Details of Research Outputs

TitleOptimizing Mobile Charger Scheduling for Task-Based Sensor Networks
Creator
Date Issued2021
Conference Name15th International Conference on Algorithmic Aspects in Information and Management, AAIM 2021
Source PublicationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
ISBN9783030931759
ISSN0302-9743
Volume13153 LNCS
Pages134-145
Conference DateDecember 20-22, 2021
Conference PlaceVirtual, Online
Abstract

Replenishing energy to wireless sensor networks is always a crucial problem as the energy capacity of sensor nodes is very limited. Scheduling mobile chargers to charge sensor nodes has been widely studied due to its efficiency and flexibility. However, most existing works focus on maximizing the charging utility or charging efficiency, which ignores the task performing function of sensor nodes. In this paper, we study the mobile charger scheduling problem with the objective to maximize the task utility achieved by sensor nodes. We consider two different scenarios where sensor nodes are deployed on a line and a ring, respectively. We prove the NP-Hardness of our problems and design two approximation algorithms with guaranteed performance. We prove the approximation ratio of our algorithms through theoretical analysis, and conduct extensive simulations to validate the performance of our algorithms. Simulation results show that our algorithms always outperform the baselines, which demonstrates the effectiveness of our algorithms.

KeywordMobile charger Wireless power transfer Wireless sensor network
DOI10.1007/978-3-030-93176-6_12
URLView source
Language英语English
Scopus ID2-s2.0-85122031637
Citation statistics
Cited Times [WOS]:0   [WOS Record]     [Related Records in WOS]
Document TypeConference paper
Identifierhttp://repository.uic.edu.cn/handle/39GCC9TT/9101
CollectionResearch outside affiliated institution
Corresponding AuthorDing, Xingjian
Affiliation
1.School of Information,Beijing Forestry University,Beijing,100083,China
2.BNU-UIC Institute of Artificial Intelligence and Future Networks,Beijing Normal University at Zhuhai,Zhuhai,Guangdong,519087,China
3.School of Software Engineering,Beijing University of Technology,Beijing,100124,China
4.School of Information,Renmin University of China,Beijing,100872,China
Recommended Citation
GB/T 7714
Meng, Xiangguang,Guo, Jianxiong,Ding, Xingjianet al. Optimizing Mobile Charger Scheduling for Task-Based Sensor Networks[C], 2021: 134-145.
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