized Max Stress versus Saturation of Degradation for 10% Degraded Material Models.

Fig. 5: Averaged Strengths of Degraded Samples.

Each graph represents a maximum stress analysis where the degraded material properties are reduced from the baseline material strength. Analyses were executed for multiple materials representing 5%, 10%, and 20% degraded material strength, each examined separately. In addition, the saturation of degradation through the test coupon was varied from 10% to 70% saturation by 10% increments. Each of these analyses were repeated at least 97 times to achieve the desired 95% confidence level in the results. The clean alignment of scattered distribution in the box graphs demonstrates a smooth curve of results which agrees with statistical significance. Of particular interest is the effect of randomly generating the samples for the necessary runs. The scattered maximum, minimum, and average metric values for each of the repeated tests across the design space are potentially interesting results. The benefit here is understanding the full potential scope of analyzing unstructured datasets. In practice, often engineers will use their judgment and experience to identify and evaluate what they deem as a worse case scenario of a project as a conservative starting point. With our method, we can identify the true worst case, and best case, without estimation. For many applications, this can help manage risk or help justify a design modification which might not be easily identifiable with manual methods.

While in practice leaning towards the most conservative approach seems safest, excessive safety usually trades with increasing overall system weight and costs, and those in turn negatively impact schedule and performance parameters. Finding an ideal solution can drive parameters towards an optimum overall solution for a given project. Additionally, the conservative approach described is only achievable when the analyst already has knowledge and intuition about system performance. With our method, we characterize system performance more fully, which is more thorough for a non-expert, and can reveal non-intuitive responses even experts may not have expected. Finally, this method has as much application in theoretical research and concepting, as it has with practical design. For the former, a broader understanding of the system’s statistical performance may be invaluable. Figure 5 depicts the average strengths of all the results overlaid onto a single graph. Besides illustrating the aforementioned trends, an intention of this graph is switching from measuring a single variable like max stress across samples in previous graphs, to charting a final evaluation function across the design space. In our example, the evaluation function is kept simple as strength is evaluated as a function of max stress relative to the baseline. If we define a variable Fev as the value of this final evaluation function, then for this simple example:

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Fig. 6: Stress Results of Varyingly Saturated Test Samples.

Fev = f (σmax )

(1)

However, this could be expanded into a more complex evaluation function measuring the participation of multiple variables. A simple example would be if we were also interested in the axial displacement of the test coupon. Such displacement strain could be evaluated together with maximum stress and both contribute to the final assessment of strength. So then, for this more general case, we define a function as follows: Fev = f (σ, , δ, ...)

(2)

where σ is stress,  is strain, and δ is displacement. This method could be expanded for more elaborate evaluation criteria or across multiple evaluated zones. For example, if a sample contains n zones of interest, each with their own values for σ, , δ, etc., then our evaluation function could be extended as follows:

Fev = f (σ1 , 1 , δ1 , ...) + f (σ2 , 2 , δ2 , ...) + ... + f (σn , n , δn , ...) (3) The values for these zones could also be weighted separately to further extend the generality of this definition, as follows: Fev = W1 · f (σ1 , 1 , δ1 , ...) + W2 · f (σ2 , 2 , δ2 , ...) + ... + Wn · f (σn , n , δn , ...) (4) where Wi is the weighting factor for the ith zone of interest, and each Wi could be unique. Beyond examining the statistical effects of random degradation, the following examines the specific stress results within the samples. Figure 6 illustrates the stress contour

results of the initial test coupon as baseline with comparison to two degraded samples; specifically 30% and 70% of material degraded, referred to as saturation of degradation. The images are of the center section of the test coupons (see model setup) which are the evaluated datasets of our steel test coupons. These contour plots differentiate between the higher and lower stress states within the samples, from black to white respectively. As expected, maximum stress levels increase with increasing saturation of degradation. In the results illustrated in Figure 6, the max stress increased by 16% relative to baseline in the sample with 30% saturation of degradation, and the sample with 70% saturation of degradation increased max stress by 20%. The original results are mono-colored as expected, because the model was intentionally set up to provide a clean baseline. The contour plots of the degraded samples illustrate a contrast between localized regions within the model experiencing elevated stress states. This is most visible in the stress results of 70% degradation saturation, where black zones contrast sharply to indicate high stress concentrations. Figure 7 illustrates a zoomed in perspective of the 30% and 70% saturation samples for easier viewing. The stress results on the left demonstrate fewer light gray zones relative to the stress results on the right with more dark gray zones, where the saturation increases from 30% to 70% from left to right. Typically these sorts of stress results are visualized better on a rainbow contour plot ranging from blue to red, but a grayscale was chosen for ease of publication. Of potential interest is also that the location of maximum stress shifts between different samples. In this case, this shift is due to stress concentrations generated between degraded material adjacent to original material, which accounts for the random shifting. However in a more complex model, it would be interesting to note the various clustering of shifted max stress locations, as this may likely correlate with potential weak spots in the geometry, which may not be

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Int'l Conf. Modeling, Sim. and Vis. Methods | MSV'17 |

Fig. 7: Close-Up Stress Results of 30% and 70% Saturated Test Samples.

intuitive to the design engineer. These results also demonstrate a phenomenon outside the scope of this current research, but explained because this phenomenon ties in with intended future research. The stress contour plot of the 30% degraded saturation sample illustrates zones of lighter gray relative to baseline. These lighter regions experience reduced stress, and are an artifact resultant of large contiguous zones with common properties, in this case strength. With low levels of degradation saturation, localized regions appreciably far from degraded zones can experience lower stress if their distance from the critical load paths internal to the structure is significant. These internal load paths are generated by the generation of degraded zones which introduce new paths of least resistance relative to the baseline structure. These phenomenon are reduced with greater saturation, as evidenced by their reduced presence in the 70% saturation stress results. To more ideally control this phenomenon, clustering of degraded zones is proposed and explained later in this paper. Clustering should achieve similar results to increasing saturation in this application. Another improvement would be to evaluate with average stress across each test sample instead of max stress to avoid the localized effects of this phenomenon.

5. Applications and Future Work The intent of this initial effort was to validate our method and demonstrate how it could be applied. Tensile test samples of steel are not likely to experience random degradation. An application closer to reality would be to approximate the corrosive effect of rust on steel, by modeling preset ranges of the elements with degraded properties (in our case with reduced participation factors). Such a set of models could predict decreased performance when the structure is, for example, 10% covered in rust compared to 15% or 25% relative to baseline. Naturally, the simplicity of a tensile

test coupon would be replaced by real structures such as brackets, trailers, vehicle hulls, bridges, etc. Alternate evaluation criteria would make sense for a variety of differently interrogated subjects. This method could be applied to spot welds between sheets, to predict the resultant behavior if a percentage of the welds randomly fail. Nondestructive testing and evaluation of composite structures can use ultrasounds to measure variation of impedance through the thickness of walls. This method could introduce random noise into that impedance to help bound the test signal. Electrical conductivity can be modeled similar to such impedance, so this method could be extended to electrical wires or batteries potentially as well. One could apply this to polymers to measure the random decaying effect of UV radiation, or to transparent materials, such as glass, to determine how much visibility or transmissibility remains after a defined amount of cracking or number of impacts. In such examples, truly random dispersion of degraded properties is not enough. We would like to model the natural clustering of rust formations, the branching crack growths between impacts on glass, and other localized and patterned spreading of degradation. The work outlined in this paper forms a solid basis that can be extended in a number of ways moving forward. We intend to follow-up on several of these applications, but our immediate next step is to implement the aforementioned clustering and then branching capabilities, as they seem critical for several real-world applications.

6. Conclusions This process successfully demonstrated a method to turn unstructured data into usable models with defined statistical confidence. Natural phenomena like metals rusting or structures cracking can be very difficult to model due to lack of measurements or variability between samples. However, by

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Int'l Conf. Modeling, Sim. and Vis. Methods | MSV'17 |

converting the unstructured data into a range of degraded states, we can analyze thousands of perturbations from a baseline state to determine possible outliers as well as a statistically expected average with more confidence and fidelity than an analyst relying solely on engineering judgment and experience could. With this baseline established and validated, the path forward is to add more real-world effects into the computer models, first by introducing the options for clustering and branching effects so as to better reflect physical reality.

References [1] Altman, Douglas, et al., eds. Statistics with confidence: confidence intervals and statistical guidelines. John Wiley & Sons, 2013. [2] Bali, Madeleine, Herbert De Gersem, and Annette Muetze. "Finiteelement modeling of magnetic material degradation due to punching." IEEE Transactions on Magnetics 50.2 (2014): 745-748. [3] Boukhoulda, B. F., E. Adda-Bedia, and K. Madani. "The effect of fiber orientation angle in composite materials on moisture absorption and material degradation after hygrothermal ageing." Composite Structures 74.4 (2006): 406-418. [4] ElTobgy, M. S., E. Ng, and M. A. Elbestawi. "Finite element modeling of erosive wear." International Journal of Machine Tools and Manufacture 45.11 (2005): 1337-1346. [5] Fang, Z., and J. P. Harrison. "Development of a local degradation approach to the modelling of brittle fracture in heterogeneous rocks." International Journal of Rock Mechanics and Mining Sciences 39.4 (2002): 443-457. [6] Sfantos, G. K., and M. H. Aliabadi. "Multi-scale boundary element modelling of material degradation and fracture." Computer Methods in Applied Mechanics and Engineering 196.7 (2007): 1310-1329. [7] Wang, Yu-Fei, and Zhen-Guo Yang. "Finite element model of erosive wear on ductile and brittle materials." Wear 265.5 (2008): 871-878. [8] Weiss, Thomas, Siegfried Siegesmund, and Edwin R. Fuller. "Thermal degradation of marble: indications from finite-element modelling." Building and Environment 38.9 (2003): 1251-1260.

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Increased Realism in Modeling and Simulation for Virtual Reality, Augmented Reality, and Immersive Environments J. Wallace1, S. Kambouris1 1 Infinite Dimensions Integration, Washington, District of Columbia, United States

Abstract - Improved realism in modeling and simulation (M&S) efforts is increasingly viewed as a requirement across a wide spectrum of Department of Defense projects. M&S realism differs across the community of practice. The goal is to clarify the requirements for realism; elucidate the meaning of realism as it pertains to different communities; and frame key concepts in the representation of reality. In general, M&S efforts support several functional areas: training, mission rehearsal, test, evaluation, experimentation, acquisition, analysis, and planning. Increased realism supports and improves the functional areas listed above. In order to provide identification with, and understanding of realism, perspectives from each functional area will be discussed in addition to the role of modeling paradigms and implementation concepts. Keywords: realism, training, augmented reality, virtual reality, real-time, modeling and simulation, complex system representation

1

Introduction

Improved realism in modeling and simulation (M&S) efforts is increasingly viewed as a necessity or requirement across a wide spectrum of Department of Defense (DoD) projects. However, the definition of M&S realism differs considerably across the community of practice. The goal is to clarify the requirements for realism; to elucidate the meaning of realism as it pertains to different communities and to frame key concepts in the representation of reality. Augmented reality (AR) is a live direct or indirect view of a physical, real-world environment whose elements are supplemented by computer-generated sensory input (e.g. sound, video). Augmentation occurs in real time and in semantic context with environmental elements. With the help of advanced AR technology (e.g. adding computer vision and object recognition) the information about the surrounding real world of the user becomes interactive and digitally manipulatable. In AR, information about the environment and its objects is overlaid on the real world. This information can be virtual or real, e.g. seeing other real sensed or measured information. Augmented reality brings out the components of the digital world into a person's perceived real world. By

contrast, virtual reality (VR) replaces the real world with a simulated one. M&S supports realism adding to the effectiveness of the simulation. Training skills should be challenged without risking injury to people, or damaging equipment, while encompassing the full range normal activities a trainee would need to succeed. M&S analysis aims to provide a powerful set of tools to systematically improve training. In order to provide identification with, and understanding of realism, perspectives from each M&S functional area by discussing the role of modeling paradigms and implementation concepts. First, several common-sense questions regarding realism are needed for training systems: • How do we measure the realism of a model or simulation? • What is the relationship between realism, verification, validation, and accreditation (VV&A)? • How do we describe differences in realism across various models or simulations? • What underlying M&S development capabilities are needed to support the implementation of realistic simulated entities? • What type of computational infrastructure is required to realistically portray a large scenario in a M&S supported activity? o Is it visual? o Is it related to M&S resolution and fidelity? o Is it the physical motions and characteristics of simulated entities (e.g., kinetic aspects)? o Is it cognitive behaviors (e.g., non-kinetic)? o Are terms like kinetic, non-kinetic, visual, and nonvisual sufficient? • Does the representation of the environment fit in terms of a kinetic/non-kinetic taxonomy that currently appears in M&S vocabulary? The environment affects kinetic and non-kinetic phenomena. For example, weather affects sensors and vehicle mobility, yet the same conditions also affect human behavior at the humanin-the-loop grain of analysis. Therefore, it is important to consider how the environment is represented. The question arises whether to represent individual environment phenomena, or rather to represent correlated, consistent

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phenomenology. Unfortunately, there is no unified or widely accepted understanding for representing, or implementing, the interaction of entities and different phenomenologies. This makes objective assessment of M&S efforts extremely difficult as realism is often defined differently from one project to the next. Subsequently, a key question must then be raised: how can coding be simplified to permit implementation of numerous, extremely complex entities, phenomenologies, and systems? Consider the representation of human behavior and the act of driving where at times, we behave according to known rules – we stop when the light is red and accelerate when it turns green. We recognize external stimuli and events around us – a ball rolls into the street and we slam on the brakes or swerve to avoid it. We also strategize – what is the best way to navigate around a traffic jam? In order to effectively model behaviors, an infrastructure is needed with ability to accept rule-based implementations of decision making and behavior. The infrastructure must also have ability to execute many different model types that represent different aspects of behavior simultaneously. The ability to integrate any technique representing any aspect of behavior is needed. For example, consider neural networks to recognize phenomena from a collection of data, or the ability to implement strategic planning activities - e.g., utilizing techniques such as genetic algorithms (GA) or evolutionary programming (EP). Humans consistently strategize and make “optimal” decisions based on certain metrics such as traveling to a destination in the least amount of time. A warfighter will strategize, and choose a course of action to minimize casualties. GA can be utilized to model these types of mental activities. The computational methods must be flexible enough to operate synchronously (serial execution) or asynchronously (simultaneous execution). Other examples will be discussed later to describe the above concepts more fully in our representative M&S training example of the Quarterback Trainer.

1.1

Role of Realism

The training should be strikingly close to actual situations the trainee would experience. For this reason, phenomenologies such as visual depictions, and the ability to move in the environment must be correct. It is also necessary to obtain realism from a cognitive simulation point of view. The system should behave in accordance with the behavior of the entities in the relevant training environment. The interactions would then be cognitively and culturally plausible. If the training is not cognitively plausible, it may be ineffective, and will ultimately be a disservice to the trainees. Software must be readily updated, or interoperated with new software and systems in order to provide correct, expedient, and efficient training. Many systems are unable to fully benefit from the astounding visual resolution and fidelity, because the cognitive plausibility of the threats is not

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implemented. The paradox can be likened to a big budget movie with amazing special effects but minimal plot. Training suffers when the trainee does not see realistic information, and/or when systems do not respond realistically. Renewed effort is being made to fine tune the representation of individual pieces of the system based upon the challenges of our existing training systems. Fine tuning individual pieces of the training system may lead to major integration issues forcing system components to be compromised in the very areas extensive effort had previously been placed. A plausible solution might involve integrating the skeletal framework of system components, and developing an effective overall system in concert. However, then problems of scalability may arise due to inability to apply modern computing resources and techniques. The current generation of integration and interoperability techniques and technologies has focused around a network of workstation-type infrastructure which does not address all the new and needed requirements for training. System components frequently run in serial rather than parallel and the inter-processor communication primitives have high latency as well as relatively low throughput. This makes a full integration of phenomenology models limited in creating a realistic whole capable of effectively mentally engaging the trainee. Another problem arises when different phenomenology representations and implementations are combined. Often, a certain type of phenomena is represented in multiple M&S components, but is not represented consistently across all the components. The problems will persist until a comprehensive understanding of the interplay between representation infrastructure, computational infrastructure, and underlying system architecture are addressed.

2

Perspectives on Realism

The requirements in realism for Training vary widely depending on the task being trained. For instance, pilot training requires realistic graphics and behaviors of synthetic entities to be represented in an accurate and credible way. Underlying phenomena need to be represented in a way that does not require as much fine scale physical detail (such as pitch, roll, and yaw of an entity). Rather, we rely on the ability to represent underlying cognitive, or decision making, behaviors of the simulated entities (and gross physical motion) to respond realistically for the trainee. The input, as sensed by the trainee and the subsequent response of the system, needs to be plausible – it needs to look like a real system in a real-world environment. Otherwise, negative training can occur and the trainee suffers – this must be avoided. Negative training refers to practicing procedures in a manner inconsistent with how an action would be performed in combat, which results in developing bad habits; this occurs most when the trainee receives simulated data that does not reflect how the real-world system operates. At the other extreme, some training systems require high fidelity of visual and physical realism meaning representation of military

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equipment and human beings in a variety of capacities. As an example, some systems are flight simulators and “first person shooter” games that are used to teach culture, customs, and protocol – these are particularly dependent on accurate real world replications. What is difficult is ascertaining the correct level of fidelity for each component of training that is necessary in order to replicate the real world in ways that matter to the brain of the trainee. What are the statistically important stimulants to that decision process? We have observed that drawing photorealistic images is important up to a certain point, but if the system does not behave “correctly”, or move “normally” within the training environment, the brain rejects the training. Sometimes photorealism is required for fidelity. Oftentimes what matters most to the brain is the real world characteristic motion and behavior of a system being replicated to stimulate the decision-making process – information realism is critical for effective, efficient training. Traditionally, this has been the Achilles’ heel of M&S training systems. A training system is only as good as the quality of the data and information going in and coming out of it. Requirements for realism in Experimentation are similar to training because the ability to virtually predict the outcome of new training systems plays a critical role in making better investment decisions in a budget-constrained future (e.g., prior to full-scale production, or prediction of change outcomes in tactics, techniques, and procedures (TTP) before employment in real-world scenarios). The need to improve outcome prediction has led to the realization that we must improve our ability to model human behavior, groups, and organizations - hence, the term “non-kinetic” modeling. There is increasing need to understand how to better represent the highly-connected nature of non-kinetic phenomena, and the role of stochastic and deterministic models and modeling constructs. Realism pertaining to Test and Evaluation (T&E) is similar to training and experimentation. T&E focus has been on representation of physical phenomena, and interaction with system-under-test. Physical phenomena representation, with increased detail in real-time, drives the state-of-the-art. Due to the advent of net-centric warfare (NCW), the need exists to represent different types of phenomena accurately. NCW also requires representation of diverse systems, and system interactions, over various networks. Requirements include: creation of complex, realistic, and scalable networks of component inter-relationships, distribution of autonomous controls and monitors, implementation of complex webs of cause and effect, dynamic alteration of the component execution structure, and adaptation and evolution of the system. M&S goal for Analysis is to accurately predict aggregate behavior and data, requiring different mathematical approaches, versus replication of detailed physical characteristics and behaviors. The language of probability and

statistics tends to dominate modeling in this domain and in terms of realism, this has very different implications. Accurate prediction and representation of asymmetric threats, or irregular warfare, is a primary challenge to the abstract, or aggregate, type of M&S. The challenge is shared by all the disciplines, but particularly by analysis and planning. Cognitive modeling of Political, Military, Economic, Social, Infrastructure, and Information (PMESII) effects is another example.

2.1

Obstacles to Greater Realism

Achieving realism requires complex and simplistic phenomenology representation methods and techniques. The ability to relate models, and the influences between one another, is a key limitation. Representing cause and effect networks between models, with all of the interrelationships, in a manner that can scale, remains an important objective. For example, representing the human nervous system (how activation and information sweep through the body or the intricacy of an accurate cognitive model that provides real behavior prediction capabilities) presents extreme challenges. Significant need exists to easily represent a wide variety of asynchronous behavior for many independent activities. Also, the independent, yet connectedness of the real world is difficult to represent in current approaches to computer languages and programming. Some techniques do provide a few of the required capabilities, but there are no techniques that scale to millions of independent, interacting entities. Another feature needed to realistically represent phenomena involves representing sequences of associated activity, or behavior. Some systems have methods to support this capability, but scalability and ease of programming remain a challenge. The need to easily start and stop an action, based on the dynamically-changing conditions, is a related challenge. One of the most difficult requirements to develop realistic, complex models involves representing a network of activities triggered by changes in the physical world, or the logical world (e.g., cognitive events). Furthermore, the need exists to trigger a large number of processes based on a single changing activity. While not usually considered a phenomenology representation primitive, it is necessary to treat it as such in order to represent control of physical and non-physical systems. In this way for a model, multiple disparate sources of control inputs and/or dynamically generated inputs, in the case of non-physical systems, can be efficiently implemented. Flexibility provides value in the context of Live-Virtual-Constructive environments, including the ability to implement external and internal sources of control. The topic of non-kinetic modeling and the need to improve it, has received much attention in the industry. The need remains to identify cognitive phenomena and accurately represent cognitive events. The next section discusses methods in which this type of representation can occur, and uses analogies in nature describe the methods.

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Finally, no discussion on the topic of obstacles to realism would be complete without mentioning scalability. Scalability refers to the ability to represent and execute large numbers of active physical and non-physical processes simultaneously in real time. At the present time, the execution of millions of processes in real-time is beyond most systems. This is a function of software algorithm design and the interplay with complier construction, processor design and capabilities, the network capabilities, as well as memory and storage. In general, these relationships are not universally well understood in a variety of DoD M&S development endeavors and operational systems.

2.2

Complex System Representation Primitives and Realism

Complex system representation (CSR) employs a set of primitives that provide a powerful tool used not only in the development of highly complex systems and applications, but also in the development of realistic models and simulations [4, 5]. There have been a few cases of application development and integration software products which contain some elements of the CSR approach. However, no products currently exist that offer the full spectrum of representation primitives that allow functional, causal, and temporal synchronization and execution characteristics, as found in the framework employed on the Joint Strike Fighter Shared Synthetic Environment project [4,5]. This discussion utilizes several biological examples to highlight aspects of representing reality that have been historically difficult to solve in a scalable, yet easily comprehensible framework. This section outlines a set of representation primitives that enable improvements in representational realism. These examples have wide explanatory value since the human body is more complex than any mechanical system built to date. The following sections describe various complex system representation paradigms and the biological analogs which motivate them: • Asynchronous and synchronous internal characteristics or mechanisms • Asynchronous and synchronous external characteristics or mechanisms • Irregular time-scale internal characteristics or mechanisms • Irregular time-scale external characteristics or mechanisms The human heart is used as an example to illustrate CSR principles: a synchronized sequence of activities that occur naturally each time the heart beats (without thinking about it). The heart operates asynchronously from the rest of the body; but within the heart itself, a tightly synchronized set of activities occurs. The ideal system development methodology should permit this type of system development, implementation, and operation.

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Threads and processes are commonly used to instantiate such implementation paradigms [1,2,3]. However, numerous problems exist, including: Scalability – threads and processes rely on saving and restoring large amounts of memory or storage, which breaks down when millions of active threads and processes are required in both serial and parallel fashions. Performance – when underlying activity or processes are fairly simple, the overhead of memory storage and retrieval may limit utility. Implementation complexity – implementation syntax is typically arcane and complex usage is beyond the reach of all but the most talented programmers. Portability – most thread and process packages are not portable. Eliminating the use of stack frames is central to resolving the difficulties. Typically, large amounts of data needed to restore the computational context. The framework should provide a method to minimize the data required to restore the process state once it has been suspended. Some mechanism is needed to indicate which variables must be saved and restored. In this way, a large amount of memory can be saved; and the execution performance of processes can be drastically improved. This same situation occurs in many militarily significant situations – from the representation of complex kinetic phenomena to non-kinetics. The ability to have millions of active processes that can be synchronous or asynchronous, with the ability to be suspended and resumed, would be quite valuable in simulating cognition or social phenomena. Any method on an object should be able to become a process. A process can be represented as an event that passes time by suspending execution and resuming (maybe several times) before exiting (sometimes called “persistent events”) due to persistence over time. In this manner, a unified execution environment could be developed. Processes should support at least two ways of passing time. The framework for realism, ideally, should provide at least three types of primitives in order to provide the ability to represent and implement complex functional, causal, and temporal synchronization between components. The framework should support this primitive, providing an implementation of processes as objects. Interactions Between Individual Models, Components, or Systems – the construct of interactions provides a mechanism to represent and implement both data transfer and functional activation between models, components, or systems. They also provide polymorphism in event scheduling and processing, and are active, interoperable, and synchronous. Data can be transferred in sets of parameters, which is a container class used to store values of known types, and pass parameters in interaction handlers. They can contain values of type integer, float, double, string, and buffer (a byte array of arbitrary length) and are accessed through keys of type integer or string. This provides a general and scalable method for conceptualizing and implementing complex relationships between different aspects of the system, and flexibility in

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changing one part of the system without breaking the overall functioning of the overall system.

3

Implementation Examples

In the Code Examples, the problem of synchronizing the actions of components in the system is addressed. Not only must messages and a variety of disparate data be broadcast and received, activity and actions between the components must frequently be addressed to truly solve integration and interoperability problems. The implementation of this in a virtual simulation trainer is the example for this effort – the Quarterback Trainer and Evaluation System (QTES).

Figure 2. Components of the Virtual Reality Quarterback Training and Evaluation System

Figure 1. Virtual Reality QB System Evaluating correctly and optimizing the training and readiness of professional and collegiate football athletes is dependent on replicating full-speed, high-performance, game-like and practice environments. A mistake picking the wrong quarterback in the draft or free agency is an eight to nine-digit financial error, and can set a franchise back years (e.g. Detroit, Cleveland, Miami). There are many current examples where teams have recently made these costly mistakes. There are no current solutions in the market allowing for full-scale, real-time, dynamic game-like speed and performance levels on demand that generate the stress reactions of real life. One of the questions that must then be asked is “What are the Components of a VR QTES?”: a high fidelity virtual human Quarterback (QB) modeling, virtual human editing and simulation, stereoscopic visualization framework, 3D cinema, high performance simulation engine, and next-generation material modeling. The QB must be real and interact with surrounded and immersed in virtual reality (VR). Everything happens at realtime speed where a real QB, training in scene, throws a real football and interacts with his environment. The thrown ball hits a net while the virtual football continues in game atmosphere. The realistic simulation includes scene changes as the play executes. Virtual players represent the same attributes as the real players (offense and defense) and there is a roadmap that escalates to full 11-on-11 football. Why is this problem so difficult?

Training in VR versus the real world requires realism and it is only possible with a specialized real-time, distributed computing engine [6]. Realism requires extreme mathematical complexity which must be computed accurately and in real time. In order to succeed, it is necessary that the simulation reacts to a real QB. Using this method, there are no precomputed scenes like movies or games, the QB can and should be able to go anywhere and the VR players follow in response to the QB’s movement. This training system is quasicompetitive and uses VR headsets, and because the trainer is real-time, there is no system lag which then reduces simulator sickness. Often VR headsets provide no frame of reference or sense of placement on the field and have limited interactivity – these all lead to a lack of realism thereby greatly affecting training systems, by not engaging the QB decision-making process. What does a realistic trainer look like? It has a full-scale immersive VR system, Player Editing for physical appearance and individual characteristics, such as speed, strength, and football skills. Play Editing institutes the notion of a virtual playbook as well as a Coach/Auto-Coach Supervision, and Post Session Analytics. Using a QTES it is possible to nearly eliminate all practice injuries and allow coaches/owners to more accurately and realistically evaluate QB talent before they invest team funds. Furthermore, a QB can virtually practice against next week’s team as often as necessary to focus on strengthening and honing his skills. The QTES supports four progressive modules of increasing complexity allowing the QB to focus on areas of improvement: Receiver Coordination and Timing (RCT) Module - with the simulator running only the receiver routes, the quarterback will be able to practice throwing to each receiver in the pattern. Full Receiver Sets (FRS) Module - the full complement of receivers are present to run pass play patterns. Receivers Against Defenders (RAD) Module - linebackers and defensive backs and standard, simple defensive packages are provided to go against the full receiver sets. The quarterback runs the play with all coverages and fronts such as Cover 2,

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Cover 3, 3-4 and 4-3 sets. Full 11-on-11 Module - the full 11-on-11 action is simulated and the 12 standard blitz packages used by NFL teams will be added to the defense sets and then individually altered to simulate the plays run by specific opponents.

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High fidelity virtual human modeling requires the integration of every type of algorithm known: visual representation, movement, and behavior. Virtual human editing and simulation allows for the use to edit and customize humans. This simplifies data entry for the wide array parameter or data sets and enables a preview mode so that less time is wasted in scenario setup. One of the challenging parts of stereoscopic scene generation is that the system must generate two or more images simultaneously and update them in real-time.

Figure 4. Stereoscopic Scene Generation

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Figure 3. Play Editing and Physics-Based Human Modeling By designing the complete system down to parts list the entire system infrastructure for the real-time, distributed computing engine facilitates ease of external input streaming for the QB and Ball, as well as the external output streaming for threedimensional (3D) integration and data. The trainer uses high fidelity human motion models, football motion graphics detailing, while integrating the VR hardware. Using the Player Editor and Play Editor, a realistic real-time, interactive 3D stereoscopic scene can be generated comprised of: a virtual football stadium and football player clothing, football equipment and uniform using our physics-based materials framework, football play details and motion paths by Playbook Play, multi-screen, 3D stereoscopic, interactive VR scene generation (like the movies, but better), real-time computation of big data scale mathematical complexity, external real-time data I/O integration with compute streams (motion capture of QB), and accuracy of all phenomenology models.

Summary

This paper highlights the major problems of realism in M&S development and systems integration. Scalable solutions to complex, real-world problems are still elusive, as is seen in the lack of realistic non-kinetic models and simulations. As such, the time is ripe to begin cross-disciplinary discussions to define what realism means in the DoD context, and to identify potential solutions by using a QTES. Not surprisingly, the concept and meaning of the phrase “realism in M&S” is purpose-specific. There are some common themes, however. The ability to represent complex webs of synchronized cause and effect is central to the implementation of realistic M&S systems. Representing many simultaneously evolving phenomena that are interrelated is part of this capability, and critical to implementing realistic non-kinetic and complex kinetic phenomena. Components required to build a VR Training and Evaluation System are high fidelity virtual human modelling, virtual human editing and simulation, complex systems representation, stereoscopic 3D visualization framework, high performance simulation engine, and nextgeneration material modelling. Another key feature for solving M&S training is to be able to stop one activity based on some arbitrary web of logic, and start another in response to changing conditions. Not the least, scalability must be achieved while providing all these capabilities. This means the ability to support 106 or larger entities or distinct phenomena – to which there are few known solutions. Robust solutions are the gold standard, because they do not fail with perturbations to the information transacted. A robust solution is easy to maintain when systems are modified

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or upgraded, and straightforward to alter when new information is required, or additional constituent systems are added. Unless the problems of realism in complex phenomena and systems representation are studied and addressed, the value of M&S investments will underperform and consequently M&S investments will be devalued. Increasingly complex architecture theories, and computer software infrastructure fads, all require developers and resource sponsors to re-implement basic behaviour. As such, solutions to the hard problems are elusive. Trainees, in turn, do not receive the effective training and tools to improve their performance. The QTES is a way of bringing increased realism to the QB decision-making process.

5

References

[1] Microsoft, Inc., Microsoft .NET Homepage, (2005). Retrieved January 15, 2006, from http://www.microsoft.com/net/default.mspx. [2] Parallel Virtual Machine, Oak Ridge National Laboratory, (2005). Retrieved January 15, 2006, from http://www.csm.ornl.gov/pvm/pvm_home.html. [3] POSIX Threads Programming, Lawrence Livermore National Laboratory (2005). Retrieved June 30, 2005, from http://www.llnl.gov/computing/tutorials/pthreads/. [4] Wallace, Jeffrey and Hannibal, Barbara (2005). “An Enterprise-capable Application Development and Integration Solution for Complex Systems”, In Proceedings and the 2005 International Conference on e-Business, Enterprise Information Systems, e-Government, and Outsourcing, CSREA Press, Las Vegas, NV, June 20-23. [5] Wallace, Jeffrey and Hannibal, Barbara (2006). “A Naturalistic Approach to Complex, Intelligent System Development and Integration”, In Proceedings and the 2006 International Conference on Artificial Intelligence, CSREA Press, Las Vegas, NV, June 19-22. [6] Wallace, Jeffrey and Kambouris, Sara (2012). “A Naturalistic Approach to Complex, Intelligent System Development and Integration”, In Proceedings and the 2012 International Conference on Artificial Intelligence, CSREA Press, Las Vegas, NV, July 16-19.

