UAV Network for Surveillance of Inaccessible Regions with Zero Blind Spots

Kumar, Nikhil; Ghosh, Monalisa; Singhal, Chetna

doi:10.1109/infocomwkshps50562.2020.9162686

Cited by 16 publications

(7 citation statements)

References 17 publications

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“…However, we have noticed that if we reduce the height to 80m, the system capacity improves by 5%, but the UAV coverage decreases by 35%. On the other hand, if we increase the height beyond 100m, the packet loss increases to an unacceptable level that is more than 4% [36]. Therefore, we fix the UAV height at 100m and evaluate the proposed scheme by comparing the performance results with other schemes.…”

Section: Number Of Iot Devicesmentioning

confidence: 99%

Multi-UAV Assisted IoT NOMA Uplink Communication System for Disaster Scenario

Barick

Singhal

2022

IEEE Access

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Unmanned aerial vehicle (UAV) communication has become a prominent technology that can effectively assist in IoT systems. The inherent features of UAV such as mobility, flexibility, and fast deployment make it preferable for emergency Internet of Things (IoT) applications. In this paper, we consider a multi-UAV assisted wireless network to support uplink communication for IoT devices distributed over a disaster area. The network involves two types of UAVs: sector UAV (SU) and anchor UAV (AU). The SU hovers at a fixed height over the sector around the temporary base station (TBS), collects the information from the respective IoT devices and relays them to the TBS via AU. The AU revolves continuously around the TBS and relays the information between the SUs and TBS periodically. We aim to improve the uplink capacity of the system. To achieve this, we employ non-orthogonal multiple access (NOMA), where we jointly optimize the positions of SUs and the power control of IoT devices. We propose a two-step approach to solve this. First, we optimize the position of SU in each sector by minimizing the sum distances of SU from the respective IoT devices. Then, by considering the optimal SU location, we optimize the transmit power of IoT devices using Lagrange dual method. Finally, the experimental results show that the proposed scheme improves the system capacity by 22% compared to the state-of-the-art schemes.INDEX TERMS UAV network, Internet-of-Things, uplink transmission, NOMA, system capacity.

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Section: Number Of Iot Devicesmentioning

confidence: 99%

Multi-UAV Assisted IoT NOMA Uplink Communication System for Disaster Scenario

Barick

Singhal

2022

IEEE Access

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“…It has been shown in [17] that determining the minimum number of drones for guaranteed coverage of a target area is NP-complete. [18] propose a methodology to decide the network topology to conduct surveillance with minimum number of UAVs. The authors consider a static UAV setup and decide the optimal UAV positions based on the camera specifications in order to cover the target region with least number of UAVs possible.…”

Section: Related Workmentioning

confidence: 99%

ECMS: Energy-Efficient Collaborative Multi-UAV Surveillance System for Inaccessible Regions

Singhal

Barick

2022

IEEE Access

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The evolution and popular adaptation of drone technology in diverse applications has necessitated advancement of UAV communication framework. UAVs inherently support features like mobility, flexibility, adaptive altitude, which make them a preferable option for dynamic surveillance of remote locations. Multiple UAVs can cooperatively work to accomplish surveillance missions more efficiently. However, the intermittent network connectivity and the limited onboard energy storage impose a great challenge on UAV-assisted remote surveillance. This paper presents an Energy-efficient Collaborative Multi-UAV Surveillance (ECMS) system for surveillance of inaccessible regions. The system employs an optimal Multi-UAV Collaborative Monocular Vision (MCMV) topology to facilitate the surveillance with zero blind spot using minimum number of drones. We also propose an application-aware Multi-Path Weighted Load-balancing (MWL) routing protocol for handling congestion by distributing traffic among all available resources in UAV network and adaptively selecting the of source datarate (i.e. switching video resolution). The simulation results demonstrate that the proposed surveillance system achieves coverage with lesser number of UAVs compared to the existing systems. It also achieves higher throughput, higher packet-delivery ratio, higher residual energy of UAVs, and lower end-to-end delay.INDEX TERMS Unmanned aerial vehicle, remote surveillance, UAV network topology, multi-path routing, load balancing.

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“…In these cases, the delivery component is downloading (or uploading) information, which is accomplished by having the drone travel to close proximity to a base station or other drone. Reference [17] considers a system with one drone route that conducts video surveillance over a rectangular region using repeated parallel passes of the drone, followed by travel to a delivery location to communicate the information collected to a base station. This research focuses on specifics of the communication channels and communication protocols, rather than the drone routing.…”

Section: Introductionmentioning

confidence: 99%

The multi‐purpose K‐drones general routing problem

et al. 2023

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In this article, we present and solve the multi‐purpose ‐drones general routing problem (MP ‐DGRP). In this optimization problem, a fleet of multi‐purpose drones, aerial vehicles that can both make deliveries and conduct sensing activities (e.g., imaging), have to jointly visit a set of nodes to make deliveries and map one or more continuous areas. This problem is motivated by global healthcare applications that deploy multipurpose drones that combine delivery trips with collection of aerial imaging data for use in emergency preparedness and resilience planning. The continuous areas that have to be mapped may correspond to terrain surfaces (e.g., flooded areas or regions with a disease outbreak) or to infrastructure networks to be inspected. The continuous areas can be modeled as a set of lines so that each area is completely serviced if all the lines covering it are traversed. Thus, given a set of nodes and a set of lines, the problem is to design drone routes of shortest total duration traversing the lines and visiting the nodes, while not exceeding the range limit (flight time) and capacity (loading) of the drones. Unlike ground vehicles in classical routing problems, drones can enter a line through any of its points, service only a part of that line and then exit through another of its points. The possibility of flying directly between any two points of the network offered by drones can lead to reduced costs, but it increases the difficulty of the problem. To deal with this problem, the lines are discretized, allowing drones to enter and exit each line only at a finite set of points, thus obtaining an instance of the ‐vehicles general routing problem (‐GRP). We present in this article an integer programming formulation for the ‐GRP and propose a matheuristic algorithm and a branch‐and‐cut procedure for its solution. Results are provided for problems with up to 20 deliveries and up to 28 continuous areas.

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UAV Network for Surveillance of Inaccessible Regions with Zero Blind Spots

Cited by 16 publications

References 17 publications

Multi-UAV Assisted IoT NOMA Uplink Communication System for Disaster Scenario

Multi-UAV Assisted IoT NOMA Uplink Communication System for Disaster Scenario

ECMS: Energy-Efficient Collaborative Multi-UAV Surveillance System for Inaccessible Regions

The multi‐purpose K‐drones general routing problem

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