Internet of Unmanned Things (IoUT) and Mission-based Networking (Internet of Things) 3031334930, 9783031334931

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Internet of Things

Chaker Abdelaziz Kerrache Carlos Calafate Abderrahmane Lakas Mohamed Lahby   Editors

Internet of Unmanned Things (IoUT) and Mission-based Networking

Internet of Things Technology, Communications and Computing

Series Editors Giancarlo Fortino, Rende (CS), Italy Antonio Liotta, Edinburgh Napier University, School of Computing, Edinburgh, UK

The series Internet of Things - Technologies, Communications and Computing publishes new developments and advances in the various areas of the different facets of the Internet of Things. The intent is to cover technology (smart devices, wireless sensors, systems), communications (networks and protocols) and computing (theory, middleware and applications) of the Internet of Things, as embedded in the fields of engineering, computer science, life sciences, as well as the methodologies behind them. The series contains monographs, lecture notes and edited volumes in the Internet of Things research and development area, spanning the areas of wireless sensor networks, autonomic networking, network protocol, agent-based computing, artificial intelligence, self organizing systems, multi-sensor data fusion, smart objects, and hybrid intelligent systems. Indexing: Internet of Things is covered by Scopus and Ei-Compendex **

Chaker Abdelaziz Kerrache • Carlos Calafate • Abderrahmane Lakas • Mohamed Lahby Editors

Internet of Unmanned Things (IoUT) and Mission-based Networking

Editors Chaker Abdelaziz Kerrache Université Amar Telidji de Laghouat Laghouat, Algeria

Carlos Calafate Polytechnic University of Valencia Valencia, Spain

Abderrahmane Lakas UAE University Al Ain, UAE

Mohamed Lahby University of Hassan II Casablanca Casablanca, Morocco

ISSN 2199-1073 ISSN 2199-1081 (electronic) Internet of Things ISBN 978-3-031-33493-1 ISBN 978-3-031-33494-8 (eBook) https://doi.org/10.1007/978-3-031-33494-8 © The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 This work is subject to copyright. All rights are solely and exclusively licensed by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed. The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. The publisher, the authors, and the editors are safe to assume that the advice and information in this book are believed to be true and accurate at the date of publication. Neither the publisher nor the authors or the editors give a warranty, expressed or implied, with respect to the material contained herein or for any errors or omissions that may have been made. The publisher remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. This Springer imprint is published by the registered company Springer Nature Switzerland AG The registered company address is: Gewerbestrasse 11, 6330 Cham, Switzerland

Preface

Internet of Unmanned Things (IoUT) is considered as a promising paradigm resulting in numerous applications including shipment of goods, home package delivery, crop monitoring, agricultural surveillance and rescue operations. IoUT nodes are Unmanned Aerial Vehicles (UAVs) that collaborate with each other in an ad hoc manner through a Line-of-Sight (LoS) link to exchange data packets. However, UAVs can also communicate with fixed ground stations, with an air traffic controller, or through a Non-Line-of-Sight (NLoS) link with a satelliteaided controller, generally based on preloaded missions. Besides the dissimilar communication technologies, various problems appear in these inter-UAV and UAV-to-X communications, including energy management, lack of security and unreliability of wireless communication links, and handover from LoS to NLoS, and vice versa. In this book, our approach was to invite front-line researchers and authors to submit original research and review chapters that explore emerging technologies for IoUT and mission-based networking. The authors present key applications of Unmanned Aerial Vehicles and AI that have witnessed significant successes towards smart cities. The current edited volume collates nine chapters that cover a plethora of emerging technologies and applications of Internet of Unmanned Things (IoUT) and mission-based networking. The technological examples presented in this book illustrate how IoUT and AI have been successfully implemented to allow for a better appreciation of the advancement of these technologies. We want to take this opportunity to express our sincere thanks to the contributors to this volume and the reviewers for their outstanding efforts in reviewing and providing interesting feedback to the authors of the chapters. The editors would like to thank Prof. Giancarlo Fortino (Series Editor-in-Chief), Ms. Mary James (Executive Editor) and Ms. Kausalya Boobalan (Production Editor), for the editorial

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assistance and support to produce this important scientific work. Without this collective effort, this book would not have been possible to be completed. Laghouat, Algeria Valencia, Spain Al Ain, UAE Casablanca, Morocco

Chaker Abdelaziz Kerrache Carlos Calafate Abderrahmane Lakas Mohamed Lahby

Contents

UAV Main Applications: From Military to Agriculture Fields. . . . . . . . . . . . . . Ludovica De Lucia and Anna Maria Vegni Mobility, Traffic Models, and Network Management for Internet of Unmanned Things by Using Artificial Intelligence . . . . . . . . . . . . . . . . . . . . . . . . Arunima Sharma A Blockchain Trusted Mechanism (BTM) for Internet of Unmanned Things (IoUT) Using Comprehensive and Adaptive Schemes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Geetanjali Rathee, Akshay Kumar, and Chaker Abdelaziz Kerrache Mobile Edge Computing in Internet of Unmanned Things (IoUT). . . . . . . . . Nina Slamnik-Kriještorac and Johann M. Marquez-Barja

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Mobile Edge Computing Enabled Internet of Unmanned Things . . . . . . . . . . 101 Abderrahmane Lakas, Abdelkader Nasreddine Belkacem, and Parag Kulkarni Accelerating Classification of Symbolic Road Markings (SRMs) in Autonomous Cars Through Computer Vision-Based Machine Learning. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 119 Arfan Ghani and Rahat Iqbal Enhancing the End-User’s Mobile Equipment Serviceability via UAV Green Technology: Sustainable Development . . . . . . . . . . . . . . . . . . . . . . . . . . 139 Irshad Hussain Inter-UAV Communication Over Future Internet Architectures . . . . . . . . . . . 155 Ahmed Benmoussa and Spyridon Mastorakis Experimental Validation of Networked Aerial IoUT Solutions: Testbeds and Measurements . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 173 Raheeb Muzaffar and Karin Anna Hummel Index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 201 vii

UAV Main Applications: From Military to Agriculture Fields Ludovica De Lucia and Anna Maria Vegni

1 Introduction The term unmanned aerial vehicle (UAV) indicates an aircraft without pilot onboard. The Federal Aviation Administration (FAA) classifies an UAV also as a semiautonomous aircraft, meaning that it works using sensors, a ground control system, and specific software programming. According to the FAA, the term “drone” is a colloquial term for all remotely piloted aircraft, regardless of size, shape, or capabilities. In this chapter, these two terms are used as synonyms. Historically, the birth of the unmanned aerial vehicles (UAVs) is associated to military purposes; in fact, the UAVs rise as weapons of attack. The early appearance was during the First World War where it was extremely complicated to rescue lost airplanes and pilots operating on warplanes. For this reason, it was considered the use of unmanned aircrafts. It followed that a suitable application was the flying bombs, and in November 1917, the Kettering Bug was introduced, but it was not used on the battlefield. The first aircraft to be used was built in the United Kingdom in 1935 under the name “Queen Bee.” It had the remote control system in the backseat, and it could reach its top velocity at 180 km/h. As shown in Fig. 1, the control system consisted of a simple wheel very similar to that of an old analog phone by which the pilot could literally “dial,” as he would for a telephone number, the commands to be sent to the drone. The numbers on the wheel corresponded to simple commands such as turn right, left, beat, etc. Clearly, the handling was reduced as compared to what it is possible to do by controlling the plane from the cockpit; in fact, the ailerons were always locked in a neutral position, and the pilot

L. De Lucia () · A. M. Vegni Department of Industrial, Electronic and Mechanical Engineering, Roma Tre University, Rome, Italy e-mail: [email protected]; [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 C. A. Kerrache et al. (eds.), Internet of Unmanned Things (IoUT) and Mission-based Networking, Internet of Things, https://doi.org/10.1007/978-3-031-33494-8_1

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Fig. 1 Image shoot in the 1935 representing the “Queen Bee” during the flight, [5]

used for rudder, balancers, and throttle. It was the first aircraft referred to as “drone,” representing a huge breakthrough in the history [1]. Before drone meant UAV, drones were male honeybees. The word is from the Old English dran, and as this article of 1946 in the Popular Science magazine testifies, the term drone was used publicly: Drones, as the radio-controlled craft are called, have many potentialities, civilian and military. Someday huge mother ships may guide fleets of long-distance, cargo-carrying airplanes across continents and oceans. Long-range drones armed with atomic bombs could be flown by accompanying mother ships to their targets and in for perfect hits.

The evolution from “Queen Bee” to drone is just an extension of the metaphor. Drones have continued to be a cornerstone in the army, playing critical roles in intelligence, force surveillance and protection, artillery sighting, target tracking and acquisition, battle damage assessment and reconnaissance, as well as weapons. Nowadays, the uses of these devices are not limited only to the military sector, but they are suitable also for agriculture and civilian usage. Due to the collaborative nature of drones, moving in swarms to accomplish common tasks, the term Internet of Drones (IoD) [2, 61] has gained its own role in the huge framework of Internet of Things (IoT). This term was coined by replacing “things” with “drones” in order to highlight the similar properties. However, we can clearly distinguish IoD in the IoT context due to its high adaptability to a wide variety of complex and dedicated scenarios. The IoD framework is comprised of flying devices that are transmitting and receiving data from each other in close proximity. Its structure and architecture is also known as Flying Ad hoc NETwork (FANET), which belongs to the well-known term of Mobile Ad hoc NETworks (MANET) [3]. In general, the IoD network architecture consists of five layers, which from the bottom to the top are airspace, node to node (N2N), end to end (E2E), service, and application. The airspace is the

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physical space where drones move and is segmented in airways that are comparable to roads, intersections where two airways intersect each other, and nodes where there are multiple intersections. In the airways, UAVs follow a precise path and are inside the boundaries made by the airways, while in the nodes they are “free” to move. In this layer are implemented features regarding maps, airspace broadcast, plan trajectory, precise control, collision avoidance, and weather conditions. In this way, a device is always aware of its coordinates in time and space, its destination and estimated level of fuel left, the speed level it should adopt, and if there are birds or other drones that can interfere their mobility path. The next layer N2N aims to monitor the status of the UAVs, notifying to the ground station (a.k.a. Zone Service Provider (ZSP), emergency messages, or to the other drones congestion notifications or contingencies. The E2E layer realizes the inter-zone graph with the information about the gates and the transit costs, routing and hand off from a ZPS to another. Finally, in the service layer, it is detected the broadcast zone, whereas the application layer allows to enable all the range of applications simultaneously [4]. This chapter is organized as follows. In Sect. 2, we provide the main classification of drones, in terms of their features and equipment onboard. Then, we will present their different applications. Specifically, this chapter provides a detailed description of the possible applications of UAVs, starting from the military ones up to the agriculture field. More in detail, Sect. 3 deals with the nowadays military applications of UAVs. Then, Sect. 4 explains the different kinds of civil applications, from photography and film making, to safety, medical health, and civil surveillance. In Sect. 5, it is underlined the role of UAVs in precision agriculture (PA) and smart farming. For all the main applications, we discuss about the use of technology tools, such as machine learning techniques and Blockchain, to serve as support for UAV applications and security aim. Specifically, in Sect. 6, we present how machine learning techniques are exploited for UAV networking and communications, while in Sect. 7 the use of Blockchain for security of IoD communications is addressed. Finally, conclusions and open discussion will be addressed at the end of this chapter.

2 Classification of Drones Depending on the field in which drones are used, they have different features. Based on their aerodynamics, UAV can be divided into four types, i.e., (i) Single Rotor Drone (SRD), (ii) Multi-Rotor Drone (MRD), (iii) Fixed-Wing Drone (FWD), and (iv) Fixed-Wing Hybrid Vertical Take-off Landing (VTOL). Table 1 collects the main UAV classification with the related features, as described in the following. In general, the rotary-wing type is composed of several rotors that generate the power necessary for lifting. For this group of UAVs, the control is accomplished by the torque force and thrust of the rotors. As the name suggests, the SRD consists of only one rotor, which is as one big spinning wing. It has a small tail to control its direction and stability. The SRD looks like an actual helicopter in structure and design but carries less load. There are many advantages, such as the benefit of much

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Table 1 Comparison between the four types of drones, underlining their features and applications Class SRD MRD FWD VTOL

Applications Agriculture Photography, surveillance Aerial mapping, security Delivering, rescue operations

Main features Strong, durable, greater efficiency over a MRD Better control, cheap, can fly close to the buildings Flight time up to 16 h, high altitude, heavy loads Great stability, simple architecture, high speeds

greater efficiency over a Multi-Rotor one, which increases if it is gas-powered for even longer endurance. This UAV type allows to include very long blades, which are more like a spinning wing than a propeller, giving great efficiency. If there is the need to hover with a heavy payload (e.g., an aerial LiDAR laser scanner) or have a mixture of hovering with long endurance or fast forward flight, then a SDR is the best option. Furthermore, these drones are built to be strong and durable. On the other hand, they can show some disadvantages, since they are complex and expensive, and suffer from excessive vibrations. They also require a lot of maintenance and care due to their mechanical complexity. The long, heavy spinning blades of a single rotor can be dangerous. This type is suitable for Aerial LIDAR laser scan, drone surveying, and carrying heavy payloads. MRDs use multiple propellers (blades) for navigation and flying in space. Such drones have common uses for photography and video surveillance. They can be categorized based on the number of propellers, i.e., tricopter, quadcopter, hexacopter, and octocopter, respectively, with three, four, six, and eight rotors. Quadcopters are the most popular multi-rotor drones by far. They are the easiest and cheapest option to get started with UAVs. They also offer greater control over position and framing; hence, they are perfect for aerial photography and surveillance. This type of UAV provides a better control of the aircraft during the flight, due to its increased maneuverability. It can fly much more closely to structures and buildings and is able to take multiple payloads, which results in an increase of the operational efficiency and reduction of the time taken for inspections. However, MRDs have limited endurance and speed, making them unsuitable for large-scale aerial mapping, long-endurance monitoring, and long-distance inspection such as pipelines, roads, and power lines. They are very inefficient and require a lot of energy just to fight gravity and keep them in the air. With the current battery technology, they are limited to around 30 min when carrying a lightweight camera payload. However, heavy-lift multi-rotors are able of carrying more weight, but in exchange for much shorter flight times. FWDs are designed with one rigid wing that is designed to look and work like an airplane, providing the lift rather than vertical lift rotors. They have a predefined air foil of static and fixed wings that allows to regulate the lifting process based on the UAV speed. Hence, this drone type needs only the energy to move forward and not to hold itself in the air. As a result, FWDs are energy-efficient drones. They cannot hover at one place, and they fly on the set course till their energy source is functional. The control is enabled by elevators, ailerons, and rudder that are fixed to

