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Unmanned Aerial Vehicles, popularly known as UAVs, have been used in many applications, from healthcare services to military assignments with diverse capabilities such as data transmission, cellular service provisioning, and computational offloading tasks. UAV’s have been recently used to provide Security as a Service (SaaS). SaaS involves technical solutions like anti-virus and anti-spam software, firewalls, using secure operating systems, etc. UAV’s are resource constrained devices, and thus they are connected to the base station (BS) so that they may avail the computational facilities of the BS. The UAV’s connect themselves to the base station using cluster heads (intermediary devices). At times several UAVs cooperatively come together to serve a given region and such a group of UAVs is called a swarm of UAVs. In real-world scenarios, many stakeholders come together to form UAV swarm configuration proving services to users. Each stakeholder wants to maximize his gains. In this work, we propose a pricing Stackelberg game among UAVs, Cluster heads, and BS by formulating their behavioral utilities. Using particle swarm optimization on each entity’s utility functions, we create an optimal price strategy for each entity to maximize their profit.

Unmanned aerial vehicle (UAV) is widely deployed to assist wireless user access for providing low latency and highreliability service. In this paper, we consider a communication scenario where ground user is far away from ground base station and focus on the multi-hop UAV assisted backhaul system. In particular, we aim to maximize the end-to-end throughput with full consideration of two different resource allocation schemes, namely orthogonal frequency (OF) scheme and power control (PC) scheme. The optimal UAV placement is derived in the OF scheme and an iterative algorithm is proposed to jointly optimize the UAV placement and transmit power in the PC scheme. Numerical results show that an additional UAV will bring about 19% performance gain for long-distance communication in both schemes, but when the distance exceeds 1.2 km, the PC scheme performs better than the OF scheme.

In this paper, we propose a mobile edge computing (MEC)-enabled unmanned aerial vehicle (UAV)-assisted vehicular ad hoc networks (VANETs) architecture, based on which a number of vehicles are served by UAVs equipped with computation resource. Each vehicle has to offload its computing tasks to the proper MEC server on UAV due to the limited computation power. To counter the problems above, we first model and analyze the transmission model from the vehicle to the MEC server on UAV and the task computation model of the local vehicle and the edge UAV. Then, the problem is formulated as a multi-objective optimization problem by jointly considering the MEC selection, the resource allocation, and task offloading. For tackling this hard problem, we decouple the multiobjective optimization problem as two subproblems and propose an efficient iterative algorithm to jointly make the MEC selection decision based on the criteria of load balancing and optimize the offloading ratio and the computation resource according to the Lagrangian dual decomposition. Finally, the simulation results demonstrate that our proposed algorithm achieves significant performance superiority as compared with other schemes in terms of the successful task processing ratio.

In recent years, unmanned aerial vehicles (UAVs) have been widely used in wireless communication, attracting intensive attentions. UAVs can not only serve as relay, but also serve as aerial base station for ground users (GUs). However, limited energy means that they cannot work for long and cover a limited area of services. In this paper, we investigate 2D UAV trajectory design and power allocation in order to maximize the UAV's service time and downlink throughput. Based on deep reinforcement learn-ing, we propose a deep deterministic strategy gradient algo-rithm for trajectory design and power distribution (TDPA) to solve the energy efficient and communication service quality problem. The simulation results show that TDPA can extend the service time of UAV, improve the communi-cation service quality, and realize the maximization of downlink throughput, which are significantly improved compared with existing methods.

An Unmanned Aerial Vehicle (UAV) is used for establishing communication in a Non-Functional Area (NFA) or disaster area where Base Stations (BSs) get damaged. This paper investigates a UAV based cognitive hybrid multi-hop Device to Device (D2D) communication using a clustering technique in a downlink scenario. The UAV can communicate through a multihop D2D network to a maximum number of victims. The UAV assisted cognitive hybrid multi-hop D2D network is proposed to improve the Spectrum Efficiency (SE) and Energy Efficiency (EE) in a NFA. The overall outage probability, SE, and EE are investigated for different values of the number of clusters, the distance between two D2D users, the transmit power of D2D users, and the impact of maximum allowable interference power on outage probability is also studied. The impacts of SE and EE are also shown.