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A Toolbox versus a Tool — A Design Approach H.-P. Bischof Department of Computer Science, Rochester Institute of Technology, Rochester, NY, USA Center for Computational Relativity and Gravitation, Rochester Institute of Technology. Rochester, NY, USA Abstract— Tools are built to be used as they are. The members of the development team of the tool are most likely the only ones who can make modifications to the tool. Others, even if the source code is available, could in principle modify the tool, but this is in most cases extremely difficult to achieve. A toolbox, on the other hand side, is intended to be extended, or modified often by many people. Unix is a good example for a toolbox. Everybody can add new libraries or commands used by one or many. Unix itself does not need to be touched in order to add user-defined functionality to it. This paper describes design criteria for a visualization toolbox which can easily be extended. Keywords: Visualization System Design, Toolbox Design, Tool Design, Data Flow

1. Introduction The first notion about the functionality of a Unix[3] pipe was written by Doug McIlroy[7]. McIlroy wrote on October 11, 1964: “We should have some ways of coupling programs like garden hose - screw in another segment when it becomes necessary to massage data in another way.” The idea of how to use a pipe was later described in[5]. In order for the individual components to be able to interact with each other they must agree on a communication mechanism. McIlory described in a conversation how this could be accomplished[8]: “Write programs that handle text streams, because that is a universal interface.” One philosophy behind Unix was write programs, which could do one thing well, and combine the programs in shell scripts using pipes for the communication. The author of a new command needs to know how write the output to stdout and how to read from stdin. This allows pipe sequences like: cat f i l e

| sort | lpr

cat sends the file to sort and then it gets printed. It is trivial to make this line into a distributed version without changing any of its used components: cat f i l e

| ssh computer s o r t | l p r

The shell is responsible for redirecting in and output channels. Adding new components to the Unix toolbox requires little special knowledge and is easy to accomplish: add an executable anywhere in the file system and make sure that the directory is in the search path. The rest of

this paper describes how the same philosophy is applied to a visualization toolbox and discusses the consequences of this. The paper describes how to design a system, which is intended to be modified by many.

2. Visualization Tools Most visualization systems like ParaView[6], VisIt[4], etc. apply a data flow principle to execute their programs. A visualization program is typically represented by a graph where the nodes are components and the data flows along the edges. The components’ purposes ranges from read the data, filter parts of the data, stream, analyze the data, create a visual of the data, store the image, and others. Distributing the components across a network, controlling the data flow through the system, etc. is done by the runtime system of the visualization system. The visualization graph is typically constructed with the use of a graphical programming environment. The graphical programming environment allows, besides creating the program, typically many more things, like selecting colors, specifying labels, adding decorative features, etc. A Unix pipe is a linear graph in which each component reads from stdin and writes to stdout. The program in Listing 1 illustrates how little knowledge is required to fulfill the communication requirement for a pipe (include files are omitted). First let us look at the writer. The writer writes the content of a character array to the standard out file descriptor and then exits with exit code 0. Listing 1: writer.c. i n t main ( ) { c h a r * msg = " H e l l o World \ n " ; w r i t e ( STDIN_FILENO , msg , s t r l e n ( msg ) ) ; exit (0); } The reader reads from the standard in file descriptor and stores the read information in a character array of length 20, prints the input text, and then exits with exit code 0. This program does not deal with buffer overflow issues. Listing 2: reader.c. i n t main ( ) {

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c h a r msg [ 2 0 ] ; r e a d ( STDIN_FILENO , msg , 2 0 ) ; w r i t e ( STDOUT_FILENO , msg , s t r l e n ( msg ) ) ; exit (0); } The two programs can be used in any UNIX shell environment like: $ c c −o w r i t e r w r i t e r . c $ c c −o r e a d e r r e a d e r . c $ writer | reader H e l l o World The communication requirements for a visualization program are a bit more complicated, because it must be possible to connect n output channels with k input channels, n and k ≥ 1. This is required in order to write components that do one thing, but do this one thing very well. A program of this kind is shown in Figure 1. This program reads the data for Black Hole and Gravitational Wave visualization, and sends the data to the visualization components and both components send the visual representation to a display component where the visuals will be combined.

4. Separation of Functionality A visualization tool should be divided up into a runtime system, which executes then visualization program, and programming environment. Most programming environenment are graphical programming environment because this is the easiest way to construct a program. We modeled our system based on how a shell executes components in a Unix pipe. Unix is buffering the data, and sending the data along through the system. The shell is responsible for connecting the input channels and output channels of the individual components. The functionality to execute a command of a pipe sequence remotely is implemented in the shell, not in the command.

5. Need to Know about Adding New Components Adding a new component to the system should only require an understanding of the communication agreements and where and how to add it to the system. The next few chapters describe how this can be achieved in Spiegel.

6. Communication The following code illustrates the definition of the communication channels. The input channels must have a name, and a type, which later on allows type safe connections. In Listing 3 we define two input channels, of type Boolean and Integer.

Fig. 1: A snippet of a visualization program.

3. Design Principles for a Visualization Toolbox There is no reason to modify a visualization system if its functionality provides what is needed. The source code for many visualization systems is available. In the case of ParaView it is even possible to request a new feature. ParaView is a wonderful visualization system and as such has received the Best HPC Visualization Product Editor Choice 2016 award. As of April 5 2017, the top four requests have more than 150 votes and many more requests have single digit support. The question is: How easy or difficult is it to add functionality to an existing system. Two “Community Contributed Plugins” are available for download. Why only two? The modification of ParaView is in its complexity comparable to modifying a Unix Kernel; doable but not easy. Spiegel[2], a visualization toolbox, implemented in Java, shall be used as an example of how to design a visualization toolbox in such a way that it is easy to add functionality.

Listing 3: Input Channel Definition. public s t a t i c DataInput . Info [] input = { new P a r a m I n p u t . I n f o ( " r a n d o m C o l o r " , Boolean . c l a s s ) new P a r a m I n p u t . I n f o ( " a I n t e g e r " , Integer . class ) } The output channel follows the same structure as the input channel. More channels could be added to the block structure if needed. In Listing 4 we define one output channel, of type Integer. Listing 4: Output Channel Definition. publ ic s t a t i c DataOutput . Info output [ ] = { new D a t a O u t p u t . I n f o ( " C h a n n e l " , Integer . class ) } Having typed input channels prevents the connection of an input with a ncompatible output channel. Most visualization systems use graphical editors to build the visualization programs. The connectable connections can be highlighted if typed channels are used.

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7. How to Add a new Component Adding a new component in a Unix system requires adding a component to a directory, adding the directory to the search path if necessary, and making the command executable. The Java equivalent to this is: Create a component, compile it, and add a class to CLASSP AT H. Adding a new component to the Spiegel visualization system requires only an understanding of the Java CLASSP AT H mechanism, and how to code in Java. In other words no additional knowledge is needed in order to extend the system. It is useful to have a visual programming editor to write programs for visualization systems. This requires having menus populated in a meaningful way. The Spiegel system examines the directory structure below the directory plugin. Every directory in this sub tree becomes a menu item like “visual”. Directories below first level directories, like “atomic” become automatically sub menu items. It is not desirable to include every class found in the class path below, and it is now and then desirable to include a component in more than one category. Determination of including a class and in which menu it appears, is decided by the values of a variable named “category”. Currently the configuration is analyzed during compile time, but in the future it will be done via reflection. Listing 5 shows the example for a component appearing under the visual and filter menu. Figure 2 shows an automatically populated menu. Listing 5: Category. public s t a t i c String [] category = {" v i s u a l " , " f i l t e r " , } ;

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information from an input stream. The black hole data structure needs to be added to the system, because it does not exist at this point. In most cases this is not required because a large number of data structures do exist. We add this data structure just to show how it is done in principle. The file structure for the input data is describe in Listing 6 Listing 6: Category. black hole id x y z An example of the input is shown in Listing 7 Listing 1.3 1.5 4.5 # 1.5 0.2 0.2 # 1.0 0.0 0.0 # ... # and s o on

7: Input Example snippet. time step 1 time step 2 time step 3

The following new developments need to be made in this order: 1) a new data structure, and we will name it BlackHole.java 2) a component which reads the input data and sends out the BlackHole data structure and we will name it BHreader.java. The new data structure will be used in this component. We assume there is only one black hole per time step. 3) a filter, and we will name it BHf ilter.java. The new data structure will be used in this component. 4) a test program and we will call it bhT est.sprache. The complete system can be downloaded from http://spiegel.cs.rit.edu/∼hpb/Spiegel. bhT est.sprache will not be shown in this paper. A visual representation of bhT est.sprache is shown in Figure 3.

Fig. 3: Visual Representation of bhTest.sprache.

Fig. 2: Automatically populated Menu.

8. A Complete Example This chapter is a complete exmaple of how to add a new filter to the system. The filter will read black hole

In most cases only the filter component would need to be developed, because the other pieces do already exist. We implement 2) only in order to be able to develop a complete and running program. In most cases this would not be required, because the component would exist. First, we implement BlackHole.java. Listing 8 shows the complete source code. BlackHole.java is a data structure which represents one Black Hole, to be more precise the x, y, and z position of it. The constructor sets the three position instance variables, which can be accessed via the get-methods.

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Listing 8: BlackHole.java package s p i e g e l . viewcontrol . function . datatypes ; p u b l i c c l a s s BlackHole { p r i v a t e double x ; p r i v a t e double y ; p r i v a t e double z ; p u b l i c BlackHole ( double x , double y , double z ) this .x = x; this .y = y; this . z = z;

Fig. 4: Component Information. {

} p u b l i c d o u b l e getX ( ) { return x; } p u b l i c d o u b l e getY ( ) { return y; } p u b l i c double getZ ( ) { return z ; } public String toString () { r e t u r n x +"/"+ y +"/"+ z + " / " ; } } The next class we implement is responsible for reading Black Hole data one by one from a file. The first part of this file is shown in Listing 9. This part shows all necessary initializations. The BHReader component is highlighted in the visual program, and the values are shown below. The input hostName is automatically set, because the base class Function includes this instance variable. The component would automatically distributed to a host, if this value would be initialized with a host name. The DataInput and DataOutput variables store the type and names of the communication end point channels. The category for this component is extractor, because of the functionality of the component. The values of the initializations are used by the visual programming environment as shown in Figure 9 and Listing 4. This allows the developer to see the parameter types for the component, and information about who wrote the component, and a very short description about the functionality of the component. Listing 9: BlackHoleReader Part 1

package s p i e g e l . v i e w c o n t r o l . function . plugins . extractor ; # some i m p o r t s a r e n o t shown import spiegel . viewcontrol . function . d a t a t y p e s . BlackHole ; p u b l i c c l a s s BHreader e x t e n d s F u n c t i o n { p u b l i c s t a t i c S t r i n g displayName = "BH e x t r a c t o r " ; public s t a t i c String description = " E x t r a c t s BH’ s from a f i l e . " ; public s t a t i c String [] authors = { " Hans−P e t e r B i s c h o f " } ; public s t a t i c DataInput . Info [] input = { new P a r a m I n p u t . I n f o < String >(" fileName " , ull , String . class ) , }; publ ic s t a t i c DataOutput . Info output = new D a t a O u t p u t . I n f o ( " b l a c k H o l e " , BlackHole . c l a s s ) ; public s t a t i c String [] category = {" e x t r a c t o r " } ; p r i v a t e ParamInput < Str in g > fileNameIn ; p r i v a t e DataOutput blackHoleOut ; The last part of the BHreader, shown in Listing 10, implements the method update. This method will be called from the runtime system when needed. First, the file name is read and then the file is opened. The file will only be opened the first time update is called. Only one line will be read to initialize the BlackHole data structure. This data structure is then sent to the next

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component, in this case BHfilter. Listing 10: BlackHoleReader Part 2 BufferedReader theBlackHoleFile = null ; p r o t e c t e d void update ( ) { String thePosition [] = null ; S t r i n g fileName = fileNameIn . get ( ) ; try { i f ( t h e B l a c k H o l e F i l e != n u l l ) { theBlackHoleFile = new B u f f e r e d R e a d e r ( new F i l e R e a d e r ( f i l e N a m e ) ) ; S t r i n g aBH = t h e B l a c k H o l e F i l e . readLine ( ) ; t h e P o s i t i o n = aBH . s p l i t ( " \ \ s + " ) ; a B l a c k H o l e = new B l a c k H o l e ( new Double ( t h e P o s i t i o n [ 0 ] ) , new Double ( t h e P o s i t i o n [ 1 ] ) , new Double ( t h e P o s i t i o n [ 2 ] ) ) ; blackHoleOut . s e t ( aBlackHole ) ; } } catch ( Exception e ) { blackHoleOut . s e t ( n u l l ) ; }

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public s t a t i c String [] c a t e g o r y = {" v i s u a l " } ; p r i v a t e ParamInput b l a c k H o l e S i z e I n ; private SingleDataInput bhIn ; p r i v a t e DataOutput objectOut ; The second part is shown in Listing 12. The method update gets the values for size and the Black Hole Data structure first. This creates a Branchgroup, representing the visual which will be sent to the next component via objectOut.set(theBH). Listing 12: IBlackHoleReader Part 2 public void update ( ) { B l a c k H o l e aBH = b h I n . g e t ( ) ; double s i z e = blackHoleSizeIn . get ( ) ; i f ( aBH ! = n u l l ) { BranchGroup theBH = new BranchGroup ( ) ; # committed o b j e c t O u t . s e t ( theBH ) ; } else {

}

objectOut . set ( null ) ;

The last component we need to implement is the class BHfilter. The first part is shown in Listing 11. Like before we need to initialize the components used for the visual programming environment. Listing 11: BlackHoleReader Part 1 public class BHfilter extends Function { public s t a t i c String displayName = " B H f i l t e r " ; public s t a t i c String description = " D i s p l a y s BH’ s " ; public s t a t i c String [] authors = { " Hans−P e t e r B i s c h o f " } ; public s t a t i c DataInput . Info [] input = { new P a r a m I n p u t . I n f o (" blackHoleSize " , new Double ( 0 . 0 3 ) , Double . c l a s s ) , new S i n g l e D a t a I n p u t . I n f o < B l a c k H o l e > ( " bh " , BlackHole . c l a s s ) }; publ ic s t a t i c DataOutput . Info output = new D a t a O u t p u t . I n f o (" object " , BranchGroup . c l a s s ) ;

} }

9. Conclusion The described paper lays out a framework which has been proven that it is easily extensible. Many undergraduate students used it on their REU experience here at RIT. We gave the students on purpose minimum instructions on how to add components and within an hour they started to add components. The students did not need to modify the runtime system.

10. Future Work At this point the runtime system and the graphic programming environment is combined. These individual components need to be separated. The runtime system allows a distribution of the components across a network, but there is no functionality provided for a finer distribution mechanism. This needs to be improved. Functions can be created, but not easily. This functionality needs to be improved. The runtime system is not optimized for speed, memory use, disk access etc. This functionality needs to be improved. Neither the visual programming environment or the runtime system is using reflection[10] to detect usable components. The use of Java reflection would enhance the modifiability of the system.

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11. Acknowledgements The authors would like to thank all members of The Center for Computational Relativity and Gravitation at RIT. Their visualization needs drove much of the development of Spiegel system.

References [1] D., Foulser, “IRIS Explorer: a framework for investigation,” ACM SIGGRAPH Computer Graphics - Special focus: modular visualization environments (MVEs), vol. 29, Issue 2, pp. 13-16, Nov. 1995. [2] H.-P. Bischof, E. Dale, and T. Peterson, “Spiegel - A Visualization Framework for Large and Small Scale Systems”, in Proc. MSV’06, 2006, paper, pp. 199-205. [3] Dennis M. Ritchie, “The Evolution of the Unix Time-sharing System”, (April/2017) [Online]. Available: http://www.princeton.edu/~hos/ Mahoney/expotape.htm. [4] DHank Childs and Eric Brugger and Brad Whitlock and Jeremy Meredith and Sean Ahern and David Pugmire and Kathleen Biagas and Mark Miller and Cyrus Harrison and Gunther H. Weber and Hari Krishnan and Thomas Fogal and Allen Sanderson and Christoph Garth and E. Wes Bethel and David Camp and Oliver Ruebel and Marc Durant and Jean M. Favre and Paul Navr at all, “VisIt: An End-User Tool For Visualizing and Analyzing Very Large Data”, High Performance Visualization–Enabling Extreme-Scale Scientific Insight, pp. 357-372, Nov. 2012. [5] Brian W. Kernighan and P. J. Plauger, “Software Tools”, AddisonWesley Publishing Company, 0-201-03669-X [6] Paraview. (April/2017) [Online]. Available: http://http://www.paraview. org/ [7] Paraview. (April/2017) [Online]. Available: http://www.princeton.edu/ ~hos/Mahoney/expotape.htm [8] Paraview. (April/2017) [Online]. Available: http://doc.cat-v.org/unix/ pipes/ [9] David Eberly. (April/2016). Kochanek-Bartels Cubic Splines (TCB Splines). [Online]. Available: http://www.geometrictools.com/ Documentation/KBSplines.pdf [10] Ira R. Forman, and Nate Forman. “Java Reflection in Action.” Manning Publishing Company, 2004.

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Generating Shapes and Colors using Cell Developmental Mechanism Sezin Hwang1, Moon-Ryul Jung2 Department of Art Therapy, College of Medical Science, Daegu Haany University, Korea 2 Corresponding Author, Graduate School of Media, Art Technology, Sogang University, Korea 1

Abstract – We present a generative algorithm that creates various shapes and colors using a process analogous to cell development mechanism. Starting from the single initial cell, the cells divide, grow, place themselves, and acquire shapes and colors by the artist-defined intuitive rules and random variations to the results of the rules. The rules determine the attributes, i.e. the position, size, and color of each generated cell relative to the parent cell. The algorithm draws the cells in various figures, such as circles, lines, and circular arcs. Unlike traditional painting that mixes color pigments, this method creates unpredictable colors by blending thousands of lights emitted by each shape directly. By adding randomness to the attributes of children cells, the algorithm creates a process that produces unpredictable moods with various shapes and colors holding the viewer's attention. Keywords: Cell Developmental, Generative Art, Algorithm Painting

1

Introduction

This research aims to look at painting from a new perspective, that is, by means of computational capability of computer. In computer arts, computers are often used as software editing tools for creating shapes or images. The authors, however, believe that employing computer programming directly without using an editing or modeling tool is a worthwhile approach to creative computer arts. In particular, we want to employ cell developmental mechanism for the purpose of shape generation. All living things in the world are generated by means of cell developmental mechanism. So, it would be interesting to see what happens when we apply this mechanism to painting. In this algorithm a basic shape element acts as a cell. And the first shape is divided as in cell division, creating numerous shape elements that have various sizes, colors, and positions, and a painting is created by combining them. Each cell is divided when it is grown to become as twice as the original size. When a cell is divided from the parent cell, its position, size, and color is determined in its relationship with the parent cell. When numerous cells are overlaid upon one another, the colors are mixed and create unpredictable colors. When a ‘cell’ is drawn in this is drawn in this algorithm, it is drawn in various basic figures such as circles, lines, and circular arcs.

2

Background

The first case of using computer for artwork was arguably paintings produced by computer algorithms. From the mid1960s, scientists and artists tried such projects, and some of them called themselves “The algorists”[1].

2.1

Harold Cohen

Cohen is an artist who used to be recognized as top 5 British artists in the late 1960s, but suddenly stopped painting and started developing an artificial intelligence-based automatic painting system called AARON. Cohen, by creating an Expert System [2], made a freehand line drawing program. The rules of AARON were inspired by children’s doodling styles. AARON starts to draw a figure from an arbitrary point on the canvas and stops when an adequate number of figures are drawn [3]. (Figure 1-a) is an example of such drawing. Different drawings are created each time due to random aspect of the painting system [4]. AARON materialized the traditional genre of line drawing with artificial intelligence and was expanded to draw concrete forms.

Figure 1-a. (Left): AARON's Line Drawing 1979. © Harold Cohen, 1-b. (Right):Kawaguchi Yoichiro, 1996. © Kawaguchi Yoichiro.

2.2

Growth Model of Kawaguchi Yoichiro

Kawaguchi, by using a method called “growth model,” has visualized developmental process of life forms [5]. Growth model algorithm starts from the initial form and grows and regenerates it, by repeatedly applying simple rules. This growth algorithm uses a genetic algorithm. The result of the growth algorithm is represented in constantly moving animation and (Figure 1-b) is a snapshot. Growth model continuously creates an abstract life form starting from the initial form. The present study aims to create pure abstract painting using only forms and colors in the framework of traditional painting.

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2.3

Automatic Geometric Abstract Painting by A. Michael Noll

In 1965, Noll had the first computer art exhibition in the US with Belaulesz at Howard Wise Gallery [6]. Noll was highly interested in the unpredictability of drawings created by computer and recreated artworks of existing painters. (Figure 2-a) is Composition with Lines by Mondrian, who is well known for geometric abstract painting. (Figure 2-b) is a drawing produced by using random numbers of computer. One hundred participants received a paper in which (Figure 2a) and (Figure 2-b)were printed on one paper and asked to distinguish the one generated by computer. 59 out of 100 participants judged (Figure 2-b), which was generated by computer, to be Mondrian’s actual painting, and only 28 chose (Figure 2-a), the artwork by Mondrian [7]. Noll argued many people who participated in the test linked randomness to human creativity and confused it with Mondrian’s painting [8]. Noll’s algorithm painting does not allow intervention by an artist and many of his works (Figure 2-c) use simple black and white geometric forms and lines.

3 3.1

The Decision Rules of Cell Attributes Composition Rule

In this work, a simple “cell” developmental mechanism is used to generate painting. In general, rules that compose a painting include so many elements [9]. However, in this work we want to use simple rules to see what they produce when they are repeatedly applied. We introduce three simple rules for determining the attributes of children cells derived from parent cells. In the process of devising composition rules, Kandinsky’s painting of (Figure 3-a) has been examined carefully. standard rules is necessarily limited, and that the images from such systems do not exhibit continuous change. A child cell is defined by three attributes, as shown in (Figure 3-b): (1) the size of the cell, (2) the color of the cell, and (3) the displacement from the parent. The rules are designed so that the size of a child cell is determined in inverse proportion to the size and color brightness of the parent cell, and the color brightness of the child cell is determined in inverse proportion to the color brightness of the parent cell, and the displacement from the parent is determined in proportion to the difference between the color of child cell and the color of the parent cell. These are simple and intuitive rules designed so that shape elements forming a painting should be balanced in their attributes, such as size, color, and displacement, rather than being one-sided in these attributes.

Figure 2-a. (Left): Mondrian, Composition with Lines, 1917. © Mondrian, 2-b. (Center): Michael Noll, Composition with Lines, 1964. Composition of Mondrian’s lines regenerated a computer. © Michael Noll, 2-c. (Right): 3-Dimensional Projection of a Rotating 4-Dimensional Hypercube, 1962. © Michael Noll. For interesting composition, the present method allows direct involvement of an artist and various combinations of geometric forms and colors. The differences between several existing studies and the present research are shown in (Table 1). Artist/System

Characteristics

Characteristics of This study

AARON by Cohen

• Automatic painting system using the expertise of the artist • Line drawing with concrete forms

• Although the artist’s knowledge is used, the painting is abstract and geometric in form

Growth Model by Kawaguchi

• Visualization of developmental process of a life form • Artificial life forms

• Pure abstract painting created only with forms and colors

Geometric abstract automatic painting by Noll

• Arbitrary recombination of empirical knowledge of existing artists • Simple geometric black and white forms

• Various geometric forms and color combinations by active involvement of the artist

Table 1. Characteristics and Difference of Existing Studies.

Figure 3-a. (Left): Several Circles 1926. © Kandinsky, © 3-b. (Right): Visual grammar of Several Circles: In this painting, the colors, sizes, and relative distance among circles seem to be the most distinct attributes.

3.2

The Rules Determining the Attributes of Children Cells

3.2.1 The Decision Rule of the Child Cell Size The child cell size is determined to be in inverse proportion to the size of the parent cell for the following reason. If the parent cell is big, the feel of its presence in the painting is strong. It is reasonable for the child cell to have a weak presence and so be small. If the parent cell is small and has a weak presence, the child cell is made big so that the cells as a whole have some balance in size. The child cell size is

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determined in inverse proportion to the brightness of the parent cell as well for the following reason. If the parent cell is dark, it has a weak presence. It is reasonable for the child cell to have a strong presence and so be big. 3.2.2 The Decision Rule of the Child Cell Color The brightness of the child cell is determined in inverse proportion to parent cell brightness for the following reason. When the parent cell is bright, the child cell is made dark, and vice versa. It is to make the brightness of the parent and child cells in balance. The brightness of the child cell does not uniquely determine its color. So the color of the child is determined by randomly choosing a color whose brightness matches the given brightness.

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in (Figure 4). Each cell grows until it becomes double the original radius. Then the cell divides and produces a child cell.

4.2

The Decision Rules of Cell Attributes

4.2.1 The Decision Rules of Cell Attributes For the development-based painting algorithm, the initial state of the root cell must be given. The color of the root cell, that is, the R, G, B, A [12] values are randomly chosen between 0.0 and 1.0 and the maximum cell size is set to be 40 [13]. The position of the root cell is set to be (0.0, 0.0, 0.0) so that it is right at the center of the global coordinate system. The initial size of the canvas is set at 500x500 pixels. The initial setting can be adjusted.

3.2.3 The Displacement from the Parent Cell The displacement between the parent and child cells is expressed in terms of rotation and translation in 3-dimensional space [10]. The rotation is determined randomly while the distance from the parent is in proportion to the difference between the child cell color and the parent cell color. In other words, when the difference in color is small, the displacement of the child from the parent is made small, and vice versa, in order to avoid putting contrasting colors close together.

4

Painting Algorithm

Figure 5-a. (Top left): Deciding child cell radius, 5-b. (Top right): Calculation of brightness coefficient and child brightness, 5-c. (Bottom left): Calculation of child cell color, 5-d. (Bottom right): Calculation of displacement from parent cell.

Figure 4. Tree structure of the algorithm. Fig. ① root cell divides cell 1 and takes it as its child. Fig. ④: the root cell divides cell 2 and takes it as its cell along with cell 1. Also, cell 1 divides cell 3 and takes it as its child; cell 3 and 2 do not have a child and nodes without a child are leaf nodes.

4.1

4.2.2 The Decision Rule of the Child Cell Size As explained in Chapter 3, the child cell radius is set to be in inverse proportion to the parent cell radius, and the parent cell brightness. The child cell size is obtained by using the following formula. Formula 1.

Overview of the Algorithm

Each cell is represented as an object in the programming language (a script language called Python) [11], and the attributes of an object include the color, size, displacement from the parent object, and the list of the children objects linked to the object. Through this children list, the algorithm visits the children objects of a given object and performs various actions on them. The flow of the algorithm is shown

Formula 2.

Formula 3.

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(Formula 2) shows that the size coefficient of the child cell is in inverse proportion to the ratio between the parent cell radius and the maximum cell size, and the parent cell brightness of (Formula 1). Here, the parent cell brightness is the average value of ( ). (Formula 3) shows that the size of child cell is determined y multiplying the size coefficient of (Formula 2) by the parent cell radius. (Figure 5a) shows a flow chart for determining the child cell radius. 4.2.3 The Decision Rule of the Child Cell Color The child cell color is randomly chosen after first determining the child cell brightness. Formula 4.

Formula 5.

elif

else :

The child cell color ( ) is determined by the rule described in (Formula 8). First, select a random number by using f = random ( ) [14]. f becomes a random number between 0 and 1. If f < 0.33, of the child cell color is chosen randomly, and, if f < 0.66, of the child cell color is chosen randomly. And is determined by using the brightness condition. (Figure 5-c) shows a diagram for calculating the color of the child cell. 4.2.4

The brightness coefficient is determined according to (Formula 4) so that the child cell brightness is in inverse proportion to the parent cell brightness. The child cell brightness is obtained by (Formula 5). If the parent cell is bright and has a strong presence, the child cell has a dark color and a weak presence, and, if the parent cell is dark and has a low presence, the child cell is made bright so that it has a strong presence. It is to make the parent cell and the child cells are balanced in brightness. (Figure 5-b) shows a flow chart for calculating the child cell brightness. The color of the ). The α value of the child cell is expressed as ( child cell ( ) is set to be in inverse proportion to the α value of the parent cell by (Formula 6). The child cell color ( ) is chosen randomly among the colors that has the same brightness as the child cell brightness obtained by (Formula 7).

The Decision Rule of the Displacement from the Parent Cell The displacement from the parent cell is expressed in terms of rotation and distance. The distance is determined by a given rule, whereas rotation is chosen randomly. The distance between the child cells from the parent cell is set to be in proportion to the distance between the child cell color and the parent cell color. The color distance coefficient is set as follows: Formula 9.

When the color distant coefficient is obtained, the distance between the parent cell and the child cell is calculated by Formula 10.

Formula 6. Here, basicDistance is the distance between the centers of the two cells. Formula 7. Formula 11. Formula 8. If there is a large difference between the child cell color and the parent cell color, the child cell is located far from the parent, and if there is a short distance, the child cell is located near the parent. This is to place cells with similar colors close and cells with contrasting colors far from one another, in order to reduce the feel of contrast between the parent and the child.

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(Figure 5-d) shows a flow chart for calculating the displacement from the parent.

4.3

Morphological Expression of Cells

4.3.1 Shapes of Cells In this algorithm, to draw a cell, a polygon expressed by (Formula 12) is used. Specific polygons are defined by specifying the parameters and θ in (Formula 12). This algorithm uses four forms of cells. A sequential number is assigned to each cell as a cell id, each time they are created. If the cell id number modulo 4 is equals 0, the first cell form is drawn, if the cell id number modulo 4 equals 1, the second form is drawn, and so on. Formula 12. [15]

In (Formula 12), when a > b, it looks like an oval long in the direction of the x axis, and when a < b, long in the direction of the y axis. Especially, larger difference between a and b creates an extreme oval which looks like a line segment. 4.3.2

Figure 6-a. (Top left): Expression of (Formula 13), 6-b. (Top right): Result of painting by overlaying 3,000 polygon from (Formula 13), 6-c. (Bottom left): Expression of (Formula 14), 6-d. (Bottom right): Result of painting by overlaying 1,200 polygon from (Formula 14).

Examples of Cell Shape

When the parameters are set as in (Formula 13), we obtain a polygon as shown in (Figure 6-a). Formula 13.

(Figure 6-b) shows the result of painting by overlaying 3,000 polygons of this form. Formula 14.

As another example, a polygon drawn by (Formula 14) creates a polygon as shown in (Figure 6-c). When three different cell shapes are used and 1,200 polygons are overlaid, the result is (Figure 6-d).

4.4

Blending of Cell Colors

When the proposed algorithm generates and draws numerous cells, many cells are overlapped in position. In this case, the colors of the cells become blended. In OpenGL programing, blending is performed automatically if the blending function is set in advance. If the initial background image is given for the frame buffer, each time a new image is drawn, the color blending between the current frame buffer image and the newly drawn image takes place according to the given blending function.

5

Conclusions

In this paper, we have described a painting algorithm that uses a simple cell developmental mechanism. It is analogous to developmental mechanism in which cells are divided and an individual creature is developed. The painting algorithm proposes an intuitive algorithm that determines the color, size, and displacement of a child cell relative to the parent cell, when the child cell is divided from the parent cell

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Figure 7. Sequence of Generating shapes and colors © Sezin Hwang and Moon-Ryul Jung. In the beginning, we had some doubts that an interesting painting would be generated by a development-based algorithm, but the result (Figure 7) was surprising[16]. An extremely interesting painting was by generating and combining basic shape elements according to three simple decision rules. Although the algorithm is basically automatic, the paintings were made with an active involvement of the author in setting the parameters of the algorithm. In this sense, it is differentiated from conventional automatically generated paintings. It is a new painting technique available to artists who know how to do computer programming.

6

and illusion of space, illusion of movement, brightness, and color. [10] Sumanta Guha, Computer Graphics Through OpenGL, (CRC Press, 2011) [11] Allen Downey, Think Python-How to Think Like a Computer Scientist, (Green Tea Press, 2008) [12] Four channels of 32 bit graphic system. A refers to Alpha channel. [13] The units of measurement used in 3-d computer graphics are arbitrary, and only the relative relation is important. However, it can be thought of in terms of' cm or m for convenience’s sake. [14] Function random( ) creates a random number between 0 and 1 [15] Fletcher Dunn, Ian Parberry, 3D Math Primer for Graphics and Game Development, (CRC Press, 2001) [16] https://www.youtube.com/watch?v=9-Q_65Qsap8

References

[1] http://www.algorists.org/algorist.html [2] An expert system is a program that expresses knowledge of an expert by using a number of ‘If...then... else’ rules to solve a given problem. [3] Harold Cohen, “ How to Draw Three People in a Botanical Garden,” (Technical Report, University of California, San Diego, 1988) [4] Pamela Mc Corduck, AARON'S CODE: Meta_Art, Artificial Intelligence, and the Work of Harold Cohen (W.H Freeman and Company, New York, 1992) [5] http://design.osu.edu/carlson/history/tree/kawaguchi.html 6. Frank Dietrich, The First Decade of Computer Art (19651975), Vol.19, No.2, 159 (1986). [6] Frank Dietrich, The First Decade of Computer Art (19651975), Vol.19, No.2, 159 (1986). [7] A. Michael Noll, Human or Machine: A subjective comparison of Piet Mondrian’s “Composition with Lines”(1917) and A computer-generated picture, (The Psychological Record, 1996) 1-10. [8] A. Michael Noll, The digital Computer as a creative medium, (IEEE Spectrum, Vol.4, No.10, 1967) 89-95 [9] David A. Lauer & Stephen Pentak, Design Basics, (Sixth Thomson Wadsworth, 2005) According to Design Basics, which is widely used as a textbook for basic design education in universities in South Korea and elsewhere, elements to compose a design include unity, balance, emphasis, focal point, scale, proportion, rhythm, line, shape, volume, texture,

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Modeling Business Processes: Events and Compliance Rules Sabah Al-Fedaghi Computer Engineering Department Kuwait University Kuwait [email protected]

Abstract—Business process modeling has become a main tool for capturing, analyzing, and developing business activities. Each business process model acts as a blueprint for instances. The ordering of activities can be controlled through process orchestration that includes a choreography representing the start and end events of the interacting processes. Events play a crucial role in expressing interrelationships between business processes. This paper re-explores the notion of events in business processes through proposing that an event is a thing that can be created, processed, released, transferred, and received. This idea is developed such that events are recognized from the description of the processes involved. Accordingly, the choreography of the execution of processes can emerge from the specification of events. The same idea can be applied in the area of ensuring compliance with imposed rules, where the problem is how to specify the constraints and rules in preparation for applying them. The system description (schemata) can be constructed from the compliance rules. Keywords—business process compliance; process modeling; diagrammatic language; conceptual model

I.

