Revolutionary Applications of Intelligent Drones 1685079911, 9781685079918

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Computer Science, Technology and Applications

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Mohit Angurala and Vikas Khullar Editors

Revolutionary Applications of Intelligent Drones

Copyright © 2022 by Nova Science Publishers, Inc. DOI: https://doi.org/10.52305/HMVC5993 All rights reserved. No part of this book may be reproduced, stored in a retrieval system or transmitted in any form or by any means: electronic, electrostatic, magnetic, tape, mechanical photocopying, recording or otherwise without the written permission of the Publisher. We have partnered with Copyright Clearance Center to make it easy for you to obtain permissions to reuse content from this publication. Simply navigate to this publication’s page on Nova’s website and locate the “Get Permission” button below the title description. This button is linked directly to the title’s permission page on copyright.com. Alternatively, you can visit copyright.com and search by title, ISBN, or ISSN. For further questions about using the service on copyright.com, please contact: Copyright Clearance Center Phone: +1-(978) 750-8400 Fax: +1-(978) 750-4470

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Library of Congress Cataloging-in-Publication Data Names: Angurala, Mohit, editor. | Khullar, Vikas, 1983- editor. Title: Revolutionary applications of intelligent drones / Mohit Angurala, PhD, editor, Assistant Professor, Department of Computer Science and Engineering, Chitkara University Institute of Engineering and Technology, Chitkara University, Punjab, India, Vikas Khullar, PhD, editor, Associate Professor, Department of Computer Science and Engineering, Chitkara University Institute of Engineering and Technology, Chitkara, University, Punjab, India. Description: New York : Nova Science Publishers, Inc., [2022] | Series: Computer science, technology and applications | Includes bibliographical references and index. | Identifiers: LCCN 2022032497 (print) | LCCN 2022032498 (ebook) | ISBN 9781685079918 (paperback) | ISBN 9798886971231 (adobe pdf) Subjects: LCSH: Drone aircraft--Industrial applications. | Micro air vehicles--Industrial applications. Classification: LCC TL685.35 .R48 2022 (print) | LCC TL685.35 (ebook) | DDC 629.133/39--dc23/eng/20220812 LC record available at https://lccn.loc.gov/2022032497 LC ebook record available at https://lccn.loc.gov/2022032498

Published by Nova Science Publishers, Inc. † New York

Contents

Preface

.......................................................................................... vii

Chapter 1

Introduction to Drone Technologies ................................1 Jyoti Kandpal, Pankaj Kumar Mishra, Bhavna Chilwal, Abhay Kumar Singh and Geetanjali Balutia

Chapter 2

Drones for Society and Industry ....................................25 Pankaj Kumar Mishra, Jyoti Kandpal, Abhay Kumar Singh, Geetanjali Balutia, Bhavna Chilwal and Bhongiri Prasuna

Chapter 3

Cloud-Based UAV Architecture, Security Concerns and Challenges ................................................45 Vrajesh Sharma and Nipun Chhabra

Chapter 4

Drones and Their Utilities Using Emerging Technology......................................................55 Harsh Taneja and Ashish Sharma

Chapter 5

Introduction to Machine Learning in UAVs .................67 Geetanjali Sharma and Rajeev Kumar Bedi

Chapter 6

Decision Making Using Machine Learning in Drones...........................................................................81 Harmeet Singh

Chapter 7

UAV Applications in Agriculture ...................................91 Prabhjot Kaur and Anand Muni Mishra

Chapter 8

Drone Centric IoT-Fog-Cloud Based Smart Agriculture Support Service .........................................107 Prabhdeep Singh, Kiran Deep Singh and Mohit Angurala

vi

Contents

Chapter 9

Disaster Management Using Cloud-Based UAVs .......125 Nipun Chhabra and Vrajesh Sharma

Chapter 10

Blockchain-Based Methods to Overcome Security Issues in Drones ..............................................133 Harmeet Singh

About the Editors ......................................................................................143 Index

.........................................................................................145

Preface

Drones are quite demanding and have turned out to be a boon for many applications. Drones are now commonly used for real-world aerial works including drug delivery, agricultural applications, vertical structure inspection, construction site survey, and many more. Such platforms provide high-level independent operations by reducing user interventions. Further, machine learning, more specifically deep learning is widely used by many researchers of drone technology. The practical aspects of machine and deep learning methods in autonomous drones, medical drones, agricultural drones, military drones, etc., have changed human life in many ways. Based on cutting-edge technologies mentioned above, masses have shown a tremendous interest in using aerial robots based on deep-learning techniques. This interest has grown for improving the capabilities and level of autonomy of drones and is expected to change society. This work includes chapter wise incremental details starting from basics of drone technology that discuss diverse aspects related to drone designing and implementation. Further chapters detail about the impact of drone technology on society in terms of development, challenges and security concerns. Then, implementations of artificial intelligence techniques such as machine learning, deep learning, etc., are discussed to translate drone technology into intelligent drone technology. As per our surveys, there are diverse applications in the area of intelligent drone technologies such as agriculture, disaster management, security, military, etc. Hence, in this book some of the drone-specific applications are discussed.

Chapter 1

Introduction to Drone Technologies Jyoti Kandpal1, Pankaj Kumar Mishra2,, Bhavna Chilwal3, Abhay Kumar Singh2 and Geetanjali Balutia4 1

NIT Arunachal Pradesh, India College of Technology, G.B.P.U.A. and T., Pantnagar, India 3 Junior Research Fellow, DRDO, DGRE, RDC, Manali, India 4 Department of Electronics and Communication Engineering, Tehri, India 2

Abstract Drones can help industries improve operational performance and effectiveness, reduce workload and development costs, improve accuracy, optimize service and customer interactions, and solve security challenges on a large scale. Drone technology adoption across industries is due to application scenarios and as more organizations understand its potential, scope, and global reach. Drones can reach the most remote regions with or without human involvement and need the least amount of work, time, and energy, whether they are operated by a remote or accessed via a smartphone app. This is one of the primary reasons for their widespread use, particularly in the military, commercial, personal, and future technology sectors.

Keywords: drone, industry, remote region, smartphone, military applications



Corresponding Author’s Email: [email protected].

In: Revolutionary Applications of Intelligent Drones Editors: Mohit Angurala and Vikas Khullar ISBN: 978-1-68507-991-8 © 2022 Nova Science Publishers, Inc.

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Introduction Unmanned aerial vehicles (UAVs), also sometimes generally regarded as drones, are aircraft that do not have a human pilot, crew, and passengers on board [1]. It is fundamentally an aerial robot that may be commanded electronically or flown automatically via software-controlled flight plans in its integrated modules. It functions with sensing capability and a global positioning system (GPS). The drone and the controlling system together constitute an unmanned aerial vehicle unit. Unmanned aerial vehicles (UAVs) constitute part of an unmanned aircraft system (UAS) that usually involves a ground-based director and a communicating infrastructure with a UAV. UAVs can also fly remote controlled by a human controller, known as remotely piloted aircraft (RPA), or even with varying autonomy measures, including such autopilot aid, all the way up to entirely automated aircraft without a user interaction [2]. UAVs were indeed constituted in the 20th century for defence operations, which seemed too gloomy, cluttered, and considered deadly for living creatures and other species. After that, in the 21st century, they became indispensable elements for many military units. In 1935, a complete restructuring of the de Havilland DH82B “Queen Bee” biplane became the earliest widely utilized drone. It mainly was flown unmanned to permit artillery raiders in coaching to practice shooting. “drone” came from a play on the “Queen Bee” pseudonym. The armed services persisted in being highly interested in UAV technologies, although it was sometimes inaccurate and pricy. The army reviewed the question of unmanned aerial vehicles when serious concerns regarding spy planes being shot down occurred. Armed drones pretty fast assumed responsibilities as leaflet droppers and espionage decoys. In 1982, the Israeli Air Force utilized unmanned aerial vehicles (UAVs) to destroy the Syrian squadron with relatively low Israeli casualties. Israeli UAVs served as decoys, jamming communications and providing realtime video surveillance. Many enterprises and government agencies will use drones and autonomous aircraft. Drone business rise is anticipated to be fuelled by the emergence of diverse techniques such as 5G, augmented environment, and computer vision, which seem to help enhance drone exchange of information and intellectual ability. Government agencies will refine existing standards and policies as personal and commercial drone utilization grows. Drones will also bring with them new safety flaws and attacking avenues. Drones tend to serve two primary objectives: navigation and flight mode. Drones need a controller, which facilitates the operator to officially load, navigate, and park back the aircraft via remote commands.

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Radio frequencies, including Wi-Fi, are used by operators to converse with the drone [3]. The drone’s navigating applications, like GPS, have been usually positioned in the nose of a drone. A drone’s GPS transmits its specific geolocation to the operator. Altitude data could be communicated using an inbuilt altimeter. If the operator determines a certain height, the altimeter assists in keeping the drone there. Drones require an energy provider to operate, such as batteries or fuels. Rotor blades, propellers, and frames are also incorporated. To minimize load and boost manoeuvrability, a drone’s structure is most often constructed of a relatively lightweight composite product [4]. Figure 1 shows the architecture of a drone. Sensor-Positioning and displacement sensors offer data on the status of the aircraft. Exteroceptive sensors work primarily on exterior data such as calculations, and exproprioceptive sensors link to different interior and exterior factors. Non-cooperative sensors are utilized to separate reassurance and obstacle detection since they can acknowledge targets independently. The quantity and efficiency of sensors on board are referred to as degrees of freedom (DOF): 6 degrees of freedom denotes 3-axis gyroscopes and accelerometers (a standard inertial measurement unit – IMU), 9 degrees of freedom denotes an IMU plus a compass, 10 degrees of freedom denotes the addition of a barometer, and 11 degrees of freedom denotes the addition of a GPS receiver. Actuators-Digital electronic pace regulators connected to motors/engines and blades, servomotors (primarily for aircraft and helicopters), weaponry, payloads actuators, LEDs, and loudspeakers are an example of UAV actuators. Software-The flight stack, often known as the autopilot, is a UAV system. The flight stack’s responsibility is to acquire information through sensors, operate motors to keep the UAV stable, and communicate with ground control and mission planners. UAVs are real-time systems that must react quickly to sensor data alterations. Drones, initially constructed for the armed forces and aviation fields, have made their way towards the mainstream due to their increased effectiveness and security. Several unmanned aerial vehicles (UAVs) fly without a pilot and with varying degrees of automation. The drones can extend from remotely controlled (where a user commands their manoeuvres) to sophisticated, where it calculates their moves using a combination of sensing devices and LIDAR trackers.

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Figure 1. Drone Architecture.

The following are several last few drone accomplishments: United States Customs and Border Protection Service was the first to introduce unmanned aerial vehicles (UAVs) to examine the US-Mexico border in 2006. Late in 2012, Chris Anderson, the editor-in-chief of Wired magazine, resigned from the corporation to focus exclusively on his drone business, 3D Robotics Inc. The organization began by emphasizing personal drones for hobbyists. It now sells unmanned aerial vehicles (UAVs) for aerial photos and cinematography. It also sells to the construction, utility, telecommunications, and public safety industries. Late in the year 2013, the CEO of Amazon officially launched a strategy to distribute products using commercial drones. In the year 2016 of September, Drone shipments were assessed by Virginia Polytechnic Institute and State University in collaboration with Project Wing, a division of Google owner Alphabet Inc. They began by ordering burritos from a nearby Chipotle eatery. In March 2021, As a significant component of the United Nations’ COVAX campaign, Zipline began supplying COVID-19 vaccines to health service suppliers in Ghana. In August of 2021, Project Wing, a division of Alphabet, declared that it had delivered 100,000 drones, establishing yet another milestone in illustrating that drone shipping at magnitude is achievable.

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Types of Drones With the advancement in science and technology, different flying devices are available, small and massive in size, which kids could operate and the military. There are different types of drones available with their specific properties. This section is going to discuss different types of drones.

Single-Rotor Drones The most basic types of drones. It has a single rotor and efficiently generates thrust compared to a multi-rotor and is an ideal option for longer flights. However, the cost could be $52k to $300K. The drawbacks with these are that they are not stable, balanced, could hover over areas, and have a higher cost than their counterparts. G. Andrikopoulos et al. used single rotor drones to present vertical take-off and landing single rotor unmanned aerial vehicles (SR-UAV). The design properties have three parts 1—flight control. The system formed was smooth and had accurate tracking performances [5].

Multi-Rotor Drones These drones have the placement of several rotors at different points. The multiple rotors make the craft balanced and hovering. These drones offer stability and are very common for commercial uses. But as drawbacks are not suitable for carrying heavy loads. The cost unit is $5k to $65k. Yan li et al. considered these drones an innovative technology that could be utilized for surveillance mapping in mining. Their work investigated the potential of multi-rotor drones in logistics, maintenance, on-site construction and demolition [6]. Ahmed Bahabry et al. witnessed the increase of multi-rotor drones in several applications due to flexibility, 3D mobility and low cost. Their work investigates the routing problems for drones in urban regions where different height obstacles exist and provide collision-free navigations. A linear program was developed, and two heuristic algorithms were designed to solve trajectory problems [7].

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Fixed-Wing Drones These drones lack rotors and fixed-wing; they are not helicopter style drones because of their controlled design. They are suitable for vertical lifts, need the energy to move forward, and are efficient for long ranges. Some of these drones are gas powered and could remain in the air for sixteen hours of continuous flight. But as limitations, it’s difficult for them to land; they need soft belly landing and experts to handle it. The cost range is $25k to $120k. Adam Seewald et al. presented a study to estimate computational and mechanical energy for fixed wing drones. The computational energy is advanced, handles hardware, and includes specifications for the robotic system. The computational modelling tool was developed, generating energy models according to specifications [8].

Fixed-Wing Hybrid Drones These drones hybrid the properties of the rotor based and fixed-wing rotor’s designs and features. These drones have some rotors attached to the ends of fixed wings. These are still in the experimental and developing phase. Many researchers use fixed wing rotors for different aerial vehicles. Maxim Tyan et al. discussed the hybrid VTOL-fixed wing, which inherited the control and maneuvering properties and extended the efficiency level to fixed-wing aircraft [9]. JanithKalpa, et al. described the use of a fixed wing hybrid rotor for the development of unmanned aerial vehicles by which it could achieve superior qualities for flying like taking –off, landing and cruising autonomously [10].

Small Drones Small drones are the most cost-effective and easy to handle. The size range is just 10 to 100 inches. These are light in weight. But for picture perfect aerial views, these are not good in balance and do not perform well for commercial purposes. However, they are good exploration options for hobbyists and children. Zhongli Liu et al. performed a survey on specifications of mini or small drones based on maximum payload, flight time, and regulations [11]. GianlucaCasagrande et al. focused on small flying drone applications while conducting surveys geographically. They developed a methodological

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framework for data translation, acquisition and configuration as per a small flying drone’s specifications [12].

Micro Drones Militaries widely use micro drones. These tiny 1*4 drones have micro cameras which provide valuable intelligence. These can fly for up to 30 minutes on a single charge, and the range is up to long miles. Also, nowadays, these micro drones have fitted infrared cameras. Markus Quaritsch et al. discussed the applications and research challenges of micro drones. Micro drones are among small scale unmanned aerial vehicles. The provide bird’s eye view for environment surveillance, monitoring and surveillance. These micro drones will deliver the sensor data for analysis in real time use [13].

Tactical Drones The tactical drones are for surveillance and security work. These are fitted with unique infrared cameras to provide good quality pictures of the area to military soldiers even at night. This drone type has GPS technology and easy to handle properties with much special training. SezerCoban, et al. discussed tactical drones as heavier UAVs that could fly at higher altitudes. These drones have six subtypes: Medium altitude, long endurance, endurance, long range, medium range, short range and close range. Long range is used for satellite platforms. These Tactical UAV are also responsible for reconnaissance missions. Male UAV can operate at 3000 Km for around forty hours with precision-guided missiles [14].

GPS Drones The GPS drones working depends on the linking up of satellites via GPS; they map the functions with their flights and create data for analysis and extraction purposes. These are useful in large topologies. However, they need a large power backup for their functioning; they return to their base area as the power depreciates. KhairulNizamTahar et al. found a technique for establishing ground control points for small projects. GPS drones are powerful tools for acquiring data [15]. A. Bhardwaj et al. formed a package delivery system by

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using GPS drones as GPS will track the live location of the customer and deliver the product. The OTP will be provided to avoid collisions and fraud. These GPS Drones will be helpful for both dispatcher and consumer [16].

Photography Drones These drones are fitted with professional-grade cameras and can take highresolution pictures. They use automatic flight modes and stable precision for taking vast area pictures. Yi-Ling Chen et al. discussed the advancement in the photography and videography field because drones hold cameras to take compelling photographs. They proposed an approach to control and balance the flying photography drones by view manipulation with different touch gestures that help users arrange the elements in the photo before capturing. The viewfinder of the drone and mobile screen are mapped for implementation. The manipulation operations are scaling and translation with touch gestures [17].

Racing Drones The racing drones are usually favourite for hobbyists who want to race around. These drones have an engine and can speed up to 50 miles per hour. Shuo Li et al. discussed autonomous drone racing. The racing drones have become a significant part of giving rise to drone racing sport, where users control their flying drones according to the competition. They developed an autonomous system for racing drones without the help of users. In addition, they developed algorithms which help drones to detect gates and obstacles [18].

Components of Drone Remote Control Transmitter Although the flight controller can pilot the UAV autonomously, it’s usually a terrific concept to have an RC transmitter to manage the UAV if something is going wrong or so that you may fly manually. The complexity of the UAV determines the optimal transmitter for a UAV. A handheld transmitter is usually sufficient, but it is preferable to have a base station to assist with all

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the controls for more oversized area vehicles. The number of channels available to a broadcaster is used to assess it. A transmitter’s number of channels corresponds to the number of unique signals it can send out. You’ll need extra transmitter channels as your UAV becomes more sophisticated. Beginning UAVs can be operated with just a 7-channel radio [19]. It’s also good to check if the radio has a three-position switch or a variable knob while hunting for UAV transmitters. This is how most autopilots switch between different flight modes. Usually, a receiver is supplied with the transmitter and must be synchronized or bound to it. This is usually a straightforward procedure that may be completed by simply pressing a button on them simultaneously. Figure 2 shows such devices.

(a)

(b)

Figure 2. (a) Handheld transmitter and (b) base station.