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Fig. 2 Prototype of the drone for the Amazon Prime Air project carrying and delivering a package

the wings. Through these features, it is possible for the UAV to turn around, to roll, and to move on the yaw angles. Fixed-wing drones can cover longer distances, map much larger areas, and loiter for long times monitoring their point of interest. The average flight time is a couple of hours, which can reach up to 16 h through the use of many fixed-wing devices. This drone type can fly at a high altitude, carry more weight, and is more forgiving in the air than other drone types. One of the most relevant drawbacks is that it can be expensive. A short period of training is usually required to fly fixed-wing drones. The relevant areas in which FWDs are used are aerial mapping, drone surveying (i.e., forestry, environmental, pipeline, and coastal), agriculture, inspection, construction, and security. Fixed-Wing Hybrid VTOLs merge the benefits of fixed-wing- and rotor-based designs. Hybrid VTOL drone types use propeller(s) to lift off and wings for gliding. This drone type has rotors attached to the fixed wings, allowing it to hover and take off and land vertically. This category of hybrids is still new on the market, but as technology advances, this option can be much more popular in the next few years. One example of fixed-wing hybrid VTOL is Amazon’s Prime Air delivery drone, as depicted in Fig. 2. Quadcopter is the most popular drone of this kind, and it consists of several mechanical and electrical parts such as propeller or wings, body or

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Fig. 3 Classification of UAVs based on their aerodynamics properties, i.e., (a) SRD, (b) MRD, (c) FWD, and (d) VTOL [6]

chassis, landing gear, DC motors (prime mover), battery, flight controller, electronic speed controllers, transmitter, receiver, GPS module, and application modules such as cameras. This type of UAV has multiple benefits. For instance, the autopilot can do all the hard work of keeping the drone stable, leaving to the human pilot the easier task of guiding it around the sky. They offer the possibility to realize either hovering or forward flight. The downsides are represented by an only handful of fixed-wing hybrid VTOLs that are currently on the market and the technologies used in these drone types are still in the nascent stage. In general, a rotary-wing UAV holds a better and easier control and can carry a heavier payload compared to the fixed-wing type. On the other side, a fixed-wing UAV presents an efficient and simpler architecture facilitating the maintenance processes and is also characterized by a longer flight duration and larger coverage. The structural differences can be appreciated in Fig. 3. Apart from the main classification of UAVs that considers their equipment, we can rate the autonomy of UAVs. The United States Department of Defense distinguishes four types of autonomy depending on the role of human to control the UAV operations remotely. The first type of UAV autonomy is defined as humanoperated system that charges the system operator for controlling all operations of the unmanned system. An advance system is the human delegated system, characterized by a higher level of autonomy compared to the previous one, because it has the ability to take autonomously some restricted decisions. The third type is the human supervised system where it is possible to take various decisions based on the directions of the system operator. It is interesting that in this case the system operator and the unmanned system can perform various actions based on the data received. Lastly, the fully autonomous system considers the UAV as fully responsible for all its operations. Here, the unmanned system receives data from the system operator and interprets it into specific tasks. This feature is particularly useful in case of

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emergency because the system operator could intervene in the function of the unmanned system. One of the most used and popular classification is to distinguish the UAVs on their weight and size. Specifically, if a UAV weights more than 150 kg, it is catalogued as heavy; otherwise, it is classified as light. For the size, it is conceivable to consider that the fixed-wing UAVs, whose weight is between 20 and 150 kg, can be characterized as large. On the other side, if a fixed-wing UAV weights less than 20 kg, then it can be classified as small. Likely, the rotary-wing UAVs, whose size generally goes from 25 to 100 kg, are considered as large. If a rotary-wing UAV does not exceed 25 kg, then it is small. Furthermore, it is possible to identify a new category, i.e., mini. Mini UAVs are characterized by a weight that ranges from some grams to several kilograms. UAVs can also be categorized based on the fuel consumed for their flight. There are four main fuels for a UAV, i.e., (i) kerosene, (ii) battery cells, (iii) fuel cells, and (iv) solar cells. Kerosene is used for large fixed-wing UAVs, generally employed for military purposes. The small rotary-wing UAVs usually have battery cells, since their functional needs require less operating time. A fuel cell is an electric device that transforms chemical substances into electrical energy and can be integrated only into fixed-wing UAVs because they comprehend a mechanical and an electronic part. These devices are also able to maximize the flight distance. Finally, solar cells can be used for fixed-wing UAVs and rotary-wing UAVs. Last, another relevant characteristic of the drones is the type of payloads that can be integrated on them, which generally depend on their size and weight. The first kind of payloads are sensors. The most used sensor is a camera that can help the device to orient itself in the surrounding space, to identify obstacles and possible dangers. Based on the purposes, there are three main technologies of cameras, i.e., (i) multispectral, (ii) hyperspectral, and (iii) thermal. The multispectral integrates five optical bands, such as red, green, blue, red edge, and near-infrared, while the hyperspectral camera includes more bands. The thermal uses the infrared radiation to form a heat zone image, operating at wavelengths of .≈14000 nm. Other types of sensors that can be integrated on drones are chemical, biological, and meteorological. Chemical sensors can identify chemical compositions and specific organic substances. Biological sensors can identify various kinds of microorganisms, whereas meteorological ones possess the ability to measure various values, such as wind speed, temperature, and humidity of the air. Lastly, there are many payloads that do not belong in the sensor category, like the ability of a drone to carry goods, liquids, or other types of objects [7, 8].

3 Military Applications One of the main applications of UAVs since their rise is the military one. UAVs are refined for the improvement and security of military actions and are used to enhance

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intelligence operations, in which the purpose is to retrieve information from a given distance. They can also be used for radar purposes to detect any unwanted signal [9]. In this section, we will provide an overview of main UAV military applications, such as the bomb recognition and in general surveillance applications. The implementation of scanning systems can allow UAVs to operate as bomb detectors. Through the use of an infrared camera (IR) and a GPS module, it is possible to detect and estimate the location of the bomb and send this information to a reference computer. The use of the IR camera is motivated by the fact that it is able to detect temperature changes, and thanks to these variations of heat, it is possible to determine the presence of the bomb. Thanks to this system, a quick intervention of bomb disposal engineers can be operated to take the right measures and precautions [10]. The use of UAVs as surveillance systems provides many advantages, such as collecting large information in a short time, monitoring a big geographical area, and low costs. The use of UAV swarms allows to monitor a given area, through an optimized deployment of UAVs on the sky. This was made possible by connecting the drones to a control ground station (GCS) via 5G connection, and it resulted to a monitored area with zero blind spots [11]. It is also possible to use drones for video surveillance in war zones to spy on enemies. In [12], a war environment was simulated and implemented smart drones for military surveillance purposes. UAVs are also often used in conflict zones where enemy monitoring and the safety of people are priority activities, operating at any time they can ship essential goods to soldiers or locate missing and injured soldiers. They guarantee a real-time view of the area affected by the conflict. The goal for the future is to make them lighter, more manageable, and more durable in order to save more and more lives.

4 Civil Applications Drones represent a developing technology especially for large sectors of the civil and consumer market. Currently, it is possible to see such aerial platforms for personal use, such as, for example, hobby, or professional, for video recordings or aerial photography. However, the potential of this technology goes far beyond that. Indeed, the availability of low-cost flying platforms that can move freely in both indoor and outdoor scenarios is already revolutionizing entire sectors, providing completely new approaches, such as the ability to quickly monitor critical infrastructure or fly over critical scenarios, but also to easily deliver goods and materials . Several companies are investing in drones capable of carrying different types of cargo, weighing around .≈50 kg, in remote areas difficult to reach by other means. In the delivery scenario, Amazon is already notoriously experimenting and offering examples of same-day goods delivery using UAV platforms, capable of flying miles to deliver packages to users’ home.

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Furthermore, in an emergency situation, it is possible to “teach on-the-fly” the drone swarm to analyze video purchases during the flight to identify the presence of people (e.g., people dispersed in a forest at night via infrared cameras) or vehicles, or locate buried smartphones still active in order to facilitate the operations of first aid personnel. In the following, the main implications for the use of drones in civilian environments have been analyzed.

4.1 Photography, Film Making, and Photogrammetry Commonly, drones are largely used to take photos and videos of the area they are flying over, representing the most popular and common application. With these devices, it is possible to obtain high-quality images with lower costs. Indeed, as compared to helicopters, UAVs can fly in places where it is difficult to access, capture objects closer, and from different perspectives. Using drones for shooting and filming is a very common practice in many professional contexts, including high-budget film and documentary productions. In fact, the number of high-level television companies that are exploiting footage and materials collected by drones is constantly growing. Depending on the type of drone (i.e., consumer or professional), there are drones with different cameras built in. Depending on the needs, there are different types of resolution that the camera is able to produce (i.e., Video Graphics Array (VGA), High Definition (HD), Full HD, Ultra HD (4K), etc.), as well as the type of camera stabilization to attenuate vibrations. For instance, professional drones are often equipped with an advanced stabilization system called Gimbal, which keeps the camera on its axis, so the images are still without smudging or interference from vibration or instability of the drone in flight. Among the most advanced camera drones, there are also those equipped with the so-called first person view (FPV) functionality, thanks to which drone pilots can see in real time the photos taken by the camera or the video taken by the integrated camera of the drone. Furthermore, a new application has been developed, which consists of a platform for remote photogrammetric autonomous or semi-autonomous measurements without pilot, i.e., the photogrammetry UAV. Photogrammetry is a technology that allows to define the shape, size, and position of objects placed on the ground, using the information contained in photographic images of the same objects taken from different points. Photogrammetry uses images captured by drones to create topographic maps and orthophotos, also useful for the Geographic Information System (GIS). Moreover, thanks to the use of integrated cameras on the UAVs, it is possible to operate, identify, and investigate archeological sites as was done for the theaters of Pompeii [13–15].

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4.2 Medical Applications Medical drones are intended to deliver drugs, vaccines, blood, and in general life-saving equipment to harsh environments, then covering “the last mile” in the delivery of medicaments. The usefulness of drones for the medical sector is evident, and for several years, research has been focusing on spreading and consolidating this application field, as much as possible [16, 17]. The first aid brought by drones dates back to 2010, after the earthquake in Haiti, when they were used by the International Organization for Migration (IOM) for mapping the most affected areas and distributing basic necessities and health items. Since then, thanks to the continuous technological research and the subsequent development of medical drone prototypes, it has become increasingly real. Medical drone applications can be divided into three main categories, i.e., (i) public health/disaster relief, (ii) telemedicine, and (iii) medical transportation. Each of these three macro-categories is being developed in specific sectors, according to the geographical area of origin and thanks to the increasing investments by public and government bodies. For instance, in a continent such as Africa, fixedwing medical drones have been used for years for the distribution of vaccines, and the demand for such drones with onboard life-saving medical equipment is kept increasing. Thanks to the use of these medical drones, the main limits of overland transport are being exceeded, namely traffic congestion and remote delivery sites. Very often vaccines destined for inaccessible harsh areas arrive at destination ruined due to temperature changes due to long journeys. On the contrary, the medical drone that flies for 40 km through a mountainous area can transport the vaccines in a special thermal container equipped with a sensor that is activated in case of temperature higher than that of product storage. In general, medical drones allow to reach harsh environment and deliver medicaments and other tools in a fast and protected manner. One of the cases where the time factor is crucial for survival is when you are the victim of a heart attack. Also in this circumstance the medical drones are giving a fundamental help by transporting the external automatic defibrillator (EAD) directly to the people affected by a heart attack. The use of EAD has greatly improved the survival rate in case of extra hospital cardiac arrest, but the most important variable for the success of treatment is the time between the illness and the actual use of the defibrillator. In general, the EADs are usually stationary in places open to the public not always reachable in a short time or in case of cardiac arrest at night. The first drone project with an integrated defibrillator onboard was created by Alec Momont, an engineering student at Delft University of Technology, who designed the prototype to navigate independently to a place in a few minutes. In addition to the DAE, a webcam is integrated on the drone that allows people on the scene of a cardiac arrest to: (i) communicate with emergency personnel and (ii) be guided in the use of the defibrillator to carry out treatment immediately pending the arrival of the ambulance.

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Similarly, air transport, carried out with the help of helicopters, is widely used in emergency medicine for a rapid action. The transport via drone avoids the typical restrictions of road transport and allows to quickly reach distant or difficult to reach places. Another medical use case where drones facilitate the operational activity is the patient transport. The first investigation into the usefulness of drones in patient transport was conducted by the US Army’s medical research division and demonstrated the possibility of using drones to extract wounded soldiers in war scenarios [17]. Also, since the time of the COVID-19 pandemic and lockdowns, the use of this drones to ship and deliver medicines has been deepened and extended to any kind of good [60]. They play a key role in situations where medicine cannot be transported by vehicles commonly used by delivery agencies. Besides medicine, UAVs are also used to deliver food, mail, and packages. Companies such as Amazon are working to make this mode of delivery routine. It has been shown that, in this field, drones significantly reduce external contamination, costs, and delivering time, while relieving traffic [16, 18]. One of the most promising uses of medical drones is in the field of telemedicine, or remote diagnosis and treatment of patients, specially in disaster scenarios where communications are not always available. In such scenario, the use of drones is intended to create an Instant Telecommunication Infrastructure (ITI). This involves building a drone platform that focused on providing communications to perform pre- and post-operative evaluations of telemonitoring patients in remote areas. The telemonitoring consists in the remote guide of an experienced surgeon to a less experienced colleague, and vice versa, with the aim to carry out the treatment remotely. Drones can also be used to establish a wireless communication network between the surgeon and a robot to perform telesurgery and surgical procedures assisted by a robot. Ultimately, the goal of this technology is to provide life-saving care for disaster victims, especially when first responders cannot quickly arrive overland and bridge this delay by providing rapid treatment directly to victims by using remote physicians to instruct anyone on the spot. Drones are also widely used for site surveillance after a disaster, in areas with biological and chemical risks, and for monitoring the spread of disease. They are used to assess initial damage and prioritize rescue by providing, in a sort of Pre-Triage, information on the number of patients in need of urgent care. Finally, in the area of public health, they are used to identify zone at risk before a disaster occurs. Drone technology is used to detect health risks such as heavy metals, aerosols, and radiation, e.g., to detect radionuclide, which is typical of nuclear accidents.