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In recent years, the Internet of Things (IoT) enabled drones, also called as unmanned aerial vehicles (UAVs), are widely used in many applications ranging from military to civilian applications, such as wildlife monitoring. Since the drones provide a risk-free as well as low-cost facility in order to quickly and persistently monitor natural circumstances at high spacial temporal resolution, they help in wildlife monitoring research. Due to wireless communication nature, the communication among the deployed drones in their respective flying zones and the IoT smart devices installed in animal bodies, and also among the drones and their respective ground station server (GSS), is susceptible to various passive and active attacks. To mitigate these issues, we propose a public blockchain based access control implementationfor wild-life monitoring purpose. The application of both accesscontrol and blockchain at the same time not only protects various attacks, but also maintains immutability, transparency as well as decentralization properties. Next, we simulate the proposed security framework for the blockchain part to measure the total computational time needed to add a varied number of blocks inna blockchain and also a varied number of transactions per block. Finally, a practical testbed experiment has been implemented tonshow the feasibility of the proposed framework.

Drones, also called Unmanned Aerial Vehicles (UAVs) are attracting significant attention in the research community for their many military and civil uses. They are especially being deployed for assistance in 5G communication networks. As a particular technology starts to gain widespread applicability, it is crucial that it becomes resistant to malicious entities. In particular, the communication between UAVs and the 5G-base station needs to be secured without leaking sensitive information to any unauthorized entities. The constraints on UAV in terms of computation time, impose the condition that any authentication protocol required for authenticating the UAV with the 5G base station must be lightweight in order to be feasible for deployment. To address this issue, a Physical Unclonable Function (PUF)-based mutual authentication scheme is proposed in this paper. Security analysis of the proposed protocol and a computation time comparison with state-of-the-art authentication protocol in the same field are also presented.

The massive growth of IoT devices and the need for digitization drive future wireless communication technologies intensively. The end-users are always demanding all kinds of network services including the time-critical and mission-critical ones. The Quality of Network Services (QoNS) of the future 5G and 6G networks are aiming to address all possible types of flexible user demands optimally. Telecom. Service Providers (TSPs) are rigorously looking for suitable network architectures that can assist them to realize such user-friendly systems. vSDN or virtualized Software Defined Network Architecture is the key technology enabler that makes such functionalities possible. In the recent paradigm shift where everything is being based on IoT, service availability and system stability are two major concerns. For keeping the quality index of the network services high, it is essential to orchestrate and deploy the network infrastructures that will provide additional and upgraded network services. Keeping these fundamental issues in mind, in this work, we have proposed a novel multi-instance deployment strategy for Controllers and Hypervisors over an actual network topology. While defining the work, our prime concern targets improving service availability within the ULL (Ultra-Low Latency) environment. Thus, we have developed a Multi-Objective Mixed Integer Linear Programming (MILP) problem for optimally placing the hypervisors and controllers over the network topology to minimize the H-plane service agreements along with propagation latency of network function demands. After analyzing the results, it can be claimed that our approach has a significant impact in improving the QoS of network parameters. The proposed model can be applied in similar time-critical relocation problems associated with UAV-assisted networks depending upon various application domains.

The exponential growth of drone technology and its computational methods has led to a surge in agricultural applications employing drones. In this paper, a Drone-Assisted Weed Detection using a Modified multichannel Gray Level Co-Occurrence Matrix (GLCM-M) and Normalised Difference Index with Red Threshold (NDIRT) indices (DA-WDGN) is proposed to aid in the process of weed detection. In DA-WDGN, the drones combine both information and communication technologies for the far-field data acquisition and precise detection of weeds. Accurate detection of weeds limits the need for pesticides and helps to protect the environment. Traditional systems use an object-oriented classification system for weed detection, which suffer from the issue of close similarities between the shape features of crop plants and weeds, making it impossible to uniquely distinguish the weeds. Therefore in the DA-WDGN system, shape, texture, and spectral features are integrated to establish a unique pattern for every plant. These patterns are then used to differentiate between crops and weeds. The proposed DA-WDGN system improves the accuracy of weed detection to 99.4% thereby establishing its supremacy over other conventional weed detection algorithms.