Multi Rotor Frame The construction of the aircraft is crucial because it must be light enough for the UAV to take off while being strong enough to provide support and not break in the case of a minor collision. It is not a good idea to build personal frames. UAVs have unique flight dynamics and structural integrity requirements that must be met, and it may be difficult for first-time designers to understand exactly what is required. Several frames available for purchase have been extensively tested and approved. There are numerous distinct frames to pick from, each with its functions. Of course, the Quadcopter is the most frequent frame, but we also have the

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Hexacopter and Octocopter for more oversized loads and the Tricopter for a distinct aesthetic, as shown in Figure 3.

(a)

(b)

Figure 3. Multi-rotar frame.

Motors/Speed Controller The motors you choose will also have a considerable influence mostly on flight time and load capacity of the UAV. It is hugely recommended that all rotors use the same engine type so the UAV can accomplish the same quantity of effort. Even though motors are of the same brands and model, their speeds may differ. The bulk of UAV motors are DC motors since gasoline motors are too heavy for the UAV and a battery pack produces a Dc supply, making DC motors the ideal alternative. The most crucial factor to take into consideration is the KV rating. The KV rating of a motor indicates how fast it will rotate at a given voltage. A low KV, such as 500–1000 KV, is desired for steadiness in most multi-rotor UAVs [20]. It is vital to exceed the thrust rating supplied by some manufacturers. Usually, they will give it a variety of propeller options, as the propellers you obtain will affect the overall thrust you can accomplish. This thrust is crucial to any design because if the thrust is just 2.5 kilos and your UAV weighs 2.5 kilos, it would struggle to take off and maintain altitude. Therefore, choose propellers and motors that will give you the most thrust. The typical motor used for drones is presented in Figure 4(a). The ESC (Electronic Speed Controller) allows the flight controller to independently regulate every motor shown in Figure 4(b). The flight controller may use this device to provide the appropriate amount of voltage at the appropriate moment to maintain the whole UAV steady.

Introduction to Drone Technologies

(a)

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(b)

Figure 4. (a) DC Motor, a typical model UAV motor (b) ESC.

Flight Controller Flight controllers are your UAV’s brains; they help stabilize and synchronize your motors so that the UAV can retain stability even if the motors deliver varying amounts of thrust. Depending on the flight controller purchased, they may even be programmed to take off and fly to waypoints. Because it ties all the elements together, flight controllers are an essential part of the UAV architecture. Understanding the UAV’s mission is critical to providing it with the appropriate computing power. The APM (Application Performance Managemen) autopilot comes highly recommended. Here’s what it has to offer. Programming/configuration using point-and-click to get you up and running quickly. Acro, Stabilize, Loiter, Alt-hold, Return to Launchpoint, Land, Simple, Guided, Position, Circle, Follow Me, GeoFence, and Auto are some of the command modes available (which run fully scripted missions using GPS waypoints). Three-axis camera control and stabilization, shutter control, and live video link with configurable on-screen display. Data transceivers provide actual monitoring and command between the ground station computer, APM, and joystick control options. Full data logging allows thorough post-mission analysis with graphing and Google Earth mapping tools. No dead ends – Advanced users will be able to customize their missions to their heart’s content. It’s simple to adapt APM or develop new apps on top of it, thanks to the 3DR Robotics Open UAV Platform shown in Figure 5.

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There is already a plethora of compatible third-party applications, such as mobile apps and image editing tools [21].

Figure 5. APM Autopilot.

Batteries Lithium-ion batteries, which are lighter and have a higher carrier concentration than older lead-acid and nickel-based batteries, account for most batteries marketed today. However, rechargeable batteries are more costly, and they have significant side effects in some cases because they’re more extensively used. The battery must be built around the motors used for the UAV. The battery pack’s capacity is measured in amp-hours (Ah), determining how long we can run on it. However, consider that the higher the capacity, the heavier the battery. The average flight time of a model UAV with a 2-3Ah battery is 10–20 minutes [22]. Typical Lithium ion used for drones is presented in Figure 6.

Routing Problems in Drone Technology During the pandemic, the demand for contactless service has highlighted the drone delivery system among customers in the e-commerce market, making way for instant delivery or delivering the demand on the same day. Amazon, Walmart, UPS, Google and other giant companies for package delivery have used delivery drones such as Zipline autonomous, Wingcopter, Flirtey, Wing,

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RakutenTenku, and Condor parcel delivery drones [23]. Moreover, drones are also used for delivering medical parcels/ healthcare products in critical emergencies.

Figure 6. Lithium Ion Batterie.

Drones may travel faster than traditional delivery vehicles; also, drones are not required to follow a specific route. However, drones show a limited travelling range and load capacity. Still, these restrictions may be overcome, and drone’s usability increases when more if they are used in collaboration with ground vehicles such as trucks. However, a drone can only visit a certain number of consumers. As a result, the drone-truck functions to fulfil product distribution, surveillance, investigation, and monitoring tasks. In most cases, the UAV collaborates with a delivery vehicle, which serves as the drone’s moving depot. To improve the delivery system’s overall efficiency, the total travelled distance by both the truck and the drone should be reduced to simultaneous demand optimization of the systems. Despite different operational problems emerging, present drone research is focused solely on engineering issues, and research examining these other issues is few [24]. Drones’ distinct qualities have presented unique challenges, such as the drone’s flight endurance being influenced by its battery capacity and weather conditions, weight, and speed [25]. A fleet of drones with a truck should be considered. The resulting optimization challenge involves choosing the optimal path for the system’s vehicles to meet the specific goal, such as reducing total mission time. The research on the Routing Problem, de+pending on the number of trucks used, is divided into two types, namely, the Travelling Salesman Problem with Drone (TSPD), in which a single truck is used, and the Vehicle Routing Problem with Drone (VRPD), in which several trucks are used. Travelling Salesman Problem with Drone (TSPD) is a problem of optimization algorithms determining the shortest route possible to travel the

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minimum distance possible. Transportation services, product distribution and delivery, planning, and logistics are among the applications that TSPD specializes in. Drone technology advancements have resulted in new TSP types. For example, a drone or fleet of drones that assists parcel delivery trucks in one or more ways adds numerous advantages. Murray & Chu presented a routing issue that considers a truck and a drone working together to deliver packages. The authors describe mathematical formulation for mainly two variations of the traditional Traveling Salesman Problem (TSP) entitled the Flying Sidekick TSP (FSTSP) and the Parallel Drone Scheduling TSP (PDSTSP). These two variations have sparked a lot of academic interest since then, leading to a new study area in truck and drone routing. In both scenarios, a group of customers need items delivered from a depot with the help of a vehicle and a fleet of drones, however, due to practical limits such as bulky shipments or remote individuals far from the depots. In terms of how drones make deliveries, the two variations are distinct. Unlike the FSTSP, there is no synchronization between drone and truck with the PDSTSP. The aim is to keep the time between truck departs and back to the depot as short as possible, noting that each customer must only be served once. When it comes to truck-drone collaboration, the FSTSP only allows one drone. The drone will be launched from a truck as the driver makes deliveries after delivering the parcel to a single customer before the truck and the drone meet up at a new customer location. As long as the drone has enough charge to hover and wait for the truck, the vehicle distributes packages to other customers while the drone is on the way. However, the truck and drone can only leave and return to the same distribution facility (i.e., depot) working in tandem. PDSTSP, on the other hand, is considered where most of the customers are near the depot. Therefore, PDSTSP allows a single drone or a fleet of drones launched from the depot to deliver packages to customers and then return to the depot. Instead, the truck will move independently following the TSP route, reaching all consumers who cannot be reached by drone. Both variations of the TSP are solved using a simple Heuristics Approach to reduce the overall time taken for all products to be delivered to their respective customers, with the help of truck-drone collaboration [24]. A mathematical model proposed by Agatz et al. a modification of FSTSP, intends to reduce operational costs. This research offers a MILP model and two-phase route-first cluster-second heuristics based on local search and dynamic programming. [25] and [26] offered initial formulations that demand significantly significant computational time.

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Vehicle Routing Problem with Drone (VRPD) is a problem of optimization algorithms and mixed-integer linear programming problems for optimizing the set of routes for one or more trucks to deliver products to their respective customers. It generalizes the problem of TSP to reduce the cost of the operation as much as possible. Each route may have its requirement, representing the number or weight of products it demands and a delivery time window. Typically, vehicles depart either from a central depot or one or more depots, deliver their product, and return to the depots. The VRP defines the routing problem for exclusively truck-drone delivery and delivery by drone. When one or more vehicles and drones are available, VRP with drones is a generalization of the TSP-D problem. Drone Delivery Problem (DDP) can be understood as a variation of the VRPD in which trucks are abolished and the fleet of drones is used. Because this is a problem that only considers drones, the techniques more focus on the drone features such as power consumption, limited capacity and flying range, and battery charge, which may prevent the smooth performance of a delivery job. As only drones are used for deliveries that are battery operated and have a much smaller payload capacity and flying range than vehicles, they can’t make all of the deliveries on their own. Dorling et al. addressed a DDP where drones can take multiple visits to reach multiple customers, targeting decreasing delivery time. The authors provide MILP formulation and simulated annealing algorithm heuristic [27]. Carrier-Vehicle Problem with Drones (CVPD) solves the technology challenges outlined in the DDP problem. It predicts the use of a cooperative vehicle team consisting of a vast carrier (such as ships or heavy ground vehicles) capable of transporting many small and quick vehicles in this context, drones/UAVs. The carrier represents a moving depot, carrying packages and allowing quick delivery vehicles to leave and return several times to cover all customers efficiently. Mathew et al. considered the Heuristics Approach to analyze the heterogeneous delivery problem considering total distance as the objective function. The author considered a truck and a drone where the truck carrying the drone along with packages moves along the route. The drone is responsible for delivering packages to consumers. Before moving on to the next customer, the truck waits for the drone to return [28]. Recently, the CVPD problem was extended by Dukkanci et al. presenting the Energy Minimizing and Range Constrained Drone Delivery Problem (ERDDP), which involves choosing launch locations, assigning consumers to launch locations, and determining the drones speed between a customer and launch locations. The author

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proposes a nonlinear model that reduces operational costs by calculating the drone’s energy consumption as a function of drone speed [29]. Liu et al. developed a genetic algorithm-based mixed integer programming model (SDSMGA). The least number of drones expected for the delivery service sequence is determined using a scheduling decision support model [30]. Numerous methods for package delivery methods involving truck-drone collaboration were presented since 2015; some are mentioned in Table 1 describing the author with their approach to solving respective routing problems and their objective function. Table 1. Different Routing Problems [34] Reference Murray and Chu (2015) Ferrandez et al. (2016) Luo et al. (2017) Marinelli et al. (2017) Carlsson and Song (2017) Agatz et al. (2018) Bouman et al. (2018) Yurek and Ozmutlu (2018) Phan et al. (2018) Chang and Lee (2018) Ha et al. (2018a, 2018b) Poikonen et al. (2019) Jeong et al. (2019) Freitas and Penna (2020) Murray and Raj (2020) Salama and Srinivas (2020) Dayarian et al. (2020) Moshref et al. (2020a, 2020b) Gonzalez-R et al. (2020) Agardi et al. (2020) MbiadouSaleu et al. (2018) Li et al. (2018) Kim and Moon (2019) Schermer et al. (2020) Reference Dell’Amico et al. (2020) Wang et al. (2017) Poikonen et al. (2017) Di Puglia and Guerriero (2017)

Routing Problem FSTSP, PDSTSP FSTSP FSTSP FSTSP FSTSP FSTSP FSTSP FSTSP FSTSP FSTSP FSTSP

Approach

Objective function

heuristic

completion time, makespan

heuristic exact/heuristic heuristic heuristic heuristic exact exact/heuristic heuristic exact/heuristic heuristic

FSTSP FSTSP FSTSP FSTSP FSTSP FSTSP FSTSP FSTSP FSTSP PDSTSP PDSTSP PDSTSP PDSTSP Routing Problem PDSTSP VRP-D VRP-D VRP-D

heuristic heuristic heuristic heuristic heuristic heuristic heuristic heuristic heuristic heuristic heuristic heuristic Approach

delivery time drone routing time operations costs completion time operations costs completion time completion time routing cost delivery time operational costs,drone waiting & routing cost operations costs completion time completion time completion time completion time and routing cost maximize the orders customers waiting time completion time distance completion time operations costs delivery time makespan Objective function

heuristic -

completion time completion time completion time and routing cost travel costs

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Daknama and Kraus (2018) Schermer et al. (2018) Ulmer and Thomas (2018) Ham (2018) Wang and Sheu (2019) Kitjacharoenchai et al. (2019) Kitjacharoenchai and Lee (2019) Sacramento et al. (2019, 2019a, 2019b) Chiang et al. (2019) Kitjacharoenchai et al. (2020) Liu et al. (2020) Di Puglia Pugliese et al. (2020) Tamke and Buscher (2021) [31]

VRP-D VRP-D VRP-D VRP-D VRP-D VRP-D VRP-D VRP-D

heuristic heuristic heuristic exact heuristic heuristic heuristic

delivery time completion time number of customers served makespan logistics costs delivery time delivery time operational cost, makespan

VRP-D VRP-D VRP-D VRP-D VRP-D

heuristic heuristic heuristic

Yadav and Narasimhamurthy (2017) Dorling et al. (2017) Coelho et al. (2017)

DDP

heuristic

costs and CO2 emissions completion time operational costs travel costs maximum completion time & total completion time completion time

DDP DDP

heuristic heuristic

Liu (2019)

DDP

heuristic

Troudi et al. (2019)

DDP

heuristic

Savuran and Karakaya (2020) Mathew et al. (2017) Bin Othman et al. (2017) Gambella et al. (2017) Boysen et al. (2018) Dukkanci et al. (2019) Karak and Abdelghany (2019) Wikarek et al. (2019) Bai et al. (2019) Poikonen and Golden (2019) Poikonen and Golden (2020) Moeini and Salewski (2020) Han et al. (2020)

CVP-D CVP-D CVP-D CVP-D CVP-D CVP-D CVP-D CVP-D CVP-D CVP-D CVP-D CVP-D CVP-D

heuristic heuristic exact heuristic heuristic heuristic heuristic exact heuristic heuristic heuristic

Dukkanci et al. (2021) [29] Liu et al. (2020) [30] Huang et al. (2021) [32] Gómes-Lagos et al. (2021) [33]

CVP-D CVP-D CVP-D CVP-D

Exact heuristic heuristic

delivery time makespan, total travelled distance, number of drones and its max speed, maximize batteries load minimize lateness & maximize efficiency distance, number of drones and batteries targets number total distance and time total distance completion time makespan energy and operational cost operational costs total distance completion time completion time completion time total distance trucks number & energy consumption energy and operational cost number of drones delivery time makespan

Applications of UAV (Drone) Drones have been around for about two decades, but their roots can be traced back to World War I, when the US and France collaborated on developing

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unmanned aircraft. However, the previous several years have been noteworthy in drone adoption, application extension across industries, and global awareness. Following are the countries which have five deadly drones power in the world:     

United States Israel China Iran Russia

Figure 7 represents the involvement of drones in different industries. In the following section, we have studied the different applications of drone technology [35-39].

Figure 7. Top industries using the Drones.

Geographic Information System (GIS) Base Mapping and Data Acquisition Aerial drone mapping can efficiently and cost-effectively offer precise data in asset management, forestry, environmental, remote sensing, agriculture, oil,

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and gas. Moreover, we can take the realistic aerial footage into 3D models and 2D orthomosaics (maps) of small and medium-sized sites using an easy-todeploy aerial mapping drone.

Design Mapping by Civil Engineers Civil engineering designers require reliable topographic mapping data to ensure that design parameters meet practical conditions. Traditional survey procedures emphasize drainage structures, hard surfaces and inverted elevations; however, drone mapping can supplement them. Drones can also provide video, digital orthophotos, and graphics to help with strategy, planning, and subsequent construction observation while offering faster results.

Search and Rescue Drones are being used to help missing people and terrible victims, especially in challenging terrains or severe conditions. In addition to detecting fatalities, a drone can deliver supplies to hard-to-reach areas in military conflict or tragedy-stricken countries. A drone, for example, might be used to drop a GPS locator, walkie-talkie, medicines, clothing, food supplies and water to injured people before rescuers can transport them to a safer location.

Disaster Management Drones can quickly gather information and traverse obstacles and wreckage to hunt for injured victims after a natural or man-made disaster. Its highdefinition cameras, sensors, and radars provide rescuers with a broader field of view, reducing the need for human-piloted helicopters. Furthermore, with their small size, drones can also provide a close-up view of regions where larger aerial vehicles would be unsafe or inefficient.

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Border and Coastal Security Management UAVs are well-known for playing a vital role in many countries’ defence strategies. Then, however, drones were used by developed, wealthy countries. At the same time, today, technological improvements and a competitive market have allowed many countries to incorporate them into their defensive strategy, making them a familiar sight for marine, land, and border control. Central governments protect their borders and crucial sea routes using drone technology.

Swachh Bharat and Medical Applications Local municipal administrations and police deploy drones for vigilance in many parts of India as part of the ‘Swachh Bharat Abhiyan,’ a scheme run by the Indian government to clean India. Drones are being used in urban and rural areas to prevent water contamination. In addition, drones have shown to be quite helpful in delivering emergency medicines to distant locations and therefore saving lives. Drones could also help solve the need for blood products in the pre-hospital situation quickly and economically by transporting expensive and infrequently used drugs like antivenin for snake bites.

Weather Forecasting Drones that fly into the outermost layer of the Earth’s atmosphere are known as weather drones. They’re equipped with sensors that collect data on the humidity, temperature and wind in the atmosphere, enabling better weather forecasting models. In addition, using drones data collection is much easier with respect the traditional data collection methods like weather balloons and satellites. As a result, drones improve the accuracy of weather forecasting models significantly. NOAA and Professor Phillip Chilson of Oklahoma are currently collaborating on several projects to improve weather data collection and forecasting.

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Food /Restaurant Industry Due to the COVID19 epidemic, food delivery from restaurants and hotels has been replaced by a machine to ensure safety. In 2016, Flirty and Domino’s teamed up in New Zealand to deliver the first pizza by commercial drone. Flirty also started working with 7-Eleven in Reno, NV, in 2016 and completed 77 delivery orders. In addition, McDonald’s and Uber Elevate have teamed up to distribute food by drones by 2023.

Conclusion Drones have a significant history, but their economic potential has only just been recognized, providing substantial benefits and prospects to enterprises worldwide. Drones and sensors, as well as accompanying the software (flight planning, data processing), infrastructure (unmanned traffic management (UTM), regulations, and standards), are all advancing as a result of collaborative research and development. Developing an ecosystem that ensures safety, reliability, and accountability will be critical to integrating drones into daily life. The ecosystem is rapidly expanding through improved technologies and a more stable regulatory environment.