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Fig. 4 Representation of the main applications of drones in a smart city

4.3 Smart Cities The definition of smart city has evolved over time with the rapid evolution of technologies, digitalization processes, and the changing needs of cities. From the European Union’s definition [19], we remind: A smart city is a place where traditional networks and services are made more efficient by using digital solutions for the benefit of its inhabitants and businesses. An intelligent city goes beyond the use of digital technologies for better resource use and lower emissions. It means smarter urban transport networks, improved water supply and waste disposal facilities, and more efficient ways of lighting and heating buildings. It also means a more interactive and responsive city administration, safer public spaces, and better meeting the needs of an aging population.

Basically, a smart city is a city that manages resources intelligently and aims to become economically sustainable and energy self-sufficient, attentive to the quality of life, and the needs of its citizens. It is a territorial space that knows how to keep up with innovations and the digital revolution but is also sustainable and attractive [20]. In this section, the use of drones has largely been exploited into several applications. As shown in Fig. 4, drones can be cooperative and collaborative in this scenario.

4.3.1

Disaster Management and Rescue Operation

One of the main drone applications in the context of smart cities is the disaster management. The fronts on which research has worked and is actually working imply the ability to predict disasters through continuous environmental monitoring and alert in a fast and efficient way. Immediately after a disaster, the drones are the

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only devices able to enter the harsh environment to assess the damage caused to the infrastructure and through video surveillance determine the presence of people in danger. Very often, in case of natural and man-made disasters, communication could be damaged or completely demolished, so another goal of these devices is to restore the Internet connection, replace the cellular Base Station (BS), and forward messages to the competent authorities, via a multi-hop devices-to-device (D2D) propagation mode, thus improving and extending the radio coverage. Drones could be a real and valuable help for firefighters, police, and ambulance, allowing them to intervene quickly where needed. During the recovery phase, the main objective is to improve the reconstruction process in an intelligent way, filling the shortcomings that caused the disaster. In the event of natural disasters such as earthquakes, floods, fires, and avalanches, UAVs are equipped with sensors and cameras and can fly over the affected region to record data to assess damage. Through the use of GPS sensor, the UAVs can also detect the location of the victims by leading the rescue teams to that precise location. An example of rescue operations of UAVs is represented by the monitoring of the dammed reactors, the assessment of vegetation health, and the possibility of rebuilding the infrastructure, such as at Fukushima Daiichi after the earthquake in 2015 [18, 21, 22]. Disaster management is also related to the weather monitoring and the use of drones for meteorology. The main objective is to replace the current aerial and ground detection techniques with these aircrafts, thus achieving considerable economic and time savings. In fact, current weather forecasting systems are very expensive and provide data only for a limited area as they cannot be easily moved. In addition, in the case of extreme weather conditions, the data provided are not particularly accurate. It should also be observed that each piece of equipment is usually capable of performing a single function, so it is necessary to invest in different tools that can complete the picture of the required data. The use of drones for meteorological surveys and forecasts could be the answer to these problems as drones can easily and quickly reach different areas and thanks to sensors and modules that can be mounted on only one aircraft is possible to perform different tasks such as analyzing and measuring temperature data, humidity, wind, and air pressure. In this way, the drones are able to predict even extreme weather conditions by implementing early warning plans. The use of drones for meteorological surveys is mainly found in areas where extreme weather conditions occur and in which accurate and timely predictions play a fundamental role. UAVs are also able to detect gases concentrations, temperature, humidity, and locations of pollution sources through electrochemical sensors (set with a fixed threshold) and cameras [23, 24].

4.3.2

Public Safety and Traffic Monitoring

Recently, criminality and terrorism have increased, and so in public places such as squares and stadiums or during parade, it is important to protect citizens from possible threats. For this reason, it is necessary that the detection of criminals and terrorists is rapid and correct. The use of UAVs has results very effective for this

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purpose and can help the authorities monitoring and ensuring security through realtime video facial recognition methods. In the long term, drones could be used for local, day or night patrols, not only on patrol level, but also for demonstrations and events. Drones could be very useful in case of inspections or accidents. As an instance, in the event of an accident, they can allow better verification by viewing the scene from above, or by recording events from the moment the police arrive. They can also help in finding any means or people involved, but not easily spotted by staff on the ground [25]. Other uses are being studied in support of law enforcement, where drones allow the operation centers to monitor the scene in which the men are engaged and decide whether reinforcements are needed. Surely drones can help a technical office to make structural inspections of public and other buildings, pilings, infrastructure, such as bridges. The same concept works in the utilities sector, one of the most widespread sectors of drones to support the maintenance and construction teams of new gas, light, fiber excavations, or other. Even where permits need to be issued, for example, for excavation work, a smart office could use a drone (as it now uses Google Maps) to check the status of a road, without moving from the desk and carefully considering the context and detail of a request. Also, the use of drones in harsh environments where the human intervention can be risky represents a solution. Drones can be very useful, for example, to quickly understand the front of movement of a fire if it is a compact front or several outbreaks and then give directions to planes and ground teams engaged in extinguishing [25, 26]. In addition, the drone can also be used to enter buildings at risk of collapse, to verify the structure, or where there has been an earthquake, for an internal survey of homes, without putting human lives at risk. Last but not least, drones can be used in case of floods or water bombs, to check the effect of rising rivers, follow a flood, understand which basins open to absorb the flood itself, and check the status of the embankments. In most of the situations described above, the drone can be used as a sentry. Let us imagine that there is a flood and there are banks to control; the drone can continuously act as a sentinel following the same path back and forth on a bank and sending images that are processed by artificial intelligence and recognize a possible leak with timely intervention and evacuation of people living nearby [27]. One of the main problems facing modern cities is traffic congestion. Traffic creates bottlenecks that paralyze urban streets, contributing to the production of smog and noise pollution. Drones can easily fly over the road network and signal the presence of traffic to drivers, indicating alternative routes and facilitating the lightening of vehicle flow. The advantage is that this is a continuous service, available indoors or outdoors anytime. Thanks to a GPS module, drones are able to detect the place of the accidents and forward the exact locations to the emergency services. Furthermore, they are used to give an alternative route and free the roads. They are also adopted to track speed velocity in highways and report who brake the limits imposed by laws. So, the real-time road traffic monitoring objectives can be summarized in the following points [28]:

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• • • • • • •

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Reporting accident agents Flying police eyes Pedestrians monitoring Free ways monitoring Coordination of traffic signals Emergency vehicle guidance Parking lots managing

Another recent application of drones for public safety is the one related to the control and monitoring of pandemics. A network of drones can be used for monitoring of Standard Operating Procedure (SOP) violations and performing an action, such as producing alarm sound, and sends the information to the interested authorities. In order to achieve this goal, wearable sensors are adopted to capture the movement of persons in conjunction with drones, as well as temperature and respiratory rates. If a sensor detects high temperatures or an abnormal respiratory rate, the sensor warns the drone that spreads the message. After the report, a medicine drone can be involved to support the infected person. The contact tracing algorithm can be done also exploiting both a network of drones and an Online Social Network (OSN) as implemented in [29, 30].

5 Agriculture Applications One of the areas where drones are being noticed is agriculture. Here, in fact, drones hardly facilitate activities and save both time and cost, thanks to the use of sophisticated monitoring tools. Recently, the agricultural sector has been forced to face several threats such as climate change, the decrease of the arable land, and the exponential increase in population. A possible solution was provided by the rapid development of UAV technology, adopted for Precision Agriculture (PA) and smart farming applications.

5.1 Precision Agriculture The task of PA is combining Information and Communications Technology (ICT) services to obtain useful and various information about the soil, the humidity level of the crops, on the quantity of fertilizers and pesticides used, weather forecasting, and in general all those activities related to agriculture. Once collected, this information must be analyzed to produce agronomic recommendations. Through the use of these technologies, the PA reduces greenhouse gas emissions, prevents water pollution, and helps the prevention of soil degradation [8, 31]. UAV applications in PA are divided into three main categories [32]:

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• UAV-based monitoring applications: In addition to the monitoring of crops, it deals with image processing activity to detect soil problems, such as diseases and pests harmful to human health and the environment. • UAV-based spraying system: It is used to spray the proper amount of fertilizers and pesticides, but also utilized for irrigation purposes. • Multi-UAV applications: UAV swarms are employed in a cooperative manner to accomplish PA tasks. Let us first examine the monitoring applications, and among them, we focus on the analysis of the field soil. The soil analysis performed by UAV allows to extract data that can be used for irrigation, as well as information about the level of nitrogen in the soil, adequate fertilizer dosages, etc. Through this continuous control, the soil will have the right amount of nutrients that will allow a healthy growth of cultivation. In [33], it is proposed a method to estimate soil salinity and chemical properties over a large agricultural farm, covered by different crops and harvest areas at the coastal saline–alkali land of the Yellow River Delta of China. Another important aspect is the health of the cultures that can affect bacterial or fungal infections, and the identification of their presence is useful to intervene immediately. Using UAVs helps simplify the crop scanning process by taking agronomic measures to ensure safe production. In [34], several research projects about health and soil assessment of crops are presented. The monitoring of crops is another interesting PA application. The soil condition control is a very difficult process, especially in adverse weather conditions. Before the use of UAVs, satellite photography was adopted, but the quality of the images provided was very low. With the use of drones, the photographs can be taken at very close distances to the ground, and by implementing a suitable camera, they can be the result of excellent quality. An example is shown in [35], where the use of appropriate cameras leads to an accurate surveying process and a good normalized difference vegetation index (NDVI) map of the considered agriculture land. A consequence of crop monitoring and environmental health assessment is the use of drones to spray the required amount of fertilizer and pesticides, adjusting soil requirements, the jet height, and the mode. These operations for farmers are very complicated to carry out manually and often do not have enough information to know the real state of the land, then resorting to an excessive use of products. By calculating the right amount goods, drones can strongly reduce the doses of pesticides. In California, a pesticide spraying system was developed and used on wine grapes located at the University of California Oakville Field Station [36]. Another application considers the use of drones for irrigation purpose. At the global scale, the .70% of water extracted is used for irrigation of crops. The utilization of UAVs allows to find methods that guarantee the minimum consumption of water and energy. To achieve this goal, UAVs can be exploited to fly along an optimized path that allows the UAVs to irrigate the soil in an efficient way. The optimization process has been realized through a reformulation of the travelling salesman problem (TSP). Along the way, the drones changed speed and direction to ensure the minimal amounts of water and energy [37].

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5.1.1

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Smart Farming

The concept of PA is complemented by smart farming, which indicates a highly efficient and optimized management of agricultural activities. In more details, this means opening up to new technologies and innovation to make agricultural work more efficient, while drastically reducing errors of assessment, waste of water, and economic resources. One of the major applications for drones in this field is the animal well-being. Either in farms or grazing is essential to manage livestock. In fact, this task has been simplified and modernized with the use of UAVs. In this way, farmers and breeders can have a complete view of the animals even from far away. One of the major applications is the detecting, monitoring, and counting of the livestock. By virtue of the video recording and the sensors mounted on the UAV, it is possible to keep tracks of the cattle. In general, the aim is to realize not only “smart farms,” but also every field in which animals are included. The steps that the technology is taking to improve such contexts are reported as follows [38–40]: • • • •

To guarantee to the farmers the safeguard herds from potential threats. To detect, localize, and count animals. To track livestock while grazing. To monitor livestock health through the monitoring of their behavior. This helps to find diseased animals.

In addition, they are also useful to protect wild animals from poaching; in fact, with surveillance applications, they are also used to design protocols for those who violate the rules during hunting. UAVs can also be used to monitor the hunters in areas where hunting is not allowed. Furthermore, it is essential for farmers to always know where the animals are, and this is for the reason that they count the animals repeatedly. UAVs can help in this process. As an instance, in areas where the fauna can be dangerous to humans, these devices help to detect these creatures and possibly reduce the number of them. For what concerns wild animals, their protection is essential, drones allow not only to detect their location but also give the possibility of feeding them. They are also involved in bio-technical and reproductive activities. Thanks to sensors, UAVs can detect if the habitat in which the animal is located is polluted or destroyed.