INTRODUCTION

Business processes refers to those activities that take inputs and create an output that is of value to the customer [1]. A process itself is described in an abstract description of a collection of activities [2]. One example is an ordering procedure, where an order is received and processed, an invoice is sent, payment is made, and the products are shipped. Business processes are considered the key instrument to organizing business activities and to improving the understanding of their interrelationships. Information systems play an important role in supporting such processes [2]. In many companies there is a gap between organizational business aspects and the information technology that is in place. Narrowing this gap between organization and technology is important, because in today’s dynamic markets, companies are constantly forced to provide better and more specific products to their customers. [2] Business process modeling has become a main tool for capturing, analyzing, and developing business activities. Summarizing from Weske [2], a model consists of activity models and execution constraints. An instance represents a concrete case in the operational business of a company. Each business process model acts as a blueprint for instances. The

ordering of activities can be controlled though process orchestrations in a similar manner to a conductor who centrally controls the musicians in an orchestra. The process choreography represents start events and end events of the interacting business processes. Process modeling techniques include event-driven process chains. Events play a crucial role in expressing interrelationships between business processes. Events are happenings in time and space. In business process modeling, an event represents “a state transition [emphasis added] of an activity instance” [2]. This paper reexplores the notion of events in business processes. According to Penicina and Kirikova [3], there is a gap between business process models and lawful states of business objects. This gap hinders compliance of business process models with internally and externally imposed regulations. Existing modeling methods such as BPMN and ArchiMate lack an explicitly declarative approach for capturing flow of business objects, their states and laws of state transitions [emphasis added]. Such deficiency can cost organization potential legal problems, make the ability of BPMN and ArchiMate to capture real-world phenomena questionable and drive modelers to employ additional standards. This paper contributes to the area by introducing an alternative approach to conceptualizing events where an event is a thing (to be defined later) that can be created, processed, released, transferred, and received. This idea is developed such that events are recognized from the description of the processes. Accordingly, the choreography of the execution of processes can emerge from the specification of events. The same idea can be applied in the area of ensuring compliance of business processes with imposed regulations stemming from various sources, such as security constraints, domain-specific guidelines, corporate standards, and legal regulations [4]. The system description (schemata) can be constructed from the compliance rules. The next section will review the diagrammatical modeling tool that will be used in process specification. This tool will be used in section 3 to illustrate the modeling techniques and, simultaneously, to develop the notion of events. II. DIAGRAMMATIC LANGUAGE: REVIEW This section summarizes the Flowthing Machine (FM) model [5, 6], which provides a diagrammatic language

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proposed as a high-level description suitable for event-like notions. FM is a generalization of the well-known input → process → output model, utilized in many scientific fields. It involves handling of flow things: things that can be created, processed, released, and transferred, arrive, and be accepted as shown in the flow system depicted in Fig. 1. If all things that arrive are accepted, the Arrive and Accept stages can be combined into a single Receive stage. Hereafter, a flow thing will be referred to merely as a thing. The arrows in the figure represent flows of things. Fig. 1 can be considered as a type of abstract machine. (in/output)

Create

Process

Release

Accept

Transfer

Arrive

Receive

Concurrent activities can be executed [emphasis added] in any order, and any overlap in the execution time of concurrent activities is allowed. . . . The ordering process can be used as a blueprint [emphasis added] that allows the reseller company to organize its work. . . . Each order that is processed according to this model is a business process instance. Therefore, there is a one-to-many relationship between business process models and business process instances. [2] Figure 2 shows the corresponding FM representation. Because of the simplicity of the example, different flows are not included in boxes. In the figure, the order is received and processed (circle 1) to trigger the creation of an invoice (2). The invoice flows to the customer who sends the payment (3) and this triggers processing (e.g., packaging) and sending the shipment (4).

Fig. 1. Flow system.

Order Transfer

The environment of flow is called its sphere (e.g., data flow within the sphere of a company). Note that a flow system itself is a special type of sphere. The stages of a flow system are mutually exclusive; that is, a thing always exists in one and only one of these states or stages at any moment. Assuming the thing is a datum, Process in this model is any operation on the datum that does not produce a new piece of data. Creation denotes the appearance of a new datum in the flow system. There are many types of things, including data, information, money, food, fuel, electrical current, and so forth. The life cycle of a thing is a sequence of stages through which it passes in a stream of flow. Other “states” of things, for example, stored, are secondary states; thus, we can have a stored created thing, a stored processed thing, and so forth. In addition to flows denoted as arrows, FM includes triggering mechanisms represented by dashed arrows. Triggering denotes activation, such as starting a new flow. III. MODELING AND EVENTS Weske [2] gives an example of an ordering process of a reseller company that is shown as a set of activities performed in a coordinated manner. The coordination between the activities is achieved by an explicit process representation using execution constraints [emphasis added]. The process starts with the company receiving and checking an order, followed by activities in concurrent branches [emphasis added]. In one branch, the invoice is sent and the payment is received; in the other branch, the products are shipped. When both branches complete their activities, the order is archived, and the business process terminates. The Business Process Model and Notation, BPMN [7] is used to model this process of a reseller company. In BPMN, activities are represented by rectangles with the name of the activity. Events are circles with icons (e.g., envelope, indicating the type) that mark the start and end of the process.

Receive

1

2

Process

Invoice

Create

Release

Transfer

Transfer

Receive

Process

3

Payment Transfer

Process

Release

4

Shipmen t Fig. 2. FM representation of the example

Note that the FM representation has no start or end. These are dynamic features that appear at the execution level through what we call events. Events are also flow things that can be created, processed, and so on. Usually, the concern in business modeling is with “meaningful” events. From the description of the problem in the example above, it seems that the interest is in the following events.  Receiving an order (V1)  Processing an order (V2)  Creating and sending an invoice (V3)  Receiving payment (V4), and  Shipping the product (V5). Figure 3 shows these events in the example of the reseller company.

V1 Create

Create Process

V2

Process

Create

Order Transfer

Receive

Process

V4 Create

Transfer Process

V5 Create

Create

Release

Transfer

Receive

Process

Transfer Process Release Process

V3

Invoice

Payment

Fig. 3. Events

ISBN: 1-60132-465-0, CSREA Press ©

Process

Shipmen t

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These events can be ordered according to desired implementation. V1, V2, V3, V4, V5 V1, V2, {V3, V4}, V5; where the curly brackets indicate parallelism. It is also possible to split V5 in Fig. 3 into two events,  retrieving and processing (e.g., packaging) the product, V5 in Fig. 4, and  transferring the product to the customer, V6 in Fig. 4. In this case we can order the events as V1, V2, {V3, V4, V’5}, V’6.

V”6

Create

Process Transfer

Process

Create Process

V”5

Release

Shipmen t Fig. 4. Dividing V5 of Fig. 3 into two events

Accordingly, modeling the dynamic behavior in FM is a phase separate from the task of describing static actions (Fig. 2). The start and end in the BPMN diagram of the reseller company (see Weske [2]) belongs to a different ontological level than action descriptions such as receiving an order, issuing an invoice, and so on. The static description (Fig. 2) is the abstract definition while the dynamic behavior is its realization in time. For example, the definition of rain is “moisture condensed from the atmosphere that falls visibly in separate drops” (can be modelled in FM). It has no start or end. But, the event of rain can be objectified as “the rain in New York at 7:15 that lasted until 8:10 on a certain date” as modelled in Fig. 5. For simplicity’s sake, the time flow was not shown in previous descriptions of events (Figs. 3 and 4). The time specification of an event reflects the singularity of events (they can only occur once). The body of the event in the modeled context is what “lives though” the event (i.e., in Fig. 5: moisture, drops, flows). It is the things out of which the event is constructed in addition to time.

Process: Moisture condensed

Create

Process: takes its course Transfer

Release

Drops Create

Release

Ground Receive Process

Receive

Transfer

Transfer

Transfer

Figure 5 provides a clear illustration of the notion of the event “raining in New York.” Events can be interrelated (e.g., intersections, sub-events) and have properties (e.g., intensity or how swift is the performed operation is), and so on. In this conceptualization, events are not such BPMN events as start and end; rather they are “what lives though the process” (e.g., moisture, drops, and flows) in time (Fig. 5). In the field of process modeling, there are different ideas regarding the typical treatment of events. As stated in the introduction, for Weske [2], “a state transition of an activity instance is represented by an event.” In another place, he says, “Examples of events are the receipt of an order, the completion of processing an order, and the completion of shipping a product.” In FM terminology, these are bodies of events or what “live though” them. This example shows a different approach to business process modeling by producing a flow-based schemata and then specifying events of interest as sub-diagrams of the representation of the system. According to defined requirements, we can select different types of execution of events for the reseller company as shown in Fig. 6. The FM representation embeds operational semantics (e.g., run-time execution) that simplify the simulation of the chronology of events. The resulting operational semantics are conceived along a causality model specification in which events are used to define fine-grained behaviors. V1 V2 V3 V4V’5 V ’6

V3 V4

V3

V ’5

V4

V ’6

V ’5 V ’6

Fig. 6. Possible sequences of events

IV. APPLICATION: COMPLIANCE RULES This Lego-like technique that constructs a (big picture) schema from pieces can be applied in different design ventures. According to Knuplesch and Reichert [4],

Event: Raining in New York Create

63

Time

Fig. 5. An event involves a sequence of actions at a specific time

A fundamental challenge for enterprises is to ensure compliance of their business processes with imposed compliance rules stemming from various sources, e.g., corporate guidelines, best practices, standards, and laws. . . . Providing a visual language is advantageous in this context as it allows hiding formal details and offering an intuitive way of modeling the compliance rules. However, existing visual languages for compliance rule modeling have focused on the control flow perspective so far, but lack proper support for the other process perspectives.

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They introduce an extended Compliance Rule Graph language, which models compliance rules utilizing a healthcare scenario dealing with compliance rules and processes captured in the context of a large process engineering project in a university hospital. Diagrams significantly increase the comprehensibility of and foster communication among business analysts and subject matter experts on one hand and process engineers on the other. Notations for compliance rules include Compliance Rule (e.g., Graphs [8] and BPMN-Q [9]) and process modeling languages (e.g., YAWL [10], and BPMN [7]) combine an intuitive notation with the advantages of a formal language. [4] In the following subsection, some of Knuplesch and Reichert’s [4] compliance rules are given and their FM representations are drawn. As we did with events, the compliance rules will be used to produce a flow-based schemata where rules become an integral part (sub-diagrams) in the composition of the total representation of the system. 3.1 Compliance Rule 1 For an inpatient, an x-ray examination must be ordered by a ward physician. The same physician must fill in the respective order form. ([4] based on [11]) Figure 7 shows the FM representation of this rule. The patient flows to the physician (circle 1) to be processed (2), accordingly, an x-ray order is created (3). For simplicity’s sake, the patient and x-ray things are not enclosed in a box. Physician 1 Transfer

Patient

2

Receive

Process

3

Create

Release

Release

Transfer

In Fig. 8, the patient (accompanied by his/her x-ray order) flows to the MTA (1) to trigger the creation of “informed consent” (2). This, in turn, triggers releasing the patient (3) to go to the radiologist (4) to produce the x-ray image (5). Radiology department

MTA Patient

Informed consent

X-ray Process

Create

Create

Process

2 Transfer Receive Release

3

Receive

Transfer

Transfer

Release

4 Transfer

1 Transfer

Receive

Radiologist

X-ray Order

Fig. 8. Rule 2

The figure specifies Rule 2 through modeling it as a flow system. The patient cannot flow to the MTA without an x-ray order and as soon as he/she arrives, an informed consent is produced to permit him/her to go to the radiologist. 3.3 Compliance Rule 3 Diagnoses shall be provided by physicians only after receiving all x-ray images from the radiology department; i.e., no x-ray image may be received afterward. [4] As shown in Fig. 9, x-rays are received and processed (2) to keep track of the arrival of all of them. The process stage may store the x-rays until all arrive, then trigger the diagnoses of the patient (3). This is performed within the sphere of a physician of a patient. As will be discussed later, instances of such an process will be tracked by events.

Transfer

Physician

X-ray Order Process:

Fig. 7. Rule 1

Diagnoses

Figure 7 is a rule in the sense that it describes a specific situation where a patient is processed by a physician and, accordingly, an x-ray order is issued. This rule would “melt” in the system schema, thus, it is the only flow that generates an xray order. This realizes the MUST in the rule. It is analogous to specifying that electricity for an air-conditioning unit in a building must come directly from the main power supply. In this case, the schemata of any building includes a direct supply line from the main electricity supply to the unit. The schema “expresses” the constraint in a diagram instead of in English. As will be shown later, all constraints are merged to form the schemata of the healthcare scenario in the involved hospital. 3.2 Compliance Rule 2 An x-ray examination in the radiology department must be performed by a radiologist. Before the exam, the informed consent of the patient must be received and checked by a medical technical assistant (MTA) in the radiology department. [4]

Receive

Patient

Transfer

3

1

Process: 2 All?

Receive

Transfer

X-ray

Fig. 9. Rule 3

3.4 Compliance Rule 4 Before requesting an informed consent, a physician must inform the patient about risks. [4] In Figure 10, the informed consent is created only as the result of receiving and processing risks flowing from the physician. Process:

Create

Patient Release

Transfer

Receive

Release

Transfer

Transfer

Informed consent Fig. 10. Rule 4

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Create

Risks

Physician Receive Transfer

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3.5 Compliance Rule 5 At least one day before a surgery takes place, blood bags must be ordered.

V. A COMPLETE DESCRIPTION FROM THE RULES The given rules can be merged to form a complete conceptual description of the hospital as shown in Fig. 14. First, the referrals (1) of the patient (2) flow to the hospital (3). Within one week (4), he/she is admitted to be under the care of a physician (6). In this situation the patient is sent to the radiology department (7) to:

As shown in Figure 11, when the date of a surgery is decided (1), then the processing of this data includes “watching” the specified range (at least one day) to trigger (2) ordering blood bags that flow to, say, the blood bank, that process such an order (3) and release and transfer the ordered blood bags to the surgery. Note that a surgery, here, denotes a recognizable work unit.

 

have x-rays taken (8) and have additional x-rays taken (9) in preparation for surgery.

3.6 Compliance Rule 6 If an additional x-ray examination is ordered to prepare a scheduled surgery, the x-ray must be completed before the surgery.

Patient Hospital Create

This rule is similar to rule 2, but, here, the ordered x-rays are triggered by the preparation for surgery (see Fig. 12).

Release

Transfer

3.7 Compliance Rule 7 A patient shall be formally admitted within one week after her referral to the hospital.

Release Transfer

Process: within one week

Transfer

Transfer

Create: Receive

Admission Fig. 13. Rule 7

Order Transfer

Receive

Date

Patient

Surgery

Receive Transfer

Transfer

3

4

Process

Blood bags 3

Process

Referral

As shown in Fig. 13, the referral and its date flow to the hospital (1) where the date is processed (2) such that “within one week,” it triggers (3) the creation of admission of the patient.

Process

Receive

1

Date Create

Transfer

2

Release

Create

Transfer

Receive

Process: At least one day

Create

1

Date

4 Release

Transfer

Transfer

Receive

Fig. 11 Rule 5

Informed consent

Radiology department

MTA Patient Create

X-ray

Receive

Process

Additional x-ray Order Preparation for surgery

Create

Create

Release Release

Receive

Transfer Transfer

Transfer Receive Release Transfer

Receive

Create

Receive

Release

Process

Physician Transfer

Process

Transfer

X-ray Order

Transfer

Fig. 12. Rule 6

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Transfer

Radiologist

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2

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The resultant “big picture” of the integration of rules embeds them all; hence, the rules are enforced by design. Additionally, the integration can expose the interrelations between rules, thereby optimizing their design. For example, it seems that Rules 1, 2, and 6 (see Fig. 15) that deal with x-ray orders and additional x-ray orders can be coordinated to produce a better designed diagram.

Diagnoses (10) are provided by physicians only after receiving all x-ray images (x-rays [11] and additional x-rays [12]) from the radiology department. For simplicity’s sake, the familiar notion in computer science—a thick horizontal line (13)—is used to denote a synchronization of the conditions of receiving ordered x-rays and additional x-rays. Upon deciding the surgery date (14), blood bags (15) are ordered as explained previously in Rule 5. Additionally, the informed consent (16) is received after the physician explains the risks involved (17).

Transfer

Transfer

Create

Release

Transfer

1

Create

Release

Date

2

Referral

Patient 3

Hospital

Transfer

4

Process

Receive

Transfer

6

Process: within one week

Receive

Physician

Date

Patient

Transfer

Transfer

Release

Transfer

Receive

Admission Release

Process

Receive

Transfer

Process: All?

Informed consent

11 Receive

Create

Release

Create

Create

8

Transfer Receive Release Transfer

Transfer

Release

Receive

Radiology department X-ray Process

Create

Receive

Release

Process

Transfer

X-ray Order

Transfer

7

MTA Patient

Transfer

X-ray Order

Transfer

Transfer

Radiologist

9 Preparation for surgery Create

Transfer

Additional x-ray Order

Receive

Process: All?

12

13

Physician

Process: Process:

Create

Diagnoses 10

16

Release

Receive Transfer

Transfer

Transfer

Receive

Process: At least one day

Create

Risks

Informed consent

Process

14

17

Release

Physician Receive

Transfer Transfer

Surgery 15

Order

Create Create

Release

Transfer

Date Receive

Transfer

Receive

Process

Blood bags Transfer

Transfer

Fig. 14 Integrated schemata of rules

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Release

5

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Physician Patient

Transfer Receive

Release

Process Process: All?

11

Receive

Transfer

Rule 1 Create

Release

Create

Release

8

Transfer

9 Preparation for surgery Create

Additional x-ray Order

Create

Transfer Receive Release

Rule 6 Transfer

Receive

Radiology department X-ray Process

Create

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Release

Process

Transfer

X-ray Order

Transfer

Informed consent

7

MTA Patient

Rule Transfer 2

Transfer

X-ray Order

Transfer

Radiologist

Transfer Receive

Process: All?

13

Physician

12

Process:

Diagnoses 10

Fig. 15. Rules 1, 2, and 6

VI. CONCLUSION This paper has examined the notion of events in business processes by treating events as flow things. This idea is developed such that events are recognized from the description of the processes. Accordingly, the choreography of the execution of processes can emerge from the resultant specifications. This led to constructing the total diagrammatic description from the events diagrams which seems to avoid the complexity of building one schemata of the system. The same idea of developing a “big picture” specification from small diagrams has been applied to compliance rules imposed upon business processes with the goal of ensuring compliance with these rules. The rules have been specified and integrated to generate a system description (schemata). The proposed approach seems to be a viable alternative method for analyzing and designing business processes. Further research in developing FM modeling would enhance the understanding of basic notions such as events in the field of business processes. REFERENCES [1] [2]

M. Hammer and J. Champy, Reengineering the Corporation: A Manifesto for Business Revolution. Location: Harper Business, 1993. M. Weske, Business Process Management. Berlin, Germany: Springer Berlin - Heidelberg, 2012.

L. Penicina and M. Kirikova, “Towards compliance checking between business process models and lawful states of objects,” 2nd International Workshop on Ontologies and Information Systems, 13th International Conference on Perspectives in Business Informatics Research Lund, Sweden, September 22–24, 2014. [4] D. Knuplesch and M. Reichert, “A visual language for modeling multiple perspectives of business process compliance rules,” Software & Systems Modeling, 21 April 2016. DOI: 10.1007/s10270-016-0526-0 [5] S. Al-Fedaghi S. and H. AlMeshari, “Social networks in which users are not small circles,” Informing Science, Vol. 18, 205–224, 2015. [6] S. Al-Fedaghi, “Conceptualization of various and conflicting notions of information,” Informing Science, Volume 17, pp. 295–308, 2014. [7] OMG: BPMN 2.0. Recommendation, OMG (2011). http://www.omg.org /spec/BPMN/2.0/ [8] L.T. Ly, S. Rinderle-Ma, and P. Dadam, “Design and verification of instantiable compliance rule graphs in process-aware information systems,” in CAiSE’10, LNCS, vol. 6051, Location: Springer, 2010, pp. 9–23. [9] A. Awad, G. Decker, and M. Weske, “Efficient compliance checking using BPMN-Q and temporal logic,” in BPM’08, LNCS, vol. 5240, Location: Springer, 2008, pp. 326–341. [10] W.M.P. van der Aalst and A.H. ter Hofstede, “YAWL: yet another workflow language,” Information Systems, vol. 30, issue 4, pp. 245– 275, 2005. [11] I. Konyen, B. Schulthei, and M. Reichert, “Prozessentwurf f• ur den Ablauf einer radiologischen Untersuchung,” Tech. Rep. DBIS-15, University of Ulm, 1996. [3]

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Constructing a Takagi-Sugeno Fuzzy Model by a Fuzzy Data Shifter Horng-Lin Shieh1 and Ying-Kuei Yang2 1 Dept. of Electrical Engineering, St. John's University, Taipei, Taiwan 2 Dept. of Electrical Engineering, National Taiwan University of Science & Technology, Taipei, Taiwan

Abstract - This paper proposes a fuzzy-based data sifter (FDS) to locate good turning-points to partition a given nonlinear, multi-dimensional data domain into piecewise clusters so that a Takagi and Sugeno fuzzy model can be constructed with fewer rules and less error. One experiment is illustrated and its result has shown the proposed approach has better performance compared with other three related approaches.

2

The Proposed Fuzzy Data Sifter In Equation (1), the premise parameters

Aij are defined

in this paper by a Two Sides Gaussian Function (TSGF) [9] that, as shown in Figure 1, indicates a membership function includes 4 parameters (1,σ1, 2, σ2) where i, σi ，i=1,2, respectively stand for the mean and deviation of the Two Side Gaussian Function.

Keywords: fuzzy-based data sifter, fuzzy cluster, TS fuzzy model, fuzzy matching degree, turning point .

1

Introduction

Constructing a fuzzy model can be basically categorized as Mamdani’s model and Takagi and Sugeno’s model (TS model). The Mamdani’s model describes the relationship between inputs and outputs of a control system through a set of linguistic control rules and membership functions [1]-[8]. On the other hand, the TS model partitions an input space into several subspaces to describe either a static or a dynamic nonlinear system. One fuzzy rule is then created for each of these clusters. Below is a representation of rules in a TS model: Ri

：IF x1 is i

THEN yi= a 0 +

A1i and x2 is A2i ……. and xk is Aki

a1i x1 + a 2i x2+ …….. + a ki xk

(1) i

where Ri (i=1,2,…c) represents the i’th rule, a b (i=1,2,..c, b=0,1..k) is a constant called consequence parameter, yi is the output of the i’th rule (i=1,2,…,c),

Aij (i=1,2,..c, j=0,1..k)

represents a linguistic term characterizing the membership function of the j’th input variable of the i’th rule and is called premise parameter. In this paper, a fuzzy-based data sifter is proposed to better partition a nonlinear system’s domain into several piecewise linear subspaces so that a TS model can be contrusted with less fuzzy rules and less error for the domain.





1

1



2

2

Fig. 1 Two Side Gaussian Function (TSGF) For TS fuzzy modeling, the data domain is divided into subspaces and each of these subspaces is represented by a linear fuzzy model. The locations where the data domain is divided are named turning-points. Therefore, one of the critical issues on establishing a TS fuzzy model is to locate the turning-points existing in a data domain so that the TS fuzzy model can best represent the system model of the data domain with less error and simplest model structure. This paper proposes a slip-window-based recognition system, namely the fuzzy data sifter (FDS), to search the best turningpoints of a nonlinear function. The obtained turning-points are then used to divide the given data domain into clusters. The piecewise linear regression algorithm (PLRA) is then applied to calculate the regression parameters for each of clusters. The FDS is an iterated sifting system that uses a turningpoint sift network (TPSN) to search out the local maximum and minimum data points in the data domain. For simplicity of explanation, the discussion is detailed in a 2-dimension figure as Figure 2. The turning-points can be basically classified into peak pattern in Figure 2(a) and valley pattern in Figure 2(b). The core idea of FDS is to use the 2 patterns as templates to search out all of the turning-points in a data domain. Because FDS is using fuzzy membership degree to indicate the bending level (angle) of a located turning-point, only the peak and valley patterns are used by FDS. The rest of not so

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obvious (sharp) patterns can be represented by less membership degrees to indicate their turning angles are not as obvious and critical as peak and valley. The higher membership degree a turning-point has, the higher priority the turning-point has to be used as a dividing point in a data domain. Therefore, the use of only peak and valley patterns as matching templates does not affect the turning-points searching result of FDS. Figure 3 shows a peak and a valley patterns in 3-D.

(a) peak (b) valley Figure 2: Patterns of peak and valley turning-points

69

The TPSN set is to perform the task of searching out all turning-points and generates their fuzzy matching membership degrees against turning-point patterns. A TPSN does the task for a particular turning-point pattern. There are two TPSNs as the peak and valley patterns are used in this paper. A TPSN takes the m data points in slip window, compares the shape of these data points with the turning-point pattern designated by the TPSN and measures the fuzzy matching degree between the shape and the pattern. The result of a TPSN is a fuzzy turning-point membership degree (FTPD) indicating the matching degree of the turning angle existing in the m data points covered in the slip window against the designated pattern. Because the length of whole data stream n is longer than the window width m, values in a FTPD form a matrix. In Figure 4, the FTPD1 matrix holds the matching results against peak pattern performed by TPSN1. Similarly, the FTPD2 matrix holds the matching results against valley pattern performed by TPSN2. As stated previously, the turning-points with high fuzzy matching degrees will be selected with high priority as points from where to divide a data domain into clusters.

2.2 (a) peak

(b) valley

Figure 3: 2-D turning-point patterns (a) peak and (b) valley

2.1

The Fuzzy Data Sifter (FDS) Architecture

The architecture of FDS is shown in Figure 4. FDS is a combinational network and can be divided into 4 blocks: slip window, turning-point sift network (TPSN), fuzzy turningpoint matching degree matrix (FTPD matrix) and indexes pool.

Measuring Fuzzy Matching Against Turning-Point Patterns

Hypothesize that a multiple-inputs-single-output (MISO) function has input variable xi，i=1,2…k, and output variable y. For the convenience of discussion on how TPSN measures the fuzzy matching degree of a turning-point existing in slip window against a designated turning-point pattern, the multidimensional turning-point patterns are projected onto xi-y planes. For example, the projection results of peak and valley patterns respectively are shown in Figure 5 and 6 in which the horizontal axis represents a certain input variable xi and the vertical axis designates the output y of system function. The point ps is where the center value of pattern bottom xis corresponds to the maximum output ymax in Figure 5 and to the minimum output ymin in Figure 6. The u indicates the bottom width of the referenced pattern.

y

y

ymax

ps=(xis,ymax) yma

Tp(xih) Figure: 4 The Structure of fuzzy data sifter (FDS) The slip window takes input into FDS for processing like a water flow and is therefore called data stream. In Figure 4, d1..dN respectively mean the first and last data points of a data stream that the slip window covers to be currently being processed (matched) against turning-point patterns in Figure 2. The m stands the width of the slip window, namely the data point numbers in the slip window. The slip window advances one data point in a data stream for each time of processing.

Degrees

Tv(xih)

ymin

ymin

xi

xih u

Figure 5: Projected peak pattern

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ps=(xis,ymin)

xih

xi u

Figure 6: Projected valley pattern

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Assume the m data points in slip window in Figure 4 are = min(xi1,xi2…xim)

xij(1ik,1jm) of the i-th input variable of the j-th data point in the slip window, the corresponding Tp(xij) value in the peak pattern can be calculated by Equation (2) to form

= max(xi1,xi2…xim) respectively indicate the

Y jcp ={ y1cpj , y2cpj  ykjcp } where yijcp =Tp(xij)，1ik, 1jm.

minimum and maximum values of the i-th input variable for the m data points covered by the slip window. That is, the

Similarly, the value of Tv(xij) in the valley pattern can be

{(x1j,x2j…xij,..xkj,yj)|1jm}. Let and

x

max

i

width of pattern bottom u =

x

x

min i

max

i

-

x

min i

. Let ymin =

min(y1,y2…ym) and ymax = max(y1,y2…ym) respectively represent the maximum and minimum of the output variable y for m data points. For the input value xih of a data point in the slip window, the peak and valley patterns in Figures 5 and 6 can be defined as Equations (2) and (3) respectively.

calculated by Equation (3)to form where

Y jcv ={ y1cvj , y2cvj  ykjcv },

yijcv =Tv(xij)，1ik, 1jm.

Peak pattern: Tp(xih)=

  y min   y  min



( y max  y min )  ( xih  ximin ) , if xih  xis ( x s  ximin )

(y  y )  maxmax min  ( ximax  xih ) , if xih  xis ( xi  xis )

(2)

(a) m data points

Valley pattern: Tv(xih)=

( y max  y min )   y min  ( x  x min )  ( xis  xih ) , if xih  xis  is i  y y (   y min  max min )  ( xih  xis ) , if xih  xis  ( x imax  xis )

(3)

That is, in a MISO system, the corresponding output value in a typical peak pattern for the h-th data point of xi input variable is Tp(xih). Similarly, the corresponding output value in a typical valley pattern for the h-th data point of xi input variable is Tv(xih). For a data point ph，h=1,2…n, the slip window selects the m-1 points closest to ph making totally m data points in the slip window. Naturally, the pattern formed by the m data points often does not conform to the definition of either peak pattern in Equation (2) or valley pattern in Equation (3). For example, for a point p3, the slip window selects the closest points of p1, p2, p4 and p5 for TPSN to examine the turning degree at point p3, as denoted in Figure 7(a).

(b) Pattern comparison Figure 7: Sample m data points and peak pattern comparison As mentioned previously, the value yj of j-th data point, where 1jm, is often different from

yijcp of a peak pattern.

The matching degree between value yj and value

yijcp can be

measured by a fuzzy membership function defined in Equation (4). k

1 μ j1 (y j )  exp (  2

(yj i 1

 yijcp ) 2

y range

)

(4)

Similarly, the matching degree between value yj and These 5 points are projected onto (x1, y) plane, as shown in Figure 7(b) where circles denote the projected points of p1 ~ p5 in the slip window and solid lines denote the projected peak pattern in Figure 5. It is obvious that the pattern formed by p1 ~ p5 does not match against the peak pattern totally, but to some degree. Fuzzy theory is therefore applied here to define the fuzzy matching degree between these two patterns in Figure 7(b). For the value

value

yijcv in a valley pattern can be measured by a fuzzy

membership function defined in Equation (5). k

μ j 2 ( y j )  exp( 

1 2

 ( y j  yijcv ) 2 i 1

y range

where yrange = ymax - ymin .

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)

(5)

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Since there are m data points in the slip window and each data point has its own matching degrees derived from Equations (3) and (3), the overall matching degree between the pattern formed by the m data points in the slip window and a designated pattern can be defined as Equation (6). m

 qs 

w μ j 1

j

jq



m

w j 1

(yj )

w1 μ 1q ( y1 )  w2 μ 2 q ( y2 )    wm μ mq ( ym )

j

is normalization

1

shown in Figure 8. The width of slip window is set as 5 with the weighting value of w={3,2,2,1,1} and threshold value of =0.08. After the proposed FDS is applied to the data domain using the peak and valley patterns in Figure 3, the data domain is divided into 3 subspaces resulting in following 3 Takagi-Sugeno fuzzy rules with SME = 0.05384

(6)

w1  w2    wm

where q=1,2, s= 1,2…n and

y  (1  x12  x21.5 ) 2 ,1  x1 , x2  5 cited from [10], as

If x1 is

A11 and x2 is A21 then y = 2.743 x1+0.0435x2 -0.3562

If x1 is

A12 and x2 is A22 then y = 9.449x1 -1.194x2-2.061

If x1 is

A13 and x2 is A23 then y = 5.1x1-0.8331x2 -0.2055

m

w

j

1

factor The s indicates the slip window is centered at the s-th data point, q=1 stands for the overall matching degree against peak pattern and q=2 stands for the overall matching degree against valley pattern. The wj(j=1,2…m) are the weight values on the influence of deciding matching degree for the turning angle in the slip window against peak or valley patterns. The weight w1 is assigned for ps and w2,w3,…wm are respectively assigned for the rest of m-1 points in the slip window. It is obvious that w1 should have the highest weight value since it is where the turning angle takes place, as shown in Figure 5. The second highest weight w2 is assigned for ps-1 and the third highest weight w3 is assigned for ps+1 and so on. The relationship of these weights is w1>w2>w3>…>wm indicating the fact that the farther from point ps, the less influence on deciding matching degree of a turning angle at point ps against a designated pattern. Equations (4)、(5) and (6), in conjunction with action of slip window, calculate the fuzzy matching degrees against designated patterns for every data point of {(x1j,x2j…xkj,yj)|1jn}. A large value of qs indicates a certain data point ps has a turning angle having high matching degree against a designated pattern. Consequently, the task of partitioning a data domain into clusters can be done in accordance of the descending list of qs value.