In this paper, we propose a Real-Time Immersive Surveillance System with a reduced number of Unmanned Aerial Vehicles (UAVs) required to cover any inaccessible area for video surveillance. To start with, we have developed a twin servo based camera assembly which is capable to move in horizontal and vertical axis (Pan and Tilt) providing us flexibility to change our field of view without physically moving the UAV. The movable assembly for sensor can be controlled in two ways - Auto mode and Movement Based on accelerometer data of mobile phone. The Real-Time video feed can be viewed on Big screens as well as on HMDs (Head Mounted Displays) using mobile phones. Furthermore, a holistic view of this area can be created using feed from multiple drones, which provides an immersive experience. The maximum distance between GCS and Anchor UAV have been obtained experimentally based on acceptable packet loss and Quality-of-Experience (QoE) for video streaming. In order to minimise the number of drones to cover the entire area, we have utilised the capability of this movable camera setup. To calculate the area covered in various position of sensor, the concept of Field of View (FOV) of the on-board camera has been used. We have also discussed the necessary limitations of the extent of camera movement and the camera setup.

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Increasing the transmission range of a signal in sub-Terahertz (0.1-10 THz) frequency band has been a challenge due to its vapor loss and molecular absorption. It is more challenging in unmanned aerial vehicle (UAV) communications because of dynamic network topology and weather conditions. However, there are many attempts to tackle this issue. Ultra-massive multiple input multiple output (MIMO) communication is discussed in this paper and has been tested in an UAV communications scenario. Also, analysis and simulations are presented to illustrate the feasibility of improving the THz communications up to 60 meters in line of sight (LoS) and non-line of sight (NLoS) areas. Finally, an analysis for Secrecy Outage Probability is presented for 0.06-0.3 THz to illustrate the performance of a physical layer security added to sub-THz communication.

Unmanned aerial vehicles (UAVs) can be used as flying base stations (BSs) for providing wireless communications and coverage enhancement in fifth/sixth-generation (5G/6G) wireless networks. Operating multiple UAV-BSs to guarantee reliable device connectivity, intelligent control of UAV movements, and resource allocation plays an important role in dynamic UAV-assisted wireless networks. In this paper, an asynchronous advantage actor-critic (A3C) based UAVs placement and resource allocation approach is proposed to maximize the network capacity while guaranteeing the wireless service requirements of ground devices. The approach enables UAVs to intelligently update their locations and resource allocation strategy according to devices' locations, to support the favourable channel gain between UAVs and devices, and maximize network benefit. Simulations show that our presented approach achieves higher learning efficiency, network capacity and QoS satisfaction level compared to popular approaches.

Deploying large numbers of unmanned aerial vehicles (UAVs) within a region can result in an overcrowded radio frequency (RF) spectrum, requiring UAVs to coordinate frequency selection and mobility to prevent data loss. Current work in interference coordination for multi-UAV systems reduces interference through the use of either trajectory and power control or channel assignments, but not both. We propose a novel controller which selects channels, creates trajectories, and controls transmit power for each UAV to increase the networking capacity of a multi-UAV system. Results show that the proposed controller yields 27% increased network capacity over state of the art UAV frequency reuse algorithms, 152% increased network capacity over state of the art UAV trajectory and power controllers, and 135% faster control overall.

Unmanned Aerial Vehicles (UAVs), commercially known as drones, have received great attention. This is due to their versatility and applicability to a large number of domains such as surveillance system, aerial photography, traffic control, flyable base stations to provide a broadband coverage and even for future urban transportation services. In this paper, the optimal distance between multiple aerial base stations has analytically been derived, based on an aerial coverage area computation. This is a fundamental wireless metric that can significantly minimise the intra-overlapped coverage and also can enhance wireless coverage connectivity and performance of aerial wireless networks. The novelty of our approach brings a better aerial optimal design understanding for UAVs communications performance without the need for establishing an aerial deployment setup.