6 Machine Learning Tools for UAV Applications The 5G cellular technology is a reality of the communication systems. It is designed to increase speed, reduce latency, and provide low power consumption, as well as improve the flexibility of wireless services. However, its biggest drawback is the low range of coverage, since a typical 5G base station can reach a few hundred meters of coverage. Another aspect to take into account is the fact that commercial network operators build their base stations only in specific areas of interest, contributing to

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this shortcomings. So, to move the communication on a worldwide perspective, the main idea is to realize a large number of 5G micro-base stations. The usage of UAV technology is perfectly suitable for this aim, since it is very high speed, flexible, and affordable. As described in [41], the design of a 5G-based UAV network is characterized by the ground control system, the UAV network, and the user terminal. The ground control system comprehends UAV flight control systems, 5G macro-base stations, and sub-systems. For the UAV network, it is possible to distinguish two networking modes, i.e., (i) star and (ii) mesh network. In the first configuration, the ground control station (GCS) interacts and communicates with each UAV. The drones are independent platforms, and so every message must go through the ground GCS before reaching another drone. This architecture is suitable for small areas, due to the delay encountered by the message delivering and the centralized topology, which can be easily compromised by failures of the GCS. On the other side, the mesh networking mode is safer, because of the presence of an UAV platform where drones are connected to each other, and the GCS can send messages directly to the platform. The communication results as faster and is able to reach longer distances [41]. In order to enhance the performances of UAVs operations, during their flight, it is useful to optimize some variables such as the UAV energy consumption, the altitude, autonomy and speed, as well as the UAV path. Several machine learning (ML) algorithms are applied as a support for the estimation of such parameters. In delivering applications, the low latency is an essential characteristic of the drone, generally as to be around 1 ms or even less especially in disaster management and rescue operations. In order to perform low latency, it is possible to use artificial intelligence techniques to estimate the channel model by integrating a long-shortterm-memory (LSTM) unit in the UAV. Moreover, the optimization of the UAVs’ paths and the guarantee of a stable wireless connection should be addressed by means of ML approaches in order to reduce the total delivery time. In [42], a swarm of UAVs can map a selected area by means of a genetic algorithm (GA). In [43], the authors introduce a ML-based energy-efficient UAV placement approach for UAVto-ground communications. It is capable of maximizing the coverage area of an UAV with the minimum power. A similar approach is presented in [44], where the role of UAVs is as a BS, able to calculate and design in real time a 3D wireless channel for dynamic outdoor environments and estimate the users’ communication link quality. This scheme uses an unsupervised learning technology that predicts the parameters of the 3D wireless channel. After initially collecting the received signal strength (RSS) and signal-to-noise ratio (SNR) parameters of the users in the network, the algorithm estimates the channel model for both LoS and NLoS propagation modes. These data represent the input data training set, based on which it is possible to predict the optimal communication range and the RSS of other users in the same scenario. The use of ML techniques is expanding also in the field of security issues. In [45], the evaluation of the security risk is predicted through a ML-based attack, which models a passive attacker that can intercept the transmission from an UAV to the ground. The UAV operation time and the number of UAVs play a fundamental

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role in the security of the UAV-to-ground communication process. By observing the communication link, the attacker can realize a scheme of cryptanalysis, i.e., the Known Plaintext Attack (KPA). With this method, the attacker can interrupt the encrypting process using a neural network (NN) and store enough pairs of plaintext, i.e., a part of the original location data, and the corresponding transmitted data called ciphertext encrypted with the same key, in order to decrypt all the data it has. Another ML-based security technique is presented in [46] and can be applied to UAV-based delivery systems, real-time multimedia streaming, and UAV-enabled intelligent transportation systems. The proposed approach relies on a CNN that detects an area with a high level of risk, by exploiting the images of the environments in which the UAV is placed. Another important aspect is to recognize strange situations or a possible attacker. Similarly, in [47], the use of different deep learning algorithms for UAV-to-ground communications is investigated, in particular for vehicle detection from UAV images. As a first solution, the use of CNNs allows to create vehicle-shaped windows from different images. Furthermore, many aspects have been taken into account such as the altitude of the UAV, same as the weather conditions, view angle, and illumination. A convolutional support vector machine (SVM) system is also presented to capture images during the UAV flight.

7 Blockchain Blockchain-based solutions are incredibly popular nowadays and represent the basic technology for cryptocurrencies. Blockchain technology greatly impacts the applications in UAVs, mainly for its characteristics of high distribution, secure data exchange, and storage. The use of blockchain for UAV communications is mainly due to prevent attack by maliciously UAVs, as well as to store data on the central control server and to forbid unauthorized access and illegal intrusions. Several works have addressed the use of Blockchain for UAV applications. In [48], a distributed UAV scheme exploits the blockchain technology to secure communications during data collection and transmission, as well as in [49] an energy-intensive blockchain-based platform aims to control drone operations while ensuring trust and security. Specifically, Ethereum Blockchain [49] has been implemented to create a blockchain network to mitigate the spoofing attacks. When an intruder gets acquired with the data in the network with a single block, it cannot affect the entire network due to the data integrity in the ledgers that has been cryptographically assigned. The blockchain network intermittently verifies the geolocation data so that any outlying data would be detected and eliminated quickly. Blockchain frameworks, such as Hyperledger Fabric [50], are applied to a swarm of UAVs to increase the security level. Similarly, in [51], a blockchain framework is used in UAV swarm for coordination and financial exchange between system users, as well as in [52] where a proposed architecture has shown effectiveness in prolonging the operating time of a drone, and a high level of transparency, traceability, and security. In [53], a new UAV swarm communication network

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architecture based on consortium blockchain is presented, showing high security requirements of the UAV swarm communication network. Similarly, in [54], each UAV node acts as a blockchain node and has onboard functionality of performing transactions interactions and consensus mechanisms via wireless ad hoc channels. In [55], a blockchain-based architecture and a user authentication mechanism are designed for the purpose of identifying and keeping track of unauthorized UAVs, also relying on a consensus model for UAV communication. AerialBlocks [56] is a unique paradigm for blockchain-enabled UAV virtualization. It provides virtual UAV-as-a-service for industrial applications and guarantees secure and persistent UAV services to the end users along with a partially decentralized blockchain model to ensure security, privacy, service quality, and transparency.

8 Conclusions and Future Directions The employment of UAVs in different aspects of life is largely discussed among the research community. In this chapter, we have provided a comprehensive overview of the different applications of IoD. We analyzed the mechanical aspects of such devices, their classification, and the uses in the military, civil, and agriculture field. Although the previous underlined utilities, UAVs are still investigated in order to exploit at the maximum level their abilities. In fact, the currently open issues are regarding the adoption of drones in the sixth generation (6G) network. In particular, a key architectural and operational element for 6G communications is the integration of UAVs, which can act as relevant “player” for heterogeneous overlapping network layers [57, 58]. UAVs can be envisioned to support a 3D connectivity by extending connectivity links both upward and downward, to space (i.e., satellites, high-altitude platforms) and ground (i.e., cellular and wireless ad hoc systems) networks, respectively. As a result, the overall 6G system is envisioned as an integrated space–air–ground communication network [59].

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Mobility, Traffic Models, and Network Management for Internet of Unmanned Things by Using Artificial Intelligence Arunima Sharma

1 Introduction A drone, also referred to as an unmanned aerial vehicle (UAV), is an aircraft that is flown remotely from the ground or by an onboard computer. Rapid deployment of UAVs has increased interest in both military and civilian Internet of Things (IoT) uses known as Internet of Unmanned Things [1]. Applications in the military include strike, reconnaissance, and border monitoring. However, the focus of this chapter is largely on mobility, traffic models and network management for Internet of Unmanned Things (IoUT) applications, such as drone light shows, delivery systems, inspection of civil infrastructure, search and rescue (SAR) activities, and precision agriculture. Artificial intelligence (AI), edge computing, and edge AI are three technologies that can be used to improve the effectiveness of such applications and Internet of Unmanned Things or IoUTs in general. AI makes computers smarter, and robots have recently used AI to carry out a variety of intellectual activities. AI has largely altered only few industries, and the aviation sector is no exception. AI can make use of the massive volumes of data that IoUT systems generate to enable more efficient, accurate, and reliable IoUTs. The ability of AI to handle large amounts of data and its quick, accurate processing are especially important for a variety of IoUT technical applications. Edge computing and IoUT systems inter-operate in such a way that either IoUTs (equipped with edge servers) can act as users themselves and offload duties to edge servers or IoUTs (provided with edge servers) can provide edge computing services for ground user equipment.

A. Sharma () Department of Computer Science and Engineering, Malaviya National Institute of Technology, Jaipur, India © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 C. A. Kerrache et al. (eds.), Internet of Unmanned Things (IoUT) and Mission-based Networking, Internet of Things, https://doi.org/10.1007/978-3-031-33494-8_2

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Fig. 1 Edge, cloud, and fog computing platforms [2]

In the latter case, tasks are sent between IoUTs and a distant centralized server according to the conventional cloud computing concept. Contrarily, edge computing makes these compute services accessible to end users (UAVs) at the network’s edge, preventing the requirement for data to travel far distances to distant centralized servers. Onboard or at neighbouring edge servers can both provide these compute functions. Figure 1 shows edge, cloud, and fog computing platforms.

2 Debates in IoUT Systems The primary technological issues with IoUT systems can be divided into six broad categories: autonomous navigation, formation control, power management, security and privacy, computer vision, and communication are the first three [1].

2.1 Unmanned or Autonomous Navigation All the way up to the navigation of fully autonomous vehicles that can travel from point A to point B without any human interaction (such as drones for package delivery), autonomous navigation can refer to the navigation of a vehicle that a human controls remotely but has some basic onboard algorithms that take over and keep it from crashing. Depending on the application, a vehicle uses localization

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and mapping, path planning, and/or collision avoidance to navigate on its own. For instance, collision avoidance is the only algorithm used when an IoUT is operated remotely and only a few basic onboard algorithms are used. In contrast, localization and mapping, path planning, and collision avoidance are required in the fully autonomous scenario.

2.1.1

Mapping and Localization

Building a map (2-D or 3-D) of a certain area is referred to as mapping in the context of robots, and establishing a robot’s position and orientation in relation to a frame of reference is referred to as localization. Any robot can find localization a difficult process, but aerial robots such as IoUTs find it especially challenging because of the environment’s 3-D structure. IoUTs frequently significantly rely on the global positioning system (GPS) to facilitate proper localization, fusing GPS location measurements with the onboard inertial measurement unit (IMU) measurements to produce an accurate estimate of the IoUTs’ posture (position and orientation). This works well because the GPS data make up for the IMU’s accumulated error (caused by measurement drift). However, there are numerous scenarios when GPS services are unavailable or unreliable, including indoors (factories, warehouses, etc.), during emergencies, or after disasters, next to tall trees or structures, or when near water bodies. These situations are appropriately referred to as GPS-denied environments, and navigating IoUTs with precise localization through such situations. The most popular method for resolving this problem is vision-based solutions, which can precisely position an IoUT without the use of GPS by combining measurements from vision and IMU sensors. An enhanced pose estimation is produced when the two measurements—IMU and visual sensor measurements—are merged. These vision-based techniques include visual SLAM (simulated localization and mapping) and visual odometery, which are the most well-known (VO). SLAM algorithms attempt to estimate the pose of a robot while simultaneously building a representation of the investigated zone. By examining the change that motion causes on a sequence of photos incrementally predicts the IoUT’s posture.

2.1.2

Path Planning

Determining a path for an IoUT from a starting point to a goal point is an issue in IoUT path planning [1]. Path planning approaches come in a wide variety, but they all aim to identify an ideal (or nearly ideal) path based on performance characteristics such as shortest time, shortest route, or lowest work cost. The combination of path planning and collision avoidance is essential, as one might expect. They are combined so frequently that it is usual to refer to collision avoidance as “local path planning” and path planning as “global path planning”. The concept is that whereas local path planning deals locally with changes in the

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environment as they are detected, performing the collision-avoidance manoeuvres accordingly, global path planning develops optimal paths while taking the entire environment into account. After performing the collision-avoidance manoeuvre, the global path is then attempted to return. There are numerous ways to classify IoUT path planning (also known as global route planning) techniques. Path planning strategies are categorized into representational, cooperative, and noncooperative techniques.

2.1.3

Systems that Prevent Collisions

IoUTs represent a high collision risk because they frequently fly through the air at high speeds, which increases the likelihood that they will collide with other objects or that others will collide with them. For IoUTs to avoid such collisions and guarantee a safe flight, a collision-avoidance system is essential. A collision-avoidance system was divided into two primary groups perception and action, with perception needed before action. Sensors pick up on obstructions in the environment during the perception phase, and during the action phase, a collision-avoidance strategy uses this knowledge to steer clear of a collision. Active sensors and passive sensors are two categories of perception. Sonar, LIDAR, and radar are thus classified as active sensors, whereas cameras and infrared sensors are classified as passive sensors.

2.2 Formation Management Performing IoUT operations with many IoUTs working together is frequently preferable to a single UAV attempting to complete the objective on its own. Extensive studies on formation-related topics have been conducted in recent decades, with formation control being the most actively researched topic. This has been motivated by applications where it is advantageous to use more than one UAV (e.g., large payload transportation or searching for objects/people in large areas) [1]. Formation control, which is the coordinated control of a group of robots in a “formation”, was inspired by the self-organization found in nature, such as in flocks of birds. According to the definition of formation, it is a network of agents connected by their controller specifications, each of which is required to maintain relationships with its nearby agents. The two classifications of leader–follower and leaderless provide a general breakdown of formation control.

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2.3 Power Control The short flying time and short battery life of IoUTs are well-known problems. By employing cutting-edge methods to acquire more energy, referred to as energy harvesting techniques, the IoUT can become more energy efficient as well as have longer battery life. Solar energy, wireless charging, and battery switching are typical methods of energy harvesting.

2.3.1

Solar Energy

Solar energy is a good source of power because it is affordable and environmentally friendly. When IoUTs need to fly at high altitudes for extended periods of time, solar cells are especially helpful. The stationary battery used by most solar-powered IoUTs serves as a supplementary source and is typically not significantly used during the day, but it might be crucial at night or in inclement weather. However, for commercial solar-energy-based IoUT applications, surface size, weight constraints, and reliance on light intensity are significant limiting considerations.

2.3.2

Wireless Charging

IoUTs do not currently come equipped with wireless charging as a standard feature. Having said that, a lot of research has been done recently to make IoUT wireless charging practical. Techniques that have been investigated include those based on magnetic resonance, capacitive coupling, and even wireless power transmission. Recharging via power lines is also a possibility. Additionally, studies examine wireless charging through tethered drone charging stations where the drones being charged use blockchain-aided FL.

2.3.3

Battery Swapping

An effective battery charging solution is to switch a depleted battery for a fully charged one rather than speeding up the charging process or charging while on the go. The idea of “hot swapping”, in which the IoUT is connected to an external power source while being swapped out, is frequently explored. When compared to the typical battery charging time, which frequently lasts 45 to 60 min, the swapping time for such systems is approximately 60 s, which is extremely quick.