1

1

2

2

3

3

where the A1 , A2 , A1 , A2 , A1 , A2 are defined by a twosided Gaussian function shown in Figure 1 with mean and standard deviation values indicated in Table 1. Table 2 is the comparisons of the proposed approach against other three methods. The comparison indicates that the proposed approach in this paper results in less rules with less MSE. Table 2: Comparisons with other 3 mthods model Sugeno and Yasukawa [10]* M.Delgado and Antonio F. [11] E. Kim and M. Park and S. Ji [12] Our model

rule number 6

MSE 0.079

5

0.231

3

0.0551

3

0.0538

Through the work of FDS, the fuzzy matching degrees of each input data point against peak and valley patterns are stored in descending order at FTPD matrix. Since higher matching degree indicates better location to divide the data domain, the values stored at FTPD is selected by the descending order. Therefore, each time a point corresponding to a selected value at FTPD matrix is used to divide the data domain into smaller clusters until the MSE (mean squre error) is less than a threshold value. Figure 8: y  (1  x12  x21.5 ) 2 ,1  x1 , x2  5

3. Experiment Due to page limit, only one 2-inputs-1-out nonlinear data domain is illustrated to show the performance of the proposed method. This example is the nonlinear equation

Table 1: Mean and standard deviation values

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Variable x1 Rule Rule 1 Rule 2 Rule 3



x2 i 11

0.08101 0.5131 0.1072



i 11

1.553 1.756 0.7524





i 21

0.08101 0.5131 0.1072

i 21

4.487 1.763 3.516

 12i

 12i

i  22

0.007458 0.1123 0.2051

4. Conclusions In this paper, a Fuzzy Data Sifter is proposed to search out good turning-points to partition an input data domain into piecewise clusters. The TS fuzzy rules and membership functions are then extracted for each of the piecewise clusters to build the Takagi and Sugeno’s fuzzy model. The experiment has shown the good performance of the proposed approach. Furthermore, the FDS is robust in the sense of: (1) the proposed FDS is an open framework that allows the addition of other shapes of standard patterns; and (2) the setup value of threshold has great impact on the number of divided subspaces (clusters).

1.307 0.007458 4.933 0.8938 0.1123 1.923 2.192 0.2051 5.483 Computing, Prentice Hall, New Jersey, 1997 [10] M. Sugeno and T. Yasukawa ,"A Fuzzy-Logic-Based Approch to Qualitative Modeling", IEEE Trans. on Fuzzy Syst., vol. 1, NO. 1, FEB. 1993 [11] M. Delgado Antonio F.Gomez-Skarmeta, and F.Martin “A Fuzzy Clustering-Based Rapid Prototyping for Fuzzy Rule-Based Modeling”, IEEE Tran. On Fuzzy Syst. vol.5,NO. 2, pp.223-233, 1997 [12] E. Kim and M. Park and S. Ji，“A New Approach to Fuzzy Modeling”，IEEE Trans. On Fuzzy Syst.，vol. 5 NO.3 pp.328-337, 1997

5. References [1] [2]

[3]

[4]

[5]

[6]

[7]

[8]

[9]

i  22

R. Yager and D. P. Filev, Essentials of Fuzzy Modeling and Control. Wiley, New York, 1994. M. Zhihong, X. H. Yu, and Q. P. Ha, “Adaptive Control Using Fuzzy Basis Function Expansion for SISO Linearizable Nonlinear Systems,” in Proc. 2nd ASCC, pp. 695–698, Seoul, Korea, 1997. J. T. Spooner and K. M. Passino, “Stable Adaptive Control Using Fuzzy Systems and Neural Networks, ” IEEE Trans. on Fuzzy Syst., vol.4, pp. 339–359, 1996. R. N. Dave and R. Krishnapuram, "Robust Clustering Methods: A Unified View," IEEE Trans. on Fuzzy Set., vol. 5, no. 2, pp270-293, May 1997. H.-H. Tsai and P.-T. Yu , “On the Optimal Design of Fuzzy Neural Networks with Robust Learning for Function Approximation,” IEEE Trans. on System, Man and Cybernetics, Part B: Cybernetics vol.30, no.1, pp217-223, Feb 2000 B. Kegl, A. Krzyzak, T. Linder and K. Zeger, "Learning and Design of Principal Curve," IEEE Trans. on Pattern Analysis and Machine Intelligence, Vol. 22, No. 3, pp.281-pp. Mar. 2000 F. Höppner and F. Klawonn, "A Contribution to Convergence Theory of Fuzzy c-Means and Derivatives", IEEE Trans. on Fuzzy Set, vol. 11, no. 5, pp 682~694, Oct. 2003 C.-C. Chuang, J.-T. Jeng and P.-T. Lin, “Annealing Robust Radial Basis Function Networks for Function Approximation with Outliers,” Neurocomputing, 56 ,pp.123–139 2004 R. Jang, C. Sun and E. Mizutani, Neuro-Fuzzy and Soft

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SESSION NOVEL ALGORITHMS AND APPLICATIONS + IMAGING SCIENCE + SIGNAL ENHANCEMENT AND WIRELESS INFRASTRUCTURES Chair(s) TBA

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Estimating Cost of Smart Community Wireless Platforms Sakir Yucel [email protected] Abstract - Smart community wireless platform has been introduced as a model to address the needs of smart community. It is a platform where municipality, communities and smart utility providers work together toward achieving smart community and smart city objectives. One important question is how much would be the total cost of building and operating such a platform. The objective of this paper is to examine several relevant dynamics in estimating the total cost of smart community wireless platforms and develop models for estimating the cost under various conditions and scenarios using an intelligence framework that incorporates systems dynamics modeling with statistical, economical and machine learning methods. Keywords: smart community wireless platform, cost of community wireless networks, smart community, system dynamics modeling

1

Introduction and Problem Definition

[12] describes the smart community wireless platform. One important question is how much would be the total cost of building and operating such a platform. The objective of this paper is to examine several relevant dynamics in estimating the total cost of smart community wireless platforms and develop a model for estimating the cost under various conditions and scenarios. We will first describe our intelligence framework for analyzing the platform and for estimating the cost. After that, we will develop models for cost estimation.

2

Intelligence Framework

We used an intelligence framework for analyzing platforms in general in our earlier work [8]. We will use the same intelligence framework for analyzing the smart community wireless platform with some additional analysis and decision making techniques. Our framework incorporates developing system dynamics (SD) models together with use of economical, statistical and machine learning models. System dynamics modeling and statistical methods have been used for analyzing municipal wireless networks in earlier work [1][2][3]. System dynamics modeling in general is used for understanding and analyzing business and management related issues such as estimating cost, benefits and return on investment, and risk analysis. To estimate the total cost over a period, simulation is a powerful tool to try different scenarios. When detailed statistical analysis could not be done due to shortage of data and exact understanding of how the system works, system dynamics models involving hypothesized assumptions can be valuable tools to demonstrate expected impact of various business decisions involving feedback

relationships. With SD model, one can model the network effects among different sides on the platform and model how the economical utilities due to network effects behave. In our intelligence framework, estimation is done by building system dynamic models together with economical, statistical and machine learning models, and running simulations, performing sensitivity analysis. Qualitative system dynamics approach improves system understanding and prediction for various scenarios, even in the absence of quantitative data. This framework has been used in [8][9][10][11]. In this paper, we focus on developing a system dynamics model for estimating the cost of the smart community wireless platforms. We will present our SD model in later sections. Although not the subject of this paper, we would like to summarize our overall approach for estimating the cost by using the intelligence framework. For this problem, we use economical, statistical and machine learning models as follows: We use economical models for cost and revenue. We use statistical methods in tests to see significant difference between different platforms and between different scenarios. We use hypothesis testing to test relation among different dynamics related to the platform. We use machine learning methods to analyze collected usage data per community network for predicting future use and demand forecasting, finding out covariance matrix, significant parameters, association rules regarding the success of the platforms. We believe statistical, economic and machine learning models and methods are not sufficient by themselves to analyze complex platforms such as this one, and SD is most suitable to be used together with these methods, hence a more powerful intelligence framework for better analysis and understanding can be constructed [8]. Application of this framework into different problem domains require utilizing additional analysis and decision making techniques and approaches. The problem domain of community wireless network requires us to further employ the following tools and methods into our intelligence framework: 1. The use of GIS data and techniques for asset mapping and geographic area characterizations while planning and designing the wireless network in the community 2. The use of network design simulators during wireless network design for how much bandwidth is needed by the users based on applications for given QoS particularly the response time, and for simulating the impact of content caching, location tracking, IoT traffic 3. The use of tools and models for wireless network security analysis and assessment

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3

Developing Model for Cost Estimation

In this paper, we focus on the SD model for estimating the initial and maintenance cost of the smart community wireless platform. We hold assumptions made in [12]. 3.1

Methodology for Developing a Cost Model

We suggest a methodology for identifying the dynamics involved in the cost of building and operating a smart community wireless platform. The methodology suggests: 1. Work out the characteristics of various cost related dynamics which will be needed in simulation and decision making. These dynamics should be elaborated for a specific community platform through efforts of needs assessment, resources analysis, partnership analysis, asset mapping, network/security/operations planning, policy development. These are done best by the community itself, as the community leaders, volunteers, stakeholders are most aware of the needs, resources and capabilities. 2. Once conceptual dynamics are characterized and cost related variables in those dynamics are identified, these dynamics are incorporated into SD models for estimating the cost. Characterization of several dynamics including financial resources, policies, strategies and utilities are outlined in [12] for the generic platform. We will not include them in this paper. We will characterize additional dynamics together with cost related variables in the subsequent sections. 3. With data collected during planning, development and operational phases of the wireless platform, build and fine tune the statistical, economical and machine learning models, integrate them with SD model, validate and fine tune the SD model. In this paper, we will apply the first two steps above. 3.2

Community Characteristics

The characteristics about the community should provide values for the following: • How big the community and how many different potential service areas exist in the community • How much volunteering from community: How big the volunteer groups in the community (for setup, for maintenance activities, for security and customer service requests)

• Availability of variety technical skills in the community • Community effectiveness for developing technical solution. 3.3

Service Area Characteristics

A community is a portion of the city like a neighborhood. A service area is an area/district within a community. Our assumption is that there could be multiple service areas within a community and each service area in a community could be different. For example, one service area could be a business district with economic development objective, whereas another service area within the same community could be a residential district with reducing the digital divide objective. Where the community has different service areas with different characteristics, it makes sense to characterize service areas separately. A community wireless platform becomes the union of possibly several wireless networks in different service areas with different dynamics. The characteristics about the service area should provide values to the following: Demographics related: Population move in, move out and growth rates. Population during day, night. Resident population, visitor population. Businesses related: Number of businesses and organizations willing to share their bandwidth and how much and/or sponsor by other means. Social responsibility awareness scale of businesses in the area. Substitutable offerings related: Availability and quality of cellular services and hotspots. Setup related: Size of the area. Availability of city light posts, buildings, municipal facilities and other physical infrastructure for attaching the equipment. Availability of IT and networking infrastructure like fiber, municipal IT resources, smart service provider resources. Other geographic and dwelling factors (building, roads, rights of ways) that will impact the difficulty of setup. Attractiveness related: Service area attractiveness for grants, sponsorships, donations. Attractiveness for other hotspot providers. Attractiveness for the visitors. Attractive places for visitors, accommodations. Social initiatives and public services.

• The amount of community help for finding grants, needs assessments, publicity and promotion, setup and installation, integration

Usage related: Projected initial usage characteristics and demand: How many residents will use the system? How many visitors will use the system? What percentage of users use how many times, when and how long. What types of digital activities does the community often perform on wireless network? Peak hour characteristics of the usage. What smart services are available in the platform?

• How successful the crowdsourcing could be

3.4

• Community effectiveness for implementing the strategies for bringing in bandwidth sponsors and smart service providers

The characteristics about the municipality should provide values for the following:

• Community help for finding sponsorship. Community effectiveness for convincing partners

Municipality Characteristics

• The extend of municipality help with allowing to use traffic lights, light posts, municipal buildings in the community.

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IT infrastructure elements such as cache servers the municipality could provide the community. • How much training municipality will support by for people maintaining the network, or for volunteers. • Municipality help with grants to this community • Municipality help with launch, publicity and promotion • Municipality help with network design, setup, installation and integration • Municipality help with ongoing operations: help with security admin 3.5

Wireless

Network

and

Infrastructure

Characteristics 3.7 The characteristics about the wireless network infrastructure should provide values for the following:

and

• Characterization of the wireless mesh network: total available bandwidth, overall throughput, latency which is averaged over all APs based on the design of the mesh network. Availability and reliability of the network. Overall security score of the network. The number of APs in the mesh. How many users can be supported at maximum. • Characterization of wireline network is similar to that of the wireless mesh network. • Characterization of the computing and storage infrastructures: overall latency and throughput for typical use case transactions (Yahoo use cases). Total transactions per second per use case. 3.6

Online Services and Applications Characteristics

The number, availability and quality of services in the area increases the utility for users and sponsors. We characterize the services in terms of availability and quality, and bandwidth demands. These are needed for 1. Estimating projected characteristics

Bandwidth demand: Expected number of users using offered services in the area, initially estimated but later monitored. How much data traffic is generated within the wireless network. How much traffic is to be transmitted outside of wireless network without Internet but using infrastructure supplied by municipality or other smart service providers. How much traffic will be transmitted to Internet. For example, security cameras feed data traffic into wireless network and infrastructure. This data mostly remains within the network and not need to go to Internet or other networks. On the other hand, vehicle traffic monitoring cameras feed data into wireless network and this data may go to other networks over infrastructure and/or over Internet and streamed to Internet possibly via separate ISP connections.

application

and

bandwidth

2. Estimating attractiveness of the service area to visitors The characteristics about the online services and applications should provide values for the following: What services offered to users: Online services from the municipality offered in the service area (for safety, security, municipal services). Services provided by smart service providers. Location based services using wireless network and IoT beacons for coupons and loyalty programs in business district. Community online services such as community social network, community cloud. Communities virtual visitor app that highlights locations, attractions, points of interest, events, local deals. Availability and quality of the services: Values for these are initially estimated but later monitored.

Smart Service Providers Characteristics

Smart service providers place sensors and other devices into the wireless network. They use these devices for their own purposes and they also offer applications (e.g. waste monitoring). An important characterization is for their IoT devices about whether IoT devices utilize mostly low power Bluetooth, or they will utilize the mesh wireless network for transmission and aggregation. In most commercial district, most IoT devices are expected to be Bluetooth low power. However, in some industrial districts, more low power Wi-Fi devices could be employed which may integrate with the mesh wireless network. One characterization is to figure out the amount of data traffic they will generate within the wireless network: the traffic transmitted over the networking infrastructure till the data reach the local infrastructure of the smart service provider and/or used by the users within the platform, or reaches the private network connection of the smart service provider. The amount of traffic generated for the Internet by their devices and by the users accessing them over the Internet. Another characterization is for finding out the amount of bandwidth they will sponsor. This sponsored bandwidth should be equal or higher than the bandwidth generated by their devices and users. There are two types of bandwidth they need to sponsor: one for the wireless network and the infrastructure for data to remain in the network, and the other one for Internet. For the infrastructure, the smart service provider should contribute APs into the mesh network. For the internet, the smart service provider should sponsor at least enough bandwidth for their own Internet traffic and for their remote users. Smart service provider may have dedicated connection from the wireless network to their own infrastructure centers for transmitting IoT data. 3.8

Quality and Attractiveness of Wireless Networks Characteristics

Initial attractiveness mostly depends on service launch strategy. Ongoing attractiveness depends on • How municipality and community promote, advertise the service

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• How they encourage the citizens to use the service • The quality of the wireless service with respect to QoS (availability, bandwidth for each wifi interface, throughput, latency). What service level can be provided. • Availability and quality of substitutable offerings such as hotspots and cellular services • Quality of customer service for security and other usage tickets • How the network evolves in response to changing user characteristics: an analytic roadmap for monitoring the usage patterns and re-engineering the network accordingly.

In this paper, we covered additional dynamics and provided a methodology characterization for them. In this section, we will incorporate them all as they apply to an area of business district in the models we will develop. We consider a service area with economic development objectives in a business district, particularly a service area with reasonable economic development opportunity. Since our focus in this SD model is economic development for a business district, the model we present may not apply directly for residential business area.

4

Figure 1 shows a simple linear SD model with no feedback loop for initial cost estimation for a single service area. It is simple and linear as there is no economic utility to calculate nor any network effect to incorporate.

SD Models for Cost Estimation

In this section, we develop SD models taking into consideration the cost related dynamics and variables. [12] outlined funds, policies, utilities and strategies for the platform. Economic development opportunity

4.1

Initial Setup Cost Estimation

Resources and Businesses

Social Initiatives and Public Services Demographics

Community Help with Need Assessment Need Assessment Fixed Cost

Potential Grants Size Area Characterization Attractiveness for grants

Grants to be Secured

Cost of Needs Assessment Attractiveness for Sponsorships

Projected Usage Characteristics

Publicity and Promotion

Attractiveness for Donations Potential Donations

Projected Initial Demand

Cost of Publicity and Promotion Municipality Help with Network Design

Community Help with Donations

Donations to be Projected Application Secured and Bandwidth Characteristics

Projected Initial Total Traffic

Cost of Raising Donations

Community Help with Grants

Municipality and Community Effectiveness for Convincing Partners Social Responsibility Awareness Scale Community Help with Sponsorships Sponsorship to be Secured

Net from Donations Projected Initial Network Characteristics

Network Design Cost Equipment and Software Cost Municipality Help with Setup and Resources

Setup and Installation Cost

Projected Initial Bandwidth to Internet Projected Initial Bandwidth Sharing Projected Initial Traffic to Municipal Resources

Net from Grants Cost of Grant Application

Grant Application Fixed Cost

Sponsorship Requesting Fixed Cost

Cost of Requesting Sponsorships

Raising Donation Fixed Cost

Community Help with Setup

Municipality Help With Grants

Net from Sponsorships

Initial Bandwidth to be Purchased from ISPs

Integration Cost Other Infrastructure Cost

Figure 1 SD Model for Initial Setup Cost Cost variables include: Needs assessment fixed cost, Cost of needs assessment, Grant application fixed cost, Cost of grant application, Net from the grants, Raising donations fixed cost, Cost of raising donations, Net from donations, Sponsorship raising fixed cost, Cost of raising sponsorship, Cost of launch, publicity and advertisement, Network design cost, Equipment and software cost, Setup and installation cost, Integration cost.

Using this model, total cost and total deductions are easily calculated. Percentages of each cost item with respect to total cost is calculated. The budget can be compared to the final cost. The net present values can be easily calculated with proper formulation. Initial demand is estimated in the model which is characterized by

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1. Projected initial total traffic 2. Projected initial traffic to wireless network, the underlying wireline network and the IT infrastructure 3. Projected initial traffic to Internet 4. Projected initial bandwidth sharing (sponsored by the sponsors): This value is provided as an estimate into the model. This value is estimated from service area characteristics in a separate SD model and fed into this model as a variable 5. Projected initial bandwidth to be purchased from ISP Economic development opportunity variable was not included in the characterization since it is not applicable for all service areas, but applicable to service areas with business development objectives such as business districts. It indicates how much economic development opportunity exists in the service area and is one of the variables used to determine the size and coverage of the wireless network. If there is not much opportunity, then no big investment will flow into the network and therefore no big wireless network. Although such initial estimation could be done with a simple spreadsheet model, SD modeling is still helpful for visualizing the different cost components and how they are related. It is also helpful to run sensitivity analysis on how different scenarios impact the cost and to for estimating the cost spanning over the duration of the setup of the wireless network. Sensitivity analysis could estimate the total cost along

with other variables in different scenarios with different size of the service area, amounts of community volunteering, municipality help, grants, involvement and sponsorship from businesses, and with varying levels of success in crowdfunding and crowdsourcing, and with varying cost components such as equipment, setup cost, consultancy cost. It would be easy to see if the setup cost is within the budget under what combinations of other variables. Decision makers could use sensitivity analysis to balance various variables to achieve the cost objectives. 4.2

Maintenance Cost Estimation

Maintenance cost includes ongoing capital expenses and ongoing operational costs. The first is due to upgrades in response to increased demand and better understanding of usage characteristics. The second one includes costs for bandwidth, electricity, contractors, equipment maintenance. Figure 2 shows an SD model for estimating the total maintenance cost. This model has feedback loops and nonlinear relations where the advantages of SD can be realized more compared to the model in Figure 1. The model does not show all the variables, rather it shows the different characteristics for simplicity. Variables exist within the characteristics in the model as per characterizations outlined in earlier sections.

ServiceAreaCharacteristics CommunityCharacteristics move ins, visitors

Policies

move outs alpha

CustomerServiceEffectiveness

Users

PotentialUsers rate of potential users

Quitters

adoption rate

quit rate

discontinue

StrategiesForPlatformPromotion come back fraction

diffusion

ServicesCharacteristics

adoption fraction

beta

QualityandAttractivenessOfWirelessNetwork UtilitiesForBeingOnPlatform

ApplicationsCharacteristics DemandCharacteristics

StrategiesForPlatformSides

EffectivenessOfSubstitutableOfferings

SmartServiceProvidersCharacteristics SponsoredBandwidth WirelessNetworkCharacteristics

CostCharacteristics

InfrastructureCharacteristics

Other Model

NeededInfra

From other model

Lifetime Customer Value

BlockedUsers

Figure 2 SD Model for Maintenance and Operation Cost Users in this model are classified into PotentialUsers, Users, Quitters and BlockedUsers. The first three are measured as stock variables in the SD model and related to the adoption of

the wireless network by users. The community needs to monitor the total payoff and estimate the lifetime customer value (LCV) and a new user’s impact on existing users’ utility. Based on these, the community should find the optimum

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number of users the wireless network should support before considering investment for updates, upgrades and expansions. There will be blocked users and that is expected by design. An ongoing evaluation of the performance of the wireless network using various measurement tools should help the decision makers whether the wireless network is up for upgrades. The number of potential users may change based on demographics, economic, or other factors as well as consumer behavior. Potential users adopt based on not only the intrinsic utility but also on expectation of future utility. Similarly for churn of existing adopters. Network externalities play an important role on the utility in addition to the intrinsic utility from the wireless network itself. The utility of the service for the user depends on interconnections among the users and utility they receive from the other sides of the platform. Existing users may become quitters when their patience level becomes low due to low service quality and poor customer service. Quitters may become adopters based on the come-back fraction variable which is set a value based on the expected future utility. One important variable is area attractiveness. This depends on many factors. It is one of the factors that attracts new users or leads them to quitters. It is influenced by the number of users. So there are positive and negative loops between the attractiveness and the number of users yielding an s-shaped curve. Area attractiveness has similar relationship with bandwidth sponsoring, yielding again s-shaped curve. The model does not show a stock variable for area attractiveness. It is retrieved from the ServiceAreaCharacteristics variable. A separate model measures the area attractiveness as a stock variable. UtilitiesForBeingOnPlatform has utility variable for each side of the platform, that is, for users, bandwidth sponsors and smart service providers. StrategiesForPlatfomSides includes assigned values for the effectiveness of considered strategies for increasing the utilities of sides of platforms [12]. SponsoredBandwidth is an aggregation of all sponsored bandwidth from any side on the platform including users, businesses and smart service providers as any side could sponsor bandwidth. For business districts, the model assumes more bandwidth sharing by the local businesses. With this model, total cost for unit period, e.g. each month is estimated over a duration, e.g. 4 years. Ongoing DemandCharacteristics for this model include total demand by users, bandwidth per user, demand for other smart service providers, bandwidth by IoT devices and wireless sensor networks, fraction of bandwidth from smart service providers to Internet, total bandwidth in network, total bandwidth to Internet, bandwidth to buy from ISPs. The amount of bandwidth sharing is to be calculated over the given period, e.g. 4 years. SD is good for this type of calculations for the reasons mentioned earlier. Ongoing capital expenses in the total maintenance cost is the cost of needed upgrades and expansions to the wireless

network and infrastructure. This cost is calculated using the first linear model in Figure 1. Ongoing operational costs in the total maintenance cost includes: 1. Cost of Customer Service: this relates to customer service effectiveness, mainly the cost of part time admins and customer service representatives 2. Cost of Bandwidth to Buy From ISPs: difference between sponsored bandwidth and the total bandwidth demand to Internet over the period 3. Electricity Cost: for all APs, nodes, servers and other equipment from WirelessNetworkCharacteristics and InfrastructureCharacteristics 4. Device Maintenance Cost: this includes replacing, repairing the APs and other equipment subject to the availability of the devices, networks and infrastructure settings (but not including the IoT devices and sensor networks of smart service providers). 4.3

Different Scenarios for Cost

The model can be run for simulating different scenarios. With simulations and sensitivity analysis, this model could be powerful tool to answer many questions. Not just estimations of various cost components, but also relations among other variables can be analyzed. Some questions to answer using this model include: • How many users use the system and how that number changes over time. Similarly for the blocked users and for uses who quit due to dissatisfaction. • Can the network provide QoS (enough bandwidth)? • How the municipality role impacts the success with respect to achieving the number of users and keeping the cost within the budget. • How the rollout strategy impacts the user adoption. • How the maintenance and management policy impacts the user adoption. • Is the maintenance cost within the budget? • How much grant is needed? • How much bandwidth from sponsors is needed? • How different strategies for incentivizing different sides of the platform affect the cost. • How different strategies for intensifying sponsors work? • How the network effects are? • How the utilities change with different strategies, with different numbers of users and blocked users, with different levels of customer service and quality of the wireless network. How all values change in response to service area characteristics particularly its attractiveness. Various scenarios can be analyzed by sensitivity analysis. Different triggers may be programmed to be active for different variables at certain intervals to see how the cost fluctuates in a long period. Different statistical distributions may be used over time for different cost items and for the characterization of community, service area and municipality.

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5

Conclusion

A wireless network (e.g. a mesh Wi-Fi network) covering most of the city is a significant contributor towards the development of a smart city. While such a network offers many benefits, a key issue with city-wide wireless network is the high fixed cost associated with laying out the infrastructure, rolling out the services, making the bandwidth available, maintaining the services. One key question is determining who will setup the network and who will fund it. Secondly, who will supply the bandwidth while broadband bandwidth is still in shortage in most cities? Lastly, who will pay for the supplied bandwidth? We believe involvement of the communities is important in building a city-wide wireless network. Our work in [12] presents a model where municipality, communities and smart utility providers work together to create a platform, smart community wireless platform, for a community in the city where different sides work together toward achieving smart community objectives. One important question is how much would be the total cost of building and operating such a platform. To estimate the cost, relevant dynamics should be identified and characterized. An intelligence framework that incorporates systems dynamics modeling with statistical, economical and machine learning methods is very useful for estimating the total cost of smart community wireless platforms under various conditions and scenarios. In this paper, we developed models for estimating the initial and maintenance costs, and outlined how these models could be used to analyze different dynamics and scenarios. These models can be used by the community which is the platform sponsor and by the city which is a main supporter of the platform. Through simulations and sensitivity analysis, these models could provide insights about different cost components as well as about other dynamics of the platform. Our work in [13] addresses the benefits and drawbacks of the smart community wireless platform and discusses the use of the same intelligence framework in measuring benefits and drawbacks, and analyzing the risks and mitigation plans for building a successful platform. Another question is how the city could inspire and assist the communities to build their community wireless network, and then coalesce them for a city-wide wireless network. We address this question by introducing the smart city wireless platform in [14]. Our future work on the cost estimation includes running the model for different scenarios and comparing results.

6

References

[1]. Lee, S.M., Kim, G. and Kim, J. (2009) ‘Comparative feasibility analysis of Wi-Fi in metropolitan and small municipalities: a system dynamics approach’, Int. J. Mobile Communications, Vol. 7, No. 4, pp.395–414. [2]. Shin, Seungjae and Tucci, Jack E., "Lesson from WiFi Municipal Wireless Network" (2009). AMCIS 2009 Proceedings. Paper 145. http://aisel.aisnet.org/amcis2009/145

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[3]. Kim, G., Lee, S.M., Kim, J., and Park, S. (2008) ‘Assessing municipal wireless network projects: the case of Wi-Fi Philadelphia’, Electronic Government, An International Journal, Vol. 5, No. 3, pp.227–246. [4]. Simon Evenepoel, Jan Van Ooteghem, Bart Lannoo, SofieVerbrugge, Didier Colle, Mario Pickavet, “Municipal Wi-Fi deployment and crowdsourced strategies”, (2013) Journal of The Institute of Telecommunications Professionals. 7(1). p.24-30. [5]. Ahmed Abujoda, Arjuna Sathiaseelan, Amr Rizk, Panagiotis Papadimitriou, “Software-Defined CrowdShared Wireless Mesh Networks”, Computer Networks, Volume 93, Part 2, 24 December 2015, Pages 359–372 [6]. Thomas Eisenmann, Geoffrey Parker, Marshall Van Alstyne, “Platform Networks – Core Concepts, Executive Summary”, http://ebusiness.mit.edu/research/papers/232_VanAlstyne _NW_as_Platform.pdf [7]. Serdar Vural, Dali Wei, and Klaus Moessner; “Survey of Experimental Evaluation Studies for Wireless Mesh Network Deployments in Urban Areas Towards Ubiquitous Internet”, IEEE Communications Surveys & Tutorials, Vol. 15, No. 1, First Quarter 2013 [8]. Yucel, Sakir: “Delivery of Digital Services with Network Effects over Hybrid Cloud”, The 12th International Conference on Grid, Cloud, and Cluster Computing, GCC'16: July 25-28, 2016, Las Vegas, USA [9]. Yucel, Sakir: “Evaluating Different Alternatives for Delivery of Digital Services”, The 12th International Conference on Grid, Cloud, and Cluster Computing, GCC'16: July 25-28, 2016, Las Vegas, USA [10]. Yucel, Sakir; Yucel, Ibrahim: “Estimating the Cost of Digital Service Delivery Over Clouds”, The 2016 International Symposium on Parallel and Distributed Computing and Computational Science (CSCI-ISPD), Dec 15-17, 2016, Las Vegas, USA [11]. Yucel, Sakir; Yucel, Ibrahim: “A Model for Commodity Hedging Strategies”, The 13th International Conference on Modeling, Simulation and Visualization Methods (MSV'16), July 25-28, 2016, Las Vegas, USA [12]. Yucel, Sakir: “Smart Community Wireless Platforms”, The 14th International Conference on Modeling, Simulation and Visualization Methods (MSV'17), July 1720, 2017 Las Vegas, Nevada, USA [13]. Yucel, Sakir: “Measuring Benefits, Drawbacks and Risks of Smart Community Wireless Platforms”, The 14th International Conference on Modeling, Simulation and Visualization Methods (MSV'17), July 17-20, 2017 Las Vegas, Nevada, USA [14]. Yucel, Sakir: “Smart City Wireless Platforms for Smart Cities”, The 14th International Conference on Modeling, Simulation and Visualization Methods (MSV'17), July 1720, 2017 Las Vegas, Nevada, USA

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Detection of Ultra High Frequency Narrow Band Signal Using Nonuniform Sampling Sung-won Park and Raksha Kestur Department of Electrical Engineering and Computer Science, Texas A&M University-Kingsville Kingsville, TX, USA [email protected] Abstract ─ For digital signal processing, an analog signal should be sampled at least at the Nyquist rate. In many applications such as radar systems and software defined radios, required sampling rates may exceed currently available analog-to-digital converters (ADCs). However, a narrow band signal can be sampled at a rate lower than the Nyquist rate when the center frequency is known. In this paper we use nonuniform sampling to identify the center frequency of the signal that is folded down below the Nyquist frequency. Nonuniform sampling results in side lobes that are proportional to the center frequency of the signal. Even when the main lobe folds down due to aliasing, the center frequency of the signal can be identified by observing the side lobes. The signal then can be reconstructed by interpolation. Keywords ─ Nyquist frequency, nonuniform sampling, interpolation

The paper is organized as follows. In section II, properties of Discrete Time Fourier Transform (DTFT) of a nonuniformly sampled sequence in terms of relations between the main lobe and side lobes for given analog signal’s center frequency are explained. In addition, how one can identify the center frequency of the signal by computing the side lobe to main lobe ratio is described. In section III, experimental results for estimating the center frequency using the side lobe to main lobe ratio are presented. Finally, a conclusion is made in section VI. II. DTFT OF RECURRENT NONUNIFORM SAMPLING Recurrent nonuniform sampling occurs with interleaved ADCs. ADC

x(t) delay 1

I. INTRODUCTION

x(t−(1+r1)T)

For digital signal processing, an analog signal should be sampled at least at the Nyquist rate. However, in some applications required sampling rate may exceed currently available ADCs for ultra-high frequency signal. Compressed sensing (CS or compressive sampling) has shown that the information from a signal can be captured with far fewer samples than the traditional Nyquist sampling theorem [1], [2]. The idea of sampling based on information rate rather than the bandwidth criteria is used for analog-to-information (A2I) as an alternative to conventional ADCs [3] [5]. In this paper recurrent nonuniform sampling is considered for A2I. Recurrent nonuniform sampling occurs with interleaved ADCs [6] [9] that are developed to increase the sampling rate. The DTFT of a nonuniformly sampled sequence results in the main lobe at the corresponding frequency and the side lobes whose heights are proportional to the center frequency of the signal. When the frequency of the analog signal exceeds the capability of the interleaved ADC system, the main lobe folds down to a lower frequency due to aliasing. By observing the side lobes the center frequency of the signal can be identified. The signal then can be reconstructed by interpolation. This allows sampling ultrahigh frequency analog signal without the need for ultra-high speed ADCs.