References [1]

[2]

[3]

[4]

[5]

Hu, J., Lanzon, A. (2018). “An innovative tri-rotor drone and associated distributed aerial drone swarm control”. Robotics and Autonomous Systems. 103: 162–174. doi:10.1016/j.robot.2018.02.019. Cary, Leslie; Coyne, James. “ICAO Unmanned Aircraft Systems (UAS), Circular 328.” 2011-2012 UAS Yearbook - UAS: The Global Perspective (PDF). Blyenburgh & Co. pp. 112–115. Hu, J.; Lanzon, A. (2018). “An innovative tri-rotor drone and associated distributed aerial drone swarm control”. Robotics and Autonomous Systems. 103: 162–174. doi:10.1016/j.robot.2018.02.019. Carlson, Daniel F.; Rysgaard, Søren (1 January 2018). “Adapting open-source drone autopilots for real-time iceberg observations”. MethodsX. 5: 1059–1072. doi:10. 1016/j.mex.2018.09.003. Carholt, Christoffer&Fresk, Emil &Andrikopoulos, George & Nikolakopoulos, George. (2016). Design, Modelling and Control of a Single Rotor UAV. 10.1109/MED.2016.7536015.

22 [6]

[7]

[8]

[9] [10] [11]

[12] [13]

[14]

[15]

[16]

[17]

[18]

[19]

Jyoti Kandpal, Pankaj Kumar Mishra, Bhavna Chilwal et al. Li, Yan & Liu, Chunlu. (2018). Applications of multirotor drone technologies in construction management. International Journal of Construction Management. 19. 1-12. 10.1080/15623599.2018.1452101. Bahabry, Ahmed & Wan, Xiangpeng&Ghazzai, Hakim &Menouar, Hamid &Vesonder, Gregg &Massoud, Yehia. (2019). Low-Altitude Navigation for MultiRotor Drones in Urban Areas. IEEE Access. PP. 1-1. 10.1109/ACCESS.2019. 2925531. Seewald, Adam & Marina, Hector &Midtiby, Henrik& Schultz, Ulrik. (2020). Mechanical and Computational Energy Estimation of a Fixed-Wing Drone.135-142. 10.1109/IRC.2020.00028. Tyan, Maxim & Nguyen, Nhu Van & Lee, Jae-Woo & Kim, Sangho. (2016). A Hybrid VTOL-Fixed Wing Electric UAV Sizing Methodology Development. Gunarathna, Janith&Munasinghe, R. (2018). Development of a Quad-rotor Fixedwing Hybrid Unmanned Aerial Vehicle. 10.1109/MERCon.2018.8421941. Zhongli Liu, Zupei Li, Benyuan Liu, Xinwen Fu, IoannisRaptis, and KuiRen. 2015. Rise of Mini-Drones: Applications and Issues. In Proceedings of the 2015 Workshop on Privacy-Aware Mobile Computing (PAMCO ‘15). Association for Computing Machinery, New York, NY, USA, 7–12. doi: https://doi.org/10.1145/2757302. 2757303. Casagrande, Gianluca&Szabo, Gergely&Sik, András. (2017). Small Flying Drones. Quaritsch, Markus &Stojanovski, Emil &Bettstetter, Christian & Friedrich, Gerhard &Hellwagner, Hermann &Rinner, Bernhard &Hofbaur, Michael & Shah, Mubarak. (2008). Collaborative microdrones: Applications and research challenges. 38. 10.1145/1487652.1487690. Oktay, Tugrul&Coban, Sezer. (2017). A REVIEW OF TACTICAL UNMANNED AERIAL VEHICLE DESIGN STUDIES. The Eurasia Proceedings of Science, Technology, Engineering & Mathematics. 1. 31-35. Tahar, KhairulNizam&Kamarudin, Sarah.(2016). UAV ONBOARD GPS IN POSITIONING DETERMINATION. ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences. XLI-B1.10371042. 10.5194/isprs-archives-XLI-B1-1037-2016. Aditya Bhardwaj, Achira Basu, Dr. A. SenthilSelvi, 2019, Package Delivery System Using GPS Drones, International Journal of Engineering Research & Technology (IJERT) Volume 08, Issue 10 (October 2019). Yi-Ling Chen, Wei-Tse Lee, Liwei Chan, Rong-Hao Liang, and Bing-Yu Chen. 2015. Direct view manipulation for drone photography. In SIGGRAPH Asia 2015 Posters (SA ‘15).Association for Computing Machinery, New York, NY, USA, Article 23, 1. doi: https://doi.org/10.1145/2820926.2820945. Shuo Li, Michaël M. O. I. Ozo, Christophe De Wagter, Guido C. H. E. de Croon, Autonomous drone race: A computationally efficient vision-based navigation and control strategy, Robotics and Autonomous Systems, Volume 133, 2020, 103621, ISSN 0921-8890, https://doi.org/10.1016/j.robot.2020.103621. Chen, J., Raye, D., Khawaja, W., Sinha, P., &Guvenc, I. (2018, August). Impact of 3D UWB antenna radiation pattern on air-to-ground drone connectivity. In 2018 IEEE 88th Vehicular Technology Conference (VTC-Fall) (pp. 1-5). IEEE.

Introduction to Drone Technologies [20] [21] [22]

[23] [24]

[25] [26] [27] [28]

[29]

[30]

[31]

[32]

[33]

[34]

[35] [36] [37] [38] [39]

23

Johnson, M. (2015). Components for Creating an Unmanned Aerial Vehicle. Tech. Rep. Park, S., Kim, H. T., Lee, S., Joo, H., & Kim, H. (2021). Survey on anti-drone systems: Components, designs, and challenges. IEEE Access, 9, 42635-42659. Rohan, A., Rabah, M., Asghar, F., Talha, M., & Kim, S. H. (2019). Advanced drone battery charging system. Journal of Electrical Engineering & Technology, 14(3), 1395-1405. Drones For Deliveries From Medicine To Post, Packages And Pizza by Fintan Corrigan, July 2, 2020. Murray, C. C., Chu, A., 2015. The flying sidekick traveling salesman problem: Optimization of drone-assisted parcel delivery. Transport. Res. Part C: Emerg. Technol. 54, 86–109. Dorling, K., Heinrichs, J., Messier, G. G., Magierwski, S., 2017. Vehicle routing problem for drone delivery. IEEE Trans. Syst., Man, Cybernet.: Syst. 47 (1), 1–16. Agatz, N., Bouman, P., Schmidt, M., 2018. Optimization approaches for the traveling salesman problem with drone. Transport. Sci. 52 (4), 739–1034. Dorling, K., Heinrichs, J., Messier, G. G., Magierwski, S., 2017. Vehicle routing problem for drone delivery. IEEE Trans. Syst., Man, Cybernet.: Syst. 47 (1), 1–16. Mathew, N., Smith, S. L., Waslander, S. L., 2017. Planning paths for package delivery in heterogeneous multirobot teams. IEEE Trans. Automat. Sci. Eng. 12, 1298–1308. OkanDukkanci, Bahar Y. Kara, TolgaBektaş, 2021. Minimizing energy and cost in range-limited drone deliveries with speed optimization. Transportation Research Part C: Emerging Technologies, Volume 125, p. 102985. Liu, C., Chen, H., Li, X., & Liu, Z, 2020. A scheduling decision support model for minimizing the number of drones with dynamic package arrivals and personalized deadlines. Expert Systems with Applications, 114157. Tamke, F., &Buscher, U. (2021). A branch-and-cut algorithm for the vehicle routing problem with drones. Transportation Research Part B: Methodological, 144, 174203. Hailong Huang, Andrey V Savkin, Chao Huang. 2021. Drone routing in a timedependent network: Toward low-cost and large-range parcel delivery. IEEE Transactions on Industrial Informatics. p. 1526-1534. Gómez-Lagos, J. E., Candia-Véjar, A., &Encina, F. 2021. A New Truck-Drone Routing Problem for Parcel Delivery Services Aided by Parking Lots. IEEE Access, 9, 11091-11108. GiusyMacrina, Luigi Di Puglia Pugliese, Francesca Guerriero, Gilbert Laporte. 2020. Drone-aided routing: A literature review. Transportation research. Part C, Emerging technologies 120, p. 102762. Tatum, M. C., & Liu, J. (2017). Unmanned aircraft system applications in construction. Procedia Engineering, 196, 167-175. https://oeaconsults.com/application-of-gis-drones-for-land-use-planning-analysis/. https://njspe.org/2021/01/28/civil-engineering-drone/. https://www.allerin.com/blog/10-stunning-applications-of-drone-technology. https://www.droneblog.com/how-drones-are-helping-with-weatherforecasting/.

Chapter 2

Drones for Society and Industry Pankaj Kumar Mishra1,, Jyoti Kandpal2, Abhay Kumar Singh1, Geetanjali Balutia3, Bhavna Chilwal4 and Bhongiri Prasuna4 1

College of Technology, G.B.P.U.A.T., Pantnagar, India NIT Arunachal Pradesh, India 3 Department of Electronics and Communication Engineering, Tehri, India 4 DRDO, DGRE, RDC, Manali, India 2

Abstract The industrial utilization of drones, also known as unmanned aerial vehicles (UAVs), has the potential to revolutionize numerous industries drastically. Drones are changing how we perceive our physical world because of their potency to retrieve statistics and transmit goods. Drones are considered carriers for sensing devices of any kind in the industrial environment, and they are mainly utilized for surveillance and inspection. Drones aid in shaping society with the help of various applications like traffic monitoring, shipping goods, etc.

Keywords: drone, automobile sector, industry traffic monitoring



Corresponding Author’s Email: [email protected].

In: Revolutionary Applications of Intelligent Drones Editors: Mohit Angurala and Vikas Khullar ISBN: 978-1-68507-991-8 © 2022 Nova Science Publishers, Inc.

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Introduction Drones, also primarily perceived as unmanned aerial vehicles (UAVs), are a fundamentally aerial robot that may be commanded electronically or flown automatically via software-controlled flight plans in their integrated modules, which functions with sensing capability and a global positioning system (GPS). UAVs are aircraft that do not have a human pilot, crew, or passengers on board [1]. Since their early utilization as surveillance drones throughout World War I, unmanned aerial vehicles (UAVs) have passed a long way. The Drone Racing League is on its way to becoming the most popular sport in the historical past. Given their ubiquitous existence not just in numerous diverse industries, drones are a rising influence upon society has established bold avenues no one could have expected. UAVs were indeed constituted in the 20th century for defence operations, which seemed too gloomy, cluttered, and considered deadly for living creatures and other species. After that, in the 21st century period, they have become indispensable elements for many military units [2]. Drones aid in shaping society, widely used in various industries, varying from oil firms monitoring pipelines to real estate brokers taking aerial photographs of a location and professionals in the entertainment industry. Drones are increasingly getting more prominent in both the commercial and non-profit sectors. Their usage will become progressively quite prevalent in the forthcoming years. The industry has adapted as technology strives to flourish and play a more significant part in consumers’ lives. As a result of pervasive Web connectivity, businesses have shifted away from brick and mortar to primarily online services. As a result of this transition, internet businesses save money on overhead expenditures such as rent and wages from running physical retail. While ordering goods and services from online shops is much more convenient, the consumers have to pay additional shipping and handling fees. Even though technology has revolutionized many businesses in the last ten years, shipping and postage have stayed untouched. A drone can help cut down the shipping cost by delivering goods to consumers, which can help in less human intervention, especially when the world is dealing with a contiguous disease like covid, where human intervention is inevitable. Drones are already breaking down obstacles in the way businesses operate. Drone delivery is being tested by major organizations such as Amazon and Google. Facebook is also using drones to give Internet access in rural areas. Even a start-up delivers food to your door using unmanned planes. Drones have caught the considerable interest of start-up companies for good reason. UAV market

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share is anticipated to achieve $48.88 billion in 2023. Drones are being used for commercial, recreational, scientific, and agricultural purposes, creating jobs for thousands of people. In 2025, for example, more than 100,000 new jobs for drone pilots are expected. As a result, drone technology is facilitating humanity in various ways. So, drones are becoming immensely significant in science, technology, industry, and society.

Key Sectors of Society and Industry Automobile Sector Locating Cars Set for Dispatch Custom autonomous drones assist Audi in identifying automobiles that are ready to be shipped in Germany. The drones fly over all vehicles on-site, identifying their locations with GPS and RFID technology. Data is automatically sent to a database as soon as the drone lands. The results are displayed on a digital map for staff to locate the automobiles ready for dispatch more effectively using a unique identification number. Before each flight, the drone collects meteorological data and only flies within safe limitations. The drone takes off, flies, and lands completely autonomously. Audi staff use a laptop or tablet to start and monitor the flight. They can use a remote control to interfere in the otherwise entirely autonomous operation in the event of an emergency. Employees were taught how to operate industrial drones. The drone automatically receives the meteorological conditions around the factory before each flight. The drone will not take off if the wind is too high, gusty, or there is too much rain. Before each flight, the programme also monitors the drone’s battery level and temperature. If something does not fall within the defined safety boundaries, it will remain on the ground [3]. Route Optimization for Self-Driving Vehicles Drones combined with self-driving cars can provide an extra set of eyes and more data to the driverless vehicle, reducing the likelihood of an accident. Even though self-driving systems include many safety features, impediments such as road-blocking incidents can cause the vehicle’s programmed path. As an added safeguard, a drone attached to the car can be used to determine the optimum alternate route that avoids both the impediment in question and other potential concerns. In addition, data from the drone can be exchanged between

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self-driving systems and other cars to assist them in altering their paths by providing a bird’s-eye view.

Reducing Traffic Congestion with Passenger Drones Passenger drones were already tested at the Consumer Electronics Show. Air taxis may be the best answer to traffic congestion and jams once they are legalized for commercial use. Passenger drones can shorten commute times while reducing pollutants. Passenger drones could minimize the number of cars and larger delivery trucks on the roads and travel time by transporting products and moving passengers. Drones are poised to alter and improve the automobile sector, making our lives easier and safer in the new mobility era. Monitoring Motorway Breakdowns with Surveillance Drones 360 Towing Solutions USA uses surveillance drones to assist in the removal of broken-down vehicles from local and international highways. 360 Towing plans to run its network of observation drones from a control room where they will dispatch the suitable vehicles and drivers to a highway breakdown. While drivers may perceive a cluster of monitoring drones as an invasion of their privacy, the presence of these drones would enable stranded drivers to receive assistance with their vehicles more quickly. Drones and the Vehicular Inspection Process Mitsubishi Electric Automotive America also plans to deploy drones to improve the ability of its vehicles to interact with their surroundings. In addition, Mitsubishi proposes more research to see if drones can keep roads uncluttered and make parking easier in metropolitan areas by pre-seizing parking spots.

How Oil and Gas Companies Will Use a Drone in the Future With increased competition, shifting pricing, and limited inventories in current wells, oil corporations are likely to use drones and other innovative technologies to shift to non-conventional sources and challenging conditions. Although unmanned vehicles will be used for various purposes in the future, these are the top three drone applications in the oil and gas business in 2023. 

Oil and gas exploration

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 

29

Inspecting and monitoring pipelines Oil and gas operations.

Oil and Gas Exploration Unmanned vehicles offer a cost-effective and safe alternative in existing and new onshore and offshore locations. The devices, in particular, can assist oil and gas corporations with aerial, subsurface, and submarine explorations. In addition, they can perform other activities, such as water sampling and building 3D maps of prospective locations and environs, and give visual views. Data from oil rigs, offshore platforms, and other places provide valuable information that organizations may use to enhance operations, output, and efficiency and anticipate and correct faults. Proper sensors and software tools will enable quicker data gathering and computation of geological and seismic data at onshore and offshore exploration sites. Drone inspections enable researchers to collect essential data from possible oil wells. Before deploying humans to the ground, the drones will provide topographical information on the new locations by utilizing other laser scanning and GPS technologies. This will also help determine the best location for the facility and access routes and other needs. In addition, the UAVs will create 3D maps with GPS and laser scanning technology, which will aid in determining the form and height of objects and the drone’s surroundings. These maps will feature landmarks such as mountains, rivers, buildings, highways, and other essential components to help engineers plan [4]. The aerial inspection provides visual pictures that organizations may use to get real-time information on activities such as building projects. They can also help discover hazards or problems that need to be addressed. The subsurface investigation is carried out using drones housed inside hollow tubes implanted in the soil. These may then be used to collect critical data, such as images, to assist researchers in establishing the presence of oil products. The same applies to subsea facilities, where the drone will be able to gather water samples from various places. To detect the presence of oil products, chemists analyze samples from offshore and subsurface sites. a) Application of drones on oil and gas pipelines - Unmanned vehicles perform several tasks to ensure that pipelines run smoothly and safely. Drones provide for more efficient and cost-effective pipeline

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inspections, which would typically take weeks or months and costly equipment, resources, and labour. Drones can also examine challenging terrain, remote places, and other difficult-to-reach regions without endangering personnel. Drones are often used for inspection, pipeline monitoring, maintenance, and natural state monitoring, among other things. Regular inspections can detect leaks, spills, and other issues that could cause spillage and environmental degradation. By equipping the drones with the necessary sensors, they can conduct visual inspections of the interior and exterior surfaces. Identifying the direction of spills or gas leaks will help evacuate or keep people away from potentially dangerous and fire dangers. Aside from the pipeline itself, the drones will monitor the structure for exterior threats such as vegetation encroachment, theft, sabotage, and natural calamities such as earthquakes. Special drones will monitor and report any seismic activity around the pipelines. With this knowledge, businesses will be able to respond promptly and reduce the effect of natural disasters. To limit possible hazards, they can, for example, temporarily block the valves and stop the flow of the items, notify people in the area, and take other preventive measures. Drones will be loaded with robots that will allow them to repair damage both within hollow locations and on the exterior surfaces as technology advances. b) Drones in oil and gas operations - Unmanned autonomous vehicles will help to speed and optimize the purification and manufacturing processes by aiding with operational activities like analysis, product segregation, and logistics. They can, for example, be used to transfer samples and check processes and products as they move through the facility’s many phases. UAVs may also inspect buildings, tanks, and other vertical structures and inaccessible areas within tanks and pipelines. The advantages include improved safety and the ability to inspect systems while they are still working, resulting in fewer shutdowns and risks because staff are not needed to climb or enter risky areas. As a result, more of these technologies will monitor flare stacks, cooling structures, tanks, and other infrastructure. Regular inspections and monitoring of these structures enable early discovery and rectification of any problems. Aside from doing routine inspections to discover issues, drones are also fantastic damage assessment tools. They will also be used by businesses to improve worker safety and

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wellbeing. The drones, equipped with the appropriate sensors and instruments, will help improve operations, safety, and emergency services such as evacuations. During exploration and extraction jobs, they will be able to highlight and notify employees of potentially risky areas and conditions. They are also instrumental in rescue scenarios since they can capture overhead photos and highlight the best escape path. UAVs may be used for medical purposes and infrastructure monitoring and inspection, enhancing operations and production. Physicians will be able to diagnose and treat field workers on oil rigs and in remote locations by integrating various technologies with teleconferencing. UAVs conduct aerial inspections during normal and emergency operations. They can keep an eye on security and provide alerts in the case of an emergency or breach. They are beneficial during emergency operations since they may offer live feeds of the situation, such as safe escape routes or the direction in which fires or liquids are spreading. Maintain a record of all asset activities and statuses. This provides documentation or backup of the facility’s actions to the maintenance and security teams, which may be helpful in the future when dealing with ethical or legal concerns. c) The benefits of using drones in the oil and gas industry are excellent for real-time surveillance of pipelines and other infrastructure. Most drones have a ready-to-use payload, while some may combine many sensors and abilities with complex inspections and data processing. The principal benefits of using UAVs are increased safety, cheaper costs, and gathering and disseminating important visual and other inspection data from critical assets. In addition, drones boost efficiency and production by performing routine, low-cost, and rapid inspections. This is due to their ability to detect both existing and future defects, allowing maintenance teams to rectify them as soon as feasible. d) Future of drones in the oil and gas industry - Future gadgets will have even higher capabilities as drones and other technologies advance. The devices will also get more innovative, allowing operators to analyze various infrastructure issues more effectively with infrared cameras, corrosion testing technologies, gas detectors, 3D laser scanning, ultrasonic testing, and other technologies. Others will concentrate on certain industries. For example, a drone robot will be able to check and repair oil and gas infrastructure such as pipelines. Companies will forgo traditional aerial inspection approaches for

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pipelines and other infrastructure that rely on fixed-wing aircraft or helicopters. Drones are becoming more affordable, adaptable, and capable, and most organizations will be obliged to use them to improve their operations, efficiency, and competitiveness. Drones will be utilized in the future to undertake initial site assessments and monitor and check pipelines, refineries, and manufacturing facilities. Furthermore, their flexibility and abilities will help organizations easily, safely, and quickly discover leaks, corrosion, theft, and other fault and security concerns. However, several challenges must be overcome for UAV applications to be practical. This includes connectivity, networks, security, and scalability. Furthermore, regulatory and compliance issues must be addressed.