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2.4 Privacy and Security Security and privacy are crucial concerns for all digital systems, but especially for unmanned aerial vehicles (UAVs). In contrast to other privacy-invading devices, IoUTs have a unique selection of agile access techniques, which makes them appealing to criminals. However, they are also susceptible to attacks that target wireless links, cyber elements, physical elements, and interfaces between cyber and physical elements.

2.4.1

Safety

The attack vectors of UAV systems were categorized as attacks against communication connections, IoUTs themselves, ground control stations (GCSs), and humans. Additionally, they outline three general UAV system security challenges confidentiality, availability, and integrity. Some attacks on IoUT are masquerade, denial of service, and GPS spoofing, and the attackers can be insider/outsider, malicious/rationale, active/passive, and local/extended. The data collected by sensors alter IoUT behaviour and significantly affect security. The GPS sensor is one that is frequently targeted because it is used to provide precise location data. The most typical methods of GPS assault on an IoUT involve “jamming” or “spoofing”. Jamming occurs when a third party sends out a disruptive signal to block the reception of other signals. Spoofing occurs when an unauthorized party captures satellite signals and transmits them to an unmanned aerial vehicle or when signals are generated based on real signals using certain programs. Adopting alternate navigation strategies, such as employing a visual and inertial navigation system that uses SLAM or VO, can prevent GPS jamming. The authentication of GPS signals, such as examining GPS observable that shows the signal’s travel duration, or spotting abrupt changes in signal strength or observable that may signal the beginning of a spoofing attempt, are solutions to GPS spoofing. Binocular visual sensors that may be spoof with ultrasonic waves are two more typical sensors that are vulnerable to attack.

2.4.2

Private

It is simple for IoUTs to invade privacy and challenging to catch intruding IoUTs. There are two primary ways to stop UAVs from violating people’s privacy. One way to prevent intrusive IoUTs from flying into prohibited regions is to register residential addresses in databases of no-fly zones. The second option is to employ methods or systems to locate, track, and destroy drones inside a given area. IoUTs can also be used by malicious software to gather personal data. For instance, malicious Snoopy software can be loaded on an IoUT to capture personal data and track/profile users of smart phones.

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IoUTs must be continuously improved to combat such rapidly changing harmful software.

2.5 Computer Vision The goal of computer vision is to enable a computer to comprehend an environment using visual data, whether this comes from a single image or a collection of images [1]. The automatic understanding of the visual data captured by IoUTs has attracted growing interest in recent years, and computer vision plays a key part in the majority of UAV applications, from aerial photography to SAR missions. Scene parsing is the main function of these apps from the perspective of computer vision. For various applications, including object recognition, object location, and exact object boundary determination, several layers of scene parsing are needed. Applications for IoUT computer vision include collision avoidance, object tracking, object detection, object recognition, and 3-D reconstruction. Such image processing can be carried out remotely at a server (either the edge or the central cloud) or onboard the IoUT (either the embedded central cloud or the UAV itself) (embedded).

2.5.1

Remote Computer Vision Processing

IoUTs frequently lack the computing capacity necessary to process images captured by UAV cameras, necessitating processing to be done remotely. Although computer vision can be processed at more remote centralized servers as well, an edge server is desirable from a latency viewpoint.

2.5.2

Real-Time Embedded Computer Vision Processing

This method is preferable to remote computer vision processing if the goal is to make IoUTs truly autonomous and reliable. Remote processing necessitates high bandwidth, low latency, and extremely dependable wireless links, none of which can always be guaranteed. The onboard computational capability of IoUTs is the main constraint of real-time embedded computer vision processing. Modern UAV computer vision algorithms have processing needs that frequently collide with hardware resource constraints.

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2.6 Communication Recent advancements in IoUT technology have made it possible to use IoUTs (from tiny commercial drones to small aircraft to balloons) for a variety of wireless communication applications. Mozaffari et al. [3] singled out the following IoUT communication-related applications while acknowledging the variety of roles IoUTs can play in wireless networks: as aerial BSs, as user equipment in cellular networks, as mobile relays in flying ad hoc networks, in wireless backhauling, and in smart cities. These applications call for a variety of communication channels, which can essentially be divided into two groups: air-to-ground (A2G) communications and air-to-air (A2A) communications. In addition, the air layer can be separated into an HAP (high-altitude platform) layer and an LAP (low-altitude platform) layer, allowing HAP-layer and LAP-layer IoUTs to fly at heights over 17 km and tens of metres to a few kilometres, respectively. Although less flexible than LAP-layer IoUTs, HAP-layer IoUTs offer greater coverage and longer endurance.

3 Mobility for IoUT When simulating network performance, the behaviour of mobile nodes can be mimicked using mobility models. The simulation results and the mobility model exhibit a significant correlation. As shown in Table 1, give certain mobility model metrics that affect the performance of wireless networks in this subsection. Path planning for IoUTs refers to the computation of the IoUT’s best route between a source and a destination under predetermined circumstances. The effectiveness of data collection is significantly impacted by the IoUT path planning. The shortest path, the quickest flying time, and the UAV using the least amount of energy are just a few of the objectives for path planning. High mobility and flexible deployment, two IoUT properties, present issues for path planning. In addition,

Table 1 Mobility metrics’ classification and characteristics [4] Metrics Based on chance Temporal dependencies Geographic dependencies Constraints due to location Hybrid architecture Pauses Relative speed

Characteristics Model without any dependencies or restrictions applied A node’s current mobility is influenced by its previous movement. In group mobility, a node’s movement is impacted by nodes around it. Node mobility is limited in a particular region To achieve the structure, all mobility metrics classes are combined. A mobile node is stationary for a set amount of pause time. Velocity measurement between two mobile nodes

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IoUT path planning is typically integrated with resource allocation to effectively maximize resource use, taking into account the limited energy and radio resources of IoUTs. As a result, given the size of the choice space, joint path planning and resource allocation are highly difficult.

3.1 Algorithms Based on Graph Theory The Voronoi diagram algorithm, probabilistic roadmap (PRM) algorithm, Hilbert curve algorithm, and others are graph-theory-based path planning algorithms. The geographic area in which the IoUT is flying is transformed into a graph using a graph-theory-based technique, and the path between the source and the destination is then looked for: [5] 1. Amount of Data Collected The quantity of data the UAV collected. 2. Time The amount of time it takes a UAV to finish collecting data. 3. Energy The amount of battery or fuel the UAV and sensors use to operate. 4. Age of Information This term refers to the freshness of the gathered data, which must be distinguished from latency. It is defined as the period of time between the generation of sensor data and the time the IoUT returns and uploads the data to the data centre. In more detail, the latency is the period of time between the sending and receiving of data, and the AoI is the period of time between the generation and use of data. AoCD, TE, EE, and AoI are the main performance indicators that can be taken into account while designing an algorithm for IoUT-assisted data collecting.

3.1.1

Voronoi Diagram

A Voronoi diagram [5], sometimes called a Tyson polygon or a Dirichlet diagram, is a collection of polygons made up of vertical bisectors linking two neighbouring points, where the points represent obstructions. The Voronoi diagram has the benefit of keeping impediments out of the produced path. A collision can be prevented as a result. According to the closest neighbour principle, the circular in Fig. 2 is treated as an obstruction, and the plane is divided into sections. In comparison to other obstacles, the point inside a polygon is closest to that obstacle. The IoUT’s flight path is then determined by searching the shortest path along the polygonal edges, which is indicated by the dotted line in Fig. 2.

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Fig. 2 Voronoi diagram [5]

Obstacle

Destination

Source

Trajectory

Four algorithms can be used to create a Voronoi diagram: the incremental approach, the intersect of half-planes algorithm, the divide-and-conquer algorithm, and the plane sweep algorithm. In order to optimize the least remaining energy of the sensors and prolong the network lifetime, update the Voronoi diagram, which estimates the ideal hovering location of the UAV based on the residual energy of the sensors. The overall length of the IoUT flight path is reduced when compared to the path planning of IoUT based on Voronoi diagram. However, in real-world application scenarios, it is challenging for a single IoUT to manage an excessive number of sensors [5]. The trajectories of several IoUTs are obtained, which significantly reduce the amount of time needed to complete data collection. This is combined with the optimization of the speed, collecting position, and transmit power of IoUT.

3.1.2

Probabilistic Roadmap

As depicted in Fig. 3, PRM is made up of sample locations and collision-free straight-line edges. The areas with no impediments are where the sampling spots are chosen. The learning and query phases make up the PRM-based path-finding algorithm. To create an indirect graph during the learning phase, PRM randomly distributes points over the area. The shortest path methods are used by PRM in the query step to determine the IoUT’s flying path. To arrive at a conclusion, this method requires only a small number of random sampling points. And as the number of sampling locations rises, the likelihood of discovering a path begins to approach 1. The data collecting mission is viewed as a physical orienteering issue that seeks to ensure a practical, collision-free trajectory in order to maximize the amount of data collected (reward) while adhering to energy constraints (budget).

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Destination

Source Sampling Points

Trajectory

Obstacle

Fig. 3 Probabilistic roadmap [5]

It is suggested to use the asymptotically optimal-sampling-based PRM* algorithm in conjunction with variable neighbourhood search (VNS). The proposed technique uses the PRM* algorithm to construct a condensed collision-free trajectory and the VNS algorithm to extend the original roadmap with low-density nodes. When compared to other algorithms with high-density initial roadmaps, the lowdensity initial roadmap had the advantage of having less computational complexity.

3.1.3

Hilbert Curve

The Hilbert curve, [5] a space-filling curve that maps from 1-D space to 2-D space, is crucial for image processing, multidimensional data indexing, and other applications. A space is partitioned into subspaces, as seen in Fig. 4, and the Hilbert curve can travel through each subspace. The Hilbert curve is depicted in Fig. 4 from the first order to the fourth order. An iteration of the order is the creation of the Hilbert curve. The curve always travels through the middle of subspaces and is arranged by straight lines. In order for the curve to have better granularity after iteration, each subspace is split into four subspaces during each iteration. Finally, a curve that spans the entire volume is created, offering the best trajectory for the IoUT to cover the data collection area. The method uses the sensor coordinates as one of its inputs. As seen in Fig. 5, the trajectory produced by the algorithm based on the Hilbert curve is capable of traversing all sensors-deployed cells while avoiding sensors-free cells.

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Fig. 4 Steps of the Hilbert space-filling curve. (a) First step. (b) Second step. (c) Third step. (d) Fourth step [5]

Sensor

Trajectory

Fig. 5 Trajectory of IoUT based on Hilbert curve [5]

3.2 Algorithms Based on Optimization Theories The techniques based on optimization theory, such as branch and bound (BB), dynamic programming (DP), and sequential convex approximation (SCA) [5], can discover the optimal or sub-optimal solution in the problem of joint path planning and resource allocation of IoUT for data gathering. However, the temporal complexity is exponential and intolerable for some NP-hard problems. Heuristic

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approaches can be used to develop a workable answer when the choice space is large.

3.2.1

Dynamic Programming

A multistage decision-making challenge is reduced by DP to a number of singlestage problems. Recursive methods can be used to find the best route. The joint path planning and resource allocation DP-based methods are outlined below in accordance with the various types of objective functions. By significantly reducing IoUT flight time, IoUT energy consumption may be significantly decreased. The IoUT’s trajectory is separated into non-overlapping periods for data gathering, and the sensors are deployed in a straight line. To reduce IoUT flying time, the data collecting interval is adjusted using DP. The ideal IoUT speed and sensor transmit power are also discovered. Furthermore, this utilized DP to enhance interval partitioning. Considering the average AoI, using the AoI as an objective function is preferable to using the IoUT’s flying time as an optimization objective. Timely data collection becomes extremely important, especially when the sensor buffer is small or when data are frequently covered. By taking into account both the data collection mode and the sensor access sequence discovered the optimal path for UAV. To ascertain the best sensor access order and reduce the average AoI, a DP-based approach is suggested. The challenge of minimizing the average AoI of gathered data was divided into two parts: the problem of IoUT trajectory optimization and the problem of time allocation and energy transfer. In contrast to prior models that took into account the fact that IoUTs have limited energy and must be fuelled by BSs, IoUTs serve as mobile power sources that simultaneously gather sensor data and provide power to the sensors. However, when there are a lot of sensors, the DP algorithms become complicated and ineffective. In order to determine the best trade-off between the UAV flight time and the sensor data transmission time, sensor association and path planning are jointly optimized with the goal of solving the problem of data collection with optimal AoI. It is established that the shortest Hamiltonian path is the one that maximizes AoI. Maximum AoI can be significantly decreased when the quantity of data collecting points, the IoUT’s flight path, and the scheduling of sensors are all concurrently optimized. The issue of IoUT-assisted data gathering based employ DP to determine the best trajectories to reduce the maximum AoI or average AoI based on the hover point and sensor access sequence of the IoUT.

3.2.2

Branch and Bound

BB is an iterative search technique. The supplied challenge is progressively broken down into smaller problems using BB. Branching is the method of sub-problem

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decomposition. The bound is the boundary of the objective function of the subproblem. A high-complexity branch, reduce, and bound (BRB) algorithm is developed to discover the global optimal solution in relatively small-scale IoT in order to gather data from as many sensors as possible and guarantee a minimum amount of uploaded data per sensor. A collection of N non-overlapping hyper-rectangles that address the optimization problem of increasing the number of served sensors are kept in the BRB method. All of the optimization model’s possible solutions are contained in the hyperrectangle. Additionally, three operations—branching, reduction, and bounding—are carried out for each iteration in the BRB to enhance the lower and upper bounds of the goal function until their difference is less than a predetermined amount. As a result, the upper and lower boundaries are updated by continuously reducing the hyper-rectangle.