ADC

delay 2 x(t−(2+r2)T)

ADC

Memory

delay N−1 x(t−(M−1+rM−1)T)

ADC

fs = 1/(MT)

Fig. 1. Interleaved ADC system.

As shown in Fig. 1, to increase the sampling rate multiple ADCs are used. Suppose that there are M ADCs and the sampling rate of each ADC is 1/MT [Hz]. Then the resulting sampling rate of the interleaved ADC system is 1/T [Hz]. Ideally each delay is exactly T seconds. However, due to the imperfection of the delay, the actual cumulative delay is given by (m+rm)T where rm are termed the nonuniform sampling ratios and should be zero for uniform sampling. This system results in recurrent nonuniform sampling. Recurrent nonuniform sampling means that a continuous-time signal is sampled nonuniformly with a periodic pattern as shown in Fig. 2 when 3 ADCs are used.

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and : Uniform sampling

x(t)

0

(1+r1)T

4T

3T

2T

T

Q ()  1  e j0 r1 e  j ( 0 )  e j0 r2 e  j 2( 0 )

: Recurrent nonuniform sampling

(2+r2)T

5T

(4+r1)T

6T (5+r2)T

t

(8)

When θ0 = 2π 0.3 rad , the magnitudes of X(θ), P(θ), Q(θ), and Xnon(θ) are shown in Fig. 3. The nonuniform sampling ratios were chosen as r1 = r2 = 0.1.

t

Fig. 2. Recurrent nonuniform sampling. T is the sampling interval for uniform sampling, rm are the nonuniform sampling ratios. There is a periodic nonuniform sampling pattern. In this case the period M is 3.

A continuous-time signal x(t) is nonuniformly sampled at

(kM  m)T  rmT  (kM  m  rm )T

(1)

where k in general goes from −∞ to ∞, m ranges from 0 to M−1, T is the average sampling interval. For example, M is 3 and r0 is zero as in Fig. 2. Let us assume that the signal to be sampled is a complex exponential signal x (t )  e j  0 t

(2)

Assume that the signal is sampled uniformly with the sampling interval T = 1, then the resulting sequence will be x ( n )  e j0 n for n = 0, 1, ⋯, N−1.

(3)

The derivation of the DTFT of the sequence x(n) is shown in APPENDIX I. X ( ) 

sin  (  0 ) N / 2  sin  (  0 ) / 2 

e  j ( 0 )( N 1)/ 2

Fig. 3. The magnitudes of X(θ), P(θ), Q(θ), and Xnon(θ) when θ0 = 2π 0.3 [rad]. (Frequency in the horizontal axis is multiplied by 2π.)

The DTFT of the uniformly sampled sequence X(θ) has only one main lobe at the frequency θ0 = 0.3π 2π rad as expected. However, the DTFT of the nonuniformly sampled sequence, Xnon(θ), has two side lobes at frequencies 2π 0.63 [rad] and 2π 0.93 [rad] in addition to the main lobe. The DTFT Xnon(θ) is obtained by the product of P(θ) and Q(θ) as in equation (6).

(4)

Now the recurrent nonuniform sampling of x(t) results in a sequence when M = 3

e j0 n , n  0,3, 6,  j0 ( n  r1 ) xnon (n)   e , n  1, 4, 7,  e j0 ( n  r2 ) , n  2,5,8, 

(5)

The derivation of the DTFT of the nonuniformly sampled sequence xnon(n) is shown in APPENDIX II.

X non ()  P()Q()

(6) Fig. 4. The magnitudes of X(θ), P(θ), Q(θ), and Xnon(θ) when θ0 = 2π 1.3 rad . (Frequency in the horizontal axis is multiplied by 2π.)

where P ( ) 

sin  (  0 ) N / 2  sin  (  0 )3 / 2 

e  j ( 0 )( N  3)/ 2

(7)

When θ0 = 2π 1.3 rad , the magnitudes of X(θ), P(θ), Q(θ), and Xnon(θ) are shown in Fig. 4. As can be seen in X(θ), the

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frequency, θ0 = 2π 1.3 rad , is folded down to 2π 0.3 rad due to aliasing. It is impossible to identify the actual frequency of the signal as the frequency from the DTFT of the uniformly sampled signal. However, the DTFT of the nonuniformly sampled signal Xnon(θ) has side lobes at the same locations as in Fig. 4 and the amplitudes of the side lobes are larger than those of Fig. 3. When θ0 = 2π 2.3 rad , the magnitudes of X(θ), P(θ), Q(θ), and Xnon(θ) are shown in Fig. 5. The frequency, θ0 = 2π 2.3 rad , is folded down to 2π 0.3 rad and it is impossible to identify the frequency. However, the DTFT of the nonuniformly sampled signal Xnon(θ) shows side lobes at the same locations as in Fig. 3 and Fig. 4. In this case the amplitudes of the side lobes are larger than those of Fig. 4.

2

R ( k )   e j0 rm e

j

2 mk M

for k = 0, 1, 2.

(12)

m 0

The plots of the magnitudes of the main lobe and the side lobes are shown in Fig. 6 for three different cases.

Fig. 6. Plots of the magnitude of R(k) for k = 0, 1, 2 for (a) θ0 = 2π 0.3 rad , (b) θ0 = 2π 1.3 rad , (c) θ0 = 2π 2.3 rad Fig. 5. The magnitudes of X(θ), P(θ), Q(θ), and Xnon(θ) when θ0 = 2π 2.3 rad . (Frequency in the horizontal axis is multiplied by 2π.)

The magnitudes of the side lobes are related to the product of P(θ) and Q(θ). Let us define R(θ) as the DTFT of the sequence r(m) which is given as



r (m)  1, e j0 r1 , e j0 r2



 R(1)  R(2) / 2 R(0)

(13)

Based on the ratio, the center frequency of the signal can be estimated. (10)

It can be easily shown that

Q()  R(  0 )

Side Lobe to Main Lobe Ratio =

(9)

Then the expression of R(θ) is R ()  1  e j0 r1 e  j  e j0 r2 e  j 2 

The DFT of the sequence r(m) can be used to estimate the side lobe to main lobe ratio. In Fig. 6, R(0) is the main lobe and R(1) and R(2) are the side lobes. The side lobe to main lobe ratio is defined as follows.

(11)

Now R(0) corresponds to the main lobe and R(2π/3) and R(4π/3) correspond to the side lobes. R(0), R(1) = R(2π/3), and R(2) = R(4π/3) are in fact the DFT of the sequence r(m) so that

III. EXPERIMENTAL RESULTS For our experiment, two kinds of signals were considered: a pure sinusoidal signal and an FM signal. Statistically independent zero-mean Gaussian random noises were added for noisy environment. The standard deviations were chosen as 0.1, 0.2 and 0.4. Nonuniform sampling ratios were chosen as r0 = 0 and r1 = r2 = 0.05. In all cases, 1000 samples were taken. The frequency at which the global maximum magnitude (main lobe) occurs is the folded center frequency of the signal. The side lobe magnitude can easily be obtained as it occurs at θ0 2π/3 or θ0 2π/3.

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(i) Pure sinusoidal signal: the signal is chosen to be

x(t )  cos(0 t  )

defined in Equation (13) is computed in each case and tabulated in TABLE I. (14)

The signal is sampled at every one second. Because the signal is a real valued signal, the highest frequency of the signal that does not cause aliasing is 0.5 [Hz]. The frequencies of the signal is chosen as ω0 = 2π 0.3 and ω0 = 2π (n∓0.3) where n = 1, 2, 3, and 4. When the signal is sampled uniformly, the DFT of the sampled signal will be identical for all frequencies chosen and there is no way to identify the frequency of the signal.

(ii) FM (frequency modulation) signal: the signal is chosen to be

x(t )  cos(0 t  sin(m t )  )

(15)

The center frequency of the signal is chosen to be ω0 = 2π 0.3 and ω0 = 2π n∓0.3) where n = 1, 2, 3, and 4 and ωm = 0.01. The spectra of nonuniformly sampled signals are shown in Fig. 8 for different frequencies when the noise standard deviation is 0.2.

Fig. 7. Plots of the spectra of nonuniformly sampled pure sinusoidal signals with frequencies ω0 = 2π 0.3, 2π 07, 2π 1.3, etc. The standard deviation of the noise is 0.2.

Fig. 8. Plots of the spectra of nonuniformly sampled FM signals with frequencies ω0 = 2π 0.3, 2π 07, 2π 1.3, etc. The standard deviation of the noise is 0.2.

The spectra of nonuniformly sampled signals are shown in Fig. 7 for different frequencies when the noise standard deviation is 0.2.

The side lobe to main lobe ratio as defined in Equation (13) is computed in each case and tabulated in TABLE II.

TABLE I Comparison of the side lobe to main lobe ratios for different noise levels to the theoretical values for pure sinusoidal signals. Frequency  ( 2π)  0.3  0.7  1.3  1.7  2.3  2.7  3.3  3.7  4.3 

σ 0  0.0324  0.0718  0.1389  0.1797  0.2505  0.2956  0.3734  0.4255  0.5151 

Side lobe to main lobe ratio  σ 0.1  σ 0.2  σ 0.4  0.0375  0.0384  0.0368  0.0760  0.0782  0.0797  0.1375  0.1398  0.1352  0.1867  0.1747  0.2175  0.2484  0.2524  0.2660  0.2915  0.2882  0.2862  0.3779  0.3722  0.4035  0.4249  0.4352  0.4534  0.5035  0.5071  0.5419 

Eq. (13)  0.0314  0.0736  0.1377  0.1816  0.2499  0.2977  0.3736  0.4278  0.5160 

TABLE II Comparison of the side lobe to main lobe ratios for different noise levels to the theoretical values for FM signals. Frequency ( 2π)  0.3  0.7  1.3  1.7  2.3  2.7  3.3  3.7  4.3 

σ 0  0.0322  0.0740  0.1420  0.1835  0.2579  0.3011  0.3856  0.4332  0.5125 

Side lobe to main lobe ratio  σ 0.1  σ 0.2  σ 0.4  0.0267  0.0218  0.0301  0.0721  0.0784  0.0826  0.1447  0.1423  0.1546  0.1800  0.1839  0.1972  0.2522  0.2703  0.2642  0.2994  0.3058  0.3512  0.3903  0.3713  0.3873  0.4370  0.4191  0.4371  0.5071  0.5104  0.5351 

Eq. (13)  0.0314  0.0736  0.1377  0.1816  0.2499  0.2977  0.3736  0.4278  0.5160 



As can be observed in Fig. 7, as the frequency increases the side lobes increase. The side lobe to main lobe ratio as

Either case, the center frequency can be easily estimated by comparing the test ratios to the reference ratio (Eq. 13).

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IV. CONCLUSION In this paper we use recurrent nonuniform sampling to identify the center frequency of a test signal that is folded down below the Nyquist frequency when the center frequency is greater than the Nyquist frequency. Nonuniform sampling results in side lobes that are proportional to the center frequency of the signal. Even when the main lobe folds down due to aliasing, the center frequency of the signal can be identified by computing the side lobe to main lobe ratio. The signal then can be reconstructed by interpolation. APPENDIX I The DTFT of the uniformly sampled sequence x(n) is N 1

N 1

X ()   e j0 n e  jn   e  j ( 0 ) n

e e

n0

n0

j ( 0 ) N / 2





e

j ( 0 )/ 2

e e

 j ( 0 ) N / 2

e

 j ( 0 )/ 2

sin  (  0 ) N / 2  sin  (  0 ) / 2 

 j ( 0 ) N / 2

 j ( 0 )/ 2

e  j ( 0 )( N 1)/ 2

APPENDIX II

[4] G. Fudge, R. Bland, M. Chivers, S. Ravindran, J. Haupt, and P. Pace, “A Nyquist folding analog-to-information receiver,” in Proc. 42nd Asilomar Conf. on Signals, Comput., Syst. (ACSSC), 2008, pp. 541–545. [5] R. Maleh, G. Fudge, F. Boyle, Member, and P. Pace, “Analog-to-Information and the Nyquist Folding Receiver,” IEEE Journal on Emerging and Selected Topics in Circuits and Systems, Vol. 2, No. 3, pp. 564578, September 2012. [6] Y. –C. Jenq, “Digital spectra of nonuniformly sampled signals: Fundamental and high-speed waveform digitizers,” IEEE Trans. Instrum. Meas., vol. 37, pp. 245251, June 1988. [7] Y. –C. Jenq, “Digital Signal Processing with Interleaved ADC systems,” Journal of VLSI Signal Processing 39, pp. 267-271, 2005. [8] D. Wei, Q. Ran, and Y. Li, “Reconstruction of bandlimited signals from multichannel and periodic nonuniform samples in the linear canonical transform domain,” Opt. Commu., vol. 284, no.19, pp. 4307-4315, September 2011. [9] Y. –C. Jenq, “Perfect reconstruction of digital spectrum from nonuniformly sampled signals,” IEEE Trans. Instrum. Meas., vol. 46, pp. 649-652, June 1997.

If N is multiple of 3, then the DTFT of the nonuniformly sampled sequence xnon(n) of equation (5) is N 3

1

X non ()   e j0 3ne  j3n  e j0 (3n 1 r1 )e j (3n 1)  e j0 (3n  2  r2 )e  j (3n  2)  n0

=

 j ( 0 ) N

1 e 1  e j0 r1 e  j ( 0 )  e j0 r2 e  j 2( 0 )  1  e  j ( 0 )3 

e e

j ( 0 ) N / 2

=

j ( 0 )3/ 2

 e

 e  j ( 0 ) N / 2 e j ( 0 ) N / 2 e

 j ( 0 )3/ 2

 j ( 0 )3/ 2

Q()

 P()Q() where P () 

sin  (  0 ) N / 2  sin  (  0 )3 / 2 

e  j ( 0 )( N  3)/ 2

and Q ()  1  e j0 r1 e  j ( 0 )  e j0 r2 e  j 2( 0 ) .

V. REFERENCES

[1] E. Candes, J Romberg and T. Tao, “Robust uncertainty principles: exact signal reconstruction from highly incomplete frequency information,” IEEE Trans. Inf. Theory, vol. 52, no. 2, pp. 489–509, Feb. 2006. [2] D. Donoho, “Compressed Sensing,” IEEE Trans. Inf. Theory, vol. 42, no. 4, pp. 1289–1306, Apr. 2006. [3] D. Healy and D. Bradley, “Compression at the physical interface [The A-to-I and MONTAGE programs],” IEEE Signal Processing Mag., March 2008.

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Smart Community Wireless Platforms Sakir Yucel [email protected] Abstract - A wireless network (e.g. a mesh Wi-Fi network) covering most of the city is a key component in the development of a smart city. While such a network offers countless benefits, a key issue with city-wide wireless network is the high fixed costs associated with laying out the infrastructure, rolling out the services, making the bandwidth available, maintaining the services once the network is laid out. One key question is determining who will setup the network and who will fund it. Secondly, who will supply the bandwidth while broadband bandwidth is still in shortage in most cities? Lastly, who will pay for the supplied bandwidth? We believe involvement of the communities is important in building a city-wide wireless network. Indeed, many community and neighborhood wireless networks have been successful. Could the city inspire and assist the communities with building their community wireless networks, and then unite them for a city-wide wireless network? In this paper, we address the first question by presenting a model where municipality, communities and smart utility providers work together to create a platform, smart community wireless platform, for a community in the city where different sides work together toward achieving smart community objectives. We present the platform with its various dynamics and describe a research plan for analyzing if this platform could be part of a viable solution to building a city-wide wireless network. Keywords: smart community wireless platform, community wireless network, smart community

1

Introduction and Problem Definition

A smart city aims to embed digital technology across all city functions including economy, mobility, environment, people, living and, governance. Many cities have taken initiatives towards becoming a smart city to attract commercial and cultural development. A wireless network (e.g. Wi-Fi network) covering most of the city is a significant contributor and a major step towards becoming a smart city. Such a network offers many benefits in tackling key challenges such as reducing traffic congestion, reducing crime, fostering regional economic growth, managing the effects of a changing climate, and improving the delivery of city services [9]. One major issue with city-wide wireless network is the high cost of laying out the infrastructure, rolling out the services, allocating adequate bandwidth, maintaining the services. One question is who will setup the network and who will pay for it. A second question is who will supply

the bandwidth while broadband bandwidth is still in shortage in most cities. Another question is who will pay for the supplied bandwidth. What should the cities do? Should they rely solely on the wireless operators to build a wireless network across the city? Should they give up on their goals of being a smart city? How could they maintain their competitiveness without a wireless network in the digital age? In general, it is unreasonable to expect the private sector to setup a wireless network for smart city objectives. If not private sector alone, then how about some private-public partnerships? Despite numerous attempts in prior years, private-public partnerships and other joint ventures between municipalities and private companies have failed to take hold. Furthermore, several states have enacted legislation to prevent municipalities from offering wireless services in many forms in the city for variety of reasons [3]. While there are so many failures in the past and there is political controversy, why should they still pursue a citywide wireless network? We think that cities could look for new approaches in realizing city-wide wireless network. City-wide wireless networks are still desired even with the availability of cellular networks, mainly due to being low cost and higher bandwidth compared to cellular networks. Plus, people are more inclined to use the wireless networks where available as opposed to using from their limited data plans. In addition to citizens, many smart IoT devices will require bandwidth and many of them will use protocols which are best supported by a city-wide wireless network. What new approaches are available for the cities? One approach is to analyze the success of community wireless networks and try to find ways to leverage their success for building a city-wide wireless network. Indeed, there are many examples of successful community wireless network implementations [5][7]. In this paper, we will bring attention to successes of community wireless networks and develop a model where municipality, communities and smart utility providers work together to create a platform, which we call smart community wireless platform, where different sides work together toward achieving smart community objectives. The purpose for this investigation is to take a new look at building a city-wide wireless network through a new model based on integrating smart community wireless networks over the span of the city. The objective of this paper is to present this platform and its various dynamics. Accordingly, the paper takes more of

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a conceptual approach rather than a technical one. The purpose is to introduce the smart community wireless platform. How such platforms could join to yield a larger city-wide wireless network is a separate discussion we address in [16]. We use the term smart to indicate the human factor in building and using the wireless network. The wireless network presented in this paper is to be built and utilized mainly by the community for the needs of the community. Smart services are developed on this network also in line with the community needs and objectives. The human factor is significant in this platform, hence the use of the term smart as opposed to terms such as digital, intelligent and ubiquitous which relate to the technological aspects of the platform [10].

2

Smart Community Wireless Platform

Earlier approaches involving municipality together with private commercial providers failed for variety of reasons [2][3][4]. We believe smart city starts with smart communities and hence the community involvement is significant in building a city-wide wireless network. Indeed, many community and neighborhood wireless models have been successful. The purpose is take it further through collaborations of communities, municipalities and other partners to realize a city-wide wireless network. In our approach, we will include the smart utility providers as a player in the smart community wireless platform. So the question becomes: can communities, the municipality and smart utility providers work together to build a platform for the community? Let us first define the smart community wireless platform. 2.1

Platform Definition

We will outline the smart community wireless platform with respect to the system architecture, sponsor and providers, sides, utilities and network externalities, financial resources, policies, strategies for how to position, present, realize and operate this platform. For platforms and platform businesses in general, see [8]. A smart community wireless platform is a community wireless network built and maintained through collaboration of the community, the municipality and the smart service providers. There are multiple sides on this platform: (1) users who use the wireless network and may also sponsor bandwidth, (2) bandwidth sponsors who sponsor bandwidth for this wireless network, particularly the businesses, (3) other smart service providers. In this model, the community and municipality assume the main roles. The amount of involvement varies by different realization of this model in different cities and even within different communities in the same city. Municipality plays an important role in this platform in both supporting the individual communities and organizing them to participate

into the city-wide bigger wireless network of community networks. The users are community members or visitors that use the wireless network. Bandwidth sponsors are entities that sponsor bandwidth used by the users. Community members may be users and bandwidth sponsors at the same time. Businesses, nonprofits and other organizations in the community become bandwidth sponsors. Smart service providers may become bandwidth sponsors. In this paper, we will focus more on businesses as the bandwidth sponsors that provide bandwidth for the wireless users to connect to Internet. Smart service providers offer smart services to the users of the platform. One typical example is the utility provider companies like electricity, gas, water, waste management. For example, waste management company provides services for smart garbage collection to the users of the platform. We will not use the term utility for them (as in electricity, water, gas) in this paper as we will use the term utility to refer to economical utility for being on the platform. We will call them as smart service providers. These smart service providers use the community wireless network for communication of their smart devices (sensors, smart devices, and other IoT devices) that they place in the network. They benefit from the platform by placing IoT devices that use the wireless network for communication, or more likely by building sensor networks that integrate with the wireless network. They sponsor bandwidth so they become bandwidth sponsors and they may provide other components into the platform as explained in later sections. Another example to smart service providers is the city offices and department. For example, parks department provides services for park resources. Another example is the community itself in providing smart services to its members, for example, smart education services. 2.2

System Architecture

Figure 1 shows the layered system architecture for the smart community wireless platform. It has users and IoT devices at the top that generate network traffic. IoT devices belong to the smart service providers that participate in this platform. User devices and IoT devices use the wireless network. Wireless network is built on top of the wireline infrastructure that has network nodes, servers and cabling provided by community, municipality and smart service providers. Computing and storage infrastructures belonging to the community, municipality, and smart service providers in this system store data and offer computing, networking, caching and data storage resources. Some core services such as analytics, location based services, user location tracking, community social networking, community cloud and other digital community services are offered using this infrastructure which is

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accessible by the wireless network. Such infrastructure by smart service providers host the data and resources for smart services. With this architecture, various services can be offered in a modern smart community. All these services are provided by smart service providers (e.g. private companies, municipal offices and communities). The smart community wireless platform creates an ecosystem around this system architecture. The smart service providers are vertically integrated to provide their services over the platform. Certain such services are only available on this platform, such as accessing the data or services maintained within the infrastructure of the platform. The providers use the wireless platform for collecting data from field devices into their infrastructure and then may offer the same services over Internet, which can be accessed by anyone. In any case, the wireless platform provides a home for the devices and for collecting their data.

Network Traffic Generating Devices (IoT devices, cameras, others)

The smart service providers preferably contribute to this network by supplying access point routers that connect their devices to the wireless network. This architecture hosts an internet of everything environment including devices connected, and users using the network, communities using the network and offering community services, smart service providers using the network and offering their smart services. The smart service provider devices (e.g IoT devices, wireless sensor networks) are connected to the wireless network and produce traffic. Some of their traffic would not leave to Internet, but rather stored, processed and analyzed by the infrastructure within the span of community wireless network. Internet access is supplied by bandwidth sponsors and commercial ISP services.

Municipal apps, local deals (coupons, attractions, events), local community apps, smart service provider apps

Services, APIs

Community routers

Bandwidth Sponsor routers

Municipality routers

Users run these applications in addition to usual Internet applications

User Applications (mobile, web)

Wireless Users (laptops, phones, others)

Other Provider routers

Location based, analytics, municipality services, other utility provider services (for smart city), community services, community social networking

All providers provide services which are accessible through the wireless network

Data is stored by these infrastructures

Wireless Mess Network

Computing and Storage Infrastructures by Municipality, Community and Other Smart Service Providers Access to Internet is provided by bandwidth sponsors and commercial ISP services Provided by community, municipality, other smart service providers

Nodes, servers, fiber, cable

Internet

Wireline network infrastructure

Figure 1 System Architecture for Smart Community Wireless Platform

We will not present a detailed design of the wireless network in this paper, rather we will state our assumption about the wireless network. We assume the smart community wireless platform uses a mesh Wi-Fi technology as it is most often the technology used in such networks. In the mesh network of smart community wireless platform, there are access points and routers supplied by the community usually having generic server hardware and running open source firmware and software. The mesh network usually runs open source mesh network routing software and open source network management

software to setup and manage a software defined wireless mesh network. In addition to routers supplied by the community, there are routers supplied by the community members and other routers belonging to municipality, to the sponsors of bandwidth and to the smart service providers. The design of the mesh network should cover the whole area by adding intermediary routers in places where no sponsor is available and should be able to redirect user traffic to any of the available access points. The smart community wireless platform relies on community members, businesses and organizations to

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share a portion of the total required bandwidth. So, a significant assumption is that ISPs allow plan sharing in the ISP service terms. When bandwidth sharing does not supply the required bandwidth completely, remaining bandwidth needs to be purchased from the local commercial ISPs. The platform provides location based analytics on the users. Participating businesses and non-profit organizations will have access to the data and analytics. The mesh Wi-Fi network uses mechanisms for access control, metering and blocking the user traffic beyond a daily cap. It enforces rate limiting of the users with respect to data rate and the amount of download/upload. It employs self-adjusting network functionalities such as enforcing dynamic rate limiting by limiting the bandwidth to each wireless interface based on current number of users for fairness. When the number of users exceeds the network capacity based on minimum bandwidth for each device, new connection requests are not granted thanks to dynamic connection admission control. Therefore, some users will be blocked and not able to join. The city officials and municipal services have guaranteed service for accessing the wireless network, and they are not blocked. The wireless network should offer enough bandwidth to fulfill the basic requirements of the users and support applications that will benefit the community and the city. This includes community social networking, community calendar of events and information about events, utilizing all that community and municipality offers, and all that commercial smart service providers offer. The wireless network hosts the community and municipality portals for accessing the smart services provided by the community and by the municipality. On the other hand, it should not be placed as a competitor to commercial cellular or wireless networks as we argue in Strategies For Platform Promotion and Positioning section. For example, it should not allow unlimited upload and download. One option is to rate limit the download/upload speeds. Another option is to limit the traffic to and from Internet while the users could enjoy unlimited access to the smart services. In other words, their Internet traffic is metered and capped, however, their traffic within the wireless network could be unlimited, or limited with higher cap subject to the whole capacity of the wireless network. It implements security and access control [6][9]. When similar networks are integrated together, a seamless network covering a bigger span of the city could be possible [16]. 2.3

Platform Control

For the smart community wireless platform, it makes sense for the community to be the platform sponsor and the primary provider. Community has the say on management and policies of the community wireless network. The

community decides on what policies and what strategies to apply. In another arrangement model, municipality and community may behave as the platform sponsors, but we will assume the community is the main sponsor and provider of this platform in this paper. We assume no commercial offerings using this platform by municipality due to existing state laws. We assume the community does not engage in seeking any profit using the platform. The smart community wireless platform is not commercial and is not for profit for the community. For this reason, many of the concerns applicable to commercial platforms do not apply, like pricing but some other concerns apply like funding. We assume the platform will be free for users but possibly with some volunteering or sponsoring bandwidth in return, or with agreeing the usage terms and giving out some privacy. The term sponsor is used in two meanings: one in the meaning of platform sponsor, the entities that control the platform. The other one is the bandwidth sponsor, for example, businesses that provide bandwidth for the wireless users to connect to Internet. The platform sponsor should not be confused with the bandwidth sponsors that will be mentioned in the paper. Community mainly supplies volunteers and bandwidth sponsors. Municipality helps the communities by allowing communities to use city owned light posts, traffic lights and municipal buildings for attaching access points, and by allowing to use wireline infrastructure; assisting the grant writers with grant applications; providing access to GIS mapping data; assisting with network design; financial help by identifying grants and tax breaks for community networks. With community and municipality working together, businesses and other smart service providers would join the bandwagon increasing the network externalities and adding value for the platform, therefore making it a viable platform. The openness of the community wireless network is controlled by the community. Normally, the community wireless platforms are open to any user provided they accept the usage policies. The community will decide on the criteria for who can join as developer and providers of services and on what conditions. The community decides whether the wireless network and infrastructure that comes with it is open to any developer to develop some service/application, or to any smart service provider to install devices and provide services. The community will decide if research tools can be deployed by universities, or by local startup companies. The community controls the quality of the wireless network and the service offered on it. The community decides and governs what complements such as location tracking and analytics can be provided and by whom. The community makes these decisions following their decision-making methodology, for example, may perform SWOT analysis for the complement providers and smart service providers. The complement

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providers could be commercial ISPs to sell bandwidth, the IoT providers that add the devices into the wireless network, other providers of the equipment, software, services and know-how. 2.4

Utilities and Network Effects

Each side of the platform have an intrinsic utility for being on the platform. For example, users have utility with being on this platform in the form of getting wireless service and additionally by receiving coupons, deals and other location based offers, and accessing smart services. Businesses have utility for promoting and advertising their businesses. Similarly for other sides. In addition to the intrinsic utility, each side experiences additional utility due to the network externalities. Network effects exist impacting the utility of different sides for being on this platform. It is assumed that the user utility and network size would follow a logistic (“S”-shaped) function. Same side effects will help increase the user population due to information diffusion initially. Increase in population would later negatively impact the utility due to increased bandwidth requirements, possible degradation in the quality of the wireless network and users being blocked in the shortage of available bandwidth. Cross side network effects exist. There could be positive network externality between users and bandwidth sponsors. Similarly between users and smart service providers. Policies and strategies would increase the network effects and thereby the utility of different sides. We will discuss some policies and strategies for taking advantage of the network externalities in subsequent sections. Network effects are so many and should be analyzed in more detail. Various network effects and their impact on the attractiveness of the platform are discussed in [14] [15]. 2.5

Platform Evolution

The community should exercise policies that will help build an ecosystem to allow users, community members, component providers to provide ideas and contributions. The platform evolves by being open to community needs, through community and university involvement, fostering innovation by allowing community startups and pilot projects and university research and being a testbed for innovation. The use of open source supports such collaboration between global developers and the community developers. Collaboration among communities and tracking what other communities do will help evolve the platform into new technologies and approaches. The community should be transparent to the users about the evolution of the platform as to provide more accurate information about the roadmap and shape the user expectations accordingly.

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The community needs to monitor the total payoff and estimate the lifetime customer value (LCV) and a new user’s impact on existing users’ utility. Based on these, the community should find the optimum number of users the wireless network should support before considering investment for updates, upgrades and expansions. Therefore, there will be blocked users due to not available bandwidth in the network. 2.6

Financial Resources

The smart community wireless platform reduces the financial responsibility for the city, but funding is still required. The municipality could continuously track and search for funding and try to maximize the amount of funding collected for the community platforms. For each grant, the municipality could maintain the area, the purpose, the conditions and constraints of the grant. Certain grants are given for specific applications and purposes, and by different sources (e.g. safety, energy, climate preparedness, transportation, health and more, by the Department of Homeland Security, Department of Transportation, Department of Energy, Department of Commerce, and the Environmental Protection Agency). Although many funding opportunities and sources exist, different communities may focus on different ones based on how the opportunities fit their needs and objectives. The community also should be on the look for funding sources by mobilizing the volunteers. Funding sources include grants, tax benefits, donations (from local organizations, businesses, and community members), bandwidth sponsorships, new exploratory research funds, new hackathon challenges and awards, free services from companies (e.g. free cloud service), testbeds (for trials of different technologies, ideas, models), special funds (e.g. system and service for first responding by department of homeland security), and definitely crowdfunding opportunities [4]. 2.7

Policies

What should be the ownership, maintenance, and security policy for the platform? It is expected that the community owns the platform as being the sponsor. Regarding maintenance policy, it is expected the community maintains the network with the help of volunteers and part-time contractors. Who should do the authentication and authorization of users? What should be the user privacy policies? For smart community wireless platform, the community should control authentication and authorization policies. The users would have to give up on some of their privacy like entering their profile (e.g. via a survey at first login) and agreeing for being tracked for usage and for location. However, this information is used for analytics, improving the wireless experience and integrate into the loyalty programs and deals of the sponsoring businesses. The

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information is shared with the sponsoring businesses, which is an incentive to bring in more businesses and increasing their utility. Additionally, this information is used to trace individuals in case any illegal content is transmitted on the network. 2.8

same strategy would not work in a business district. A strategy that provides utility to the bandwidth sponsors from business owners and non-profits in the area is desired. 2.9

Strategies For Platform Sides

Strategies are employed to increase the value of the platform for different sides and to reach the critical mass in terms of regular users within the community and by visitors. These include strategies to mobilize the volunteers, officials, sponsors, non-profits, smart service providers, commercial ISPs, component and complement providers. There should be strategies in place for: • Bringing in users by offering them a consistent service as well as access to business loyalty programs and smart services over the platform • Bringing in private investment for upgrading the broadband infrastructure. Strategies for private investment to lay down more fiber, to upgrade wireline infra and services, for improving the wireless coverage and enhancing the service to the latest should be in place. • Motivating city universities for research and development to help with technical and managerial initiatives. Provide them opportunity to test ideas, provide them with testbeds, nourish their business and management ideas. • Bringing in other organizations (non-profit, church, etc): they will not have much utility for offering their bandwidth just so that more people come in economic district, but would have increased utility with contributing to the community in developing areas and for reducing digital divide. • For bringing in smart service providers such as smart parking and waste management. • Effective crowdsourcing and crowdfunding • Convincing the ISPs to allow sharing the broadband connection as the platform depends heavily on. • Bringing in businesses as sponsors by presenting how the platform could lead to more customers, by allowing them to advertise and do directed marketing by accessing the user data, tracking data and all analytics on them. • Strategies for making sure the smart service providers contribute enough to the platform. They need to sponsor bandwidth in addition to bandwidth they should sponsor for their IoT devices. • Strategies for enforcing users sponsor bandwidth: the user is expected to share bandwidth to be able to utilize the network beyond a cap. This is effective in a residential community, not in a business community with mostly visitors. In residential area, a crowdsourcing strategy could be employed for residents to join the shared wireless network and contribute from their broadband connection [4]. The

Strategies For Platform Promotion and Positioning

Other strategies include the positioning of the platform, its launch, rollout of the wireless networks and promotion of the platform. The municipality should start using it, e.g. personnel, police etc. from day one, continues to be a user of the platform. What strategy should the municipality follow while helping community mesh wireless and rolling the smart city services on this network, and meanwhile encouraging private investment? Municipality and communities must understand the value of commercial investment in the city, should stay away any policy or strategy that will deter them. The platform should not be positioned to compete against commercial wireless services and substitutable offerings. This platform should not be for competition or for disruption. It should not be about being a winner in the market. Rather, it should be for serving a real need in the community for a specific purpose and to fill in the gap from commercial providers. All policies and strategies should be compliant with these principals, that is, keeping the availability of substitutable and commercial offerings. Otherwise, the platform could deter private investment and run into legal issues as happened in earlier attempts [2]. Policies and strategies should encourage broadband modernization by private industry both in wireline (fiber) and wireless (5G). On the other hand, community and municipality could try to convince the local incumbent ISPs to lower prices and alter terms of service agreements as this happens with community networks by grassroots groups [5]. In our opinion, this platform should not be positioned as an alternative to conventional ISPs in the last mile, rather a balance should be preserved so that commercial ISPs still find interest and profit in the community. As an example for not competing against substitutable offerings, the bandwidth in the community wireless network should be limited to basic use so that commercial providers still find interest in providing better quality services for fee.