Importance of Drone Technology in Real Estate Marketing Real estate exhibits its significance in its simplest form by including the marketing and selling places and listings for various corporate, educational, and military applications. Understanding the complexities of real estate means appreciating the industry’s severe issues while also researching creative strategies to promote its growth. The discovery and implementation of drone technology at its heart emerged from sifting through the abundance of alternatives for growing the real estate sector. The new technology has given rise to a slew of automated solutions that have unambiguously demonstrated the justification for and crucial significance of innovation in today’s climate. In this setting, drone technology has not only accelerated national growth but also acted as a stronghold for a variety of industries. Because of its beneficial and redeeming character, drone technology has had a significant influence on the running and progress of the real estate industry. For example, drones for real estate photography have brought forth a bevy of benefits that will aid this firm in the long run [5]. a) Dynamic Visuals - The ability of drone technology to capture unique and dynamic visual video is a significant benefit in the real estate industry. The mobility of UAVs accounts for their quick movement, allowing drones to monitor and collect aerial imagery. Aerial photography and comprehensive images from all angles reinforce the benefits of drones, demonstrating UAVs to be a novel development

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b)

c)

d)

e)

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choice. In addition, the creative information collected by smart sensors and high-quality drone cameras serves as a current real estate marketing technique. Better Listings - As a testament to its tenacity, aerial drone photography has sucked in a large ocean of serviceability and flexibility. The ingenuity of aerial photography and visual material has sparked many people’s attention and curiosity. Realtors may utilize this to acquire more clients. In drone photography, 3D models, mapping, and other services, listings have discovered a new marketing channel. UAVs are an effective marketing tool for selling sites and listings, emphasizing several characteristics of these listings that would not have been visible if the real estate industry had not used drones. Accuracy - The operators of all activities strive to be exact and accurate in their respective areas regardless of their scale. Drone photography, mapping, 3D modelling, airborne surveillance, and inspection have attained efficiency based on measurement precision. Residential real estate drones are designed to be precise in navigation and data collecting. Real estate photography has benefited from the commercial usage of drone images and UAV data, which has increased the property of accurate measurements. Drones drove by artificial intelligence deliver significant as precise insights as technology allows. Highlight more Property Features - When UAVs are invested in, the abundance of opportunities for success, particularly in the real estate sector, is unrivalled. Drones do this by photographing from several angles and shooting in various modes, as well as having a wide range of picture settings pre-programmed into them. In addition, their mobility, especially in remote areas, helps the aerial footage recorded by the drones, producing and casting light on a variety of previously overlooked parts and features of properties. Generate New Business -The capacity to produce and develop extra business is essential for any industry, big or small. This may be firmly integrated with the usage of drones, which are primarily associated with the real estate business. A virtual tour is one way to keep current in the real estate market. Because it is beautiful, it enhances the clients’ engagement factor significantly. UAVs also provide customers with a complete awareness of the listings’ surroundings with their aerial imaging and mapping, allowing them to make

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informed judgments. As a result, people are sufficiently captivated by conducting business through drone photography to commit to it fully. f) High-Flying Savings - Drones, in general, feature high-definition cameras and improved operating quality and the capacity to save time, effort, and money. Because of their extensive effect, the use of such profound technical wonders would only be wise. One of the most compelling reasons to employ drones for real estate photography and marketing is their efficiency and performance, but more crucially, their affordability. The introduction of new ideals frequently comes at a hefty cost. Drones, notably linked to real estate, look to be a significant exception since they dramatically lower production and operating costs. g) Outsmart the Competition - Drone technology, which is powerful and elegant, has altered numerous industries. For example, drones for commercial real estate have incrementally improved their business and performance. Immense potential in innovative aerial drone photography, clever virtual tours, high-quality drone images, 3D models, mapping, UAV data processing, and various other drone features help real estate firms outwit and outpace their competition. h) Location - Drone technology’s proactive effect helps important parts of drone deployment in real estate operations such as site inspection, aerial surveillance, drone mapping, marketing, and creative content. Because of their speed and agility, drones aid in the smooth movement and accessibility of remote locations in the real estate business. It also enables you to explore the surroundings and site listings for further in-depth surveying.

The Future of Drones in Real Estate Real estate factors that are becoming more intense, drone technology, and innovation have propelled the sector to new heights. Drones and Unmanned Aerial Vehicles (UAVs) are gaining traction by seeking to improve the experiences of real estate companies and their clients. Drones, which reduce the limits of real estate marketing, appear to be immaculate testaments to the prodigiousness of technological advancements and breakthroughs. Consequently, the future of drones in real estate is projected to be bright for both real estate and drone market sizes.

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Drones for Traffic Monitoring An uncrewed aerial vehicle (UAV), commonly known as a drone, is gaining considerable interest in transportation engineering to monitor and supervise traffic. Road transportation is the lifeline in all cities. Nowadays, traffic congestion has become a daily life problem in any metropolitan city. With the increase in the growth of traffic volume and the growth of global travel, traffic monitoring is a significant challenge in many countries worldwide. Important reasons for traffic congestion are poor traffic management and the exponential increase in the number of vehicles as many people use their transportation due to insufficient availability of public transport. Usually, we still use a manual traffic management system in many cities where there will be traffic lights at the junctions. A person needs to monitor the junction based on the automatic timing system. But in, traffic congestion and accidents is a time-consuming processes. Within transportation engineering, there are many ways where drone technology is being used, and progress is being made to explore ways to benefit from the technology. Traffic monitoring using drones has many benefits because they can be deployed in many places when we want to gather information on infrastructures, bridges, and train tracks and provides a clear insight into situations that might not be clear from the ground. The reliability of traffic measurement data becomes an essential requirement for road traffic studies to respond effectively to the significant challenges caused by the significant growth in vehicle volumes on the road network. Most of the efforts are based on collecting traffic and driving behaviour data captured through bird-eye cameras mounted on drones. This data can be used for many cases such as surveillance and monitoring, recognizing traffic violations, aiding in traffic congestion management, signal optimization and extracting vehicle trajectories to answer research questions related to accident risk assessment. Several studies have been done on intelligent traffic management systems using drone technology. FatmaOutay et al. [6] have proposed applications of unmanned aerial vehicles (UAVs) in road safety, traffic and highway infrastructure management, recent advances and challenges. In this paper, they have given a review of recent developments in the application of UAVs in transportation systems. They have considered three cases road safety, traffic monitoring, and highway infrastructure management. First, computer vision algorithms extract critical features from UAV images and videos for all these cases. Second, the road safety-based application of UAVs included detailed accident investigation, risk assessment, and overall road network surveillance.

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Traffic monitoring, the research focuses on developing a set of algorithms and methods to extract useful information from video data obtained from drones. Third, traffic flow analysis included the performance of different road geometries such as roundabouts, signalized and non-signalized intersections, and other traffic flow behaviours, including lane change behaviour, gap acceptance analysis, shock wave analysis, etc. Finally, in highway infrastructure management, monitoring and management of physical highway infrastructure are reviewed by considering two areas, i.e., bridge inspection and monitoring and pavement distress recognition.

Drones for Border Surveillance Drones are a significant part of border security and surveillance nowadays because they are suitable for real-time reconnaissance, tracking movement of vehicles & people, target acquisition, and illegal activities with high-quality video capturing. The drones with thermal detection picture cameras are best compared with stationary cameras to track movements through dense areas and forests. Mobile technologies and drones are the best way to overcome the limitations of fixed surveillance system issues in border activities. The drones have access to dangerous and dense areas and a short response time for patrolling purposes, but these drones’ flight time is short. For this purpose, SeonJin Kim et al. propose the concept of E-Line (electrification line) to charge drones wirelessly during their flight to increase their durations. Amir Mirzaeinia et al. discussed that drone technology of small drones with recharging stations near borders helps to reduce costs for border security and patrolling hours of a man near borders. Also, small drones can visit remote areas. Moreover, the small drones take less time for maintenance [7]. Boris Shishkov et al. proposed that drones benefit uncrewed security missions by supporting embedded systems and processing data. The rules made by algorithms are input to drones for decision-making and decisions that might be taken based on monitoring data [8]. Luisa Marin focuses on the placements of drone technology in border securities and accessing with impact on privacy. The uncrewed aerial vehicles and aircraft systems help form intelligent border systems like EUROSUR. One of the well-developed drones for surveillance is the fully-autonomous Blackbird drone and the smallest and highest calibre drone worldwide. This drone can be scheduled for flying patrols any time in the rain, dust storms,

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snow, day and night. It can respond to alerts, can be video feeds, recharges and airborne in a few seconds on its own. This drone features autonomous landing and takeoff, response ready, artificial intelligence, properties, processing power, and weather protection. The robotic securities provide security capabilities and enhance security. In India, the traditional approach for border security has ground sensors, human-crewed vehicles and aircraft, and video cameras. Still, with time, these technologies become ineffective in identifying different challenges related to country security. So, to ensure robust surveillance, drone technologies for border security and control become essential. Moreover, drones are cheaper and more cost-effective to deploy than more comprehensive technologies. Some border surveillance drones used by Indian defence forces are 

NETRA V Series UAV – Endurance Champion.

It is a highly portable, autonomous UAV. It covers a larger area than the auditory and visual range of humans. It has zoom-in capability and multiple failure safety features. 

NETRA Pro UAV – Versatile Workhorse

It is a versatile UAV with drop features. It is IP53 and rugged certified, used in the most demanding conditions. It has GPS and rotor systems. It can operate for two payloads as day & night video cameras and supply drops. It is used for Inspection, Surveillance, Traffic Management, and Photogrammetry. 

SWITCH UAV – All Terrain Dominator

It’s the first kind of VTOL with a fixed-wing hybrid UAV. It has advanced flight time and high safety, used for a long duration, endurance and security operations.

Drones Used in Emergency Cases Emergency services are frequently operating against the time and in situations that limit their ability to move about on the ground. Drones can be a tremendously helpful tool in making an initial assessment. Rapid deployment of the first reaction within 60 minutes of an incident (known as the ‘golden

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hour’) might be the difference between life and death. In addition, drones can be particularly valuable in assisting emergency services in determining how to effectively arrange their rescue efforts while a tragedy is underway [9]. Flood disasters 1. 2. 3. 4.

In earthquakes In search and rescue To deliver emergency supplies Drones for disaster recovery

Firefighter drones are utilized to assist fire departments. While drones get smaller, more powerful, and have more payload possibilities, one thing that will remain constant is their capacity to reach vantage points that people cannot easily achieve quickly. Through better data gathering and surveying, these remote controlled or even autonomous flying platforms can be utilized to make people’s occupations easier and more efficient. Firefighter Drones are sent to fire scenes as scouts, employing thermal imaging cameras to assist first responders in their rescue attempts [10].

Delivery of Goods Using Drones Drones for delivering parcels and other commodities are attracting significant investment from some of the world’s most influential companies. It’s only a matter of time before huge firms like Amazon, Walmart, UPS, Google, and other global postal companies invest in drone delivery programmes. The following are the top delivery drones which are employed by the prominent firms for parcel delivery and are listed below [11]. Drones for delivering parcels and other commodities are attracting significant investment from some of the world’s most influential companies. It’s only a matter of time before huge firms like Walmart, Amazon, Google, UPS and other global postal companies invest in drone delivery programs. The following are the top delivery drones    

Wing delivery drone Matternet M2 parcel delivery drone Wingcopter 178 Heavy Lift delivery drone RakutenTenku delivery drone

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  

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Condor parcel delivery drone Zipline autonomous delivery drone Flirtey delivery drone

Drones Use in COVID 19 Managements In response to COVID-19, media publications and other relevant sources have identified three essential use cases for drones. These are some of them a) Lab sample collection and delivery and medical supply transportation to reduce transportation delays and infection risk. b) Public spaces are sprayed from the air to sanitize possibly polluted regions. c) Observation and advice in public spaces during lockdown and quarantine.

Importance of Drones in the Agriculture Field Agriculture as the primary source of income for the livelihoods of households in rural areas is making India an agrarian economy. India’s economy is however strongly reliant on agricultural products, which account for a large number of the country’s exports. Despite its growing importance, agriculture is still lagging behind technological improvements. The leading causes of this situation are crop failure because of bad weather and unmanaged pest problems. Furthermore, Indian farmers still rely on the rainy season for irrigation and practice age-old farming. As a result, despite farmers’ best efforts, the quality and quantity of agricultural products are often compromised. Drones can assist farmers in responding more quickly to challenges (weeds, pests, and fungi), reducing crop scouting time (validating procedure taken), improving variable-rate treatments in real-time, and estimating yield from a field to achieve maximum use of inputs (seeds, chemical fertilizer, and water). Several agricultural projects based on drones are in progress, and some real-life examples are presented in the following section

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1. BAYER, a different invention of drones known as a multi-rotor, was introduced by China to deploy crop protection products on farms and is operated by rechargeable batteries. With only a 5-10 litre tank, a UAV can fertilize one hectare of rice in 10 to 15 minutes. These multi-rotor drones can fly and land independently, have an automatically filling tank fitted to the specific field size, track the terrain, & keep a steady level over the crop with the help of a height sensor. In addition, newer models can identify obstacles and avoid collisions while in flight. 2. Drones like the DJI Agras MG-1 (DJI, 2017) are meant to spray liquid fertilizer, pesticide, and herbicide at special fixed rates. The MG-1’s technology enables it to lift a payload of 10 kg of liquid chemicals and spray the region of 4,000-6,000 m² within 10 minutes, which is 40 to 60 times more efficient than human spraying. Furthermore, the innovative spraying system controls its spray as per the aircraft’s speed, ensuring even spray. This technique prevents pollution and saves expenses by accurately controlling the percentage of chemicals used.

Figure 1. Advantage of Drones in the Agriculture Field.

3. In the Philippines, the FAO does use drones that are integrated with navigating systems and photogrammetric data with a ground resolution of up to 3 cm. For example, using this drone, the Normalized Difference Vegetation Index (NDVI) may be used to

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detect water stress or a shortage of certain nutrients in crops. Dronebased mapping work is now being mainstreamed in the Philippines under the FAO’s disaster risk reduction and management (DRRM) and climate change adaptation (CCA) programs. Intelligent drones also inform you when to visit fields for cultural operations like fertilizer and pesticide spraying. 4. An MoU was signed between the World Economic Forum (WEF) Centre and the Indian Government for the Fourth Industrial Revolution to investigate the use of drones for a variety of government programs. As a result, farmers in Maharashtra’s DahanuPalghar tribal communities are taught how to use drones for crop rotation, organic farming, biocontrol, fish farming, hydroponics and biowaste management, along with the use of drone-based techniques on agricultural farms. Also, the International Crops Research Institute (ICRISAT) has been granted permission to utilize drones for agricultural research by the Indian government.

Importance of Drones in the Construction Industry The Construction Industry is constantly adopting innovative technology to improve the speed, efficiency, precision, and safety of construction projects worldwide. For rapid project execution, advanced technologies are making their way into Construction Industry. Monitoring mechanisms are also being upgraded to cope with the increased execution speed. Thereby, drones have entered the scene; however, they don’t contribute to the actual execution, but they significantly contribute to faster project monitoring, which aids in quick decision-making and thus reduces project delay time. According to the Commercial Drone Industry Trends survey, which Drone Deploy released in May 2018, the construction industry has surpassed all others in terms of commercial drone service usage. It’s just a matter of time before the construction industry catches up to the drone photography industry in terms of market share, with a phenomenal 239% increase in industrial usage. Most construction companies already use drone technology for rapid and secure performance assessment and real-time connection, with applications for pre-construction planning. Compared to traditional data collecting methods, drones have provided construction organizations with up to a

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twenty-fold rise in time savings, decreasing man-hours and operational expenses.

Figure 2. Advantages of Drones in the Construction Industry.

Skycatch, a US-based IT start-up specializing in developing maps and models employing drones for the mining and construction sectors, placed the largest ever order for commercial drones with DJI in 2018.

Importance of Drones in the Medical Field Drones are employed for disaster surveillance and places with biological risks and epidemiology research, and illness tracking. Telecommunication drones are employed in remote areas for diagnosis and treatment, telementoring, and perioperative evaluation. In addition, drones promise to be trusted delivery platforms for medications, microbiological and laboratory samples, vaccines, and emergency medical transportation. Drone use in the medical field has been put on the national agenda by government bodies—active research endeavours in risk management, industrial growth, and enhancing public awareness and participation.