3.2.3

Successive Convex Approximation (SCA)

This technique turns a non-convex optimization problem into a sequence of effectively solvable convex optimization problems. The principle of SCA is comparable to majorize–minimize or minorize–maximize (MM), which can iteratively solve a number of convex optimization problems such as the original problem. This is seen in Fig. 6. The solution is roughly viewed as the solution to the original problem when the final convergence condition is satisfied. In order for MM to work, the approximation function .UMM (xt ) must be one possible solution to the objective function and be the upper bound of the original function at the approximation point. Also necessary for SCA is the convexity of the approximation function .USCA (xt ). The SCA-based combined path planning and resource allocation techniques are then compiled in accordance with the objective functions. Time and energy consumption must be taken into account when designing the IoUT’s course. A mobility model for fixed-wing IoUTs consists of a straight-line segment and a circular segment. On the basis of meeting the throughput criteria,

a

f (x)

b f (x) UMM (xt)

USCA (xt)

UMM (xt+1)

USCA (xt+1) xt

xt+1

xt xt+1

Fig. 6 Comparison of (a) SCA and (b) MM for solving optimization problems [5]

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they also employ the route discretization approach and SCA to optimize the straight circular trajectory and reduce the propulsion energy consumption of IoUTs. In order to reduce the maximum energy consumption of the UAV and sensors, they jointly optimize the communication scheduling of the sensors, the transmit power allocation, and the trajectory of the IoUT. By using alternating optimization and SCA, an effective sub-optimal solution to the non-convex issue is suggested. In order to reduce the weighted energy consumption of the IoUT and sensors, they optimize the IoUT’s trajectory, data collecting time, and wake-up schedule. To find the local optimal solution, SCA and block coordinate descent (BCD) approaches are also used. IoUT optimized the maximum task completion time and made sure that all sensors had enough power to upload data. The original problem is converted into a discrete equivalent problem with a finite set of optimization variables by leveraging the bisection method and temporal discretization methodology. And by employing SCA, a Karush–Kuhn–Tucker (KKT) solution is produced. TDMA and frequencydivision multiple access (FDMA), two orthogonal multiple access techniques, are devised and contrasted. A binary search technique based on SCA was employed to reduce the amount of time needed to complete data collecting. It has been established that the FDMA scheme’s completion time is less than the TDMA scheme’s. An effective iterative technique to enhance 3-D path planning and time allocation for IoUTs. Epigraph equivalent representation, variable substitution, and equivalent representation using SCA are all combined in the procedure. Additionally, the issue of data gathering with limited energy is efficiently resolved by the combination of wireless power delivery and data collection. Under the specified beginning trajectory, the associated transmission scheduling and power allocation for sensors are created while taking into account the IoUT’s velocity constraint. A two-tier IoUT communication system with the aim of maximizing the average throughput between IoUT and sensors: In the first tier of the strategy, sensors use a multichannel ALOHA-based random access mechanism to transmit data to their CHs. In the second tier, CHs use coordinated TDMA to send the aggregated data to the UAV. The trajectory and resource allocation of the IoUT are jointly optimized via a low-complexity iterative technique based on SCA. By integrated transmit power and communication scheduling of the sensor or IoUT to maximize the system’s overall throughput in the context of IoUT mobility and transmit power limits. Additionally, they employ SCA to find the sub-optimal solution that maximizes throughput and the global ideal solution. Adaptive resonant beam charging system was invented to maximize power transmission efficiency. By jointly adjusting the trajectory of the IoUT and the power of the charging station, the Quality of Service (QoS) is ensured and the efficiency of power transfer is optimized. The issues of trajectory design and power control are specifically addressed using the SCA and Dinkelbach methods, respectively.

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Approach to Finishing the Matrix

In order to estimate the unknown matrix members and recover the full matrix, matrix completion is used. These data matrices typically have some missing data and a low rank. The goal of the low-rank matrix completion issue is to recover the matrix using the low-rank attribute of the matrix after predicting the missing data using the observed data. The matrix completion approach can be used to choose the IoUT data sampling points and recover the missing data based on the aforementioned features. Even if the IoUT only captures a little quantity of data, it may still retrieve the data for the full monitoring region by examining the data correlation because of the high correlation between the collected data. The matrix completion approach is used to direct the IoUT in choosing data gathering spots from a time and space perspective. A position/time matrix can be created using the data gathered at various locations and times. The location matrix as an example dynamically modified the probability of the selected sampling points based on the number of selected sampling points in the row of the sampling points in the location matrix, successfully reducing data redundancy. The IoUT’s trajectory is reduced using ant colony optimization (ACO), which is based on the outcomes of the matrix completion approach. The sampling spots are gradually chosen by the IoUT in accordance with the path selection probability until their number reaches a particular value.

3.3 Algorithms Based on Artificial Intelligence When compared to conventional algorithms, AI-based algorithms are better able to manage the uncertainty of path planning and arrive at a solution that is close to being ideal. Machine learning algorithms and intelligent optimization algorithms are two categories for AI-based path planning systems. Figure 7 displays the categorization in detail.

3.3.1

Supervised Learning

An algorithm for machine learning is capable of learning things on its own. Machine learning algorithms can be categorized as semi-supervised learning, unsupervised learning, supervised learning, ensemble learning, reinforcement learning (RL), and deep learning depending on the learning methods used (DL). Among these, supervised learning and RL are frequently applied to the planning of UAV paths:

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Artificial intelligence based algorithms

Machine learning algorithms

Supervised learning algorithms

Intelligent optimization algorithms

Reinforcement learning algorithms

Classification algorithms

Q-learning algorithms

Regression algorithms

Deep reinforcement learning algorithms

Individual behavior based algorithms

Simulated annealing algorithm

Swarm intelligence based algorithms

Genetic algorithm

Differential evolution algorithm

Ant colony optimization

Shuffled frog leaping algorithm

Particle swarm optimization

Cuckoo search algorithm

Fig. 7 AI-based algorithms for joint path planning and resource allocation [5]

• Classification algorithms These algorithms establish a classification model using labelled training data and then use this model to categorize new data. The travelling salesman problem (TSP) with the neighbourhood is used to model the path planning problem (TSPN). Additionally, the segment-based trajectory optimization algorithm (STOA) and the group-based trajectory optimization algorithm (GTOA) are two proposed methods for designing trajectories. The STOA determines the sensor and data collection point visit order, while GTOA groups all sensors into clusters and determines the locations and order of visits based on those clusters. Only the cross-area of the shared transmission zone of the grouped sensors must be traversed by the UAV. The sensor with the greatest number of neighbour sensors is chosen as CH in specific. The short IoUT flight time makes data collection difficult, aiming to jointly optimize the trajectory of the IoUT, data transmission scheduling of the sensors, and transmit power in order to reduce the overall energy consumption of sensors while ensuring data collection. Non-orthogonal multiple access, or NOMA, was creatively applied to IoUT data collecting. Additionally, the scheduling of sensors and transmit power are separated using the generalized benders decomposition (GBD). • Regression algorithms Regression algorithms use the sample data’s properties and forecast the continuous goal values that match to the fresh data. The measurement stream approach is to change the conventional RL issue into a supervisory learning problem. According to the power of the sensors and VoI, the CHs and data forwarding rules are based on the division of all sensors into clusters. The UAV then gathers the CHs’ aggregated data. In order for the IoUT to handle multiobjective tasks, maximize the VoI of acquired data, and ensure IoUT charge, the direct future prediction (DFP) model is utilized.

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Reinforcement Learning Algorithms

RL uses the environment’s interaction to investigate the relationship between the ideal state and the action, eventually arriving at an ideal course of action and maximizing cumulative profit. In general, supervised learning training sets are not dependent on one another. RL, on the other hand, addresses sequential decision-making issues that are dependent on one another in the sequencing. RL is typically used in IoUT path planning and obstacle avoidance: • Algorithms for Q-learning A model-free RL algorithm that uses the time-series difference technique and can do off-policy learning is called Q-learning. The value function is updated, and new actions are chosen using Q-learning. To find the IoUT’s ideal trajectory and maximize the total amount of data collected. Two trajectory optimization techniques based on state–action–reward– state–action and Q-learning are suggested to enable the optimization of the IoUT’s fly route without knowledge of the network architecture. Use of an unmanned aerial vehicle (UAV) with microwave power transfer (MPT) capability and proposed a double-Q-learning scheduling algorithm that jointly optimizes the schedule of MPT and data collection without knowing the battery level or the length of the sensor’s data queue in advance. This technique successfully reduces the sensor packet loss over an extended period of time. In order to find a minimum–maximum AoI optimal path, an RL approach is used in response to the need for time-sensitive data collection. For the first time, they optimize the IoUT’s flying path by combining AoI, data deadline limitations, and Q-learning. The IoUT’s flight time and the flight sequence over the sensors have a direct impact on the data loss rate. The outcomes demonstrate the low time and data loss rate of the optimization strategy based on RL. • UAV Collision Avoidance and Minimization of Energy Consumption The collision-avoidance algorithm based on RL to produce an ideal trajectory for the IoUT in order to deal with the potential collision of numerous IoUTs during data gathering. An RL algorithm is used to optimize the real-time field data size collected by the onboard camera, transmission time between UAV and aerial BS, and flight path of IoUT in order to minimize the total energy consumption of IoUT during data collection. This was done in consideration of the influence of limited energy and communication resources on flight endurance and data collection of IoUT.

3.3.3

Algorithms for Deep Reinforcement Learning

DL has strong perception, but it lacks the capacity for decision-making. Although RL is capable of making decisions, it is not perceptually capable. The deep RL

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(DRL) combination of them, which can complement one another, can choose the course of action to optimize revenue. The most innovative method of IoUT-assisted data collecting also involves using DPL to tackle the joint optimization problem: • Minimization of AoI A typical technique for simplification is to model the DPL problem as an MDP. To choose the best course of action and successfully deal with the tragedy of dimensionality, use a DRL technique known as the deep Q-network (DQN). The task of gathering data is treated as an MDP with a final state and action space. Additionally, by maximizing the IoUT’s flight route, the weighted sum of AoI, packet loss rate, and energy consumption is reduced. According to the simulation results, the average AoI will drop as the IoUT’s flight time, transmit power, or packet size are increased. The UAV-assisted vehicular network is where IoUTs are utilized to gather and analyse sensor data. To minimize the expected weighted total of AoI, create the deep deterministic strategy gradient (DDPG) to process the trajectory and scheduling policy of IoUTs. Carefully examine the data collection points, flying speed, and bandwidth allocation of IoUTs in order to reduce the weighted total of AoI and the number of sensors as well as energy consumption of IoUT. • UAV Collision Avoidance and Minimization of Data Transmission Failure Rate A DRL model called “j-PPO+ConvNTM” and transformed the problem of IoUT collision avoidance into a partially observable MDP in order to avoid potential collisions among IoUTs when collecting data from IoT. For all IoUTs, the model is capable of making discrete (data collecting and charging) and continuous (path planning) decisions. A path planning technique called deep-RL-based trajectory planning in order to reduce the probability of sensor buffer overflow and transmission failure caused by pathloss of the air–ground channel. In order to reduce data loss, a DRL algorithm is used in the path planning of the IoUT while taking into account the battery levels and buffer duration’s of the sensors, the position of the IoUT, and channel conditions. • Maximization of the Amount of Collected Data Minimization of the flight route and maximization of the AoCD as the optimization objectives due to the limited energy and flying time of IoUT. The UAV can independently select what to do next at each position using deep Q-learning and duelling DQL, which allows it to create a 3-D trajectory that balances throughput, data gathering time, and flight path length. A UAV that uses full-duplex mode hovers while collecting data and charging the sensors within its coverage. A multiobjective optimization issue is put forth that includes maximizing the cumulative data rate, the total energy captured by sensors, and IoUT energy consumption. Through the use of a DRL algorithm based on DDPG, the IoUT does online path planning.

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• Data collection time minimization Data collection time minimization was achieved using a DDPG and Q-learning combination approach. In addition, DDPG is utilized to implement autonomous IoUT navigation in an environment containing obstacles, and Q-learning is used to plan the IoUT’s task arrangement to expedite data collecting. Minimization of Energy Consumption A freshness function based on AoI and a DRL method that can resolve the continuous online decision-making problem involving numerous IoUTs in order to reduce the energy consumption of IoUTs. The method can significantly lower AoI and energy usage in the continuously changing environment. Algorithms for intelligent optimization local and random searches are combined in intelligent optimization techniques. In order to effectively identify the roughly optimal solution, learning strategies are applied in the process of an intelligent optimization algorithm. Then, the superiority of the answer is enhanced using a thorough searching technique.

3.3.4

Intelligent Optimization Algorithms

These algorithms combine local search with random search to produce intelligent optimization results. Optimization algorithms and learning techniques are used to gather the data necessary to accurately identify the roughly optimal solution. Then, the superiority of the answer is enhanced using a thorough searching technique: • Individual algorithms based on behaviour individual Behaviour-based algorithms start off with a workable answer. The program then refines this workable solution and probabilistically exits the local optimum to search for the solution that is close to the global optimum. The simulated annealing (SA) algorithm is a common tool for searching an expansive solution space for an approximate global optimal solution. The SA method, which differs from the genetic algorithm (GA), has the capability of probabilistically jumping out of local optimums, which helps speed up path planning. A path planning technique is developed for IoUTs based on matrix completion and SA with the aim of enhancing the EE of IoUTs. First, dominator sampling points, virtual dominator sampling points, and follower sampling points are chosen from the sampling points with degrees ranging from high to low. And by completing the matrix, all data in the monitoring region are recovered. SA is then used to decide the visit order of each sample point based on the quantity and location of the chosen sampling points. • Swarm intelligence-based algorithms These include the shuffled frog leaping algorithm (SFLA), shuffled frog optimization (PSO), differential evolution (DE) algorithm, and cuckoo search (CS) algorithm. To replicate the answers, they used a lot of people. Then, the mechanisms of learning and evolution are used to increase the superiority of solutions. The process of biological evolution is

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simulated by the genetic algorithm (GA). A chromosome represents a solution to the issue. To achieve the best result, the algorithm performs population reproduction, crossover, and mutation procedures to identify the most suited chromosome. Ant colony optimization (ACO) simulates the method by which ants choose the shortest path while foraging. A possible solution to the path planning conundrum is an ant. During the path-finding process, ants disperse the pheromone, and the best path collects the most of it. In this way, the ants are led to the best course of action, i.e., the best scheme is sought. The diversity of an ant colony can also prevent local optimization. Particle swarm optimization (PSO) comes to solving challenges involving continuous optimization, and PSO has many benefits. Typically, data retrieval and processing are done when the UAV has finished gathering its data. A PSO-based algorithm is used to reduce the flying time of many IoUTs for the NP-hard issue, examination of bird foraging. Individuals use information-sharing mechanisms to help each other find food. Differential evolution algorithms such as GA and DE are methods of intelligent optimization search that utilize both inter-population cooperation and competition. It is mostly used to resolve global optimization problems involving continuous variables because of its great global convergence performance and robustness. As an aerial anchor node and a mobile data collector, IoUTs help BSs accomplish sensor location and data collection of sensors located far from BSs. The UAV path and sensor transmission priority are optimized using a DE algorithm to reduce or increase the energy consumption of the UAV, sensors, and BSs. And the Cramér–Rao bound (CRB) is derived to assess the algorithm’s effectiveness. To reduce the amount of time needed for data collection, estimate each IoUT’s delay effective path. Four phases make up the algorithm, including: the Shuffled Frog Leaping Algorithm (SFLA) mimics how frogs communicate and share information while foraging. It combines the positive aspects of PSO with the memetic algorithm (MA). Further optimizing the placement and traversal sequence of data collection locations is done using the upgraded SFLA. These well-organized data gathering stations are then used to plan the IoUT course with the lowest possible energy usage. Cuckoo Search Algorithm discovers the best solution, and the CS algorithm simulates the special breeding behaviour of the cuckoo parasite brood and Ivy flying search mechanism. Compared to GA and PSO algorithms, it does searching more effectively. CS can quickly and accurately seek the global optimal path in a path planning area in the path planning issue.