3

Smart City Wireless Platform

When different smart community wireless platforms come together, is it possible to create a bigger platform for the whole city? We call this new platform the smart city wireless platform. In the bigger city-wide deployment of a smart city wireless platform, the municipality would be the sponsor of the platform to ensure order and control mechanisms. This platform requires additional infrastructure elements in the system architecture and has

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more platform sides compared to the smart community wireless platform. We explore this platform in [16].

4

Conclusion

A wireless network (e.g. a mesh Wi-Fi network) covering most of the city is a significant contributor towards being a smart city. Such a network offers many benefits but there are technical, economical and policy challenges for building and operating one. In this paper, we present a model where municipality, communities and smart utility providers work together to create a platform, which we call smart community wireless platform, where different sides work together toward achieving smart community and smart city objectives. We present the platform together its various dynamics. The main advantage with this platform is that communities have clear objectives and needs, and have better predictions about the demand, and are small and manageable in sizes. The municipality does not allocate big budget for initial and ongoing cost. The network provides bandwidth for smart IoT devices and access to the services offered by smart service providers. This model allows collaboration among communities, municipality and smart service providers. Benefits, drawbacks and risks together with appropriate mitigation plans for this platform should be further analyzed and researched, which is our plan. Our work on smart community wireless platform builds upon our earlier work analyzing platforms and cost using an intelligence framework [11][12][13]. Our research addresses the cost of building and maintaining these platforms in [14], benefits, drawbacks and risks in [15]. Another question is how the city can leverage these platforms to achieve smart city objectives which we address in [16].

5

References

[1]. Lee, S.M., Kim, G. and Kim, J. (2009) ‘Comparative feasibility analysis of Wi-Fi in metropolitan and small municipalities: a system dynamics approach’, Int. J. Mobile Communications, Vol. 7, No. 4, pp.395–414. [2]. Shin, Seungjae and Tucci, Jack E., "Lesson from WiFi Municipal Wireless Network" (2009). AMCIS 2009 Proceedings. http://aisel.aisnet.org/amcis2009/145 [3]. Kim, G., Lee, S.M., Kim, J., and Park, S. (2008) ‘Assessing municipal wireless network projects: the case of Wi-Fi Philadelphia’, Electronic Government, An International Journal, Vol. 5, No. 3, pp.227–246. [4]. Simon Evenepoel, Jan Van Ooteghem, Bart Lannoo, SofieVerbrugge, Didier Colle, Mario Pickavet, “Municipal Wi-Fi deployment and crowdsourced strategies”, (2013) Journal of The Institute of Telecommunications Professionals. 7(1). p.24-30. [5]. Abdelaal, Abdelnasser; “Social and Economic Effects of Community Wireless Networks and Infrastructures”, IGI Global, Feb 28, 2013 -

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[6]. Ahmed Abujoda, Arjuna Sathiaseelan, Amr Rizk, Panagiotis Papadimitriou, “Software-Defined CrowdShared Wireless Mesh Networks”, Computer Networks, Volume 93, Part 2, 24 December 2015, Pages 359–372 [7]. Greta Byrum: “What are Community Wireless Networks For?”, The Journal of Community Informatics, Vol 11, No 3 (2015) [8]. Thomas Eisenmann, Geoffrey Parker, Marshall Van Alstyne, Platform Networks – Core Concepts, Executive Summary, http://ebusiness.mit.edu/research/papers/232_VanAlst yne_NW_as_Platform.pdf [9]. Serdar Vural, Dali Wei, and Klaus Moessner; “Survey of Experimental Evaluation Studies for Wireless Mesh Network Deployments in Urban Areas Towards Ubiquitous Internet”, IEEE Communications Surveys & Tutorials, Vol. 15, No. 1, First Quarter 2013 [10]. Dimitri Schuurman, Bastiaan Baccarne, Lieven De Marez and Peter Mechant: “Smart Ideas for Smart Cities: Investigating Crowdsourcing for Generating and Selecting Ideas for ICT Innovation in a City Context”, Journal of Theoretical and Applied Electronic Commerce Research, ISSN 0718–1876 Electronic Version, VOL 7 / ISSUE 3 / DECEMBER 2012 / 49-62 [11]. Yucel, Sakir: “Delivery of Digital Services with Network Effects over Hybrid Cloud”, The 12th International Conference on Grid, Cloud, and Cluster Computing, GCC'16: July 25-28, 2016, Las Vegas, US [12]. Yucel, Sakir: “Evaluating Different Alternatives for Delivery of Digital Services”, The 12th International Conference on Grid, Cloud, and Cluster Computing, GCC'16: July 25-28, 2016, Las Vegas, USA [13]. Yucel, Sakir; Yucel, Ibrahim: “Estimating the Cost of Digital Service Delivery Over Clouds”, The 2016 International Symposium on Parallel and Distributed Computing and Computational Science (CSCI-ISPD), Dec 15-17, 2016, Las Vegas, USA [14]. Yucel, Sakir: “Estimating Cost of Smart Community Wireless Platforms”, The 14th International Conference on Modeling, Simulation and Visualization Methods (MSV'17), July 17-20, 2017 Las Vegas, Nevada, USA [15]. Yucel, Sakir: “Measuring Benefits, Drawbacks and Risks of Smart Community Wireless Platforms”, The 14th International Conference on Modeling, Simulation and Visualization Methods (MSV'17), July 17-20, 2017 Las Vegas, Nevada, USA [16]. Yucel, Sakir: “Smart City Wireless Platforms for Smart Cities”, The 14th International Conference on Modeling, Simulation and Visualization Methods (MSV'17), July 17-20, 2017 Las Vegas, Nevada, USA

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Sketch Based Image Retrieval System based on Block Histogram Matching Kathy Khaing1, Sai MaungMaung Zaw2, and Nyein Aye2 Faculty of Computer System and Technology, University of Computer Studies, Mandalay, Myanmar 2 Faculty of Computer System and Technology, University of Computer Studies, Mandalay, Myanmar 2 Computer University, Hpa-An, Myanmar 1

Abstract - Nowadays, the usefulness of scalable image retrieval (IR) systems is obvious valiant than ever. Moreover, searching desired image by describing hand-drawn sketch is popular, because of the emerging of touch screen technology. Therefore a matching algorithm for sketch based image retrieval (SBIR) system is proposed in this paper. The features of database images and query sketch are extracted by Canny edge detection algorithm. And then block histogram matching by sliding window method is applied for matching sketch edge and edge images. The retrieved images similar with query sketch are displayed by rank order. Mean Average Precision (MAP) is measured as evaluation criteria. The benchmark sketch dataset of Eitz et al. and Flickr15K are used to evaluate the performance of this system. Keywords: Blockhistogram, Canny, IR, MAP, SBIR.

1

Introduction

Image processing is a fundamental piece in the computer vision domain in such a way that the results of many computer vision applications depend on the well design of the image processing algorithms. Moreover, image processing in the context of image retrieval should be understood as a step for enhancing the image information, not for describing the content of the image in its entirety. The exponential growth of publicly available digital media during the last two decades has highlighted the need for efficient and user-friendly techniques to index and retrieve images from large multimedia databases. Despite the considerable progress of content-based image retrieval (CBIR) where the goal is to return images similar to a user-provided image query, most of the multimedia searches are traditionally text-based. Currently, out of all the big web image search engines, only Google provides the possibility to search by image. Text-based image search requires user intervention to tag all the available data and has two main drawbacks: i. Image labeling is a protracted and most importantly subjective and ii. Images cannot be compactly communicated based on words; different people would probably use different words to describe a scene based on their cultural background and experience. On the contrary, CBIR techniques allow effortless

tagging and export non-biased image summaries, but suffer from the so-called semantic gap. That is the discrepancy between human and computer representation of knowledge on a topic. Feature extraction is the main part in SBIR system. It extracts the visual information from the image and save them as the feature vectors in database. The feature extraction finds the image description in the form of feature values called feature vector for each pixel. These feature vectors are used to compare the query with the other images and retrieval. Features extracted from the whole image are called global features. Local features are extracted from an object or a segment of an image. Global features cannot provide enough information to estimate the similarity between images. Therefore, local feature descriptor is used in this system.

2

Related Works

This section reviews the related literature and describes the concept of an image retrieval system. Scientific publications included in the literature survey have been chosen in order to build a sufficient background that would help out in solving the research sub-difficulties. Although a vast number of authors have been involved in the content based image retrieval using an example image as query, some relevant works on sketch based image retrieval have just appeared in the last three years. The work of Eitz et al. [1] is one of the most relevant in this context. They propose two techniques based on the well known SIFT and Shape Context approaches for extracting relevant information from sketch representations. The extracted information, in the form of feature vectors, is clustered to form a codebook that is then used under the Bag of Features (BoF) approach. This work is also relevant because the authors propose the first systematically built benchmark for the SBIR problem. One outstanding property of this benchmark is that it takes into account the user opinion about the similarity between sketches and test images. This is highly important because the ultimate goal of a retrieval system is to satisfy the user requirements. The dataset of Eitz, et al. consisting of 31 sketches, each with 40 photos ranked by similarity. Furthermore, they develop new descriptors based on the bag-of-features approach and use the benchmark to demonstrate that they significantly outperform other descriptors in the literature. Another approach is that proposed by Hu et al. [2]. This work is also based on the Bag of Feature approach but

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addressed the extraction information step in a very different way with respect to the Eitz's approach. The novel idea in this work is the transforming of sketch representations into gradient field (GF) images. The test images are converted into sketch representations by edge maps using the Canny operator. The GF images are then used to compute HOG descriptors in three different scales with respect to each edge pixel. After that, a BoF approach is applied to form a frequency histogram. This method requires solving a sparse linear equation system where the number of variables is the order of the size of the underlying image. The majority of SBIR methods are based on histograms of orientations either to compute a global representation or a local one. Rui Hua and John Collomossea [3] described HOG seems to be the favorite descriptor in the community. However, because of the sparseness of sketches, HOG descriptors may be also sparse which may impact negatively in the final effectiveness. In [3], an extensive evaluation on several image descriptors is carried out indicating the superiority of HOG-like features in SBIR. The authors also made publicly available two SBIR databases, namely Flickr160 and Flickr15k. In 2005, Chalechale et al. [7] performed angular partition in the spatial domain of images, as a means to extract compact and effective features. The first step of the process is to obtain the edge map of all the natural images, in order to transform them in a format more suitable for matching against binary sketches. Sketch queries were preprocessed by a morphological thinning filter to better match the edge maps extracted from the images. An angular partition of an image is employed and divides images in K angular regions. K can be adjusted to achieve hierarchical coarse to fine representations. The number of edge points for each region Ri, i = {1, 2, . . . , K} is chosen to represent each slice feature. 1-D Discrete Fourier Transform (DFT) is then computed for each region of the image and by keeping only the magnitude of the DFT, scale and rotation invariance is guaranteed. Authors also note that this scheme provides robustness against translation as well. Similarity between images and sketches is measured by the l1 distance between the two feature vectors. This system was tested on a database of 3,600 images. At least 13% percent of the images had to be recovered from the database in order to retrieve the correct image, a fact that highlights the noise sensitivity of DFT. In [16], making better use of edges via perceptual grouping by Jun Guo et al. is presented in CVPR 2015.To compare the performance of our system, the data from that paper is used. Dipika R. Birari and J.V. Shinde [17] proposed a SBIR system with descriptor based on constraints. Edge extraction, designing of descriptor and selection of edges are included in their system. The system in this paper is closely related to that paper. The difference is edge matching algorithm. The result of our system is compared with theirs. The proposed SBIR system in this paper is used the Canny edge detection algorithm for edge extraction of both query and database images. For efficient matching of sketch and

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image, block histogram matching by sliding window method is developed in this work.

3

Image Retrieval System

Over the last years, the study of information retrieval systems has become a very active research area among computer sciences. Ongoing research on information retrieval is focused on multimedia information retrieval and in particular, image retrieval systems has attracted a great number of researchers coming from different communities like computer vision, multimedia retrieval, data mining, among others, leading to a vast number of related publications. The reason why image retrieval is the focus of much research is basically due to the fact that images are ubiquitous and easy to capture by users.

3.1

Content Based Image Retrieval System (CBIR)

Nowadays the application of internet and www is increasing exponentially and the collection of image accessible by the users is also growing in numbers. During the last decade there has been a rapid increase in volume of image and video collections. A huge amount of information is available, and daily gigabytes of new visual information is generated, stored, and transmitted. However, it is difficult to access this visual information unless it is organized in a way that allows efficient browsing, searching, and retrieval. A content-based approach requires extracting visual information from images. The extracted information is then used to evaluate similarity between images. Commonly, this approach also involves image processing and computer vision techniques. An appropriate feature representation and a similarity measure to rank images, given a query, is essential here. Content based image retrieval attempts to establish visual similarities between an image collection and an image query. The image feature is considered any information that extracted from an image. Some of the most common modalities are color, shape and texture. There is a vast range of fields where CBIR have been applied. Medical imaging, painting retrieval and digital forensics are a few examples. Google recently introduced a generic CBIR system for web image search where users can upload an image query. Google Goggles is a commercial CBIR application that can retrieve landmarks and logos. It follows from the above that CBIR is a well-established research field, yet in the frequent case when a user seeks a particular image an impressibility barrier rises. Specifically, there is no specified input mechanism to describe the aforementioned image; hence the search is rendered void. Sketch based image retrieval provides a solution with an interactive query generation approach.

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The aim of this paper is to develop a sketch based image retrieval system, which can retrieves images quickly, easily and efficiently.

3.2

Sketch Based Image Retrieval System (SBIR)

A sketch is s free hand-drawing consisting of a set of strokes and it lacks texture and color. SBIR is part of the image retrieval field. In SBIR system, the input is a simple sketch representing one or more objects. Although a vast number of researchers on multimedia image retrieval are mainly focused on content based image retrieval systems using a regular image as query, in the last few years the interest in the SBIR problem has been increased. This interest may be owed to the emerging touch screen technology that allow users to draw a query directly on a screen, turning the process of making a query easy and accessible. The Sketch-based image retrieval (SBIR) was introduced in QBIC and Visual SEEK systems. In these systems the user draws color sketches and blobs on the drawing area. The user has a drawing area where he can draw those sketches, which are the base of the retrieval method. The retrieval system using sketches can be essential and effective in our daily life such as Medical diagnosis, digital library, search engines, crime prevention, geographical information, art gallery and remote sensing systems [4]. Indeed, people commonly use an image retrieval system because they do not count on the desired image, thereby, having such an image query may not be possible, limiting the image retrieval system usability. An alternative for querying is by simply drawing what the user has in mind. That is, making a sketch of what the user expects as an answer could overcome the absence of a regular example image. This kind of query is also supported by the fact that emerging touch screen based technology is becoming more popular, allowing the user to make a sketch directly on the screen. Of course, a sketch may be enriched by adding color, however we claim that making a sketch only by strokes is the easiest and natural way for querying.

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Proposed SBIR System

The proposed system architecture is described as shown in figure 1. It consists of two parts. The first is processing for database images. The features of database images are extracted by Canny edge detection algorithm. The Canny edge detector uses a filter based on the first derivative of a Gaussian, because it is susceptible to noise present on raw unprocessed image. The raw image is convolved with a Gaussian filter. The result is a slightly blurred version of the original which is not affected by a single noisy pixel to any significant degree.

Fig.1. Overview of the System Architecture

Gaussian operator is:

(1) In this system, 5×5-size Gaussian mask with σ = 1.4 is used. After extracting edge feature, the block histograms of edges are calculated by sliding window method. The proposed block histogram matching algorithm is described in the below sub-section A. Similarly, the above steps are processed for query sketch. Then, the matching process is carried out by selecting the maximum overlapping histograms. Finally, the retrieval results of images are displayed by rank.

4.1

Proposed Edge Matching Algorithm

Evaluating a sketch based image retrieval system is a momentous task. It is even more difficult to match images and sketches due to the vague nature of the sketch. A sketch can depict shapes or symbols or an imaginary scene, thus semantic convergence with photographic images is not always the case. The following algorithm is proposed edge matching by sliding window method. 1: for each block in sourceimage 2: srcBlock = srcEdge(i:rows,j:cols) 3: for each block in sketchimage 4: skBlock = skEdge(1:rows-i+1, 1:cols-j+1) 5: matBlock = srcBlock .* skBlock 6: matchPx = sum(sum(matBlock)) 7: if matchPx > max 8: max = matchPx 9: end if 11: end for

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12: end for 13: for each block in sketchimage 14: skBlock = skEdge(i:rows,j:cols) 15: for each block in sourceimage 16: srcBlock = srcEdge(1:rows-i+1, 1:cols-j+1) 17: matBlock = srcBlock .* skBlock 18: matchPx = sum(sum(matBlock)) 19: if matchPx > max 20: max = matchPx 21: end if 22: end for 23: end for

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5.2

Examples of Retrieval Results

The following figures show that some of our retrieval results of Flickr15k and Eitz datasets. The images with green box are true result and those with red box are false. The location of the sketch and that of the image is approximately the same for Flickr15k dataset.

Experimental Evaluation

To evaluate the performance of the Sketch Based Image Retrieval System, the Mean Average PresicionMAP) rate is calculated. The Precision provides information related to effectiveness of the system. Precision (P) = no: of relevant images retrieved / total no: of retrieved images Mean Average Precision (MAP) = ∑Avg P (q)/Q Where, Q= no: of queries images displayed with similar shape accuracy of the system. Table1 shows that the comparison result of our system to the state-of-the-art methods described in [16].

TABLE1. SBIR RESULT COMPARISON (MAP)

5.1

Methods

Vocabulary Size

MAP

BHM (Ours) GF-HOG HOG SIFT SSIM Shape Context Structure Tensor PerceptualEdge-continuity

3500 3000 1000 500 3500 500 non-BoW

0.3347 0.1222 0.1093 0.0911 0.0957 0.0814 0.0798 0.0789

Datasets

Two datasets are used to evaluate the performance of the proposed system. EitzSBIR benchmark was published by Eitz et al. and was based on a controlled user study of 28 subjects. It consists of 31 hand-drawn sketches, 1,240 images related to these sketches and 100,000 distracter images. Flickr15k dataset consists of approximate 15k photographs and manually labeled into 33 categories based on shape, and 330 free-hand drawn sketch queries drawn by 10 non-expert sketchers.

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the hand drawn sketch. Block histogram matching by sliding window method is applied in the matching of the input query sketch and the database images. The retrieving in this system is the type of direct matching without training.

References

6

Conclusion

The sketch based image retrieval system is presented in this paper and which is developed to retrieve efficiently. It is still difficult to bridge the gap between image and sketch matching problem. Thus, the main contribution of this work is to improve the effectiveness of image retrieval by querying as

[1] Mathias Eitz, Kristian Hildebrand, Tamy Boubekeur, and Marc Alexa. “Sketch-based image retrieval: Benchmark and bag-of-features descriptors”. IEEE Transactions on Visualization and Computer Graphics, 2011. [2] Rui Hu, M. Barnard, and J. Collomosse. “Gradient field descriptor for sketch based retrieval and localization”. In 17th IEEE International Conference on Image Processing (ICIP), 2010. [3] Rui Hua and John Collomossea, “A Performance Evaluation of Gradient Field HOG Descriptor for Sketch Based Image Retrieval", Centre for Vision, Speech and Signal Processing, University of Surrey, UK. [4] N. Dalal and B. Triggs. “Histograms of oriented gradients for human detection”. In Proc. IEEE Computer Society Conf. Computer Vision and Pattern Recognition CVPR 2005. [5] N. Prajapati, G.S.Prajapti, “Sketch base Image Retrieval System for the Web - Survey", International Journal of Computer Science and Information Technologies, Volume 6, Issue 4, April 2015. [6] Prof. Balram Puruswani, Jyoti Jain, “A Preliminary Study on Sketch Based Image Retrieval System", International Journal of Modern Engineering & Management Research, Volume 1, Issue 1, April 2013 ISSN: 2320-9984 [7] A. Chalechale, G. Naghdy, and A. Mertins. “Sketchbased image matching using angular partitioning”. IEEE Transactions on Systems, Man and Cybernetics, 2005. [8] J. Saavedra and B. Bustos. “Sketch-based image retrieval using keyshapes”. Multimedia Tools and Applications, 2013. [9] A. S. Vijendran and S. V. Kumar. “A New Content Based Image Retrieval System by HOG of Wavelet Sub Bands”. International Journal of Signal Processing, Image Processing and Pattern Recognition, Vol 8, No.4, 2015. [10] M. Adoram and M. S. Lew, “IRUS: Image Retrieval Using Shape,” Proceedings of IEEE International Conference on Multimedia Computing and System, Vol. 2, 1999. [11] Fendarkar J. D., Gulve K. A. “Utilizing Effective Way of Sketches for Content-based Image Retrieval System” International Journal of Computer Applications Volume: 116, No.: 15, April, 2015. [12] B. Szanto. P. Pozsegovics, Z. samossy Sz. Sergyan, “Sketch4Match Content-based Image Retrieval System Using Sketches” SAMI 2011 _9th IEEE International Symposium on Applied Machine Intelligence and Informatics, 2011. [13] Jose M. Saavedra. “Sketch based image retrieval using a soft computation of the histogram of edge local orientations (s-helo)”. In International Conference on Image Processing, ICIP’2014.

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[14] A.K. Jain, J.E. Lee, and R. Jin, “Sketch to photo matching: a feature-based approach,” Proc. SPIE, Biometric Technology for Human Identification VII, 2010. [15] T. Hashimoto, A. R¨ovid, G. Ohashi, Y. Ogura, H. Nakahara, and A.R. V´arkonyi-K´oczy, “Edge detection based image retrieval method by sketches,” Proc. of the International Symposium on Flexible Automation, 2006. [16] Yonggang Qi, Yi-Zhe Song, Tao Xiang, Honggang Zhang, Timothy M. Hospedales, Yi Li, and Jun Guo, “Making better use of edges via perceptual grouping,” in CVPR, 2015. [17] Dipika R. Birari, J. V. Shinde, “A Sketch based Image Retrieval with Descriptor based on Constraints,” International Journal of Computer Applications, Volume 146 – No.12, July 2016. [18] Yonggang Qiy Yi-Zhe Song? Honggang Zhangy Jun Liuy “Sketch-Based Image Retrieval via SIAMESE Convolution Neural Network” ICIP 2016.

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Smart City Wireless Platforms for Smart Cities Sakir Yucel [email protected] Abstract – A wireless network (e.g. a mesh Wi-Fi network) covering most of the city is a key component in the development of a smart city. While such a network offers countless benefits, a key issue with city-wide wireless network is the high fixed costs associated with laying out the infrastructure, rolling out the services, making the bandwidth available, maintaining the services once the network is laid out. One key question is determining who will setup the network and who will fund it. Secondly, who will supply the bandwidth while broadband bandwidth is still in shortage in most cities? Lastly, who will pay for the supplied bandwidth? We believe involvement of the communities is important in building a city-wide wireless network. Could the city inspire and assist the communities to build their community wireless networks, and then coalesce them for a city-wide wireless network? To address the first question, we proposed in our separate work a model where municipality, communities and smart utility providers work together to create a platform, smart community wireless platform, for a community in the city where different sides work together toward achieving smart community objectives. In this paper, we address the second question by presenting the smart city wireless platform which is a superset and merger of the smart community wireless platforms. We present the smart city wireless platform with its various dynamics and describe a research plan for analyzing if this platform could be viable alternative to fulfill smart city objectives. Keywords: smart community wireless platform, smart city wireless platform, smart city

1

Introduction and Problem Definition

A smart city aims to embed digital technology across all city functions including economy, mobility, environment, people, living and, governance. Many cities have taken initiatives towards becoming a smart city to attract commercial and cultural development. A wireless network (e.g. Wi-Fi network) covering most of the city is a significant contributor and a major step towards becoming a smart city. Such a network offers many benefits in tackling key challenges such as reducing traffic congestion, reducing crime, fostering regional economic growth, managing the effects of a changing climate, and improving the delivery of city services [9], thereby impacting the competitiveness of cities positively. We assume the main objective with the city wireless network is to offer smart services and making them available to the span of the city. Many smart services and applications can be offered over the city-wide wireless network including smart local government apps for smart parking, structural health,

smart traffic, smart lighting, smart waste management, smart environment apps, smart water apps, smart metering apps for smart grid, smart public safety apps, smart agriculture apps, smart health apps [1]. Such a network offers value proposition to different sides. The city gets free service for its departments, for its services. It may place its sensor networks (IoT beacons, other sensors) into the wireless network. The network makes the city more competitive. The city will attract more tourists and commerce, thereby increasing the tax revenues. Additionally, the city could make more revenue by more effective use of its resources (e.g. parking). It could lower energy and maintenance costs, increase revenue from city services, help better leverage of existing assets, better traffic control, help with investing in infrastructure and filling open technology jobs. For law enforcement, it will improve situational awareness and help with better-informed decision making, allow better collaboration the different offices, improve compliance enforcement. It will help improve planning with network and people-flow analytics, better ROI and greater savings for city reinvestment. In summary, it helps increase city’s ability to meet city safety, mobility and revenues goals. For citizens, it means safer streets and neighborhoods, improved mobility and greater quality of life, enhanced economic opportunities, improved digital access to city services and other smart services, reduced traffic congestion, more efficient parking spot finding, reduced fuel consumption and frustration, better sense of safety, improve quality of digital life. For visitors, it helps with better access to events, attractions, points of interests. For shoppers, it helps with coupons and better shopping experience. For businesses, it will bring in more commerce and help them new marketing approaches for reaching out to consumers. For smart service providers (utility companies, municipality and community), it allows them to place their sensors, sensor networks, collect data and offer smart services to users. It enables intelligent sensor-based IoT innovations in transportation, utilities, public safety, and environment. For the environment, it means reduced carbon emissions, reduced resource consumption, reduced congestion. A city wide wireless network is essential for achieving smart city objectives. The questions are who would build it and how, who would maintain it and how, who would pay for it and how, who would benefit from it and how. What alternative models exist for all these questions? In this paper, we will describe various alternatives including different alternative

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platforms. After that, we will present a platform for smart cities. Smart city starts with smart communities and hence we believe the community involvement is important in building a city wide wireless network. Our research objective is to provide insights and discussions for answering the following question: if municipality, the communities, smart service and utility providers come together to build a platform, is it possible to create wireless network that spans the city and serves as the backbone of smart city? The purpose of the paper is to introduce the smart city wireless platform with various aspects. This platform is based on smart community wireless platforms described in [3]. This paper describes the platform and a research agenda around this platform concept, and therefore is more conceptual than technical.

2

Alternative Models

Different cities tried different models for planning, building, integrating, operating and sustaining city-wide wireless network. In one model, the city owned and operated the wireless network. Other models with various combinations of partnerships in building, owning and operating the wireless network have been tried. Partnership models included a city owned wireless network but operated by private partner(s), public/private partnership with city being the anchor tenant, public/private nonprofit in partnership with wireless network service providers. Cities tried various pricing strategies along with the above models [11][12]. The primary reasons these networks failed is due to investors pulling out of the projects, and happened mainly because the high cost of the network in conjunction with low demand caused a poor return on investment and investors back out. In this paper, we are interested in platforms for realizing the smart city objectives. For platforms and platform business in general, see [1]. In addition to above models, we can list the following additional possible platform models: 1. The wireless service provider builds and provides the service and owns the platform. The citizens access the wireless network and the services over it with subscription fee. The businesses pay subscription fee to the platform provider. They pay advertising fee separately to advertise over the platform. The smart service providers pay subscription fees in various amounts depending on their use. In this model, the platform is provided by a commercial service provider. The city may get free access to the city employees and city departments, or may pay a fee. 2. The city partners with component/complement providers (particularly hardware and software providers) and integrators to build the wireless network. Large equipment providers such as CISCO with their partners may provide the know-how, training and certifications. The city IT department would be responsible for maintaining the network. The city outsources (contracts, leases, licenses) running the platform to private institutions. The private institution develops business and pricing models on this

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platform for the platform sides such as users, businesses and smart service providers. The city receives free service over this platform in addition to licensing fees from the platform provider. In this model, the city is the platform sponsor and the private institution is the platform provider. In a slightly different model, the city outsources (contracts) the management of the wireless network to the same private or a different company. 3. Various other combinations of the above two models can be considered, particularly about the funding for the initial setup. The city may follow build-operate model for the platform. The city may contract different phases to different commercial suppliers. Problems similar to the ones we associated with earlier models apply to these platform alternatives. We can summarize the issues with these alternatives as follows: • It is hard to estimate the adoption and revenues. If small population of the city uses and the demand remains low, then the same issue appears, which the investors would pull out as they don’t see the return. • In general, there is doubt about benefits of wireless network in general, but particularly on the value of the municipality’s involvement. The main issues are high cost, low demand, low benefits, less private investment. • These platforms do not involve the citizens adequately. One essential aspect of building the smart city is to facilitate collaboration with citizens from the phase of planning the wireless network to the operation. We use the term smart to indicate the human factor in building and using the wireless network [17]. Smartness in this context should involve the citizens more. The above platforms offer wireless network for a digital city, but without enough involvement of its citizens to call it a smart city. What we are looking in this paper is a platform with lower cost, with the wireless network right sized for the demand, with direct and indirect benefits to the city, and the one that does not deter private investment but somehow attracts it. Additionally, the platform should engage the citizens and other stakeholders such as businesses, smart service providers and all. Before introducing our proposed platform, we want to bring up the success of community wireless projects. Such projects have been successful where municipality-based models have failed. Analysis of successful community wireless implementations point to the following factors: (1) lower cost, (2) accurate demand projections, (3) clear objectives and conceived real benefits targeting community needs, (4) small launch, pilot implementation initially and later expansions based on community needs (5) community involvement with setup and maintenance of the network. Now we can introduce our platform.