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Figure 3. Advantages of Drones in the Medical Field.

With the world in quarantine and social isolation becoming necessary due to the COVID-19 pandemic, numerous steps have been taken to meet fundamental human requirements. However, the medical sector is experiencing numerous challenges as the number of cases remains high, and it is challenging to maintain enough vital equipment and drug supplies. Using drone payloads to distribute medications, transmit tests and samples, and apply disinfectants seems to become a new approach for hospitals and the government to execute operations. Drones that can carry payloads and reach up to a certain distance have now been designed by governments and technology companies from India to Canada and the United States to allow faster healthcare and medical services.

Conclusion Drone technology improves the working environment through technical empowerment. Significant research work in drone-related applications proves the usability of UAVs. Industries are designing drones as per requirement. Different sectors will have different requirements. The use of drones significantly improves productivity and saves handling time. Popular sectors

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like agriculture, healthcare, and security are successfully using drones. Data collection in the human inaccessible area is a major advantage of the drone.

References [1]

[2]

[3]

[4]

[5]

[6]

[7]

[8]

[9] [10] [11]

Kim, Seon Jin & Lim, Gino. (2018). Drone-Aided Border Surveillance with an Electrification Line Battery Charging System. Journal of Intelligent & Robotic Systems. 92. 10.1007/s10846-017-0767-3. Mirzaeinia, Amir & Hassanalian, Mostafa& Lee, Kooktae. (2020). Drones for Borders Surveillance Autonomous Battery Maintenance Station and Replacement for Multirotor Drones. 10.2514/6.2020-0062. Boris Shishkov, Stefan Hristozov, Marijn Janssen, and Jeroen van den Hoven. 2017. Drones in Land Border Missions Benefits and Accountability Concerns. In Proceedings of the 6th International Conference on Telecommunications and Remote Sensing (ICTRS’17). Association for Computing Machinery, New York, NY, USA, 77–86. doi https//doi.org/10.1145/3152808.3152820. Nouacer, R., Hussein, M., Espinoza, H., Ouhammou, Y., Ladeira, M., & Castiñeira, R. (2020). Towards a framework of key technologies for drones. Microprocessors and Microsystems, 77, 103142. Johnsen, S. O., Bakken, T., Transeth, A. A., Holmstrøm, S., Merz, M., Grøtli, E. I., ... & Storvold, R. (2020). Safety and security of drones in the oil and gas industry. In Proceedings of the 30th European Safety and Reliability Conference and the 15th Probabilistic Safety Assessment and Management Conference, ESREL2020PSAM15 Organizers, Singapore. FatmaOutay, Hanan Abdullah Mengash, Muhammad Adnan, Applications of unmanned aerial vehicle (UAV) in road safety, traffic and highway infrastructure management Recent advances and challenges, Transportation Research Part A Policy and Practice, Volume 141,2020, Pages 116-129, ISSN 0965-8564, https//doi.org/10.1016/j.tra.2020.09.018. Hu, J.; Lanzon, A. (2018). “An innovative tri-rotor drone and associated distributed aerial drone swarm control.” Robotics and Autonomous Systems. 103 162–174. Doi10.1016/j.robot.2018.02.019. Tice, Brian P. (Spring 1991). “Unmanned Aerial Vehicles – The Force Multiplier of the 1990s”. Airpower Journal. Archived from the original on 24 July 2009. Retrieved 6 June 2013. When used, UAVs should generally perform missions characterized by the three Ds dull, dirty, and dangerous. https//www.soarizon.io/news/six-ways-drones-are-helping-in-emergency-response. https//dronenodes.com/firefighter-drones/. https//www.dronezon.com/drones-for-good/drone-parcel-pizza-delivery-service/.

Chapter 3

Cloud-Based UAV Architecture, Security Concerns and Challenges Vrajesh Sharma1, and Nipun Chhabra2 1

Panjab University Swami Sarvanand Giri Regional Centre, Hoshiarpur, Punjab, India 2 I. K. Gujral Punjab Technical University, Hoshiarpur Campus, Hoshiarpur, Punjab, India

Abstract Considerable attention has been gained by Unmanned Aerial Vehicles (UAVs) owing to the increased applications in today’s time. Further, these UAVs, popularly known as drones, make use of radio frequency (RF) transmissions for communicating with the ground stations for receiving and sending commands. Traditional technologies have been extensively used for collaborative drone-based applications which need dedication, budget, time, and effort. Also, by connecting various smart objects with the internet, a term called “Internet of Things (IoT)” has emerged to be an important part of everyone’s life. In the present times, huge amount of data is being produced by IoT devices, but the computations for this generated data at local end is not possible, hence the need to transfer the data to some external devices with better storage and computational capacities has emereged where the data is generally handled at remote sites by centralized servers using fog computing. As the sensors make use of IoT-based applications for computations and data processing, therefore, in this chapter cloud-based architecture of UAVs as well as security and challenges associated with it, have been discussed. 

Corresponding Author’s Email: [email protected].

In: Revolutionary Applications of Intelligent Drones Editors: Mohit Angurala and Vikas Khullar ISBN: 978-1-68507-991-8 © 2022 Nova Science Publishers, Inc.

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Keywords: Internet of Drones (IoD), UAVs, Internet of Things (IoT), Architecture of IoD

Introduction In the recent times, UAVs have been developed rapidly and their usage can be witnessed in civil and various military applications. Besides these prime utilities, other useful drone applications are: search and rescue operations, agriculture monitoring, surveillance for security, infrastructural and environmental monitoring and many more. However, there are many applications which involve multiple drones working collectively to achieve a desired aim. On the flip side, to keep this system running, much management and labour is required in the development and testing part of these applications which concurrently operate on numerous drones. One way to do this is to make use of Internet of Drones (IoD) that can be utilized for efficiently coordinating the operations of multiple UAVs. Also, the IoDs are used for describing an infrastructure design to give control and access, using the internet amid drones and users [1-2]. In reality, IoD allows UAVs coupling vehicle and cloud mobility functions to permit remote drone access and control scalable offloading through remote cloud storage. The IoD environment as shown in Figure 1 (given below) illustrates signal links, base stations, and cloud environments.

Figure 1. IoD environment [1].

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There are two types of UAVs, one with fixed wings and the other with rotary wings. The key benefits of fixed UAV wings in comparison to the rotary UAVs wings include less maintenance and repair overheads. The maintenance facility gives the consumer extra operating hours with minimum cost. Owing to its simple structure, these aircrafts offer longer flight durations with highspeed that covers more ground. Drones makes use of non-power supply methods to make glide more effective. As the drones rely upon RF, the applications of UAVs establish direct connections amid themselves and the ground stations. Such communications could either be wireless point-to-point links or these could be wireless multihop links traversing via other middle nodes. Nevertheless, the peer-to-peer communications are not appropriate for few UAVs having dynamic heterogeneous as well as distributed environments as it also limits the positions of the ground stations to the destined locations. Moreover, it requires drones to be in direct Line of Sight (LoS) from the ground station or available communication hubs for maintaining control alongside the communication. Furthermore, the UAVs monitoring and control applications are more complex in nature with limiting themselves to specific devices that are connected to the UAVs. Specialised techniques/interfaces integrate drones with the cloud computing paradigm and have been extended for mobile devices, computers and embedded systems also [3]. Sensors, embedded devices and actuators are smart objects which are linked to the internet via IoT architecture [4]. Therefore, the main emphasis of IoT is to establish connectivity of network between the internet and smart objects, and for the same various web protocols/tools are used to develop and interact with these objects, thus, the Web of Things (WoT) makes the application layer on top of this network [56]. Likewise, the drones having embedded systems conform to WoTs and IoTs in order to be connected to the Internet for accessing and monitoring via web [7]. To illustrate the mission progress, including the status and position of every drone, is monitored by a client application. It also enables the drone’s resources access such as: sensors, cameras, and actuators using web service request [8-9].

Architecture of IoD The major elements in the architecture of IoD are: the communication protocols and the architectural components. Thus, the design of IoD plays

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critical role in the administration and control of UAVs that equips them to effectively accomplish their operations.

Architectural Components or Elements There are many IoTs components which exist in IoD design (architecture of IoD) and the operations of such components depend upon the communication with the architecture of IoDs. Besides this, the architectural components determine the data acquisition, stability and communication techniques of IoDs. Furthermore, as discussed previously, the main objectives of IoD architecture includes controlling and deciding the drones, it also ensures that the data correctly reaches the destination node from the source. Finally, intersections, nodes and airspaces are considered for the development of IoDs architecture.

Communication Protocols The data among the nodes can be transferred and supported using multiple communications in the IoDs. Drone planner assists the MAVLink protocol and ROSLink protocol, where ROSLink integrates it with the robot operating system; the MAVLink protocol on the other hand is a lightweight message marshaling protocol. The node movement decides the efficiency of the communication protocols and its behavior within the network. Moreover, the routing competency is accelerated with the communication protocols for IoDs. Thus, the analysis of protocol selection must be thoroughly performed. The next section depicts the most important future research works and challenges on the IoDs.

Challenges and Security Related to Privacy and Security The modern day IoDs are equipped with many sensor nodes, as, these are being used in commercial and civilian applications because government agencies provide various drone usage licenses. However, these devices are susceptible to different threats such as: hijacking, mankind errors and losses.

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These issues should be given higher priority in the design of drone applications. Moreover, it’s difficult to prevent the jamming attacks in communication networks. To guarantee that this objective is met, high protection measures that safeguard the information should be utilized between the ground and flight controllers along with the federal aviation authority. Technical advancements including data protection technology and augmented telecommunications may aid in avoiding the security issues. However, with modern technologies, anti-jamming techniques, by making use of highly powered signal encoding, this is quite feasible. Communication protection relies upon the communication channel’s frequency, technology, communication media and the relationships with each other. Generally, high bit rate encryption algorithms are costlier than the low-bit rates, thus lowering the costs is often followed by the security reduction or the number of operations associated with it. The drones which are smaller in size have opened new ways in the civil and defense industries. Nevertheless, smaller drones are defenseless against the privacy and security threats because of inappropriate architectures. Evolutions in IoDs and IoTs offer new directions and additional challenges related to data security and privacy. The basic design and architecture requires amendments for providing high secure and reliablility in a network. The structure of a typical drone is based on a layered architecture [10], as shown in Figure 2.

Figure 2. Layered architecture for industrial drones [10].

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The IoT with drones is used for equipping UAVs with decision-making ability while blockchain technology can also make drones secure and private. Few security-based threats to the drones are as shown in table 1. The table shows the bifurcation of different attacks according to the protocol type. Table 1. Attacks Type Protocol-based attacks

Jammers Sensor-Based Attacks

Compromised components

Common cybersecurity threats Data Confidentiality Protection, Security of Communication Link, Replay Attack, Deauthentication attack and Privacy Leakage. Stop Packet Delivery and Denial of Service. Motion Sensors Spoofing, GPS Spoofing/Jamming, UAVs Spoofing/Jamming Attack IoT Security Threats, Control/Data Interception

An intelligent system is indeed very much required for providing the security of drones which can examine the attacks and ensure the security by proactive techniques in UAVs [11]. A secure IoDs depends on the reliability, security, and consistencies for developing a robust and reliable system. Machine learning-based solutions are also proposed for security of UAVs including its authentication and access control access mechanisms while different parameters can also be researched for cyber-security evaluation system. The aim of such parameters is to handle various metrices in better possible way [10].     

Drone cyber-security threat exposure Denial of service attacks Malicious attacks Jamming Spoofing.

The machine-learning-based research solution [10] is an efficient and effective way to secure authentication and access control for UAVs.

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.

Global Challenges There are many possibilities and potential for research and improvements in the various fields of UAVs. Below Figure 3 shows different challenges that exist:

Figure 3. Types of global challenges in UAVs.



Resource Management-Related Challenges

This type of challenge is critical to serve reduction in cost and productivity and thus can be categorized into two groups including: local allocation and global resource allocations. Here, the global resource allocation is concerned with the time, equipment and energy expenditure. 

Communication-Related Challenges

To address the issues of high throughput, delay and latency, technologies such as: intelligent routing, LoRa-based IoT systems, 5G, narrowbandInternet of things needs to be supported. Lastly, the standardized policy should be developed to use the authorized component for drones’ communication protocol.

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

Tasks Scheduling and Coordination-Related Challenges

Edge and Cloud computing are combined to handle IoT intensive applications in the future. Such intelligent external network applications need a proper centralized artificial intelligence and big data analysis for coordination. 

Deployment and Drones Distribution-Related Challenges

Data sharing, access control and data confidentiality are few challenges that IoD deployment is facing. For example, set of drones may collaborate to collect road traffic data, but securely and efficiently sharing these collected data is a major challenge.

Conclusion UAVs or drones are assisting in improving the efficiency in day to day life. Further, linking drones into IoDs help in enhancing the performance and safety of drones with the number of UAVs in low-altitude air territories. However, security and privacy during communications are still major concerns. Therefore, in this chapter we have tried to address the key challenges in cloudbased UAVs and also discussed its architecture in detail.

References [1]

[2]

[3] [4]

[5]

Abdelmaboud, “The Internet of Drones: Requirements, Taxonomy, Recent Advances, and Challenges of Research Trends,” Sensors, vol. 21, 2021. https:// doi.org/10.3390/s21175718. Sahingoz, O. K. “Mobile networking with UAVs: opportunities and challenges,” in Proceedings of the International Conference on Unmanned Aircraft Systems (ICUAS '13), pp. 933–941, IEEE, Atlanta, Ga, USA, May 2013. Mell P. M. and T. Grance, “The NIST definition of cloud computing,” National Institute of Standards and Technology, vol. 53, no. 6, p. 50, 2009. Gubbi, J. R. Buyya, S. Marusic, and M. Palaniswami, “Internet of Things (IoT): a vision, architectural elements, and future directions,” Future Generation Computer Systems, vol. 29, no. 7, pp. 1645–1660, 2013. Zeng, D. S. Guo, and Z. Cheng, “The web of things: a survey,” Journal of Communications, vol. 6, no. 6, pp. 424–438, 2011.

Cloud-Based UAV Architecture, Security Concerns and Challenges [6]

[7]

[8]

[9]

[10]

[11]

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Mahmoud, S. and N. Mohamed, “Collaborative UAVs cloud,” in Proceedings of the International Conference on Unmanned Aircraft Systems (ICUAS '14), pp. 365–373, Orlando, Fla, USA, May 2014. Lin, E. C.-R. Li, and Y.-H. Lai, “UAS cloud surveillance system,” in Proceedings of the 41st International Conference on Parallel Processing Workshops (ICPPW '12), pp. 173–178, Pittsburgh, Pa, USA, September 2012. Papazoglou, M. P. P. Traverso, S. Dustdar, and F. Leymann, “Service-oriented computing: state of the art and research challenges,” Computer, vol. 40, no. 11, pp. 38–45, 2007. Simanta, S. D. Plakosh, and E. Morris, “Web services for handheld tactical systems,” in Proceedings of the 5th IEEE International Systems Conference (SysCon '11), pp. 115–122, Montreal, Canada, April 2011. Majeed, R. N. A. Abdullah, M. F. Mushtaq, M. Umer, M. Nappi, “Intelligent CyberSecurity System for IoT-Aided Drones Using Voting Classifier,” Electronics, Vol. 10, 2021. https://doi.org/10.3390/ electronics10232926. Stephen, S. N. Christian, and W. Scott, “Automated UAV-based video exploitation using service oriented architecture framework,” in Airborne Intelligence, Surveillance, Reconnaissance (ISR) Systems and Applications VIII, 80200Y, vol. 8020 of Proceedings of SPIE, May 2011.

Chapter 4

Drones and Their Utilities Using Emerging Technology Harsh Taneja* and Ashish Sharma Department of Computer Science and Engineering, Bharati Vidyapeeth’s College of Engineering, New Delhi, India

Abstract This chapter targets to map the detailed information about drones and their types with the possibilities and threats related to the contemporary and future use of drones. As such, this chapter is beneficial as a reference for more research, along with instructions for suitable or unwanted technological trends, vital, beneficial, or undesirable packages of drones, and possible regulation of drone technology and drone use. Although this chapter provides details concerning drone technology to understand potential threats, as well as moral and legal issues created by drones, it no longer focuses solely on technology. It instead focuses on the social, ethical, and criminal consequences of drone technology. The sociocultural consequences are discussed in terms of opportunities and risks. Ethical difficulties are examined in terms of which sorts of drone use may potentially breach important ethical norms and principles, which moral conflicts may develop, and which drone usage styles may also need the application of moral standards in novel ways.

Corresponding Author’s Email: [email protected].

*

In: Revolutionary Applications of Intelligent Drones Editors: Mohit Angurala and Vikas Khullar ISBN: 978-1-68507-991-8 © 2022 Nova Science Publishers, Inc.

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Introduction The goal of this chapter is to supply a top-level view of various drones currently used, their technical specifications, capacity payloads and packages, frequency spectrum problems, and the current and close-to-future technological development in drone technology. The important traits of drones, however, will probably remain identical for future years. Drone technology is something that is constantly developing. This is simply because drones have a tremendous scope which is basic for some organizations. Unmanned flying vehicles are employed extensively in the military to obtain information. The first significant difference to make while using drones is between the drone (platform) and the device that is attached to it (the payload). In this perspective, the drone may be seen as a flying platform that can be customized to suit specific goals [1]. These goals can be realized in collaboration with the right payload for the task. A camera, for example, might be attached to a drone to make it suitable for specific inspections. This difference is used to characterize the chapter’s exclusive elements.

Types of Drones Drones have existed since 1917 in a variety of forms. Drone activities, which were originally utilized solely for military purposes, are now widely exploited in the corporate world. Professional drones may help with spying, satellite imaging, agricultural tasks, and inspections. Micro-drones the size of a ping pong ball are even utilised for biological warfare. The varying body shapes of each type of drone affect the amount of weight they can carry (payload), as well as the efficiency and flying duration. The four major types of professional drones are a.

Multi-rotor. For vertical take-off and landing, drones with multiple rotors and propellers were developed. The goal of having many rotors is to aid in better control of the drone’s location in the sky. The more rotors it has, the more manoeuvring options it has [2]. b. Fixed-wing. Unlike drones with rotors that can take off vertically, fixed-wing drones have a single long wing on either side of their fuselage and must use a catapult or a runway to get off the ground. Fixed-wing drones are occasionally employed for surveillance, such

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as in the military, although they are not commonly utilized for aerial photography or drone flight [2]. A drone would need to be able to hover and keep flying at angles to take steady images and movies. c. Single-rotor helicopter. Single-rotor drones are more efficient than multirotor drones. Single-rotor drones can be nearly as difficult to operate as fixed-wing drones, and both need a delicate balancing act. They have fewer applications than multi-rotor drones, but they can undoubtedly carry a bigger payload [2]. d. Fixed-wing hybrid VTOL. Vertical take-off and landing are referred to as VTOL. This is the fundamental cause for the creation of this hybrid. Fixed-wing drones have such a long flight endurance compared to other drones; the only drawback is that they are difficult to land. This hybrid brings the finest of both worlds together. It isn't a new concept, but it is swiftly gaining popularity and a good reputation [2].