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4 Traffic Models for IoUT Due to the potential characteristics of IoUTs like their ability to use wireless networks, unmanned aerial vehicle (UAV)-enabled wireless networks are expected to play a significant role in 5G and beyond wireless networks. Fast deployment, broad coverage, and adaptable mobility [6] Numerous services in the military and civic sectors, including search and rescue missions, firefighting, agriculture, mapping, surveying, and Internet of Things (IoT). However, future UAV-enabled wireless networks face significant difficulties due to the significantly rising service demand. UAV control can be negatively impacted by latency and packet loss brought on by poor network design, while the massive data generated by UAVs can reduce QoS, and the QoE is negatively impacted by UAVs’ communication over cellular networks. Although some research has examined the deployment of UAVs’ services across the network and employing optimum service placement in order to develop and deploy such networks, it is still crucial to predict the UAV traffic flows for various services [1].

4.1 UAV Network Services The three main subsets of the services offered by the network are telemetry data, IoT data, and streaming data, which, respectively, represent information about the status of the UAV and its component known as the telemetry data, information packets from the IoT onboard the UAVs, and videos taken by the camera onboard the UAV.

5 The UAV Subgroups The behaviour of the UAV can be divided into three groupings based on the frequency of the network utilization, from the lowest to the highest: 1. The poor subgroup represents network users who use the network very little. 2. The intermediate subgroup represents average network users who use the network to a moderate extent. 3. The affluent subgroups represent network users who use the network heavily. The Pareto distribution [1] is frequently used to describe how unevenly people’s incomes are distributed in society. The Gini coefficient is used to determine how many people belong to a particular grouping. Take it into account the Pareto distribution with parameter .Ai , where .i = 1, 2,and.3 denotes the .i th network service subset. Let .Uj stand for the share of UAVs in the .j th subgroup, where .j = 1, 2,and 3 represent the subgroups that are poor, middle-class, and wealthy, respectively. The

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following is a possible way to express Lorenz curves that correspond to the Pareto distribution with parameter. Q(A, x) = 1 − (A − U (x))

.

A−1 A

.

(1)

Let us presume that maximum of video streaming data will be produced by UAVs from the third grouping, and IoT data will be produced by UAVs from the second and third subgroups. The subgroups may then be identified using the Lorenz curves as A3

U3 = 0.9 A3 −1

(2)

.

A2

U2 = 0.9 A2 −1 − U3 ,

(3)

U1 = 1 − U2 − U3 .

(4)

.

.

6 Amount of Packets To accommodate the previous two subgroups, two classes of events are created. Events from the three separate network services are contained in the first class. Designate telemetry, IoT, and stream data, respectively, using the indexes .i = 1, 2, and 3. The three network user groupings are included in the second class. For the network users from the low, middle, and rich subgroups, respectively, .j = 1, 2, and 3. The separation of these two groups of events will result in nine segments, and in each of them, calculate the packet arrival rate (.I nij ) per UAV. On the basis of that, go on to compute how many packets a swarm of UAVs would produce. Let .Tij represent the proportion of transactions that users of the .j th network subgroup have requested for the .i th subset of network services. As shown below, there are two ways that define the share of transactions ij. The proportion of transactions dependent on how frequently network services are requested: The values of .Tij for thenine segments can be determined by combining the formulas (1), (2), (3), (4), and . j Tij = 1 as follows: Ti1 = 1 − (1 − U1 )

.

Ti2 = 1 − (1 − U1 − U2 )

.

Ai −1 Ai Ai −1 Ai

Ti3 = 1 − Ti1 − Ti2 .

.

(5) − Ti1

(6) (7)

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The proportion of transactions .Ti1 depending on .I nij ’s transaction rate: If NU is the total number of users in the network, then Sij = Uj ∗ N ∗ I nij

.

(8)

can be used to define the total rate of transactions for the .i th network service generated by all users of the .j th subgroup. .Sij stands for total rate of transactions. The formula for the total number of transactions produced by requests for the .i th network service is  .Si = N I nij Uj . (9) j

The division of the two previous formulas yields the expression for .Tij , which is shown below: Tij =

.

Sij . Si

(10)

We can determine the values of the following unknown parameters based on the initial input value of .I n11 that indicates the average packet arrival rate for the telemetry data from an IoUT of the underprivileged subgroup .I nij . .

I n12 =

I n11 T12 U1 T11 U 2j

(11)

I n13 =

I n11 T13 U1 T11 U3

(12)

.

I nij =

λi Tij

.

 j

I n1j Uj

λ i Uj

, i = 2, 3; j = 1, 2, 3.

(13)

Allow t to stand for the experiment’s time in seconds. Following is a list of all the packets that were sent when the .i th network subset was requested for services. Psent = N − t



.

I n1j Uj .

(14)

j

6.1 Size of the Traffic Data The average data size of a transaction from the .i th network service subset is indicated by the symbol .Mi . The traffic data size for each network subset can then be calculated from the transactions rate as follows:

Mobility, Traffic Models, and Network Management for Internet of Unmanned. . .

Di = NtMi



.

I n1j Uj ,

49

(15)

j

where Di is the .i th network subset. The entire magnitude of the traffic data is then determined as follows:   .D = NtMi Mi I n1j Uj , (16) i

j

where D represents the entire data size.

7 Network Management and Communication for IoUT IoUTs are a promising component of 5G and beyond networks for the capacity augmentation over existing networks due to their quick and cost-effective deployment, strong line-of-sight (LoS) communication link, and great mobility. The channel characteristics of IoUT wireless networks, IoUT use cases in 5G networks, and their practical applications are all presented in this section [7].

7.1 Channel Specifications Channel characteristics, which are dominated by a strong LoS link in IoUT wireless networks, are a key distinction between airborne IoUT stations and terrestrial stations. Additionally, channel fluctuation caused by moving IoUTs has a significant impact on how well IoUT wireless networks perform. Therefore, it is important to take into account the channel characteristics of IoUT wireless networks while deploying IoUTs for maximum application QoS. In general, air-to-ground (also known as UAV-to-ground node) and air-to-air (also known as UAV-to-UAV) channels make up UAV wireless networks.

7.1.1

Air-to-Ground Channel

Strong LoS links are present in air-to-ground channels; however, they are not always present because of shadowing from objects such as trees and buildings. To accurately assess the performance of IoUT wireless networks, an air-to-ground channel must be accurately modelled because the mobility of IoUTs also results in shadowing when they are in flight. The LoS probability is introduced as Pr (a) =

.

1 . 1 + αe(−β[a−α])

(17)

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α and .β are environmental factors, such as suburban, urban, dense, and high-rise urban; and a is an elevation angle between the IoUT and the ground node, to reflect a LoS component on channel modelling. When alt is the altitude of the IoUT and d is the horizontal separation between the IoUT and ground node, the evaluation angle is defined as

.

a = arc tan(

.

alt ). d

(18)

Take note that .Pr (a) grows as a rises. The probability of not being LoS is Prn (a) = 1 − Pr (a).

(19)

P L = Pr (a)xPL + Prn (a)PLn

(20)

.

.

is the resulting average pathloss, where the pathloss .PL is for LoS link and .PLn is for non-LoS link. .CG0 is the channel gain when the distance between the IoUT and the ground node is equal to the reference distance, and air-to-ground channel gain is defined as CG =

.

CG0 . PL

(21)

The air-to-ground channel [7] has been stated differently based on the probabilistic channel model to better precisely reflect the link characteristics. The influence of building shadowing has effects on non-LoS linkages as well as the likelihood of LoS. The two-ray ground reflection model is used to build a foundational model of data transmission from UAV-to-ground station and to effectively allocate radio resources. Medina-Pazmiño et al. [8] take into account a link budget computation between the UAV and the ground station to identify the practicable frequency band, propagation loss, antenna gain, and other factors. In addition to a Rician model, a height-dependent small-scale fading and pathloss exponent model is used to account for the interaction of LoS and multipath scatters. The air-to-ground pathloss between a UAV and a terrestrial terminal is predicted using a statistical propagation model and an air-to-ground propagation channel model for an ultra-wideband (UWB) [7]. Comprehensive measurements and computations should be made for various UAV scenarios because the air-toground channel model varies with environments and system factors.

7.1.2

Air-to-Air Channel

Multipath fading is less noticeable because of the dominant air-to-air channel feature of the LoS link. A pathloss-dependent large-scale fading with LoS probability can

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be taken into account to describe an air-to-air channel since a small-scale fading can be disregarded. Because of this channel characteristic, the mmWave protocol, a novel radio protocol for 5G networks, can be used in IoUT wireless networks to increase the capacity of the UAV-to-UAV wireless link and make it simpler to work together in 5G networks. To fully model an air-to-air link and choose the best communication protocols for a particular air-to-air channel characteristic, more research is nonetheless required.

7.2 Use Cases for IoUT Wireless Networks The following are the top three applications for IoUT in wireless communication networks: • IoUT base station (BS) to offer wireless service within a specific target area • IoUT relay station (RS) to establish wireless links to far-off users without direct connectivity from base station or command centre • IoUT aggregator to disseminate information to or gather data from the distributed sensors or devices A static IoUT is used in the first two use cases as an aerial BS and RS, but a mobile IoUT is typically used as a moving aggregator. As shown in Fig. 8a IoUT base station where no terrestrial BS is available, multiple IoUTs can serve as aerial BSs to cover the emergency or disaster area. IoUT BSs may receive wireless backhaul links from terrestrial MBSs or satellites, and IoUT BSs may change their positions to optimize network performance for UAV-to-UAV links. Keep in mind that the coverage area of aerial IoUT BSs changes according to the IoUTs’ height. In Fig. 8b IoUT relay station by deploying a potent air-to-ground LoS link, IoUT RS can increase the coverage of a terrestrial BS. In scenario 1, UE2 is taken into account. Due to a natural disaster or damaged infrastructure, UE2 cannot be served by BS2. Due to significant pathloss attenuations, the distant BS1 is unable to give UE2 a trustworthy communication link. In Scenario 2, there is a severe bottleneck that prevents a direct connectivity from BS1 to UE3. In both cases, IoUT RS can offer UE2 or UE3 a dependable communication link by receiving a signal from BS1 via a wireless backhaul link and transmitting the signal to the receiving device via a potent air-to-ground LoS access link. In Fig. 8c, IoUT aggregator flies over sensors or Internet of Things (IoT) devices to gather data from them or to communicate with them. IoUT mobility and delaytolerant transmission, also known as store-carry-and-forward transmission, can reduce the transmit power required by sensor and Internet of Things (IoT) devices. It is guaranteed that sensor/IoT devices will reliably provide data to the core network and vice versa.

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Fig. 8 Uses for IoUTs. (a) IoUT base station, (b) IoUT relay station, and (c) IoUT aggregator [7]

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Base Station for IoUTs

IoUT BS aids in expanding service coverage for the current networks and offers dependable, seamless connectivity to user devices (UEs). A backhaul link can be established by the IoUT BS from a satellite, a nearby terrestrial macro base station (MBS), or neighbouring IoUTs. Both the extremely crowded region, such as a sports stadium, where more links are required owing to base station unloading, and the emergency area, where communication infrastructures are damaged or destroyed by natural disasters, might benefit from IoUT BS. One of the major scenarios that 5G networks should ensure. As a result, IoUT BS can offer quick deployment, better network speed, in addition to extended coverage. To ensure the performance of all networks, the interference between IoUTs and the current wireless networks must be carefully taken into account.

7.2.2

UAV Relay Station

Without compromising network performance, UAV RS offers wireless connectivity to remote users or user groups that lack a direct communication link from a BS or a transmitter. This increases the capacity of the networks as a whole. The UAV relay network (URN) can be built with one or more UAVs, but for applications, the number of IoUTs, the topology, and the routing protocol should be optimized for a stable relay connection and an efficient power consumption. URN is appropriate for use in emergency situations or military communications where temporary communications between the command centre and operators are crucial. When providing mission-critical operators with a dependable relay connection, the outage probability of access links should be taken into account.

7.2.3

UAV Aggregator

To distribute (collect) information to (from) the dispersed wireless devices, such as sensors, an IoUT aggregator flies around the sky. Data should be delay-tolerant, allowing it to move within a predetermined latency. An example of monitoring is for agriculture or public safety. A major obstacle to extending the lifespan of IoUTenabled networks for IoT applications and M2M communications is the reduction of power consumption.