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Smart City Wireless Platform Requirements

The platform must support the smart city objectives bringing users, businesses, smart services and smart service providers, city functions together. Meanwhile it should be constrained by the following: The wireless solution should be within budget for setup and maintenance cost, should be scalable to cover the city, should be legal, should be developed timely, and should be easy to maintain, operate and upgrade. 3.2

Platform Definition

We will outline the smart community wireless platform with respect to the system architecture, sponsor and providers, sides, utilities and network externalities, financial resources, policies, strategies for how to position, present, realize and operate this platform. A smart city wireless platform is a city-wide wireless network built and maintained through collaboration of the municipality, the communities in the city and the smart service providers. This is based on the smart community wireless platforms [3] but it is a separate platform with municipality playing a bigger role. In the smart city wireless platform, smart community wireless platforms are integrated together to form a city-wide wireless network. There are multiple sides on this platform: Users, bandwidth sponsors and smart service providers have similar roles as in the smart community wireless platform. In addition, there are other sides which are application builders for smart services and ISPs offering premium services over this platform. Different from the smart community wireless platform, the sponsor of this platform is the municipality. The bigger city wide smart city wireless platforms should be open for communities to join. Communities become the providers of the platform being the primary contact point for the end user in different service areas assuming all the community wireless networks interoperate. The sponsor of the platform, the municipality, should consider a shared platform that evolves through a consensus-based process among the communities in the city and the city officials. Component/Complement providers for this platform include • Equipment providers for wireless and wireline equipment, for server and storage equipment • Cloud providers • Software and solution providers such as wireless networking software providers, network and management software providers, security software and solution providers, analytics solution providers, middleware providers • Integrators 3.3

System Architecture

[3] describes the system architecture for smart community wireless platform. The larger smart city wireless platform

integrates the smart community wireless platforms in the city and requires additional infrastructure elements. The resulting system architecture is shown in Figure 1. The smart city wireless platform creates an ecosystem around this system architecture. In this paper, we will provide a high-level description of the system architecture. This multi-tier smart city wireless system architecture has the following layers: Smart City Wireless Network Layer: This layer integrates the community wireless networks. The city wireless network is mainly composed of the community wireless networks. The municipality adds additional access points in areas not served by community wireless networks, thereby filling in the gaps where no community wireless network serves. The ISPs offer their wireless networks for a subscription and/or usage fee. The ISP wireless networks are integrated with the smart city wireless network but they provide higher bandwidth and quality of service. Community wireless networks are connected via private links, municipal fiber, and metro area network service purchased from network providers. Each wireless network has many connections to the Internet (e.g. bandwidth sponsor connections, purchased connection, ISP’s complement connection). The wireless sensor networks of smart service providers are connected to the city wireless network usually through community or municipality-supplied access points. These belong to the utility provider companies, the municipality and the community. These networks produce traffic into the wireless network. Some of the traffic of these sensor networks would not leave to Internet, but rather transferred by the underlying links from the wireless mesh networks to the infrastructure of the smart service provider where the data is stored, processed and analyzed. Middleware and Infrastructure Layer: This layer contains the computing and storage infrastructures by the community, the municipality, the smart service providers and the other complement providers. It contains software platforms and services including middleware, service oriented solutions, clouds (both commercial and community clouds). It runs middleware and containers to host core services and for providing software access to the sensor network microservices. Other components include reliability, security, privacy and trust solutions. Core Services and Data Layer: This layer offers core services and their APIs. Core services include machine learning and analytics, location services, search, semantics, visualization, collaboration platforms, geospatial services, access to public data and statistics, community social networking platforms. The data processing and mining is done at this layer. These core services are usually accessible to bandwidth sponsors and smart service providers. Smart Services Layer: This layer includes all smart services offered by the smart city such as smart transportation, smart health and smart government services. This layer offers APIs of the smart services for application builders.

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Admins

Wireless Users (laptops, phones, others)

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Smart Services, Dashboards Transportation, Health, Government

Sponsoring Businesses, Smart Service Providers, App Builders, Others

Network Traffic Generating Devices (IoT devices, cameras, others

Smart Services Layer

ML and Analytics, Location, Intelligent Data Processing, Data Mining, Contexts, Search, Semantics, Visualization

Core Services and Data Layer

Computing and Storage Infrastructures by Municipality, Community, Other Smart Service Providers, Clouds, Middleware, Containers, Infra of Component/Complement Providers (e.g. ISP), Reliability/Security/Privacy and Trust Solutions

Middleware and Infra Layer

Wireless Sensor Networks

Community routers

Bandwidth Sponsor routers

Municipalit y routers

Smart Community Wireless Network

Other Provider routers

Communit y routers

Bandwidth Sponsor routers

Smart Community Wireless Network

Municipalit y routers

Other Provider routers

Municipalit y routers

Smart City Wireless Network Layer

ISP Wireless Network

Internet

Figure 1 System Architecture for Smart City Wireless Platform With this system architecture, various digital services can be offered in a modern smart community, including smart government, smart transportation, smart waste, electricity, water and other utility management, smart health and tele-care services, and many other smart services. All these services are provided by smart service providers (e.g. private companies, municipal offices and communities). Users use the wireless network to access Internet and the smart services. Sponsoring businesses, smart service providers, app builders access the core services API. Sponsoring businesses access the user information including location analytics. Smart service providers use the core services and user data. App builders develop apps by invoking the core service APIs and smart service APIs. Interoperability and compatibility among community wireless networks is a big plus. The municipality could use some strategies and special funds to achieve bringing in smart community wireless platforms together in a city-wide wireless network. This means a common wireless networking interface will be offered for user and similarly for IoT devices across the city. This could be stretch goal as convincing all communities would be a challenge but the gain is big. Some smart services are available only on this network, and are not available through internet. Services that use IoT beacons will be available only in certain locations. Most services should be available over Internet so that other wireless users (e.g. 5G and other wireless networks, commercial

hotspots) could still access. Commercial wireless networks could be integrated into this platform provided their traffic is routed to their routers and then to their Internet connections. With this, the users of commercial providers may get additional paid bandwidth but still access the smart services on the network. 3.4

Platform Control

[3] explains how the smart community wireless platform could be controlled and how the community and municipality could work together to realize and maintain the platform. We suggested the community should be the platform sponsor and should control the openness of the platform as well as what complement providers could join the platform. The municipality can provide support for these platforms in many ways. For the city-wide smart city wireless platform though, the municipality should be the sponsor. The platform should be open for communities to join provided they conform to the criteria and policies set forth by the municipality, the platform sponsor. Participating communities are not rival companies trying to acquire market share through this platform, but rather providers of wireless network in their respected service areas, yet they can compete in quality and realizing the benefits of smart community wireless platforms [5]. Not all communities would like to join initially, but they would as they see the need and the value. Municipality can act like a magnet and enabler.

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The municipality should define what criteria should be in place for accepting the communities to join, what criteria for allowing the smart service providers, what criteria for complement providers, what criteria for app builders to access the core services API. Similarly, criteria for research institutions, startups and entrepreneurs should be defined by the platform sponsor. Not just controlling the openness of the wireless network, the municipality should exercise control on who can provide core services and who can access the core services APIs. The municipality should define a methodology for evaluating all the players about whether they are allowed on the platform or not with respect to the defined criteria. For example, the municipality could use a SWOT framework for communities, smart service providers, complement providers, partners. The municipality should define policies for offering services and complements on this platform as well as for using the services, as outlined in Policies and Strategies. The policies are decided by the municipality, but with discussions, consultations and input from the communities. This is a big challenge for the municipality. The municipality supplies all it can offer the communities [3], but in turn, asks the communities to agree to be part of the integrated wireless network and to conform to the policies and criteria. The communities still have the sponsor role for their own community platform and control their platform as outlined in [3], but they conform to the general policies of the smart city wireless platform. This becomes a federation of smart community wireless platforms into a smart city wireless platform. With higher level of involvement, the municipality will have more control over the quality, security and performance of the platform. 3.5

Utilities and Network Effects

As explained in [3], community and municipality have utilities with the smart community wireless platform. When many communities build such platforms and the municipality could integrate them in a larger wireless network that spans most of the city as in the smart city wireless platform, this increases the utility for the municipality greatly. The community’s utility also increases by being on this city-wide platform. Platform sides such as users, app builders, smart service providers, component/complement providers have utilities by being on the platform as briefly outlined in Platform Definition section. Many other entities find utility with being on this platform such as research institutions, startups and entrepreneurs. Same and cross side network effects exist on this platform. As explained in [3], network effects exist for businesses to join the smart community wireless platform and provide some bandwidth when more users use. For the smart city wireless platforms, network effects should be analyzed and exploited toward achieving smart city objectives. This relies on ability of the municipality to cooperate and unite community networks for smart city objectives. If community networks are disparate, community platforms alone may not be helpful for smart city objectives. Policies and strategies should be in place to

increase the network effects and thereby the utility of different sides toward achieving smart city objectives. For example, more municipality support (e.g. security personnel) will lead to more utility for users. Similarly, overall success of the platform will attract more grants and sponsors. Its success will influence the other cities also, as more cities will adopt this platform model. Network effects are so many and should be analyzed in more detail. For example, how a new smart service by itself influences the network effects and the utilities, or an app that uses core services APIs? Further research is needed to analyze such network effects. 3.6

Platform Evolution

Building a smart city wireless platform takes time and effort. The municipality should follow a phased approach. Initially, the municipality could reach out to existing community wireless networks if any, and try to sell the ideas of both the smart community wireless platform and the smart city wireless platform. If no community wireless network exists, the municipality should choose a first community with a specific objective such as economic development. The objective should resonate with the community. The municipality should consider the dynamics of cost, benefit, drawbacks and risks we identified in [4] and [5] for the communities in deciding on the first one. This community is chosen to be the first/early adopter of the idea for a pilot implementation. The municipality should provide support for the first adopter community, work closely with the community for the success. Adequate and balanced support from the city will help the community stakeholders (businesses, smart service providers) welcome and embrace the idea. The support should be balanced so that the community could build, own and maintain the network. Once the first smart community wireless network becomes operational, other similar communities could see the success and start building their smart community wireless platform. The municipality then should choose a different objective such as closing the digital divide and a community for realizing that objective. Successful community platforms lead to duplication in other communities. Once smart community wireless platforms are built with at least two distinct community objectives, then the municipality should consider the smart city objectives. This platform cannot be built with smart city objectives in mind initially. It should start with community objectives and achieve some success in community platforms. If community platforms exist, then it may consider the smart city objectives and try to combine the community platforms together. In any case, the city needs to actively sell the idea to communities. The municipality monitors the successes of communities, develop the criteria and policies for joining the community platforms. Scalability to the whole city is achieved by successes of the communities and by the success of the municipality for bringing them together on this platform. When communities find it attractive to build platforms and join the

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bigger platform, the city evolves closer to achieving smart city objectives. 3.7

Financial Resources

[4] lists funding resources for community platform. For the city platform, there is additional cost for supplying and integrating access points into the city-wide network in areas not served by any community wireless network, for additional hardware and software infrastructure elements, and for the maintenance. Ideally, city citizen’s tax money is not used for this cost. The city finds grants and distributes them to communities. The city could use some grants for the additional cost and/or compensate through some revenues earned over this platform, such as by offering its smart services for a fee. At the worst case, allocating some budget is fine and legal since this service is not a replacement for commercial private services. The municipality should not charge users for wireless service as it could run into legal issues with private service providers on the basis of offering a competing service. But for smart services, for example for using a smart parking service, the municipality could charge. Normally and ideally users should pay for wireless access. On this platform, they get the service free but at the expense of giving up on the privacy and by accepting advertisements from businesses participating in the platform. Smart service provider and complement providers may offer value-add services and may charge for usage/subscription. For example, ISP may offer better wireless service with higher upload/download speeds and may charge the users. 3.8

Policies and Strategies

What strategies the municipality should employ while helping community wireless networks and rolling the smart city services on the integrated city-wide network, and meanwhile encouraging private investment? The city should not discourage cellular services and commercial hotspots by its support to the free wireless network. There are many policy and regulation related issues with municipalities offering free and/or commercial wireless services. The city should be careful not to position the platform as a competition to private offerings. Normally this should not happen as individual community platforms is sponsored and owned by the communities, and the city sponsors the bigger platform which is composed of community platforms. All these platforms should enforce usage limits and caps. This policy allows commercial wireless network providers to provide networks (or hotspots) which integrate with city wireless network but allows higher speed Internet access. Also, smart service providers may choose to utilize services from commercial wireless providers for better service and quality, since the community networks may not have enough capacity, availability, security and customer service. This way, the commercial providers may distinguish themselves while being compatible with the city wireless network. The policies should encourage broadband

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modernization both in wireline (fiber) and wireless (5G). The system architecture diagram in Figure 1 presents a competition model in addition to the architectural elements. The city should enforce policies to keep the competition and attract competitors. What should be the ownership, maintenance, and security policy for the platform? As explained in Platform Definition section, the platform sponsor is the municipality and therefore it is the sponsor to define all policies. In [3], we suggest the community should be responsible for security policy for the community wireless network. Municipality does not enforce security policies for this type of platform. However, when the community platform becomes part of the bigger city platform, the municipality is the sponsor of this bigger platform and defines the security policies that all community platforms confirm. Therefore, recommend the wireless network security is monitored and controlled by the municipality in conformance of the security policies and guidelines set forth by the municipality for the city-wide wireless network. The municipality should employ security administrator(s) to monitor and control security related issues in the community networks across the city. Additional volunteer help and/or part-time employment could be used under the supervision of the security administrator per community wireless platform. Regarding network maintenance policy, it is expected the communities maintain their networks with the help of volunteers. For the bigger network, city makes its network and security engineers available for consultancy and for administrating the volunteers and contracts. The municipality employs its engineers and technical staff to maintain the city platform, mainly for organizing, training and administering the volunteers. The municipality may prefer to outsource maintenance and labor associated with sustaining and managing the network to a third party. The policy should be the same across all wireless networks regarding the privacy of the user and the user agreement. The user profile can be shared by all participating communities. Individual customer service policy may be different as per community. The community networks perform their own location tracking, traffic metering and policying. Similarly, use of the consumer data may be different by different community. For the bigger smart city wireless platform, the municipality can facilitate storing the user profile information and sharing with participating community wireless networks. The city needs to promote and market this idea to get communities engaged in the development process. The municipality should not overreach to dictate over the communities. The common policies should be light and agreeable by the communities. The city should be familiar with different community needs and policies, and should come up with commonly accepted policies. Another question is how the municipality distributes certain funds to service areas and to which smart community wireless platform. This depends on the priorities of objectives like economic development or

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digital divide. Then certain service area will attract certain funds.

4

Conclusion

A city wide wireless network is essential for achieving smart city objectives. The questions are who would build it and how, who would maintain it and how, who would pay for it and how, who would benefit from it and how. Earlier models tried by municipalities failed mainly due to high costs, low demand and investors pulling out of the projects. Cities now are looking for new approaches. In this paper, we described various alternatives including different alternative platforms. We believe smart city starts with smart communities and community involvement is significant in building a city wide wireless network. We introduced a platform, smart city wireless platform, built by the municipality, the communities, smart service and utility providers together. This platform builds on smart community wireless platforms described in [3] and integrates them together for achieving smart city objectives. While this platform offers many benefits, there are potential risks and issues associated with the platform. More research is required to analyze whether this could be a viable platform or not with respect to achieving smart city objectives. We studied the cost of smart community wireless platforms in [4] and their benefits in [5] by using an intelligent framework. This intelligence framework has been used in our earlier work [6][7][8]. Our further research plan is to study the cost and benefits of the bigger smart city wireless platforms using the same intelligence framework, particularly address if this platform achieves smart city objectives and under what conditions, constraints, policies and strategies.

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References

[1]. Thomas Eisenmann, Geoffrey Parker, Marshall Van Alstyne, Platform Networks – Core Concepts, Executive Summary, http://ebusiness.mit.edu/research/papers/232_VanAlstyne _NW_as_Platform.pdf [2]. 50 Sensor Applications for a Smarter World http://www.libelium.com/resources/top_50_iot_sensor_ap plications_ranking/ [3]. Yucel, Sakir: “Smart Community Wireless Platforms”, The 14th International Conference on Modeling, Simulation and Visualization Methods (MSV'17), July 1720, 2017 Las Vegas, Nevada, USA [4]. Yucel, Sakir: “Estimating Cost of Smart Community Wireless Platforms”, The 14th International Conference on Modeling, Simulation and Visualization Methods (MSV'17), July 17-20, 2017 Las Vegas, Nevada, USA [5]. Yucel, Sakir: “Measuring Benefits, Drawbacks and Risks of Smart Community Wireless Platforms”, The 14th International Conference on Modeling, Simulation and

Visualization Methods (MSV'17), July 17-20, 2017 Las Vegas, Nevada, USA [6]. Yucel, Sakir: “Delivery of Digital Services with Network Effects over Hybrid Cloud”, The 12th International Conference on Grid, Cloud, and Cluster Computing, GCC'16: July 25-28, 2016, Las Vegas, USA [7]. Yucel, Sakir: Evaluating Different Alternatives for Delivery of Digital Services, The 12th International Conference on Grid, Cloud, and Cluster Computing, GCC'16: July 25-28, 2016, Las Vegas, USA [8]. Yucel, Sakir; Yucel, Ibrahim: Estimating the Cost of Digital Service Delivery Over Clouds, The 2016 International Symposium on Parallel and Distributed Computing and Computational Science (CSCI-ISPD), Dec 15-17, 2016, Las Vegas, USA [9]. Serdar Vural, Dali Wei, and Klaus Moessner; “Survey of Experimental Evaluation Studies for Wireless Mesh Network Deployments in Urban Areas Towards Ubiquitous Internet”, IEEE Communications Surveys & Tutorials, Vol. 15, No. 1, First Quarter 2013 [10]. Andrea Zanella; Nicola Bui; Angelo Castellani; Lorenzo Vangelista; Michele Zorzi: “Internet of Things for Smart Cities”, DOI 10.1109/JIOT.2014.2306328, IEEE Internet of Things Journal [11]. Lee, S.M., Kim, G. and Kim, J. (2009) ‘Comparative feasibility analysis of Wi-Fi in metropolitan and small municipalities: a system dynamics approach’, Int. J. Mobile Communications, Vol. 7, No. 4, pp.395–414. [12]. Shin, Seungjae and Tucci, Jack E., "Lesson from WiFi Municipal Wireless Network" (2009). AMCIS 2009 Proceedings. Paper 145. http://aisel.aisnet.org/amcis2009/145 [13]. Kim, G., Lee, S.M., Kim, J., and Park, S. (2008) ‘Assessing municipal wireless network projects: the case of Wi-Fi Philadelphia’, Electronic Government, An International Journal, Vol. 5, No. 3, pp.227–246. [14]. Simon Evenepoel, Jan Van Ooteghem, Bart Lannoo, SofieVerbrugge, Didier Colle, Mario Pickavet, “Municipal Wi-Fi deployment and crowdsourced strategies”, (2013) Journal of The Institute of Telecommunications Professionals. 7(1). p.24-30. [15]. Abdelaal, Abdelnasser; “Social and Economic Effects of Community Wireless Networks and Infrastructures”, IGI Global, Feb 28, 2013 [16]. Ahmed Abujoda, Arjuna Sathiaseelan, Amr Rizk, Panagiotis Papadimitriou, “Software-Defined CrowdShared Wireless Mesh Networks”, Computer Networks, Volume 93, Part 2, 24 December 2015, Pages 359–372 [17]. R. Giffinger, C. Fertner, H. Kramar, R. Kalasek, N. Pichler-Milanović, and E. Meijers. (August, 2007) Smart cities: Ranking of European medium-sized cities. Smart cities. [Online]. Available: http://www.smartcities.eu/download/smart_cities_final_re port.pdf.

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Measuring Benefits, Drawbacks and Risks of Smart Community Wireless Platforms Sakir Yucel [email protected] Abstract - Smart community wireless platform has been introduced as a model to address the needs of smart community. It is a platform where municipality, communities and smart utility providers work together toward achieving smart community and smart city objectives. One important question is how to measure the benefits and drawbacks of these platforms to estimate the returns on investment over a period. Another question is how to model risks and mitigation plans for the success of these platforms. The objective of this paper is to examine several relevant dynamics in estimating the benefits, drawbacks and risks of smart community wireless platforms and develop models for measuring them and for estimating the success of these platforms under various conditions and scenarios using an intelligence framework that incorporates systems dynamics modeling with statistical, economical and machine learning methods. Keywords: smart community wireless platform, benefits of community wireless networks, smart community, system dynamics modeling

1

Introduction and Problem Definition

We described the smart community wireless platform in our work [9]. One important question is how much would be the total cost of building and operating such a platform. Our earlier work [10] describes the intelligence framework for estimating the total cost of smart community wireless platforms and presents models for estimating the cost under various conditions and scenarios. Another question is how to measure the benefits and drawbacks of these platforms to estimate the returns on investment over a period. Another question is how to model risks and mitigation plans for the success of these platforms. In this paper, we address these issues using the same intelligence framework in [10]. The objective of this paper is to examine several relevant dynamics in estimating the benefits, drawbacks and risks of smart community wireless platforms and develop models for measuring them. This paper addresses only those that apply to a smart community wireless platform. Those of the bigger scoped smart city wireless platform is described in [11].

2

Developing Models for Estimating Benefits, Drawbacks and Risks

A smart community wireless platform offers benefits to the platform sides: users, bandwidth sponsors and smart

service providers. Every such platform implemented by different community is unique and offers unique benefits to the platform sides. Different platforms would be built with different objectives by different communities. The success of the platform is evaluated with respect to the determined objectives of the platform. A question to be asked early in the planning phase for building such platforms should be: What should be the objectives of the platform? There are challenges in identifying the objectives for building such a platform: What the objectives should be? What benefits are sought? What beneficiaries are considered? Some benefit to one side of the platform may not be as beneficial to another side. There could be conflicting interests from different platform sides and therefore network effects may not be all positive. There could be even different interests within the same platform side, for example by different groups of platform users. There are also challenges with measuring the benefits: Some benefits are tangible, some are not. Some are short term, some are long term. Some are direct, some are indirect. Some will influence other benefits in positive way, some will do in negative way. Therefore, there are questions about benefits and how to measure them. What are the conceived benefits? How to measure if the platform provides the conceived benefits? How to define the success of the platform and how to measure the success? What are the risks for the success and how to mitigate them? How policies and strategies are related to the success of the platform? What are the drawbacks of these platforms and how to limit them? How do the drawbacks, the risks, the policies and strategies, the mitigation plans impact the success of the platform? To address these issues, we propose a methodology for developing some estimation and measurement models. The models are used for estimating benefits, drawbacks and risks. These models could be used by local government officials, local communities and local smart service providers in decision making. For developing models, we suggest the following methodology: 1. First characterize the objectives, benefits, drawbacks, risks, mitigation plans, policies and strategies for a given service area or a community.

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2. Based on these characterizations, characterize the service area and community. Identify the dynamics involved and variables to be used in estimation. 3. Then follow the intelligence framework in [10] for building models. The intelligence framework entails developing system dynamics models integrated with economical, statistical and machine learning models. 4. Use the models for simulations and sensitivity analysis in early decision making. 5. As more insights are obtained and more data is collected about the platform characteristics, build statistical, economical and machine learning models, integrate them with the SD models, validate and refine the SD models. 6. Continue simulations and sensitivity analysis using the refined models as they are valuable tools in estimating the cost, benefits, drawbacks and risks, and in deciding if the platform would be feasible for a longer term, and to apply which policies and strategies and mitigation plans for the success of the platform. We start applying this methodology by characterizing various dynamics in subsections below. 2.1

Characterization of Objectives

The main objectives for smart community wireless platforms are (1) public safety and city services in the community and civic engagement, (2) closing the digital divide, (3) convenient services for citizens and users by enhancing digital experience, (4) economic development [5]. The characteristics about the objectives should determine what objective(s) are most relevant and with what relative weights for the community. 2.2

Characterization of Benefits

The platform offers benefits to different sides as we summarize below. The characteristics about the benefit should provide values for the listed benefits. Benefits to Users: Access to free wireless Internet for citizens: Provides citizens with Wi-Fi experience and location based services. Citizens can access the Internet over their smartphone, tablet, and other computing devices when they are in public spaces and on the move. They have access to city information and city services anytime, anywhere. A community app will help the citizens to be more digital. They have access to smart services provided by the smart service providers over the platform. The number of the smart services offered to the users matters. The more the number of smart services, users are more likely to have higher utility with using the platform. However, this number by itself is not sufficient to increase the utility of the users, the quality as well as the functionalities provided by the smart services are significant factors.

Access to free wireless Internet for visitors: Enhance the visitor’s experience. Community app for visitors will enhance their visiting experience, for example, from parking to shopping and visitor attractions. Benefits to Bandwidth Sponsoring Businesses: Will the platform lead to economic growth such as more businesses, revenues, jobs, transactions, wages? Businesses can do better targeted marketing thanks to analytics which would yield density/utilization at given time of day or day of week, people flows/footfall, time spent in the area, first time versus repeat visitors. Location-based services offer new insights about user behaviour that can also be leveraged by local businesses/retailers to do better targeted offers. Businesses can operate and adjust (hours, number of employees) based on the location data. Businesses could take advantage of real-time analytics for prediction of repeat visitors as well as new visitors based on similarity, and can customize their marketing strategies. Shopping centers can boost footfall by enabling shoppers to stay connected to social networks and share their experiences as they happen. New startups may appear for example for offering smart services over the platform. Overall innovation is fostered by the platform through university collaborations and entrepreneurial engagement over the platform. If businesses and organizations benefit from the community network, these same businesses may also give back to the network. Some larger businesses may provide bandwidth and others may act as root access sites and/or connection points. With such returns, positive network effects are realized. Benefits to Smart Service Providers Smart service providers may benefit from the platform by connecting their wireless sensor networks and individual IoT devices with the wireless network. They can offer smart services for the community such as smart waste management. Municipality is also a smart service provider and may place their IoT devices/networks like the commercial smart service providers for smart environment monitoring, smart light and street management, weather monitoring, traffic monitoring, smart public safety. Benefits include reduced cost by using wireless network for delivering smart services and reducing the cost of operations with smart technology. For example, a smart service provider would save by having access to almost real-time data of its devices. More benefits are realized when smart community wireless platforms integrate to for a smart city wireless platform [11]. Benefits to Community and City One conceived benefit is a safer community with police, fire, emergency medical response teams accessing the wireless network for safer streets and neighborhoods. This helps with public safety, incident response, law enforcement, and keeping stores safe from thieves.

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Efficiency is expected to improve with the platform in delivering public services with municipality using the wireless network for their services and citizens accessing those services. This results in lower energy and maintenance costs as well as more revenue from city services for example with paid parking. The platform helps the city with better leverage of existing assets, better traffic management, improved planning, better ROI and greater savings for city reinvestment. It helps the community with economically by boosting economic recovery and city prospects, and with innovation. It helps with improved education for closing the digital divide and greater citizen compliance. It helps the communities to be greener and elegant with smart waste and trash management About the benefits listed above, some are measurable such as smart lighting by comparing the electricity cost. Others are harder to realize and measure such as benefits of reducing digital divide, and are over long term. Some are direct for example bringing in more visitors. Some are indirect such as crime rate reduction and increase in quality education which are not just due to the existence of the wireless platform but other factors as well. 2.3

Characterization of Drawbacks

The platform has some drawbacks. There are inherent drawbacks in the model itself with building the platform as well as operating it. The characteristics about the drawbacks should provide values for them which are summarize below. Legality Issues One main positive outcome with this approach is that it does not have legality issues. This is because the platform is owned and sponsored by the community. In other approaches where the municipality owns the network, the city may run into legal issues incumbent ISP can sue cities and governments for creating municipal wide Wi-Fi networks because they compete with free market practices. In general, the city cannot monetarily benefit from a municipal wireless service in many states. Even with this model, the city should be careful in their policies and strategies with supporting the community platforms. The city should not deter the private investment, rather should seek to attract more private investment. When such community platforms are integrated together to form a city wide platform, the bigger platform can be used as a tool for bringing in more and diverse private investment. This is a subject we elaborate in [11]. Harm to Private Investment A risk with this model is it may distort the competitive markets and private investors and service providers are discouraged from entering unfair markets. Another risk is if the private investment may not update the cellular infrastructure in the community, but rather prioritize other communities with no such platform. On the contrary, it

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could lead the competing private commercial providers enhance their services to offer higher quality services than the community wireless and/or reduce their fees. Anyway, the community wireless networks will produce more network traffic to/from Internet and depends on ISPs enhancing their broadband services. We elaborate this issue in our [11] for the bigger city-wide platform. Wireless Network Quality and Availability Issues The wireless network and the underlying wireline infrastructure and server computing and storage infrastructure may not be as high quality as commercial offerings. How about the network problems, for example, access point or network element problems? Who will solve them? How about any customer service? Would it be reliable and available and stable to run critical services? The availability of the network, the infrastructure and smart services may be less than adequate for supporting critical services and applications with stringent RTO/RPO objectives. Similarly the QoS supported may not be comparable to commercial wireless services. Nonetheless, the platform should be positioned to support only non-critical services. More critical services should run over more commercial and professional services. Since this is a community wireless network, it does not necessarily cater for high performing and critical networked applications unless they are needed by the community and the community takes charge of supporting them. As discussed in [9], the platform offers limited bandwidth for users both in upload/download speeds and enforces a cap in total upload/download per period (e.g. day). Also, it enforces a limited number of connected users at any time. One risk with the platform is if it cannot attract enough sponsors over time to increase the limits. Another risk if the shortage of private investment to enhance and improve the broadband infrastructure. Security Issues Various security issues exist for this platform. As an example, hackers can create rogue networks to lure the users into their network as opposed to the community wireless network. The community wireless network platform should provide security guidelines for the users, and they should monitor emergence of such rogue networks. Drawbacks to the Community The sought benefits of the platform for the community may not be realized. Rather to the contrary, some drawbacks may appear. As an example, we can mention disruption to conventional way of life and businesses. This is due to embracing an all-digital life and isolating ourselves from traditional human interactions. Another drawback is increasing the digital divide. This happens when the platform becomes a playground for tech savvy but leave

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behind the other interest groups which are not very tech savvy [2].

d. Community not becoming part of smart city wireless platform [11]

Drawbacks to Users

4. Risk of pushing out private investment

One drawback to the users with this platform is the privacy. Users give up on their privacy in return to getting free wireless service. Other drawbacks include limited bandwidth, low quality and security issues in the wireless network during use. Another drawback is the limit in total supported users at a time, which could block some users. Offering less than adequate customer service is another drawback.

The characteristics about the risk should provide values for the level of risks.

Management Issues This platform relies on joint efforts from the municipality and the community. What happens when the community disagrees on the objectives and policies. There could be conflicting interests within the community about the objectives of the wireless platform. Policies are hard to agree upon and implement. Volunteer nature of most tasks may lead to slow progress on the development and inefficiency on the operations. Financial issues This platform relies on various funding sources. If funding cannot be granted, it is possible the platform may degrade and may not serve its objectives and may be abandoned by the sides, and eventually by the sponsors. This would result in failed project and wasted resources. 2.4

Characterization of Risks

The community decides on what level of risks to accept and what mitigation plans to consider and strategize. Most drawbacks outlined in the Characterization of Drawbacks section are risks that need to be managed. We can categorize different types of risks as follows: 1. Risks of hitting the drawbacks above 2. Risks with the development of the platform a. Risk not getting enough municipality

help from the

b. Risk of political change in the city. Disagreements between the municipality and the community. c. Risk of project falling apart due to management and policy reasons. Conflicting interests within community. d. Slow progress due to bureaucracy and volunteering 3. Risks during operations a. Quality issues not handled b. Sides losing interest c. Not enough benefits realized

One risk is when the community does not get enough help from the municipality as the platform relies on municipality for various help including help with funding resources. Another risk is if the perception changes with political change by a new city administration and if the new administration does not consider the community platforms a priority. This will leave communities without help from the municipality. Another risk is that the progress may be slowed by political maneuvering and complex coordination processes. Another risk is conflicting views between municipality and the community. This relates to defining roles and the operational policy for preventing conflict between community and municipality. Conflict may lead to a risk of community not being part of the bigger smart city wireless platform or leaving it. This risk is addressed in [11]. One risk is when the community cannot secure enough funding for building a platform even at a small scale. This is because the communities most in need of wireless access are also least likely to have the resources available to them to start a network. One risk is when large stakeholders, particularly bandwidth sharing entities, may not feel incentivized to share with the network. If the community does not have access to shared bandwidth the cost of the network increases dramatically. Businesses such as hotels, shopping malls/centers have their own wireless networks and loyalty programs. Why should they be part of community? There is where the benefit of being part of a community network comes into play, especially with more IoT and smart services becoming available. The benefits should be explained well to the businesses. This depends on effectiveness of community for convincing the partners. There is risk with sustainability due to lack of sufficient funds for maintenance and upgrades, and low adoption by intended beneficiaries. There is risk of wireless providers not improving the service or not updating to latest if the mesh networks become widespread in the city. Another risk is wireless hotspot providers may end their services due to availability of community wireless network. To mitigate these risks, the community should not build the network to compete against the wireless providers as outlined in [9]. Another risk is with causing divides in the community, for example between geeks and non-geeks when tech savvy members controlling the platforms and others are falling behind. As a mitigation, the community should seek for inclusion of all from community, and not just tech savvy.

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2.5

Characterization of Policies and Strategies

Policies and strategies outlined in [9] are assumed in this paper also. 2.6

Characterization of Success

The success of the platform is evaluated with respect to the objectives of the platform. One success factor is how the economic utilities of the platform sides increase. Others include: • How policies and strategies are contributing positively toward the objectives. • How the benefits are realized, how the cost is kept to minimum with grants and crowdfunding. • How the management decision contributed toward the objectives.

and involvement

• Whether the objectives were really the right ones, or unachievable or unrealistic or unbeneficial objectives were pursued. One success indicator is the ratio of sponsored bandwidth to total bandwidth, which is an indicator to effectiveness of the community in getting bandwidth sponsors on the platform. Performance related success criteria include QoS, reliability and availability measures of the network, time to respond to tickets, whether the network can support maximum number of non-blocked users with predictable QoS. The characteristics about the success should provide values for the above. 2.7

Service Area and Community Characteristics

Community characterization is done by characterizing the above dynamics for each service area in the community, that is by identifying the objectives, sought benefits, risks, mitigation plans, drawbacks, policies and strategies, success criteria. This includes: • What the service area needs • What benefits to different sides of the platform, even to different subgroups in a platform side, for example residential users vs visitor, retail businesses vs others, commercial smart service providers (waste management) vs municipality smart services (smart parking) • How much the community and municipality will benefit • What drawbacks are possible in the service area or in the community, and how to devise policies and strategies to minimize them • What risks exist and how to manage them. For this characterization, the size of the service area matters. The resources such as social and non-profit organizations and businesses in the area matter. Opportunities such as economic development opportunities

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in the service area matter. How the municipality sees the service area matters with respect to whether municipality considers significant investment or not, and what social initiatives and public services are planned. Existence of substitutable offerings matters.