Drones for Society and Commercial Drones Multiple sensors, transmitters, and imaging equipment can be carried by drones. Drones have an impact on industries ranging from entertainment to agriculture, construction to distribution markets as the usage of drones becomes more widespread. The development and utilization of specialized high-definition imaging drones have already legitimized their use in Hollywood film production [3]. Civilian unmanned aerial vehicles (UAVs) have the potential to be a major infrastructure platform. They can be used in a variety of sectors to conduct sophisticated, costly, and risky jobs. Short battery life and a lack of effective regulation (and enforcement) are now the two most significant barriers to their widespread use. To either develop or standardize drones, several organizations and industry standards organizations have been formed. The majority of today’s commercially available drones use a similar design as shown in the figure above. The basic design includes a flight control microcontroller, four to eight motors and propellers, a radio receiver, electronic speed control, and a battery, all of which are housed in a light plastic or metal frame [3]. Gyroscopes and other sensors are also added to improve the drone’s mid-air stability, and a GPS device can be utilized for navigation. Most hobbyist drones also have a camera for aerial photography and a gimbal for picture stabilization. Other sensors can also be added however, this comes

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at a cost in terms of greater functionality and weight. Some of the top hardware manufacturers are DJI, 3DRobotics, and Parrot, which sell assembled drones and their components [3].

Figure 1. Structure of a Commercial drone [3].

Drones are considered platforms for sensors of any sort in the business space, and they have mostly been utilized for surveillance and inspection. Drones are being used to monitor crops, conduct search and rescue operations, count wild animals and keep track of animal populations, conduct land surveys, watch forest fires, and examine oil pipelines, power lines, and other distant infrastructure. Their capacity to transport heavy equipment has been used to spray crops on big fields and deliver food, medical supplies, and pharmaceuticals to remote regions. The proposed three main categories of challenges require further consideration based on an examination of the drone discussion. The first is safety and security, which covers personal and property damage as well as attacks on drones themselves. The next issue is privacy and ownership, which is relevant to the data collected by drones. The third problem is personal and business liability, which raises concerns about drone operators’ accountability.

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Internet of Drones The Internet of Drones (IoD) is a network infrastructure that allows drones and people to communicate and control each other through the Internet. In actuality, drones are rapidly becoming commodity objects, allowing any user to fly several drones in controlled airspace for various aims. Although technology aids in the mass production of onboard components such as processors, sensors, storage, and battery life for unmanned aerial vehicles (UAVs), the performance constraints of these components obstruct and restrict expectations [4]. IoD delivers drones that combine vehicle and cloud mobility functionalities to enable remote drone access and control, as well as scalable offloading and remote cloud storage capabilities. The fundamental advantage of a UAV (unmanned aerial vehicle) with fixed wings over one with rotary wings has been that the simple structure demands a less complex repair and maintenance process, supplying the consumer with more operational time at a lower cost. The basic build allows for higher-speed planes with a longer flight length and greater land coverage. Non-power supply strategies may be used by UAVs to improve gliding efficiency [4]. Also, it’s worth mentioning that fixed-wing planes can carry a larger payload for longer distances when flying at lower altitudes, allowing them to transport a combination of larger, more modern sensors as well as a pair of complementing sensors. Previously, UAVs were used alone, but currently, a larger number of coordinated drones may be used to complete complex missions. Drone communication is critically necessary for these scenarios. In other words, users must have a deep understanding of UAV communication protocols. Drone communications use a completely different type of wireless channel and network protocol, but there are several types of wireless routers and network protocols used in drone communications. As a result, the network design for UAVs is dictated by their intended use. Researchers have discovered, for example, that a point-to-point line-of-sight link between a drone and a device may continue continuous data transfer even when the transmission is extended. Drones that use satellite communications to communicate against one another for surveillance, safety protection, or more broad outreach operations are a better possibility for drones. Cellular communications technologies, on the other hand, are more widely used in civic and personal applications [4]. A remote hijacking of the drones might be carried out by exploiting a flaw in the software of the UAVs, which serve as sophisticated military tools.

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Malware programs on drones that can be used by criminal individuals for illegal purposes are influencing global positioning system (GPS) signals. As a result, the attacker may launch irrational attacks, such as dropping bombs and putting lives in danger. Because of the various communications among entities, the control signal is an important aspect of IoD environments and should never be showed or exposed. To avoid harm from security attacks, strong security measures are needed. Therefore, for personal and business drones to fly independently, authentication and key exchange methods between the two entities in the sky are needed. For future data transmission, both entities create asymmetric security keys.

Requirements of the Internet of Drones (IOD) Drones predicted increased usage in several applications might expose operators to a whole new set of risks, including third-party damages and liability. The following sub-sections classify many of the requirements for possible drones.

Communication Requirements Researchers are paying more attention to the implications of IoD communication issues. Without the use of drones, many distant sites would be difficult to reach. As a result, drones are often used for critical jobs like rescuing people, supplying surveillance, transporting, and helping in environmental conservation and preservation [5]. Therefore, critical communication requirements to support the different drone applications are discussed as follows. a.

Seamless Coverage. For aerial entertainment, hot-spot coverage (stages, tourist destinations, and industrial regions) is ideal. Inspection and coordination of power or base stations require widespread coverage in suburban, urban, and rural areas. Drone coverage will become more important for network planning in the future. Unlike traditional network coverage, which primarily serves land users, expanded sky coverage is necessary to support drone users flying at various altitudes. For plant protection, coverage of up to 10 m height is sufficient (e.g., spraying of agricultural chemicals). Power

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line inspection needs coverage of up to 50 to 100 meters in altitude. For mapping agricultural fields, coverage of up to 200 to 300 m in height is acceptable [6]. b. Real-Time and Remote Communication. Remote controllers may offer time-based command and control instructions based on the drone flight status report in real-time, such as space co-orders and equipment status, thanks to real-time and remote communication capabilities. The monitoring of flying circumstances, drone tasks, and equipment, as well as emergency control, are all done using real-time and remote commands. To allow remote control of drones, certain data latency and rate criteria must be satisfied. In many application scenarios, the downlink data rates (from the base station to the drone) are around 300–600 kbps, and existing 4G+ networks will meet this demand. The latency constraint is rigorous for future implementations, such as distant real-time operations, to ensure service accuracy and experience [6]. c. Transmission of HD Image/Video. Organizations should be able to supply a high uplink (from the drone to the base station) information rate for drones to allow for HD picture/video transmission. The required information rate is decided mostly by the size and quality of the image/video. Later, the need for greater resolution pictures/recordings in vertical companies demanding 4K/8K HD video support would need a higher Gbps data rate. The 5G networks are ideal for helping such administrations that require multi-gigabit per second data rates. Drones’ application scenarios will be greatly expanded with the transmission of HD pictures/video, including energy and electrical line examination, agricultural investigation, control, salvage, diversion, and checking. Drones connected to networks may broadcast HD pictures/recordings at high transmission speeds, enhancing the realistic experience of extended reality [6]. d. Drone Identification and Regulation. Portable organizations might help recognize and control drones by supporting drone enrolment, following, arrangement, and coordination [6, 7]. • Registration: Finding however normalizing the drone gear number, chronic number, and flight control chronic number aides track the entire interaction efficient from beginning drone creation to being used. Through normalizing enlistment of drone clients, proprietors also, versatile organizations, drone clients and proprietors can be lawfully checked.

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•

e.

Monitoring: Drone associations and information interchanges can be found and seen through versatile organizations. Drone executions can be totally followed in constant with extra administrative conventions. • Forecast: Flight circumstances can be progressively assessed and early admonition of conceivable dangers can be carried out by following drone positions and checking the flight traffic and way. • Coordination: Knowledge trade among enterprises and various organizations can be done by supported oversight of all upward ventures included. Positioning of High-Precision. For a long-time application situating is basic. In a few drone applications, vertical situating additionally is significant, despite customary situating on the level plane. The necessity for situating exactness will increment from several meters to sub-meters with the drone applications’ turn of events. Fifty meters situating accuracy is sufficient for customary checking exercises. Applications, for example, rural land planning also, computerized stacking include high accuracy situating at the sub-meter [6].

Security Requirements Researchers have developed several security and privacy ways to safeguard the position of unmanned aerial vehicles (UAVs) as well as the privacy and security issues that come with using the Internet of Drones (IoD) network. Drone location was previously inaccurate due to these localization issues, which had severe consequences for the whole IoD network. Another important goal of the IoD network is to improve security and privacy to the point where they can’t be hacked. As a result, the IoD network’s primary security and privacy criteria are authenticity, secrecy, availability, integrity, and nonrepudiation. 1. Authenticity: Before access to a restricted asset is granted or basic data is exposed, authenticity is expected for finding devices, clients, and gateway hubs. In addition, two of the imparting parts must be an observation drone and a ground-control station for joint verification of chemicals. To keep complete secrecy, it’s critical to adopt a secure key exchange mechanism that generates meeting keys that are difficult to retrieve [4].

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2. Confidentiality: The wireless communication channel’s confidentiality or privacy protects against illegal information leakage. Making data available and regulating access to that data is another important obstacle to IoD deployment (data confidentiality). When a fleet of drones captures road traffic data from many locations, for example, there is a persistent challenge with securely and effectively sharing this data [4]. 3. Availability: In case of a system denial-of-service attack, registered users should be given access to suitable network services. Both the mechanism and the system can detect if a drone is in conflict and keep track of the battle limit, which affects whether the flight management system can detect a malfunctioning drone and decide whether the availability requirements have been breached [4, 5]. 4. Integrity: Integrity is vital for ensuring the dependability of the data (for instance, that it has not been changed on the way, and the wellspring of the data is certified). To overcome these challenges (safety and security with personal and business liabilities), we need to have blockchain-connected drones.

Blockchain Connected Drones Device communication and human-gadget communication are part of the information exchange in these devices. IoT (Internet of Things) allows information to flow between devices in a variety of industries, including military, health, agribusiness, transportation, and vibrant urban communities in various regions. Drones are also classified as IoT devices because of their ability to communicate with other devices over the internet, receive input, and send yield data as needed. Current IoT networking recommends that devices verify each other when it comes to certain system administrative limits. Even though these business conventions may be secure, the security of the business device is dependent on the manufacturer. In a tactical situation involving many drones. These robots are managed from a central location where network executives may check endpoint devices and the Internet-of-Battle-Field infrastructure. Elements of Blockchain Technology that can be used in Drone Security.

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1. Digital Fingerprint: Cryptographic calculations known as secure hash algorithm (SHA) or hashing are performed in Blockchain. Hashing converts data such as recordings, text, and images into a single line of a specified length. For instance, the SHA 256 algorithm turns a piece of data into a 256-piece string that is like a string of 32 characters for Alphanumeric text. Because of their unique qualities, hashing algorithms are regarded to be secure. To begin with, they are unidirectional, implying that the yield cannot be used to generate input. Secondly, a piece of output is unique in relation to a specific piece of data. This means that if a piece of data is processed several times using the same hashing computation, the result will be the same each time, and no other data will produce the same result. Any changes in the information, no matter how minor, will affect the outcome. Because of these two qualities, these calculations are useful for figuring out the reliability of information passed from one robot to the next without having to analyse the data directly [8]. 2. Data Structure: Data is kept in blocks in Blockchain, with each square immediately connected to the next via a cryptographic connection to form a chain. The square stands for the holder data structure, in which exchanges are grouped together to be remembered for a public record, the Blockchain. The chain starts with one square, and when more transactions are stored, more squares are layered on top of it. A block has two parts: a header that contains the square ID and a body that holds the contents. There are three types of metadata in a header: data that provides information about the data stored in the square, data that provides information about the data stored in the square, and data that provides information about the data stored in the square. The primary set is the past square’s hash, or advanced unique mark. The following set holds the timestamp, which writes down when the square was created, among other things. Finally, the Merle Hash tree is a reference to the hash on the exchanges stored in the square. The Merle tree links a square’s content to a single hash value numerically. This allows a client hub to replicate a square in a noticeably short amount of time to verify the accuracy of the data stored [8, 9]. 3. Access Control: Blockchain has two kinds of access control to decide which hub access what data; permissioned and public access. In general society (un-permission) access, there is no entrance control. Any hub with suitable software can enter the organization and access

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the data on a Blockchain without looking for validation or check from any power. On the other hand, permissioned private) access permits overseer hubs to control which hub joins the organization, the pieces of the blockchain they can get to, which hubs can compose on the squares, and those hubs that take an interest in giving agreement [9]. Execution of permissioned access control in robots will control the drones cap join the organization and which data they can access or store in the Blockchain. Believed drones are the as it were ones permitted to join the agreement bunch [8, 10].

Conclusion This chapter gives an overview of the various mechanical components of drones. This review covers the many types of drones, their functions and specialized features, their applications, how blockchain technology influences drone security, and the present and near-future revolutionary advancements in drone technology.

Future Scope Firstly, research will execute to build a vision model on the drone for trial and error while making upgrades in current models for better speculation. A second significant improvement is the further expanding independence of drones. Drones are regularly viewed as controller airplanes, however, there are advancements that empower independent tasks, in which the controller by a human operator is not needed.

References [1] [2]

[3]

Sandbrook, Chris. “The social implications of using drones for biodiversity conservation.” Ambio 44, no. 4 (2015): 636-647. Vergouw, Bas, Huub Nagel, Geert Bondt, and Bart Custers. “Drone technology: Types, payloads, applications, frequency spectrum issues and future developments.” In The future of drone use, pp. 21-45. TMC Asser Press, The Hague, 2016. Rao, Bharat, Ashwin Goutham Gopi, and Romana Maione. “The societal impact of commercial drones.” Technology in society 45 (2016): 83-90.

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[5]

[6]

[7]

[8]

[9]

Harsh Taneja and Ashish Sharma Abdelmaboud, Abdelzahir. “The Internet of Drones: Requirements, Taxonomy, Recent Advances, and Challenges of Research Trends.” Sensors 21, no. 17 (2021): 5718. Zorbas, Dimitrios, Tahiry Razafindralambo, and Francesca Guerriero. “Energy efficient mobile target tracking using flying drones.” Procedia Computer Science 19 (2013): 80-87. Yang, Guang, Xingqin Lin, Yan Li, Hang Cui, Min Xu, Dan Wu, Henrik Rydén, and Sakib Bin Redhwan. “A telecom perspective on the internet of drones: From LTE-advanced to 5G.” arXiv preprint arXiv:1803.11048 (2018). Lin, Chao, Debiao He, Neeraj Kumar, Kim-Kwang Raymond Choo, Alexey Vinel, and Xinyi Huang. “Security and privacy for the internet of drones: Challenges and solutions.” IEEE Communications Magazine 56, no. 1 (2018): 64-69. Singh, Maninder Pal, Gagangeet Singh Aujla, and Rasmeet Singh Bali. “Blockchain for the Internet of Drones: Applications, Challenges, and Future Directions.” IEEE Internet of Things Magazine 4, no. 4 (2021): 47-53. Ch, Rupa, Gautam Srivastava, Thippa Reddy Gadekallu, Praveen Kumar Reddy Maddikunta, and Sweta Bhattacharya. “Security and privacy of UAV data using blockchain technology.” Journal of Information Security and Applications 55 (2020): 102670.

Chapter 5

Introduction to Machine Learning in UAVs Geetanjali Sharma and Rajeev Kumar Bedi, PhD Sardar Beant Singh State University, Gurdaspur, India

Abstract In the last five years, application of drones in various fields like transportation, agriculture, security, etc. has increased. This immense use of drones makes it so popular for commercial use and this gains the interest of various organizations in it. So, the integration of drone technology with other technologies is emerging as a path to the above target. The two main technologies in computer vision have gained popularity exponentially in the last decade. These are machine and deep learning technology and using these technologies with drone technology is a great option. The aerial vehicle industry welcomes ideas that can solve various issues like power management and automatic control of drone with the help of machine learning. This chapter covers these two technologies in detail. It also shows some literature regarding integration.

Introduction So far in this book introduction about drones is completed. Now let’s move towards other aspects related to the drone. As mentioned earlier, here in this chapter some additional technologies that can be used to improve the functionalities of the drone will be discussed. At maximum level drone is an automatic technology, but still, it needs a manual level of operation at some 

Corresponding Author’s Email: [email protected].

In: Revolutionary Applications of Intelligent Drones Editors: Mohit Angurala and Vikas Khullar ISBN: 978-1-68507-991-8 © 2022 Nova Science Publishers, Inc.

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levels. Conversion of remaining manual operation to automatic is a new research interest in drone technology. Nowadays, major drone manufacturing companies are demanding ideas and it gains high demand regarding integration of artificial intelligence in drone technology. So, in this chapter, the relationship between drones and machine learning is to be explored. For the last decade, one major subset of artificial intelligence has gained high importance due to its high computing capability and the availability of prediction. Yes, you are thinking right - it’s machine learning, so let’s explore how machine learning can be used in the case of the drone. There is also one thing, which is that if machine learning can be applied to drone technology, then the involvement of deep learning is also possible because deep learning is a subset of machine learning as described in Figure 1 [1]. In this chapter one by one every term will be explained. Section 2.2 of the chapter covers the detail regarding machine learning and its various types, whereas section 2.3 gives the knowledge regarding deep learning and artificial neural networks. The last part of the chapter covers our main concern which gives the information regarding contemporary usage of machines and deep learning in drone technology.