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7.3 Useful IoUT Wireless Network Applications Civil and public safety is one of the key applications that has garnered a lot of attention. IoUTs can assist firemen during a warehouse fire or find a criminal during a pursuit in addition to offering a dependable communication link. Supporting dependable communication links to disaster victims is also crucial, and quick and effective operations require a limited number of IoUTs. Further analyses, such as IoUT mobility and genetic algorithms for the deployment of many IoUTs, are taken into consideration to increase throughput without compromising network performance. One of the main requirements for 5G networks is to support user connectivity in crowded locations like sports stadiums, open festivals, shopping malls, and other public events, where the required average user data rates during busy times. To address the supply–demand imbalance, a study on crowd traffic modelling was conducted, and an IoUT relay system-based approach to reducing traffic congestion was provided. The most effective IoUT deployment is essential to meet the requirements for a given scenario because flexible modelling for IoUT wireless networks to support public safety communications or crowd areas is required to reduce interference from other users and guarantee the requisite QoS. In IoT networks with sensors and other small wireless devices linked, an IoUT can be crucial because these devices have limited battery life and cannot broadcast over long distances. IoUTs can function in IoT networks as a moving aggregator or as a static aerial base station (BS) for a limited range of IoT communications. Utilizing IoUTs in IoT networks has been met with a number of challenges, including ensuring reliable connectivity to a mobile IoT gateway, collecting data from distributed sensors successfully, lowering energy assumptions or speeding up response times, and minimizing transmit power at IoT devices by using the best clustering and trajectory of IoUTs. Most notably, in order to efficiently build IoT networks and extend the service time to disseminate (collect) data to (from) the distributed IoT devices or sensors, static IoUT BS deployment and the trajectory of a mobile IoUT aggregator are the key concerns.

8 Conclusion Unmanned aerial vehicles (UAVs) and other new smart linked platforms have become increasingly integrated into the Internet of Things (IoT), which is a vast global network [9, 10]. IoUTs not only provide a practical solution to the drawbacks of fixed terrestrial IoT infrastructure, but also new ways to supply value-added IoT services through a variety of applications ranging from monitoring and surveillance to on-demand last-mile deliveries and people transport.

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IoUTs are anticipated to soon play a crucial role in monopolising the common low-altitude airspace if they live up to their potential. Mobility, traffic models, and network management for IoUT are introduced in this chapter. Management of IoUT traffic is essential to ensuring compliance and secure operation within the airspace. The position or trajectory of IoUTs and the resource management of complete networks are key factors in how well IoUT wireless networks perform in order to meet performance requirements. In order to manage the performance of IoUT based on IoUT performance and communication parameters, mobility and network management would be helpful. This chapter elaborates all these points in summarized way highlighting major entities for evaluation of IoUT.

References 1. P. McEnroe, S. Wang, M. Liyanage, A survey on the convergence of edge computing and AI for UAVs: opportunities and challenges. IEEE Internet Things J. 9, 15435–15459 (2022) 2. S. Khan, Mercury systems, sailing through the fog into the edge. https://www.mrcy.com/ company/blogs/sailing-through-fog-edge. Accessed 2 December 2022 3. M. Mozaffari, W. Saad, M. Bennis, Y.-H. Nam, M. Debbah, A tutorial on UAVs for wireless networks: applications, challenges, and open problems. IEEE Commun. Surv. Tuts. 21(3), 2334–2360 (2019) 4. S. Samaoui et al., Wireless and mobile technologies and protocols and their performance evaluation, in Modeling and Simulation of Computer Networks and Systems (Morgan Kaufmann, 2015), pp. 3–32 5. Z. Wei et al., UAV assisted data collection for Internet of Things: a survey. IEEE Internet Things J. 9, 15460–15483 (2022) 6. J. Navarro-Ortiz et al., A survey on 5G usage scenarios and traffic models. IEEE Commun. Surv. Tuts. 22(2), 905–929 (2020) 7. S.I. Han, Survey on UAV deployment and trajectory in wireless communication networks: applications and challenges. Information 13(8), 389 (2022) 8. W. Medina-Pazmiño, A. Jara-Olmedo, D. Valencia-Redrován, Analysis and determination of minimum requirements for a data link communication system for unmanned aerial vehiclesUAV’s, in 2016 IEEE Ecuador Technical Chapters Meeting (ETCM) (IEEE, 2016) 9. N.S. Labib et al., The Rise of Drones in Internet of Things: a survey on the evolution, prospects and challenges of unmanned aerial vehicles. IEEE Access 9, 115466–115487 (2021) 10. A. Abada, B. Yang, T. Taleb, Traffic flow modeling for UAV-enabled wireless networks, in 2020 International Conference on Networking and Network Applications (NaNA) (IEEE, 2020)

A Blockchain Trusted Mechanism (BTM) for Internet of Unmanned Things (IoUT) Using Comprehensive and Adaptive Schemes Geetanjali Rathee, Akshay Kumar, and Chaker Abdelaziz Kerrache

1 Introduction In last few years, the technology has modified rapidly with a number of quality of sensors and intelligent systems increasing steadily. In addition, the recently smart decisions communication protocols fasten the transmission with reduced delay communicational steps [1]. The Internet of Unmanned Things (IoUT) is considered as one of the most promising paradigms for ensuring a number of intelligentbased communications in agriculture, smart industries, package delivery, and so on. The IoUT provides an environment for data transmission by ensuring a secure and accurate decision-making scheme [2]. A number of applications such as smart industry, smart vehicles, IoT-based networking, and smart cities are the most recent areas of IoUT in order to provide an independent system where decisions and computations can be made possible without any participation of human power [3, 4]. Trust computation is defined as one of the most promising areas where organizations are afraid of using any new technology in the market because of less trust on that product or framework.

G. Rathee Department of Computer Science and Engineering, Netaji Subhas University of Technology, New Delhi, India A. Kumar Department of Computer Science and Engineering, Jaypee Institute of Information Technology, Noida, Uttar Pradesh, India C. A. Kerrache () Laboratoire d’Informatique et de Mathématiques, Université Amar Telidji de Laghouat, Laghouat, Algeria e-mail: [email protected] © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023 C. A. Kerrache et al. (eds.), Internet of Unmanned Things (IoUT) and Mission-based Networking, Internet of Things, https://doi.org/10.1007/978-3-031-33494-8_3

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1.1 Need of Trust Whenever a smart system is adopted by an organization, it is highly responsibility of that framework to provide a secure and efficient communication mechanism. Any kind of forgery may drastically affect the network and reduce its market growth [5, 6]. An intruder may encounter any type of threat such as denial of service, resource consumption, network delay, authenticity delay, and so on inside the network by forging or mimicking the legitimate intelligent device. In addition, the intruder may further cause serious issue to any organization with severe growth degradation [7]. Therefore, it is very much needed to provide a secure and highly transparent communication environment in the network. Figure 1 represents a visual IoT framework of smart devices where a number of visual objects such as smart devices are being communicated via various security protocols such as decisionmaking and AHP (analytical hierarchical protocols) in order to ensure a trusted and secured communication framework [8, 9]. Blockchain is considered as one of the most trending and promising areas in the IoT networking where smart devices can be intelligently monitored and traced with full transparency while sharing or performing transmission of information among the networks [10]. Though a number of researchers have used blockchain

Fig. 1 A secured IoUT framework having intelligent devices in the network

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technique in various applications of smart environments, however, very few of them have focused on IoUT including smart cities and manufacturing systems where it is needed to establish a secure and transparent communication mechanism [11–14].

1.2 Overview of UAV Security The modern facilities of today’s era have attracted a number of individuals to move from normal life to smart era by effective use of Internet of Things (IoT) [15]. The IoT has become a necessity for each and every person to ease their life in almost all the aspects of their fields such as transportation, manufacturing, homes, cities, etc. In addition, the management and monitoring of the progress of every site is much easier by integrating the intelligent devices with the drones. There exist various drone-based applications such as health sector, smart government, goods mobility, and resource management using intelligent devices. The term Internet of Unmanned Things (IoUT) has become one of the latest and trending topics for further ensuring an efficient and effective communication and transmission of messages among various intelligent devices in the network [16, 17]. Further, the IoUT is not only used for inspecting the construction sites from a distance but also can be utilized for ensuring various surveillance and safety measures at the site. The IoUT can fly over the objects with proper altitude by acquiring the best possible pictures of the site for processing it further. However, many organizations have adopted this technology of IoUT that provides a connectivity, reliability, stability, and coverage in the network. However, the security and privacy issues of IoUT are still at its early stage [18]. Though the IoUT provides an efficient way of connecting, recording, and managing the resources and information in the network, the intruders may still reduce the performance of the network by compromising the unmanned vehicles. The intruders may compromise the drones for blurring their visions or altering the generated record of intelligent devices for their own benefits. Eavesdropping, masquerade attack, and DoS are the major security threats for IoUT while capturing the information in the network. The excessive use of resources and monitoring of network activity may further compromise the security of the network. Furthermore, the compromise of IoUT may exploit the real-time tracking of objects and data by the intruders in the network. However, various scientists and researchers [19, 20] have proposed a number of security techniques and algorithms such as encryption, trust-based, ticket-based, and cryptographic. It is still needed to propose a transparent and trusted security mechanism from preventing the real-time tracking and compromising the information or devices by the intruders during communication in the network.

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1.3 Contribution The main aim of this paper is to propose a secure and transparent communication mechanism by reducing the communication delay and more accuracy while making the decisions among information transmissions. The paper has used adaptive and comprehensive trust models in order to compute and maintain the trust while making the accurate decisions among intelligent devices in the network. In addition, the accurate decisions and transmission are vigilant continuously by the blockchain technique where each and every intelligent device is being recorded while maintaining the communication. The trusted models are further traced and get transparent using blockchain network where each block having device id, category (legitimate, moderate, malicious), size is being monitored and recorded by the network. The performance of the proposed phenomenon is validated against existing approach over various measuring parameters such as communication delay, accuracy, and probability attack. The remaining structure of the paper is organized as follows. The number of security mechanisms, techniques, and algorithms has been proposed by various researchers and scientists, which is discussed in Sect. 2. A trust-based security mechanism for ensuring the security of intelligent devices and unmanned vehicles while sharing the information is discussed in Sect. 3. In addition, the transparency can be further maintained for continuous surveillance and recording of information using blockchain network, which is further illustrated in Sect. 3. The performance and validation of proposed mechanism is discussed in detail in Sect. 4. Finally, Sect. 5 concludes the paper along with some future directions.

2 Related Work This section represents the number of trusted or blockchain-based security schemes/mechanisms proposed by various academicians and scientists. Table 1 presents the number of techniques and methods along with their measuring parameters and limitations proposed by various authors. Singh et al. [21] have proposed a blockchain-based trusted management system for Internet of vehicles by ensuring the validity and feasibility in the system. Wang et al. [22] have proposed a blockchain-based trusted tracking and data computation system while optimizing the payoff energy provider and contract generation measures. In addition, Liu et al. [23] have proposed an innovative blockchain in zero trust context where the proposed mechanism is evaluated against ETH-based platform against unauthenticated participants. However, the authentication process leads to delay. Further, Yahaya et al. [24] have proposed a mutual verifiable fairness scheme in which the proposed mechanism is verified against trust, energy cost, peak-to-average ratio, etc.; however, it may lead to several issues during dynamic behavior of the network.

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Furthermore, Zhang et al. [25] have proposed a consortium blockchain system with varied trust values where the authors tried to ensure the enhanced throughput and fault tolerance process. Further, a blockchain trusted management system is proposed where the trust of communicating devices is managed and validated by computing the trust. Furthermore, the communication process is made transparent against malicious devices using blockchain mechanism. Table 1 illustrates a brief summary of trusted and blockchain-based approaches proposed by several researchers in smart environment. Zhang et al. [26] have proposed a blockchain-based trusted management system where the authors have focused on validating the proposed phenomenon over accuracy and delay of vehicular judgment while sharing the information among each other. However, the delay while ensuring the security leads to further increase of the trust issues among intelligent devices in the network. Though a number of schemes have been proposed [14, 27–29], however, it is further needed to focus on the accuracy and computational steps of trust along with reduced transparency time using blockchain system. This paper proposed a secure and trusted communication mechanism using blockchain-based adaptive and comprehensive trust computation of each DS that is further verified against accuracy, computational delays, and probability attacks of each communicating device.

3 Proposed Framework 3.1 Adaptive and Comprehensive Trusted Model The comprehensive trust model is used to dynamically compute the each of each communicating device by assigning some weights at the network establishment and later on keeps on updating them depending upon their behavior. The mapping function F: Dn➔D having an associated weight vector V = {V1, V2, . . . Vn} in each unit interval is defined as the sum of following equation: F (d1, d2, . . . .dn) =

n 

.

Vj P φ(i)

i=1

where Pφ(i) is considered as the highest ith in the set of V = {V1, V2, . . . Vn}. In order to establish the weight vector V, various aggregation operators can be used for determining the process and accuracy speed of the devices. The Pφ(i) weights can be further computed as the below equations: v1 [(n − 1) Γ + 1 − nv1 ]n = [(n − 1) Γ ]n−1 [(n − 1) Γ − n) v1 + 1

.

Mutual verifiable fairness scheme

Consortium blockchain with trust varying devices

Blockchain-based trust management

Yahaya et al. [24]

Zhang et al. [25]

Zhang et al. [26]

Liu et al. [23]

Wang et al. [22]

Technique Blockchain-based trust management in IoV Blockchain-based trust-free private data computation and tracking mechanism Innovative blockchain in zero trust context

Author Singh et al. [21]

Table 1 Literature survey

Optimal contract generation and payoff energy provider Proposed mechanism is evaluated against ETH-based platform against unauthenticated participants Proposed mechanism is verified against trust, energy cost, peak-to-average ratio, etc. The proposed scheme is verified against fault tolerance, throughput, and low consumption The proposed phenomenon is validated against accuracy and vehicles judgment

Measuring parameter Reliability and feasibility

Needed to analyze the computation delay of trust

Need to consider accuracy

Issues during dynamic behavior of the network

Energy consumption while tracking the mechanism again and again Authentication process leads to delay

Limitation Suffer from communication delay

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vn =

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((n − 1) Γ − n) v1 + 1 (n − 1) Γ + 1 − nv1

 (n−i) (i−1) v1 vn , 1