3

Systems Dynamics Model

Once the characterization phase is complete, the methodology suggests following the intelligence framework in [10] for building models to estimate benefits, drawbacks and risks. In this paper, we develop a generic systems dynamics model taking into consideration the dynamics we characterized in earlier sections. The generic model in Figure 1 shows how these dynamics influence each other. Then we will explain how the generic model could be instantiated for specific service areas. The generic model shows the impact of success on users (e.g. visitors) and the impact of user adoption to observed benefits. More users could lead to more benefits initially, but since some users will be blocked after the maximum allowed users, the quality and attractiveness of the wireless network will degrade. That will cause some users to quit, and will impact the observed benefits negatively. Positive loop from economic utility to observed benefits exists: when the quality and attractiveness of the wireless network is high, the economic utility for being on the platform will be high for users and other platform sides. When more users join, the observed benefits will increase to some extent. Negative loop also exists from low success to benefits: the low success on the objectives will degrade the quality and attractiveness of the wireless network, which will lead to lower economic utility for users and sponsors on the platform, which will reduce the amount of observed benefits. With small number of users, no big benefits could be achieved. If the critical mass is not reached, it may be partly due to non-effective policies and strategies. Most benefits are intangible. In the simulation, some weights are assigned for benefit related attributes. Another advantage with SD analysis is to be able to simulate the uncertain benefits of the platform and compare the benefits under different scenarios through sensitivity analysis. Another challenge with measuring benefits is that some benefits are realized over a long-term. Different statistical distributions may be used to take effect over time for the characterization of community, service area and municipality, and that way, their impacts on the benefits are measured over a long term. Triggers for various dynamics at certain times help with analyzing the impacts over a long-term. The model contains all different characteristics and their relationships. Those characteristics include various variables corresponding to different aspects of characterization we outlined in earlier sections, although the variables are not visible in the diagram. When the model is instantiated for a specific service area, only the

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relevant variables are populated for each characteristic. Otherwise, a model that contains most variables is hard to build, nor would be helpful because of huge diversity in objectives, benefits, drawbacks, risks, policies and strategies. Rather the generic model is instantiated for each service area to simulate the different dynamics pertinent to the service area. A typical instantiation of this generic model involves oneto-three objectives/benefits, one drawback, one risk and one mitigation plan in the model. Also, typically one smart service is incorporated into the model to simulate how it

would impact the utility of the platform sides. More specifically, the smart service could be local to the service area and would not be available via Internet in the instantiated model. This generic model allows analyzing the network effects of a single smart service. An example is how a new smart service by itself influences the network effects and the utilities in the service area. Another example is how community beacons or augmented reality introduced in a business district influence the network effects and utilities.

ExpectedDrawbacksCharacteristics

ServiceAreaCharacteristics move ins, visitors move outs

ObjectivesCharacteristics

rate of potential users

Users

PotentialUsers adoption rate

Quitters quit rate

discontinue

PoliciesAndStrategiesCharacteristics diffusion

ExpectedBenefitsCharacteristics

LifetimeCustomerValue

InherentRisksCharacteristics MaximumAllowedUsers

ObservedBenefitsCharacteristics

MitigationPlansCharacteristics

UtilitiesForBeingOnPlatform BlockedUsers ObservedRisksCharacteristics

EffectivenessOfSubstitutableOfferings

QualityAndAttractivenessOfWirelessNetwork SuccessCharacteristics

ObservedDrawbacksCharacteristics

Figure 1 Generic Model for Measuring Benefits of Smart Community Wireless Platform

An instantiated model is used for estimating the benefits and drawbacks, and for analyzing the relations among benefits, drawbacks, risks and mitigation plans in existence of network externalities for a service area. When enough data is not available, system dynamics modeling and simulation is still helpful by performing sensitivity analysis based on assumptions, expert opinions, estimations and observations for providing insights to many managerial questions. As the next steps in the methodology described earlier, the model is further verified and validated as more data becomes available. With more data about the dynamics being available while the wireless network is operational, other models that use statistics and machine learning are built for clustering, classifying and predictions on the success of the platform. Factor analysis is done on what dynamics affect the success of the platform. Statistical methods are used to see significant difference between different platforms and between different scenarios, and to test hypothesis about the relations among different dynamics related to the platform. Machine learning methods are built to analyze collected usage data per community network for predicting future use and demand forecasting, finding out covariance matrix, significant parameters, association rules regarding the success of the

platforms. All these statistical, machine learning and economical models are integrated with the systems dynamics models and the SD model is validated and tuned using available data. With data, it is possible to do comparison between different service areas. The model should run over time for a service area to see how the benefits and drawbacks are realized over a period. The system should analyze all possible improvement strategies and tradeoffs, balancing required budgets and expected benefits. We are currently working on instentiating the generic model for different sample service areas, run simulations and compare results.

4

Conclusion

Our work in [9] presents a model where municipality, communities and smart utility providers work together to create a platform, smart community wireless platform, for a community in the city where different sides work together toward achieving smart community objectives. One important question is how to measure the benefits and drawbacks of these platforms to estimate the returns on investment over a period. Another question is how to analyze the risks and mitigation plans for the success of

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these platforms. To measure the benefits, relevant dynamics should be identified and characterized. An intelligence framework that incorporates systems dynamics modeling with statistical, economical and machine learning methods is very useful for estimating the benefits of smart community wireless platforms under various conditions and scenarios. In this paper, we developed a generic model for estimating the benefits and drawbacks, and for modeling the causal loops among benefits, drawbacks, risks and mitigation plans in existence of network externalities. We outlined how the generic model could be instantiated for specific dynamics and to analyze different scenarios. The model can be used by the community which is the platform sponsor and by the city which is a main supporter of the platform. Another question is how the city can use these platforms to achieve smart city objectives which we address in [11]. This paper presented a methodology for characterization of the relevant dynamics and for building estimation models. The characterization phase should consider objectives, benefits, drawbacks, risks, policies, strategies and criteria of success for a specific service area in a community. These characteristics are incorporated into the generic model to come up with specific instantiations. Our future work includes running the model for different instatiations and comparing results.

5

References

[1]. Abdelaal, Abdelnasser; “Social and Economic Effects of Community Wireless Networks and Infrastructures”, IGI Global, Feb 28, 2013 [2]. Greta Byrum: “What are Community Wireless Networks For?”, The Journal of Community Informatics, Vol 11, No 3 (2015) [3]. Lee, S.M., Kim, G. and Kim, J. (2009) ‘Comparative feasibility analysis of Wi-Fi in metropolitan and small municipalities: a system dynamics approach’, Int. J. Mobile Communications, Vol. 7, No. 4, pp.395–414. [4]. Shin, Seungjae and Tucci, Jack E., "Lesson from WiFi Municipal Wireless Network" (2009). AMCIS 2009 Proceedings. Paper 145. http://aisel.aisnet.org/amcis2009/145 [5]. Kim, G., Lee, S.M., Kim, J., and Park, S. (2008) ‘Assessing municipal wireless network projects: the case of Wi-Fi Philadelphia’, Electronic Government, An International Journal, Vol. 5, No. 3, pp.227–246. [6]. Yucel, Sakir: “Delivery of Digital Services with Network Effects over Hybrid Cloud”, The 12th International Conference on Grid, Cloud, and Cluster Computing, GCC'16: July 25-28, 2016, Las Vegas, USA

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[7]. Yucel, Sakir: “Evaluating Different Alternatives for Delivery of Digital Services”, The 12th International Conference on Grid, Cloud, and Cluster Computing, GCC'16: July 25-28, 2016, Las Vegas, USA [8]. Yucel, Sakir; Yucel, Ibrahim: “Estimating The Cost Of Digital Service Delivery Over Clouds”, The 2016 International Symposium On Parallel And Distributed Computing And Computational Science (CSCI-ISPD), Dec 15-17, 2016, Las Vegas, USA [9]. Yucel, Sakir: “Smart Community Wireless Platforms”, The 14th International Conference on Modeling, Simulation and Visualization Methods (MSV'17), July 17-20, 2017 Las Vegas, Nevada, USA [10]. Yucel, Sakir: “Estimating Cost of Smart Community Wireless Platforms”, The 14th International Conference on Modeling, Simulation and Visualization Methods (MSV'17), July 17-20, 2017 Las Vegas, Nevada, USA [11]. Yucel, Sakir: “Smart City Wireless Platforms for Smart Cities”, The 14th International Conference on Modeling, Simulation and Visualization Methods (MSV'17), July 17-20, 2017 Las Vegas, Nevada, USA [12]. Serdar Vural, Dali Wei, and Klaus Moessner; “Survey of Experimental Evaluation Studies for Wireless Mesh Network Deployments in Urban Areas Towards Ubiquitous Internet”, IEEE Communications Surveys & Tutorials, Vol. 15, No. 1, First Quarter 2013 [13]. Andrea Zanella; Nicola Bui; Angelo Castellani; Lorenzo Vangelista; Michele Zorzi: “Internet of Things for Smart Cities”, DOI 10.1109/JIOT.2014.2306328, IEEE Internet of Things Journal [14]. Shin, Seungjae and Tucci, Jack E., "Lesson from WiFi Municipal Wireless Network" (2009). AMCIS 2009 Proceedings. Paper 145. http://aisel.aisnet.org/amcis2009/145 [15]. Simon Evenepoel, Jan Van Ooteghem, Bart Lannoo, SofieVerbrugge, Didier Colle, Mario Pickavet, “Municipal Wi-Fi deployment and crowdsourced strategies”, (2013) Journal of The Institute of Telecommunications Professionals. 7(1). p.24-30. [16]. Ahmed Abujoda, Arjuna Sathiaseelan, Amr Rizk, Panagiotis Papadimitriou, “Software-Defined CrowdShared Wireless Mesh Networks”, Computer Networks, Volume 93, Part 2, 24 December 2015, Pages 359–372

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Magnetic Resonance Image Applied to 3-Dimensional Printing Utilizing Various Oils Tyler Hartwig1 , Zeyu Huang1 , Sara Martinson1 , Ritchie Cai1 , Jeffrey S. Olafsen2 , and Keith Schubert1 1 Department of Electrical and Computer Engineering, Baylor University, Waco, Texas USA 2 Department of Physics, Baylor University, Waco, Texas USA Abstract— Three dimensional printing has been widely implemented in the medical field. The use of 3D printing allows for accurate models of patient-specific anatomy from various scans. Given that MR is used for identifying detail within soft tissue in regards to the human body, the reconstruction of patient-specific scans for practical applications such as surgery preparations and patient understanding has already been established. However, the use of various oils to verify the internal structure of 3D printed objects has not been assessed. Coconut oil, olive oil, peanut oil, and dish soap were all established as various mediums for medical imaging. One rectangular prism with four cylindrical columns was 3D printed. Each column was filled with a different liquid and then imaged by the MR scan. It was concluded that olive oil displayed the brightest signal, producing the most accurate image of the cylindrical column.

marker as the oil is easily identifiable on an MR scan [8]. The aim of the research here is to identify other low cost, readily available oils that can be used as a contrast agent to accurately reveal the internal structure of a 3D printed model.

Keywords: A maximum of 6 keywords

3. Experiment Setup

1. Introduction

A hollow rectangular prism of dimensions 1.5 x 1.5 x 2 inches was 3D printed. The wall thickness was 0.08 inches. Each cylinder had an outer radius of 0.28 inches and inner radius of 0.2 inches. The space between columns was filled with water. Each cylinder was filled with a different liquid: olive oil, coconut oil, peanut oil, and dish soap (Figure 1).

Medical Imaging has changed the face of medicine. Questions about the internal state of the human body due to injury or disease can be answered with a simple scan revealing the source of the problem. Visualizing three dimensional (3D) structures on a computer monitor has challenges, so 3D printers have been introduced to provide clarity for doctors and patients [1]. 3D printing is also used to provide everything from prosthetics [2], bone replicants [3], and synthetic scaffold for growing tissue [4], to fabricting devices for drug delivery [3], and has been noted to be very useful in Magnetic Resonance (MR) imaging [5]. Producing 3D prints is straightforward, but verifying the correctness of the internal structure is very difficult, particularly for fine details, such as those representing tiny bones or thin membranes. MR scans, even from small research MR machines, are a quick and readily available source of verification in a medical environment, however, polymers commonly used in 3D printing, such as ABS, do not show up well in an MR scan. Furthermore, what liquid should be used as a contrast agent to maximize the identification of fine structures in 3D prints is an open question. Oils have long been known to be good contrast agents [6], [7] in other situations. Vitamin E capsules have long been found to be a common practical

2. Theory The different properties of oils are to be considered in order to better examine the internal structure of 3D print. Effectively, the oil is imaged instead of the 3D plastic. The “shadow” of the block then appears on the MR scan. To properly evaluate the internal structure, the inside of each cylindrical column must be completely filled with liquid. Air pockets within the oil may affect the imaging, and thus must be avoided.

Fig. 1: Prism With Liquid

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The prism was placed in the MR machine with each column occupying a different quadrant on the MR scanning volume (Figure 2). The quadrants were identified in counterclockwise orientation. The first quadrant was the liquid soap. The second quadrant was olive oil. The third quadrant was peanut oil. The fourth quadrant was coconut oil.

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to each slice of an image, the mean and standard deviation was recorded. The results of this experiment indicate different strengths of registration on the MR machine. For instance, Figure 4 shows the mean and standard deviation for each liquid. These results are slightly misleading for coconut oil however, as during this scan the oil leaked out from the prism and an air bubble appeared. Liquid Type Olive Oil Peanut Oil Coconut Oil Dish Soap

Mean 0.7994 0.7681 0.4166 0.2837

Std. Dev. 0.080 0.080 0.069 0.027

Fig. 4: Scan 1 Data

Fig. 2: Prism in MR Machine

Figure 5 shows one of the slices from the first MR scan. Notice in this image that the bottom right cylinder is dark, since the liquid leaked out. In other scans, this cylinder has a solid white circle. The top right cylinder is dark because the liquid soap was found to not register in any of the MR images.

Three MR scans were conducted. The first two scans were of normal imaging technique. The third scan was done with an inverted imaging technique. Each scan produced twenty four slices. All three scans were conducted in the same environment with consistent positioning of the prism.

Fig. 5: Scan 1 MR Image

Fig. 3: MR Setup

4. Results After obtaining the slices for each scan, the data was normalized on a scale from 0 to 1 for intensity. A mask was then made for each column of liquid in order to only pull out the intensities for that liquid. The mean and variance was then calculated for this set of data. After applying this

The ability to find the leak does verify that structural issues were found by this method, but the corrupted imaging results necessitated another scan with the same setup be taken, after the leaking coconut oil was fixed. Figure 6 shows these results. Here, it is shown that olive oil yields the highest intensity registration, followed by coconut oil then peanut oil. It is also worth noting that these means are not within several standard deviations of each other, indicating it would be easy to distinguish different oils in a scan. One example of using different oils would be to verify a model of the hear by putting one oil in the left chambers and a different oil in the right chambers, then scan. Each side of

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the hear would be easily identifiable. Liquid Type Olive Oil Peanut Oil Coconut Oil Dish Soap

Mean 0.7937 0.6853 0.7409 0.2802

Std. Dev. 0.072 0.073 0.024 0.069

Fig. 6: Scan 2 A raw slice from the second scan is shown in Figure 7. Notice here the bottom right cylinder is white, as the leak was fixed for this scan, an the coconut oil was able to be imaged. Fig. 9: Scan 3 MR Image

5. Conclusion

Fig. 7: Scan 2 MR Image Following the second scan, an inverted was used. This yields an interesting result, here the highest intensity was coconut oil, followed by olive oil and then peanut oil (Figure 8). It was expected that the intensities would follow the same pattern as the previous scan (in reversed order). Furthering the analysis of this data, it was noticed that there was a direct correlation between the density of each liquid to the mean of the intensity. Liquid Type Olive Oil Peanut Oil Coconut Oil Dish Soap

Mean 0.2033 0.1907 0.3647 0.8711

Std. Dev. 0.095 0.100 0.093 0.030

Fig. 8: Scan 3 (Inverted) As before, Figure 9 shows a slice from the inverted scan. Unsurprisingly the oils are now dark since the scan is inverted. Again the liquid soap does not show up, but the other oil are still very clear, with visible differences in intensity.

This experiment shows that MR machines can clearly distinguish between different oils and types of liquid. These liquids’ means were even several standard deviations away from each other. Olive oil performed the most consistent in terms of the mean of the intensity between scans, and thus it is the recommended choice, though the other oils performed adequately. The coconut oil leak detected in the first scan, while not the objective of this work, does show this method can be used for leak detection. Accuracy of the final images was limited by the MR machine noise and the presence of air bubbles. The MR machine used was a low field MR and thus this could be improved by using a system with a larger magnetic field, however the results were good enough not to require this. Air bubbles could be mitigated by, oils can be degassed and capped. Degassing is time consuming and would only be warranted in regions with many fine structures that can trap bubbles. It should also be noted that the MR machine could not detect dish soap or ABS plastic well. This may be useful when constructing models to be imaged. ABS plastic can be used as a support which the MR machine will not see, and likewise dish soap can be used as a neutral liquid as well. Another interesting result from the testing is that the inverted scan intensities followed the pattern of the densities of the liquid, which could be exploited in parts testing.

References [1] F. Rengier, A. Mehndiratta, H. von Tengg-Kobligk, C. M. Zechmann, R. Unterhinninghofen, H.-U. Kauczor, and F. L. Giesel, “3d printing based on imaging data: review of medical applications,” International journal of computer assisted radiology and surgery, vol. 5, no. 4, pp. 335–341, 2010. [2] N. Herbert, D. Simpson, W. D. Spence, and W. Ion, “A preliminary investigation into the development of 3-d printing of prosthetic sockets,” Journal of rehabilitation research and development, vol. 42, no. 2, p. 141, 2005.

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[3] B. C. Gross, J. L. Erkal, S. Y. Lockwood, C. Chen, and D. M. Spence, “Evaluation of 3d printing and its potential impact on biotechnology and the chemical sciences,” Analytical Chemistry, vol. 86, no. 7, pp. 3240–3253, 2014, pMID: 24432804. [Online]. Available: http://dx.doi.org/10.1021/ac403397r [4] B. Leukers, H. Gülkan, S. H. Irsen, S. Milz, C. Tille, M. Schieker, and H. Seitz, “Hydroxyapatite scaffolds for bone tissue engineering made by 3d printing,” Journal of Materials Science: Materials in Medicine, vol. 16, no. 12, pp. 1121–1124, 2005. [5] K.-H. Herrmann, C. Gärtner, D. Güllmar, M. Krämer, and J. R. Reichenbach, “3d printing of mri compatible components: Why every mri research group should have a low-budget 3d printer,” Medical engineering & physics, vol. 36, no. 10, pp. 1373–1380, 2014. [6] K. C. P. Li and P. G. P. Ang, “Paramagnetic oil emulsions as mri contrast agents,” June 9 1992, uS Patent 5,120,527. [7] A. L. Doiron, K. Chu, A. Ali, and L. Brannon-Peppas, “Preparation and initial characterization of biodegradable particles containing gadolinium-dtpa contrast agent for enhanced mri,” Proceedings of the National Academy of Sciences, vol. 105, no. 45, pp. 17 232–17 237, 2008. [8] L. Liberman, E. A. Morris, D. D. Dershaw, C. M. Thornton, K. J. Van Zee, and L. K. Tan, “Fast mri-guided vacuum-assisted breast biopsy: initial experience,” American Journal of Roentgenology, vol. 181, no. 5, pp. 1283–1293, 2003. [9] B. Ripley, D. Levin, T. Kelil, J. L. Hermsen, S. Kim, J. H. Maki, and G. J. Wilson, “3d printing from mri data: Harnessing strengths and minimizing weaknesses,” Journal of Magnetic Resonance Imaging, vol. 45, no. 3, pp. 635–645, 2017. [Online]. Available: http://dx.doi.org/10.1002/jmri.25526

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SESSION POSTER PAPERS Chair(s) TBA

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The Lithium-ion Battery cell analysis using Electrochemical-Thermal coupled model 1

D.C. Lee 1, K.W. Kim2 and C.W. Kim2# Department of Mechanical Design Engineering, Hanyang University, Seoul 133-791, Republic of Korea E-mail:[email protected] 2 School of Mechanical Engineering, Konkuk University, Seoul 143-701, Republic of Korea E-mail: [email protected] and [email protected]

Abstract - Nowadays, electric vehicle and energy storage systems are expanding so, the requirement of the lithium-ion battery energy density is required to be increased. Also, a high capacity of the battery is required to respond to the rapid charging and discharging. As a result, the thickness of electrodes and separator are getting thinner and surface area is increasing. However, this factor threatens safety and life cycle. In particular, excessive reaction area causes the hightemperature rise and causes a fire if it is not properly cooling. Therefore, considering the temperature rise and heat generation is essential in battery cell design. In this paper, an electrochemical – thermal coupling simulation model was implemented to estimate the temperature rise and heat generation of the battery. Keywords: Lithium-ion battery, Electrochemical-Thermal coupled analysis, heat generation Type of the submission: Extended Abstract/Poster Paper

1

rise and heat generation and these simulations were performed during discharging and charging in the battery.

2

Lithium-ion Battery model

The multi-physical battery model was analyzed using electrochemical-thermal battery modeling method in the literature. LixC6 | LiPF6, EC/DMC | LiyMn2O4 cell was applied, and parameters required for modeling were obtained from literature [4,5]. The schematic diagram of lithium-ion battery cell shown in Figure 1. This battery consists of two current collectors such as negative and positive electrode and a separator. In addition to the active particles, the electrodes also contain conductivity and additives. but in this study, only spherical active particles of uniform size were considered. The dimensions for negative and postive active particles radius are 12.5 and 8.5 μm respectively. Then, the thickness for cathode, anode and seperators are 100, 174 and 52 μm respectively.

Introduction

Lithium-ion batteries are widely used in many applications such as portable electronic devices, energy storage systems (ESS), military and transportation due to advantages such as high density/high power/high cycle compared to conventional secondary batteries. In particular, industries of a hybrid and an electric vehicle have increased; therefore, efficient and improved lithium-ion battery is required [1]. To increase power and energy density of the battery the thickness of the positive electrode and the separator have become thinner to get a large reaction area. However, this may be a factor that decreases safety and life cycle. In specific, excessive reaction area causes the hightemperature rise and causes a fire if it is not properly cooling. Therefore, considering the temperature rise and heat generation is essential for the design of the lithium-ion battery cell [2,3]. In this paper, an electrochemical – thermal coupling simulation model was analyzed to estimate the temperature

Figure 1 Schematic diagram of a Li-ion battery cell

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Results

The simulation was carried out for a cycle of CC discharge, CV discharge, CC charge and CV charge with 12A/m2. Figure 2 indicates the change in input current density and electric potential. Figure 3 shows the temperature rise and

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heat generation against time in positive electrode right end. The input conditions of 12A/m2 current density causes a temperature rise up to 7°C and a rise of 3° C after the end of the cycle. Therefore, during the open circuit time between every cycle the temperature of the battery become stable. Figure 4 shows the specific energy density and the specific power density of the discharge/charge cycle. It can be seen that the specific energy density of the model used in this study is 120J/kg and the specific power density is 54 W/kg.

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Acknowledgement

This research was supported by the National Research Foundation of Korea (NRF) grant funded by the Ministry of Science, ICT & Future Planning (No.2016R1A2B2015794). and the Defense Acquisition Program Administration and Agency for Defense Development under the Contract UD150025GD.

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References

[1] Scrosati, Bruno, and Jürgen Garche. “Lithium batteries: Status, prospects, and future”. Journal of Power Sources, Vol. 195 No. 9, 2419-2430, 2010. [2] Lin Rao and John Newman. “ Heat-Generation Rate and General Energy Balance for Insertion Battery Systems”. Journal of The Electrochemical Society, Vol. 144 No. 8, 2697-2704, 1997. Figure 2 Input current density and electric potential

[3] Meng Guo, Godfrey Sikha and Ralph E. White. “ SingleParticle Model for a Lithium-Ion Cell: Thermal Behavior”. Journal of The Electrochemical Society, Vol. 158 No. 2, A122-A132, 2011. [4] Wu, Wei, Xinran Xiao, and Xiaosong Huang. “The effect of battery design parameters on heat generation and utilization in a Li-ion cell”. Electrochimica Acta, Vol. 83 No. 30, 227-240, 2012.

Figure 3 Temperature rise and heat generation

[5] Wu, Wei, Xinran Xiao, Xiaosong Huang, and Shutian Yan. “A multiphysics model for the in situ stress analysis of the separator in a lithium-ion battery cell”. Computational Materials Science, Vol. 83 No. 15, 127-136, 2014. [6] Sumitava De, Paul W.C. Northrop, Venkatasailanathan Ramadesigan, and Venkat R. Subramanian. “Model-based simultaneous optimization of multiple design parameters for lithium-ion batteries for maximization of energy density”. Journal of Power Sources, Vol. 227 No. 1, 161-170, 2013.

Figure 4 Specific energy density and specific power density

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[7] Xue, Nansi. “Design and Optimization of Lithium-Ion Batteries for Electric-Vehicle Applications”. Diss. University of Michigan, 2014.

Conclusion

Through this study, we analyzed an electrochemicalthermal-coupling model for the temperature rise and heat generation. In additionally, electric potential change, specific energy density, and specific power density were also calculated at discharging/charging current density of 12A/m 2

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RENFRED-OKONIGENE CHILDREN PROTECTION SYSTEM NETWORK: WHERE IS MY BABY? Dorcas Okonigene1, Robert Okonigene2, and Clement Ojieabu3 Department of Physical and Health Education, Ambrose Alli University, Ekpoma, Edo State, Nigeria 2,3 Department of Electrical and Electronics Engineering, Ambrose Alli University, Ekpoma, Edo State, Nigeria 1

Abstract - This research work is centered on providing sufficient supervision system that is capable of reducing the chances of harm or risks to life threatening situation in children. The scholars are concern mainly with the aspect of children neglect (supervision). The proposed system network detects the position of the child around the vicinity that is considered to be dangerous and instantly initiate a sequence of response through the network. This research work was greatly and further inspired by the death of Renfred Okonigene, who was born on March 14, 2014 and drowned in a well on February 11, 2017. The proposed system to be developed is therefore to be called “RENFRED-OKONIGENE CHILDREN PROTECTION SYSTEM NETWORK”. The final system is to be developed, taking into consideration, most regions of Africa and Asia where there is poverty. This research work is still at its early stage. Keywords: Renfred-Okonigene, Children protection, Child Neglect, Envelope, System network, Supervision

1

Introduction

The Child Protection System is one major system of intervention of child abuse and neglect in the USA. Child protection and child safety are a consistent issue in modern society. Parents who wish to provide personal protection for their children can have insufficient level of awareness about the child. In some instants parents can become adequately protective or over-protective. As a result of increasing awareness of child protection, parents go for new children protection devices, alarms, child transmitters, pool alarms and other gadgets. We studied the several wearable gadgets for specific purposes that currently exist such as the likes of Guardian’s smartwatch, Revolutionary Tracker, Hi Tech wireless security alarm and kid alert. Therefore, focus will be on neglect or supervision of children. Child neglect is a form of child abuse [1-4] which results to Parental failure to provide for a child, when options are available. However, due to the fact that there are no specific

guidelines about child neglect makes the definition of child neglect broad. Thus, the World Health Organization (WHO), in the United States, the Centers for Disease Control and Prevention (CDC) and Child Abuse Prevention and Treatment Act (CAPTA) differently defines child maltreatment, abuse and neglect [5-10]. Thus, neglectful acts can be divided into six sub-categories which are’ Supervisory neglect, Physical neglect, Medical neglect, Emotional neglect, Educational neglect and Abandonment [11]. From literature there are lots of research works going on about children protection devices. However, none considered providing immediate protection before possible human intervention. It is this possible time lag that this research work tends to address.

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Methodology

The scholars intend to update and avail themselves with the latest technology in children protection devices, through literature review of published work as it relates to children protection. The system can be modified to achieve its objective. The system will be made up of three major components namely: the hardware, the sorftware and system security. There will be lots of sensors that will be will be deployed in the entire network. The sensor senses the presence of the child around the facility and the data of the child body radiation (heat) is processed and analyzed for further necessary action. Parents and guidance can monitor all what is happening to their children through the network. The entire proposed system network is also design to protect the child from getting into trouble or prevented from dangerous sharp objects. It will prevent the child from falling into ditches, pits, gutters, wells and gullies that can cause serious body harm and even death. When the child body is sensed to be in danger the system immediately respond to provide an immediate protection. The service to be rendered to save the child will be automatically made available when at risk due to poor supervision. In other

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words this proposed system will breach the gap between the time of possible risk or danger and intervention. This proposed system will have the capability for an immediate intervention when the child is in danger before any human intervention. The system will create an envelope instantly to engulf the child thereby preventing suffocation, body harm and burns or crushing. It will be cost effective and the technology will be compatible with iOS and Android devices. A good knowledge and understanding of the drone technology as it relates to positioning and navigating in space will be deployed. A drone is any unmanned aerial device. The devices are known to be remotely piloted aircrafts. Drones are remotely controlled devices, can transmit data back to a ground source and are unmanned

[3] Welch, Ginger, Heather Johnson, and Laura Wilhelm. "Neglected Child: How to Recognize, Respond, and Prevent". Beltsville, MD, USA: Gryphon House, 2013. [4] Polonko, Karen A. "Exploring assumptions about child neglect in relation to the broader field of child maltreatment". Journal of Health and Human Services Administration. Vol. 29 Issue 3, pp. 260–84. [5] Leeb, R.T.; Paulozzi, L.J.; Melanson, C.; Simon, T.R.; Arias, I. (January 2008). Child Maltreatment Surveillance: Uniform Definitions for Public Health and Recommended Data Elements, Version 1.0 (PDF). Atlanta, Georgia: Centers for Disease Control and Prevention, National Center for Injury Prevention and Control.

Also necessary are review in cloud computing, health informatics, biometrics, pattern recognition, artificial intelligence and robotics.

[6] McCoy, M.L.; Keen, S.M. (2013). "Introduction". Child Abuse and Neglect (2 ed.). New York: Psychology Press. pp. 3–22.

The researchers intend to build modules and then model the entire system network. The model will be simulated to test the conditions under which a real time system can be built.

[7] "What is Child Abuse and Neglect?". Australian Institute of Family Studies. September 2015.

Laboratory experiments will be carried out to test the durability and compatibility of the required sensors. The algorithm for each of the modules will be tested and then step by step the modules will be coupled together.

[8] Herrenkohl RC (2005). "The definition of child maltreatment: from case study to construct". Child Abuse and Neglect. 29 (5): 413–24.

The material for the envelope will be fabricated and several tests are to be carried out to ascertain the suitability of the material for the task of protecting a child. It will be important to educate parents that the best defense for their children is calmness and well-informed parenting. Parents would be educated to know their children habits, the child's innate sense of right and wrong.

3

References

[1] Bovarnick, S (2007), Child neglect (Child protection research briefing), London: National Society for the Prevention of Cruelty to Children.

[9] "Definitions of Child Abuse and Neglect in Federal Law". childwelfare.gov. Children’s Bureau, Administration for Children and Families, U.S. Department of Health and Human Services. [10] World Health Organization and International Society for Prevention of Child Abuse and Neglect (2006). "1. The nature and consequences of child maltreatment". Preventing child maltreatment: a guide to taking action and generating evidence. [11] "Child abuse and neglect by parents and other caregivers". World Health Organization.

[2] Barnett, W. S., and Belfield, C. R. 2006. Early Childhood Development and Social Mobility. The Future of Children, 1(2), 73–98.

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Author Index Al-Fedaghi, Sabah - 61 Asar, Azzam ul - 13 Aye, Nyein - 94 Bischof, Hans-Peter - 49 Boyle, Sara - 36 Budhi, Lokesh - 7 Cai, Ritchie - 114 Hartwig, Tyler - 114 Hasan, Khondker Shajadul - 29 Huang, Zeyu - 114 Hwang, Sezin - 55 Jun, Moon-Ryul - 55 Kambouris, Sara - 42 Kamran, Muhammad - 13 Kestur, Raksha - 82 Khaing, Kathy - 94 Kim, Chang-wan - 121 Kim, Keon-uk - 121 Lee, Dongchan - 121 Mange, Jeremy - 36 Martinson, Sara - 114 Maurer, Peter - 3 Medellin, John M. - 7 Ojieabu, Clement - 123 Okonigene, Dorcas - 123 Okonigene, Robert - 123 Olafsen, Jeffrey - 114 Park, Sung-won - 82 Sawka, Peter - 36 Schubert, Keith - 114 Shieh, Horng-Lin - 68 Tidwell, Craig - 19 Wallace, Jeffrey - 42 Yang, Ying-Kuei - 68 Yaqub, Raziq - 13 Yucel, Sakir - 75 , 87 , 100 , 107 Zaw, Sai Maung Maung - 94