Reward Maximization for Disaster Zone Monitoring With Heterogeneous UAVs

Wenzheng Xu; Chengxi Wang; Hongbin Xie; Weifa Liang; Haipeng Dai; Zichuan Xu; Ziming Wang; Bing Guo; Sajal K. Das

doi:10.1109/TNET.2023.3300174

Reward Maximization for Disaster Zone Monitoring With Heterogeneous UAVs

Wenzheng Xu
, Chengxi Wang
, Hongbin Xie
, Weifa Liang
, Haipeng Dai
, Zichuan Xu^*
, Ziming Wang
, Bing Guo
, Sajal K. Das

^*Corresponding author for this work

Department of Computer Science

Research output: Journal Publications and Reviews › RGC 21 - Publication in refereed journal › peer-review

24 Citations (Scopus)

Abstract

In this paper, we study the deployment of K heterogeneous UAVs to monitor Points of Interest (PoIs) in a disaster zone, where a PoI may represent a school building or an office building, in which people are trapped. A UAV can take images/videos of PoIs and send its collected information back to a nearby rescue station for decision-making. Unlike most existing studies that focused on only homogeneous UAVs, we here study the scheduling of K heterogeneous UAVs, where different UAVs have different energy capacities and functionalities that lead to different monitoring qualities (monitoring rewards) of each PoI. For example, one type of UAVs can take only visual images while the other type of UAVs can take both visual and thermal infrared images. In this paper, we investigate a problem of scheduling K heterogeneous UAVs to monitor PoIs so that the sum of monitoring rewards received by all UAVs is maximized, subject to energy capacity on each UAV. We propose the very first 1/3-approximation algorithm for this scheduling problem. We also evaluate the performance of the proposed algorithm, using real parameters of commercial UAVs. Experimental results show that the performance of the proposed algorithm is promising, which is improved by 25%, compared with existing algorithms.

© 2023 IEEE. Personal use is permitted, but republication/redistribution requires IEEE permission.

Original language	English
Pages (from-to)	890-903
Journal	IEEE/ACM Transactions on Networking
Volume	32
Issue number	1
Online published	23 Aug 2023
DOIs	https://doi.org/10.1109/TNET.2023.3300174
Publication status	Published - Feb 2024

Funding

The work of Wenzheng Xu was supported in part by the National Natural Science Foundation of China (NSFC) under Grant 62272328 and in part by the Double World-Class Project for Sichuan University under Grant 0082604151352. The work of Zichuan Xu was supported by NSFC under Grant 62172068. The work of Bing Guo was supported in part by NSFC under Grant U2268204. The work of Sajal K. Das was supported in part by NSF under Award CCF-1725755, Award CNS1818942, Award SCC-1952045, and Award SaTC-2030624.

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 4 Quality Education

Research Keywords

approximation algorithm
Disaster area monitoring
heterogeneous UAVs
multiple UAV scheduling
orienteering problem

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10.1109/TNET.2023.3300174

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title = "Reward Maximization for Disaster Zone Monitoring With Heterogeneous UAVs",

abstract = "In this paper, we study the deployment of K heterogeneous UAVs to monitor Points of Interest (PoIs) in a disaster zone, where a PoI may represent a school building or an office building, in which people are trapped. A UAV can take images/videos of PoIs and send its collected information back to a nearby rescue station for decision-making. Unlike most existing studies that focused on only homogeneous UAVs, we here study the scheduling of K heterogeneous UAVs, where different UAVs have different energy capacities and functionalities that lead to different monitoring qualities (monitoring rewards) of each PoI. For example, one type of UAVs can take only visual images while the other type of UAVs can take both visual and thermal infrared images. In this paper, we investigate a problem of scheduling K heterogeneous UAVs to monitor PoIs so that the sum of monitoring rewards received by all UAVs is maximized, subject to energy capacity on each UAV. We propose the very first 1/3-approximation algorithm for this scheduling problem. We also evaluate the performance of the proposed algorithm, using real parameters of commercial UAVs. Experimental results show that the performance of the proposed algorithm is promising, which is improved by 25\%, compared with existing algorithms. {\textcopyright} 2023 IEEE. Personal use is permitted, but republication/redistribution requires IEEE permission. ",

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AB - In this paper, we study the deployment of K heterogeneous UAVs to monitor Points of Interest (PoIs) in a disaster zone, where a PoI may represent a school building or an office building, in which people are trapped. A UAV can take images/videos of PoIs and send its collected information back to a nearby rescue station for decision-making. Unlike most existing studies that focused on only homogeneous UAVs, we here study the scheduling of K heterogeneous UAVs, where different UAVs have different energy capacities and functionalities that lead to different monitoring qualities (monitoring rewards) of each PoI. For example, one type of UAVs can take only visual images while the other type of UAVs can take both visual and thermal infrared images. In this paper, we investigate a problem of scheduling K heterogeneous UAVs to monitor PoIs so that the sum of monitoring rewards received by all UAVs is maximized, subject to energy capacity on each UAV. We propose the very first 1/3-approximation algorithm for this scheduling problem. We also evaluate the performance of the proposed algorithm, using real parameters of commercial UAVs. Experimental results show that the performance of the proposed algorithm is promising, which is improved by 25%, compared with existing algorithms. © 2023 IEEE. Personal use is permitted, but republication/redistribution requires IEEE permission.

KW - approximation algorithm

KW - Disaster area monitoring

KW - heterogeneous UAVs

KW - multiple UAV scheduling

KW - orienteering problem

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Reward Maximization for Disaster Zone Monitoring With Heterogeneous UAVs

Abstract

Funding

UN SDGs

Research Keywords

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