Machine Learning Drones are automated devices used for a variety of purposes. Initially developed drones were manually and remotely controlled, but now drones often incorporate artificial intelligence and automation in some or all operations [2]. Machine learning is not a new term; it was coined in 1959 by Arthur Samuel, an American IBMer and pioneer in the field of computer gaming and artificial intelligence. Before moving toward the history of machine learning let’s first check the formal definition of machine learning. Arthur Samuel defined machine learning as a “field of study that gives computers the ability to learn without being explicitly programmed” [3]. Arthur Samuel introduced machine learning in his paper as a subfield of computer science that gives computers the ability to learn without being explicitly programmed. For a very long time, this definition remained widely accepted, and even in his definition also the key features that a machine cannot be explicitly programmed which means the machine should be self-learned and this requires high computing and major use of statistical models which is still to be explored at that time. With the change in the time and technology, issues

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related to self-learning of the machines are resolved and in the late 90s again a definition of machine learning is coined by Tom. M. Mitchell. He defines machine learning as “A computer program is said to learn from experience E concerning some class of tasks T and performance measure P if its performance at tasks in T, as measured by P, improves with experience E.” Here, performance measure checks the learning power of machine and machine will learn from data E which is fed as input to it. Data E is related to Task T or T is the topic or point on which the machine learns. Now, for machine learning as defined before some statistical model or algorithm is to be used. Therefore, the main two objectives of machine learning include: classifying data based on models which have been developed, and the other to make predictions of future outcomes based on these models. A machine algorithm specific to classifying data may be like a computer vision of weather forecasting where day is classified as sunny, cloudy, rainy or cold; and in case of predictions, a machine learning algorithm used in stock trading may inform the trader of future potential.

Figure 1. Relationship between AI, ML, and DL.

Figure 2. Machine Learning Process.

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In Figure 2, the process of machine learning is elaborated. Let’s take the example of email spam detection. Thus, to incorporate machine learning into our spam detection system process will be like: First of all, task T is to detect whether particular mail is spam or not, and to find out this task input data E that is the past information related to the task is reviewed. The past information present related to the task is collected from the records that have been maintained from emails received before the task date. All emails will be given as an input to the machine and then some statistical algorithm will be applied to it for the generation of a model or rule. The model defines how machines outline whether particular mail is spam or not. To illustrate it, any email that contains words like the lottery, won, etc. will be treated as spam. The generated model defines information in the form of rules and the machine will operate on these rules. Eventually, the machine learns how it can be found out whether a particular email is spam or not. This concept of learning is known as self-learning which was defined earlier. But the major concern here is to check whether a generated model is best or not. Machine is evaluated by performance measure P (as per the definition, it is also called accuracy in general language). To check the accuracy, the model is to be tested, and for this, input data E is divided into two parts; first is training data, and the second is testing data. In machine learning, data is split into training and testing data. The first split of data is used to develop the model, whereas the second is used to test data. The process of training and testing is like, after successfully developing a model based on the training data, testing of the model on the remaining data is to be performed. To train a model, there are different types of learning present. In the case of machine learning, three types of learning are mainly used: supervised learning, unsupervised learning, and reinforcement learning.

Supervised Learning To get an accurate model, splitting of input data into training and testing is performed, but selecting an accurate algorithm is a major aspect of training a machine. Various studies are made regarding the selection of an appropriate algorithm. To understand how a particular algorithm is selected, first knowledge regarding the learning system in the machine is to be explored. Before directly defining categories of learning, let’s see what learning means and why it is important. One of the most heard criticisms of drone

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machines is that these machines cannot learn new things and cannot adapt to the new situation on their own. Humans have the capability of reacting in new situations and they have these capabilities by learning from their past experiences or taking advice from near ones etc. Here, when a machine also gains information from its experiences, then it can be coined that machines also start learning from experience and this learning helps the machine to react in a new environment. Let’s look in detail at each type of learning. In supervised learning [4], the training data you feed into the machine will be explored by the algorithm, and the relationship between variables and outcome is generated. Let’s consider an example of an email filtration task. It clearly defines that the rules are generated by a machine algorithm and based on these rules the machine predicts whether mail is spam or not. The key point here in this learning is that the machine already knows what it is going to predict, for example here whether mail is spam or not. Possible outcomes of the task are known before machining. Therefore, from the start of learning to its ending, everything is defined in the machine. It is as like the mother is saying to the child that this is a spoon, this is a pencil and because of this supervising nature, particular type of learning is called supervised learning. Statistically supervised learning finds the relationship between X (represents input here) and Y (represents output here) as defined in Figure 3. After exploring rules machine generates a model based on these rules only. In supervised learning, let’s see which types of algorithms are used. Supervised learning is all about outcome labels. As defined earlier in supervised learning, prior knowledge regarding output is there, but the possible value of output is either definite or indefinite. Definite here means that the possible values are countable, like in email filtration all possible values of task that are whether email is spam or not are already known to you. Similarly, if you have to predict the color of an object, then it will be any possible value from the color name, etc. Thus, in both examples, the output values are labeled and countable. In these cases, the classification algorithm will apply, whereas if the output goes uncountable, then go for the regression algorithm. Uncountable or indefinite is the case when values of outputs cannot be calculated. Possible values for number prediction may range anywhere between 0 to infinite. Different algorithms are present in both categories. In the classification category decision tree algorithm, Naive Bayes, etc., whereas in the case of regression go for linear and non-linear support vector Machine. Now the question in mind is, what if the output is not known prior? The answer is the second type of learning, i.e., unsupervised learning.

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Unsupervised Learning In the case of unsupervised learning [5], the output is not known beforehand, which means the system tries to learn without any supervision. Let’s understand the term with the help of an example if the task is to find what the customer is going to purchase or in general language task is to find out purchasing behavior of any customer. In this type of problem, learning behavior of any person is hard to predict with the help of supervised learning because no rule is present for it. The purchasing behavior of humans is unpredictable, but still, unsupervised learning can help us to generate a statement that if a person is going to purchase product 1, then there are higher chances of purchasing product 2. It is similar to the situation if a person buys a laptop, then the chances of buying antivirus are also higher. Here we categorize purchasing behavior not by generating rules. The system regarding purchasing behavior is known as recommendation system and is the biggest example of unsupervised learning. Based on the back history regarding watching movie or show, further recommendations are shown. In unsupervised learning, no supervision is present, therefore, to generate a model, the system groups similar types of objects into a cluster as shown in Figure 4. All objects that are grouped into a cluster must have a common property like all persons who purchase top-wear are grouped into a cluster and get the recommendations of bottom-wear. The formation of clusters is called clustering and in unsupervised learning, clustering algorithms are used. Various clustering algorithms are K-Means, DBSCAN, etc.

Figure 3. Supervised Learning Process.

When clusters are formed based on some common property of objects, the relationship between two recommendations is discovered. To resolve this

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question, the association between two objects is analyzed. Like in an example of top-wear purchasing, the associations between top-wear and bottom-wear find a link that if a person is purchasing top-wear, then he/she will purchase bottom-wear as well.

Figure 4. Unsupervised Learning Process.

The results of these associations are rules and the generation of rules is called association analysis. Association analysis is used to find a relation between two objects or clusters. To perform an association analysis, the a priori algorithm is majorly used and for best rule generation, the algorithm uses support and confidence variables which will be explained in machine learning.

Figure 5. Reinforcement Learning Process.

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Reinforcement Learning The third type of learning is reinforcement learning. Reinforcement learning is majorly used in the case of intelligent machines. Whenever artificial intelligence is used with any technology, then, reinforcement learning is used. This is complex learning because here instead of supervision machine learns from its past experiences. Here, the system improves its performance by interacting with the environment and getting feedback from interactions [6]. If the interaction is positive, then feedback is a reward and for negative interaction, feedback is punishment (Figure 5). From the rewards, system learns that it’s a good move and performs it again and again, but in the case of punishment, that action never repeats. Here, the learning system is called as an agent who learns it by generating policies or rules. The rule for which maximum rewards can be accumulated is selected as the best rule for the model. Robots and gaming are examples where reinforcement learning is used. Now that the types of learning have been explained, let’s move into the second section of the chapter, which provides knowledge regarding deep learning.

Deep Learning For a diverse set of problems, a new type of learning is to be used. So, here we introduce a new type of learning called deep learning and it is a subset of machine learning that is based on artificial neural networks with reinforcement learning. Learning here can be supervised and unsupervised. Deep learning is mainly involved in the process of learning machines from their own experiences like humans. To make human-like machines, researchers try to copy the brain system of humans. Humans make decisions from the brain and the brain consists of neurons so these networks also have neurons like the brain and this is the reason that these networks are known as neural networks. Deep learning architectures such as deep neural networks, deep belief networks, recurrent neural networks, and convolutional neural networks have been applied to fields like computer vision, speech recognition, natural language processing, machine translation, bioinformatics, drug design, medical image analysis, climate science, material inspection, and board game programs, where they have produced results comparable to and in some cases surpassing human expert performance [7]. Now let’s see in detail these neural networks.

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Model of Neurons It is already defined that neural networks are like our brain system. In our brain system, there are different types of neurons in the nerve structure and each neuron is having a particular shape, size, and length which depend on its function and utility in the nervous system. At one end of the neuron, there is a multitude of tiny, filament-like appendages called dendrites. These dendrites come together to form larger branches. These large branches attach to the cell body and the body of the nerve cell. Now neuron is connected to a single filament leading out of the cell body and called an axon, which has extensive branching on its far end. Now let’s see the working of neurons. As defined, neurons are connected via axons and dendrites. Signals are sent through the axon of one neuron to the dendrites of other neurons. Thus, dendrites are represented as the inputs to the neuron, and the axon as its output. The axon of each neuron creates connections with the dendrites of many other neurons. Here, each branch has exactly one dendrite of another cell and this junction is called a synapse. Above is a small description of the biological neuron system. Now let’s see the perceptron algorithm because artificial neural networks use the perceptron algorithm only. Perceptron Algorithm The perceptron algorithm is categorized as the simplest algorithm for neural networks. Here, in this algorithm neurons are basic computational units and define more than one input, a process, and only one output. In 1957, Frank Rosenblatt invented this algorithm at the Cornell Aeronautical Laboratory [8]. This algorithm is used when supervised learning is applied to neural networks. It is a linear classifier used specifically for neural networks. The perceptron is an algorithm for learning a binary classifier called a threshold function. Threshold function maps its input x (a real-valued vector) to an output value f(x) (a single binary value): 1, 𝑖𝑓𝑤. 𝑥 + 𝑏 > 0 f(x) = { 0, 𝑜𝑡ℎ𝑒𝑟𝑤𝑖𝑠𝑒 where ‘w’ is a vector of real-valued weights and b is the bias [9]. The product of input values by weight is the first step of the perceptron algorithm. After it, all the production values are added to create the weighted sum. Then, bias, which is the line of intercept in a linear equation, is calculated. The bias shifts the decision boundary away from the origin and does not depend on any input

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value. Finally, the output f(x) is calculated. The value of output is either 0 or 1 and is used to define ‘x’ as either a positive or a negative instance. In a perceptron algorithm, the Activation function plays a vital role in ensuring that output is mapped between required values (0, 1) or (-1, 1). It is important to note that the weight of the input is indicative of the strength of a node. Similarly, an input bias value gives the ability to shift the activation function curve up or down. The perceptron learning algorithm does not get terminated if the learning set is not linearly separable or if the vectors are not linearly separable or if learning never reaches a point where all vectors are classified properly. There are two types of perceptron models: the first is Single Layer perceptron and the other is a multi-layer perceptron. In the context of neural networks, a perceptron is an artificial neuron using the step function as the activation function. Single layer perceptron consists of only input and output layer in a feed-forward manner and also includes a threshold transfer function inside the model. The main objective of the single-layer perceptron model is to analyze the linearly separable objects with binary outcomes, whereas in the multilayered perceptron a number of hidden layers is one or more than one.

Artificial Neural Networks When the biological neural system is implemented on machines, then an artificial neural network is implemented. Three basic functions of neural systems are: checking of input and counting of each input signal, second is to perform different functions on input using some threshold function and last is the conversion of intermediate value into output. To calculate these functions, an artificial neural network consists of input, output, weighting factors, and activation functions. Let’s look at it in detail as shown in Figure 6. Artificial Neural Networks consist of three layers: 1. Input Layer: This layer is responsible for accepting inputs from various sources in different formats. 2. Hidden Layer: Hidden Layer is an intermediate layer. The hidden layer can be one or more than one depending upon the problem stated. Artificial neural networks also are of two types one is the single layer where the input layer sendsdata to the next receiving layer and the second is multilayered. Hidden Layer takes input from the input layer and adjusts its weight according to the problem and then transfers it to the layer for further activation. This is the same as like synapses

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function that defines which input is more important from all inputs. In the Hidden layer, only the vector summation of nodes is converted into the scalar product as defined in the diagram. After this, there is a function of activation, which mainly computes activation as a function of the total input stimulus. The activationfunction is also called a transfer function. The hiddenlayer plays a vital role in deciding the performance of the model [9]. 3. Output Layer: As input goes into a series of transformationsin the hidden layer, the output of a hidden layer is finally transferred to the output layer. This layer finally conveyed the output to the user. In the above section information regarding machine learning and deep learning is explained. Now let’s move to the next section which defines how this particular learning can be used in the case of drone technology.

Figure 6. Artificial Neural Networks.

Application of Machine and Deep learning in UAVs Now in the last section of the chapter, let’s discuss the integration of both technologies [10]. Huge research has been performed on this topic in the last two years. The Commercial Unmanned aerial vehicle (UAV) industry, which is commonly known as the drone has extensively grown in the last five years. The main reason behind the emergence of technology is to make these devices accessible to the public and this reason only gives the large scope of computer vision to be used in this technology. Machine learning improves a lot of issues

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like remote processing, data collection, and processing, location prediction in drones, etc. [11]. Some researchers use various machine learning algorithms like Naïve Bayes, Support vector machines, etc. for power management in drones, whereas some use machine learning-assisted handover and resource management for cellular-connecteddrones [12]. Even Machine learning can also be used in various applications like building security using drones [13] or in the detection, tracking, and classification of aircraft and drones in digital towers [14]. Not only does machine learning helps drone in increasing theirfunctionality, but also used for the detection of drones themselves [16]. As drone technology gained importance, its usage in various realtimeenvironments like agriculture [16], smart cities, wireless networks, securities, etc. also increases [17]. To work efficiently in the above areas, the integration of drone technology with deep learning is a great solution. With the help of learning, we can predict various actions, and then drones can work accordingly to improve situations.

Conclusion All three Artificial Intelligence, Machine Learning, and Deep Learning are subsets of each other. Machine learning are a “Field of study that gives computers the ability to learn without being explicitly programmed.” Learning is categorized as supervised, unsupervised, and reinforcement learning. Supervised learning, the training data you feed into the machine will be explored by the algorithm and the relationship between independent and dependent variable is generated. Unsupervised learning is a grouping of outcomes based on some common property. These groups are known as clusters. Reinforcement learning is when a machine learns from its action. If the agent’s action is positive, then the reward will be awarded whereas for negative action punishment will award. Implementations of the human neural system in networks called artificial neural networks and deep learning is used for the artificial neural network. An artificialneural network consists of threelayers, one is input, second is output, and intermediate is hidden layer. Depending upon layers artificial neural networks are of two types one is single layer and the other is multilayered. Implementation of deep learning and machine learning with drone technology solves numerous issues related to drone technology. In this chapter along with details of machine learning, the application of machine learning in drones is also covered.

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References [1] [2]

[3] [4] [5]

[6] [7] [8] [9] [10]

[11]

[12]

[13]

[14]

[15]

[16] [17]

Brynjolfsson, E. and T. Mitchell, “What can machine learning do? workforce implications,” Science, vol. 358, no. 6370, pp. 1530-1534, 2017. Yadereli, E., C. Gemci and A. Z. Akta, “A study on cyber-security of autonomous and unmanned vehicles,” The Journal of Defense Modeling and Simulation, vol. 12, no. 4, pp. 369-381, 2015. Samuel, A. L., 1959. Machine learning. The Technology Review, 62(1), pp. 42-45. Kahn, G., P. Abbeel and S. Levine, “Badgr: An autonomous self-supervised learning-based navigation system,” 2020. Ang, C., A. Mirzal, H. Haron and H. N. A. Hamed, “Supervised unsupervised and semi-supervised feature selection: A review on gene selection,” IEEE/ACM Trans. Comput. Biol. Bioinf., vol. 13, no. 5, pp. 971-989, Sep. 2016. Khan, M. M. et al., “A systematic review on reinforcement learning-based robotics within the last decade,” IEEE Access, vol. 8, pp. 176598-176623, 2020. Liu, W. et al., “A survey of deep neural network architectures and their applications,” Neurocomputing, vol. 234, pp. 11-26, Apr. 2017. Frank, R. “The perceptron a perceiving and recognizing automaton,” 1957. Jain, A. K., J. Mao, and K. M. Mohiuddin, “Artificial Neural Networks: A Tutorial,” Computer, vol. 29, pp. 31-44, Mar. 1996. Hu, J., H. Zhang, L. Song, Z. Han, and H. V. Poor, “Reinforcement learning for a cellular internet of UAVs: Protocol design trajectory control and resource management,” IEEE Wireless Communications, vol. 27, no. 1, pp. 116-123, 2020. Pajares, G., “Overview and current status of remote sensing applications based on unmanned aerial vehicles (UAVs),” Photogrammetric Eng. Remote Sens., vol. 81, no. 4, pp. 281-330, 2015. Azari, F., Ghavimi, M. Ozger, R. Jantti, and C. Cavdar, “Machine learning assisted handover and resource management for cellular-connected drones,” arXiv preprint arXiv:2001.07937, 2020. Sharma, V. D. N. K. Jayakody, I. You, R. Kumar and J. Li, “Secure and efficient context-aware localization of drones in urban scenarios,” IEEE Commun. Mag., vol. 56, no. 4, pp. 120-128, Apr. 2018. Thai, V. P., Weixian Zhong, Thinh Pham, Sameer Alam, and Vu Duong, “Detection Tracking and Classification of Aircraft and Drones in Digital Towers Using Machine Learning on Motion Patterns,” Integrated Communications Navigation and Surveillance Conference (ICNS), 2019. Saqib, M., S. Daud Khan, N. Sharma and M. Blumenstein, “A study on detecting drones using deep convolutional neural networks,” 2017 14th IEEE International Conference on Advanced Video and Signal Based Surveillance (AVSS), pp. 1-5, 2017. Zhang, C., and M. K. John, “The application of small unmanned aerial systems for precision agriculture: A review,” Precision Agric., vol. 13, no. 6, pp. 693-712, 2012. Shahbazi, M., J. Théau, and P. Ménard, “Recent applications of unmanned aerial imagery in natural resource management,” GISci. Remote Sens., vol. 51, no. 4, pp. 339-365, 2014.

Chapter 6

Decision Making Using Machine Learning in Drones Harmeet Singh Sant Baba Bhag Singh University, Jalandhar, Punjab, India

Abstract Unmanned aerial vehicles (UAVs), also known as drones, are an essential component of wireless networks. In today’s era, wide adoption strategy in various communication fields is likely to expand the spectral efficacy including its coverage in comparison to other traditional solutions. Nevertheless, new challenges will increase in the network if these UAVs are introduced in communication-based applications. Therefore, various machine learning (ML) architectures or solutions are expected to deliver better solutions for such issues that may arise when drones communicate with other devices. This book chapter details all the relevant decisionmaking techniques based on ML for drone-based communications. This in turn would help in making better designs and functional aspects.

Introduction Unmanned Aerial Vehicles (UAVs) include main features such as: costefficient, easy accessibility, and many more [1]. These characteristics attract numerous sectors related to aerospace. Therefore, these UAVs, also known as drones, are used for delivery of first aid, management of disasters, law 

Corresponding Authhor’s Email: [email protected].

In: Revolutionary Applications of Intelligent Drones Editors: Mohit Angurala and Vikas Khullar ISBN: 978-1-68507-991-8 © 2022 Nova Science Publishers, Inc.

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implementations, inspecting infrastructures, monitoring crops, rescue operations, defense intelligence, etc., UAVs are also known as low-altitude drones and often these drones come across various obstacles including mountains, skyscrapers, poles, trees, and so on while accomplishing their targets. These UAVs must be installed with sensor nodes to observe the atmosphere by avoiding hazards. An autonomous navigations study has been conducted by various researchers to efficiently manage drone usage in a cluttered environment and one way to auto navigate UAVs is to perform localization in vehicles. The technique of Global Positioning System (GPS) is implemented for UAVs localization, but this technique is unreliable in cluttered environments. One of the researchers fused the Inertial Navigation System (INS) as well as visual camera inputs for localizing drones. It is also possible to mix GPS inputs and INS along with the vision sensors for automatically navigating the drones. Further, the GPS input inclusions minimize the vision-based localization of UAVs error and thus improve navigational accuracy [4] proposed a methodology to systematically analyze the input data efficiency for better drone navigation with higher accuracy. Based on the flying technique, the drones are categorized into: 1. 2. 3. 4. 5. 6.

Remotely piloted vehicle Multi-rotor or Rotary-wings drone Fixed-wing drone Hybrid fixed-wing drone Robot plane Pilotless aircraft

These drone types differ in size from little UAVs to larger defense vehicles. The communication equipment, sensors, and cameras have around 10 grams to 100 kilograms weight and due to these features, the drones offer universal cost-efficient wireless access. The cost-effective wireless access is over huge coverage zones at higher elevation angles. The wireless access also has a modest height along with good line-of-sight connections with ground nodes.

Machine Learning After developing the first computers, Alan Turing coined the Artificial Intelligence (AI) term in 1950. The Turing test proposed by Alan Turing

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distinguished people and machines via questioning and answering sessions. In case the test is passed by the computer, then it is considered similar to human intelligence. Finally, in 1956 artificial intelligence was formulated based on the Turing tests. AI perceives varying environments for deciding what needs to be done with problem-solving and learning. Nevertheless, AI was unpopular until the 21st century due to many challenges including computational limitations. Currently, the technology has been used in various fields including knowledge representation, automated reasoning, natural language processing, and machine learning. Machine learning, however, is a major research area for AI and a lot of researchers are trying to identify different patterns for predicting from the available data by making use of machine learning. The important point here is that the prediction is done without computer programming. Machine learning is trying to improve the autonomous intelligence of UAVs and enables them to solve distinct issues including object detection and intelligent control strategy. Further, the term machine learning is categorized into supervised learning (SL), unsupervised learning (UL), and reinforcement learning (RL). SL is the part of machine learning that has an agent that observes a few pairs of input-output, and studies or learns a function that maps from the input. Labeling of output is performed by the agent. On the other hand, if an output is a discrete number for ‘x’ inputs, it is the problem of classification and if it is continuous then it is a regression problem. UL has the non-labeled output and the agent here focuses solely on observation of the world and its learning patterns. The major objective of UL is to look for hidden patterns, its structures, and the data embedded characteristics. Clustering is one such example of UL. RL is involved in looking at the finest policies that are learned via feedback mechanism and this feedback mechanism is also known as a reward or reinforcement. Trial and errors are repeatedly performed in RL until an agent chooses an appropriate action for maximizing the reward. Thus, the RLbased task learns a perfect policy using observed rewards for the environment.

Overview of Path Planning The issue of path planning determines a route for the drone from the starting location to the destination point. The route selection must be collision-free for the drones and its planned motion must be satisfied in case of UAV’s physical constraints. The physical constraints, also known as kinetic constraints,

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include kinetic and electrical energy. The drone’s route plan contains the following key terms: A) The first term is motion planning which is connected with robotics and the planning satisfies the flight route. Motion planning optimizes the path. It decreases the route length and reduces the angle of turn. B) The second term associated with the decision route plan involves the trajectory planning and encloses the route plan with the drone’s time, velocity, and kinematics. C) The Third term is navigation which is a part of motion plan, collision avoidance, trajectory plan, and localization. In this, drone monitoring as well as controlling takes place from one place to another. Threedimensional (3-D) views for the complex environment are needed when path planning is done for drones as the two-dimensional (2-D) route planning methods cannot find out the obstacles and objects [4]. The 3-D environment scenario is as shown in Figure 1 below:

Figure 1. Scenario of a 3-D environment [4].

Machine Learning For UAVs Also, in Figure 2, different application areas of machine learning and AI solutions for drone-enabled services are presented. Usually, numerous researchers have outlined intelligence in wireless communications. Machine learning activates the devices which are wireless to intelligently analyze the

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surroundings [5-8]. The data is exploited for training for learning, predicting, and adapting the surrounding characteristics. This includes mobility and traffic patterns, wireless channel dynamics, and the network composition among others. As a result, various performance parameters maximize the satisfying probability [9]. Further, machine learning takes input data from various sources and via different learning technique applications, it permits the network for adapting to the wireless environment in autonomous and dynamic methods [10].

Figure 2. AI and Machine Learning Applications in Drones [5].

The development of potential applications of drones and the technical progress in the hardware of UAVs has resulted in novel associated issues and challenges [11-14]. Keys towards such challenges based on machine learning as well as AI have resulted in highly active research works in different areas. Technologies like AI and machine learning have not essentially a solo universal definition. Next, Figure 3 depicts the connection between AI and machine learning. Flying drones, on the other hand, also play numerous roles in the wireless communication field. Particularly, drones have auspicious applications in

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smart cities and wireless backhauling. Base stations of UAVs can greatly improvise the capacity and wireless network coverage [15-17]. Besides this, UAVs can be positioned for enabling connectivity within the public safety information distribution situations. Drones also facilitate the Internet of Things and wave communications. UAVs with cellular connection can move freely and improve their path for rapidly completing their targets and task deliveries and this requires low-latency and reliable communications [18]. Flexible and self-forming flying drone-based ad-hoc networks provide greater coverage for areas having incomplete wireless infrastructures. Such applications also have technical as well as social impacts. Nevertheless, for the true deployment of drone-centric applications, numerous technical challenges must be resolved. Figure 4 below shows decision-makers learning continuously from their observation.

Figure 3. Taxonomy of terminology in artificial intelligence and machine learning.

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Figure 4. Role of UAV as decision Maker [19].

A modified reward mechanism is introduced for considering various constraints, particularly the limited battery capacity of the flying units, the time windows of events, and the delays caused by the UAV navigation between events.

Conclusion Drones have been extensively used in several communication areas owing to their higher mobility, lower cost, and high flexibility. In this chapter, a thorough complete study on drone use in wireless networks and other fields is discussed. For UAVs, various challenges, issues, and applications are also studied. Furthermore, the major state of the art is presented in various challenges in drone-enabled rest. The use of UAVs is quickly escalating with their advanced features and as they have distinct key applications in different fields, they are widely used in academia and industries. Similarly, the chapter also discusses the cellular-connected UAVs which enable applications including real-time streaming of videos for delivery of medical or other equipment.

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References [1]

[2]

[3]

[4]

[5]

[6]

[7] [8]

[9]

[10]

[11] [12]

[13]

[14]

Na Z., J. Wang, C. Liu, M. Guan, and Z. Gao, “Join trajectory optimization and communication design for UAV-enabled ofdm networks,” Ad Hoc Networks, vol. 98, Article ID 102031, 2020. Challita U., A. Ferdowsi, M. Chen and W. Saad, “Machine Learning for Wireless Connectivity and Security of Cellular-Connected UAVs,” in IEEE Wireless Communications, vol. 26, no. 1, pp. 28-35, February 2019, DOI: 10.1109/ MWC.2018.1800155. Mozaffari M., W. Saad, M. Bennis, Y. -H. Nam and M. Debbah, “A Tutorial on UAVs for Wireless Networks: Applications, Challenges, and Open Problems,” in IEEE Communications Surveys & Tutorials, vol. 21, no. 3, pp. 2334-2360, third quarter 2019, DOI: 10.1109/COMST.2019.2902862. Aggarwal S. and N. Kumar, “Path planning techniques for unmanned aerial vehicles: A review, solutions, and challenges,” Computer Communications (2019), DOI: https://doi.org/10.1016/j.comcom.2019.10.014. Bithas P. S., E. T. Michailidis, N. Nomikos, D. Vouyioukas, and A. G. Kanatas, “A Survey on Machine-Learning Techniques for UAV-Based Communications,” Sensors, 19, 2019. doi:10.3390/s19235170 Liu J., F. Gao, W. Jia, and W. Zhang, “Gridless compressed sensing based channel estimation for UAV wideband communications with beam squint,” IEEE Transactions on Vehicular Technology, vol. 70, no. 10, pp. 10265–10277, 2021. Chen L., “Intelligent ubiquitous computing for future UAVenabled MEC network systems,” Cluster Computing, vol. 2021, no. 25, pp. 1–10, 2021. Liu Y., J. Shi, C. Liu, and Z. Gao, “UAV-supported clustered NOMA for 6Genabled internet of things: trajectory planning and resource allocation,” IEEE Internet of 6ings Journal, vol. 8, no. 20, 2020. Deng D., X. Li, V. Menon, M. J. Piran, H. Chen, and M. A. Jan, “Learning-based joint UAV trajectory and power allocation optimization for secure IoT networks,” Digital Communications and Networks, vol. 54, pp. 2352–2361, 2021. Liu J., J. Jiang, and F. Gao, “Efficient deployment with geometric analysis for mmwave UAV communications,” IEEE Wireless Communications Letters, vol. 9, no. 7, pp. 1115–1119, 2020. Ni S., “Multiband cooperation for 5g hetNets: a promising network paradigm,” IEEE Vehicular Technology Magazine, vol. 14, no. 4, pp. 85–93, 2019. Yang N., L. Wang, G. Geraci, M. Elkashlan, J. Yuan, and M. Di Renzo, “Safeguarding 5g wireless communication networks using physical layer security,” IEEE Communications Magazine, vol. 53, no. 4, pp. 20–27, 2015. Sun X., Q. Li, and X. Ma, “Edge caching and computation management for realtime internet of vehicles: an online and distributed approach,” IEEE Transactions on Intelligent Transportation Systems, vol. 22, no. 4, pp. 2183–2197, 2021. Wang B., F. Gao, S. Jin, H. Lin, and G. Y. Li, “Spatial-and frequency-wideband effects in millimeter-wave massive MIMO systems,” IEEE Transactions on Signal Processing, vol. 66, no. 13, pp. 3393–3406, 2018.

Decision Making Using Machine Learning in Drones [15]

[16]

[17]

[18]

[19]

89

Xie H., F. Gao, S. Zhang, and S. Jin, “A unified transmission strategy for TDD/FDD massive MIMO systems with spatial basis expansion model,” IEEE Transactions on Vehicular Technology, vol. 66, no. 4, pp. 3170–3184, 2017. Hu X., C. Zhong, Y. Zhu, X. Chen, and Z. Zhang, “Programmable metasurfacebased multicast systems: design and analysis,” IEEE Journal on Selected Areas in Communications, vol. 38, no. 8, pp. 1763–1776, 2020. Li G., H. Liu, G. Huang, X. Li, B. Raj, and F. Kara, “Effective capacity analysis of reconfigurable intelligent surfaces aided 4 Wireless Communications and Mobile Computing NOMA network,” EURASIP Journal on Wireless Communications and Networking, vol. 2021, no. 1, p. 198, 2021. Tao Q., J. Wang, and C. Zhong, “Performance analysis of intelligent reflecting surface aided communication systems,” IEEE Communications Letters, vol. 24, no. 11, pp. 2464–2468, 2020. Bouhamed O., H. Ghazzai, H. Besbes, and Y. Massoud, “A Generic Spatiotemporal Scheduling for Autonomous UAVs: A Reinforcement Learning-Based Approach,” in IEEE Open Journal of Vehicular Technology, vol. 1, pp. 93-106, 2020, DOI: 10.1109/OJVT.2020.2979559.

Chapter 7

UAV Applications in Agriculture Prabhjot Kaur and Anand Muni Mishra Chitkara University Institute of Engineering and Technology, Chitkara University, Punjab, India

Abstract In developing countries like India, agriculture is the primary source of revenue. Agriculture employs about 60% of India’s population and accounts for 17% of the country’s total GDP. Although research workers and growers concentrate on different factors that can increase productivity, losing crops because of ailment is a crucial problem faced by them. Crop development observation and prior pest infestation identification remain challenging. Food stability access puts pressure on decision-makers to ensure that our globe has enough food for everyone. For observing and advancing the detection of agrarian disease, “Unmanned Aerial Vehicles” play an important part. This study focuses on delivering a complete outline of the most effective strategies for precision crop observation and pest control in agrarian areas by the use of Unmanned Aerial Vehicles (UAVs)/Unmanned Aircraft. The mentioned study gives knowledge of the uses as well as implementations of UAVs in agriculture to encourage UAV usage in agriculture and ensure long-term reliability. The purpose of this article is to examine the usage of Unmanned Aerial Vehicles (UAVs) in agricultural implementations. According to the literature, UAVs can be used for a variety of agricultural purposes. A complete review of other research in this field is included in the methodology. As a result of the change, it was discovered that different sensors provide varied agricultural analyses. As 

Corresponding Author’s Email: [email protected].

In: Revolutionary Applications of Intelligent Drones Editors: Mohit Angurala and Vikas Khullar ISBN: 978-1-68507-991-8 © 2022 Nova Science Publishers, Inc.

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Prabhjot Kaur and Anand Muni Mishra a result, the project’s goal must be determined before employing UAV technology to improve data quality and analysis. To summarise, before employing a UAV to collect correct stats and perform an error-free investigation, some appropriate detectors and UAVs must be known.

Keywords: UAV, Precision agriculture, crop management, stress detection, aerial vehicles

Introduction For better decisions and accurate results in terms of the right rate, right times, and places the farmer uses analytical advanced tools in conjunction with the crop data sources is called “Precision agriculture (PA).” In recent times, a surge of curiosity showed up about “PA” around the world giving viable ways to meet the unprecedented demand for higher-quality food and energy more sustainably by reducing outwardness [1]. In recent years PA practices have dramatically changed with the worldwide market expected to grasp $43.4 billion in 2025. Remote sensing (RS) technology can be used to execute PA based on an earlier record of any of the variables of interest. Straightforwardly and cost-effectively, RS lets growers gather, visualize, and analyzed the health conditions of soil and crop during various stages of production. It can be used as an advanced caution structure to identify probable problems as well as provide opportunities for quick solutions. The platform type determines attributes like deployment range and time, sensor distance from the article of interest, picture capture frequency, image attainment timing, position, and coverage range in RS [2]. Conventional ‘RS’ medium monitor crops instantaneously, trees categorization, water strain evaluation, weed recognition, disease recognition, produce an estimate, and numerous nutrient and pest managing tactics could be revolutionized through capabilities of Unmanned Aerial Vehicles (UAVs). In the outcome of the growth of UAV technology, various research has been conducted on the implementation of Unmanned Aerial Vehicle (UAV) technology, which has the largest capability for UAVs. As per Association for Unmanned Vehicle Systems International (AUVSI), agrarian UAVs are predicted to take up 80% of the commercial UAV industry in the future [3]. Agrarian UAVs are attractive as they are projected to perform a significant part in conquering some of the current agriculture issues. A new agricultural UAV structure is required to ensure the long-term viability of agricultural productivity, which is now challenging to

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uphold due to climate change, as well as the increasing demand for agrarian items with an increasing populace. Agrarian UAVs have now been used mostly in pest monitoring and control of crops including vegetables, soybeans, corn, and rice. The agricultural UAVs, on the other hand, are likely to get utilized for field and soil surveying, sowing, spraying, observation, irrigation, progress assessment, plotting, “remote sensing,” investigation, and conveyance. Work time and labor demands have been greatly decreased by incorporating a UAV in conventional agriculture, and agricultural work efficiency has been enhanced remarkably. Still, as a UAV’s main power source is a limited battery, it is further effective to employ a multi-UAV system to conduct agricultural tasks than the existing single-UAV system [4]. A single UAV, for example, is employed for agrarian tasks including spraying or observing large farmland; nevertheless, it is ineffective as it takes extensive time and consumes a lot of energy. When employing a multi-UAV, on the other hand, it is feasible to do collaborative or individual agricultural chores on the allotted acreage at the same time (a division of labor). As a result, on big farmland, agricultural duties can be completed fast. In other cases, when numerous UAVs are used to locate unhealthy crops, the agricultural process accuracy gets boosted or equalized as the mission regions of each UAV overlap. When looking at existing UAV applications for agriculture, for example, most researchers use a UAV with independent control to do agricultural chores. Few studies are there to complete agricultural tasks using a multi-UAV system; therefore, research is still in its early stages [5]. Created one independent structure concerning “precision agriculture” inspections grounded upon using sole and numerous UAVs. Furthermore, a precision agriculture mechanism grounded upon the deployment of a set of UAVs capable of taking georeferenced photographs to construct a comprehensive map using mosaicking processes for after-processing has been investigated in [6]. Even though [6] uses a multi-UAV arrangement for agricultural functions, it uses centralized regulators via commercialized software or several computers and didn’t execute a measurable assessment since the number of UAVs amplified; therefore, ignored the ease of the swarm regulators used.

UAV and Precision Agriculture Over the last few years, the Unmanned Aerial Vehicle (UAV) appeared as an economical choice in identifying data analysis approaches and technology. The remote sensing approach identifies a target object’s qualities from a

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distance using electromagnetic energy, having the advantage of being comprehensive, timely, flexible, and non-invasive. “Remote sensing” computes soil’s attributes is different compared to authentic figures reason being a complicated character of “remote sensing,” agrarian produce, and soil. As stated in [7], several initial types of research by the use of multi-spectral satellite pictures aimed at soil studies, also mapping was carried out, but those pictures stood incapable to offer quantifiable evidence about particular soil qualities. As a result, UAVs may be a viable choice for gathering precise infield figures. It was recognized as one viable technology competent to produce “high-spatial-resolution” imaging (