Placing Wireless Chargers With Limited Mobility

被引:40
作者
Dai, Haipeng [1 ]
Wang, Xiaoyu [2 ]
Lin, Xuzhen [1 ]
Gu, Rong [1 ]
Shi, Shuyu [1 ]
Liu, Yunhuai [3 ]
Dou, Wanchun [1 ]
Chen, Guihai [1 ]
机构
[1] Nanjing Univ, State Key Lab Novel Software Technol, Nanjing 210023, Jiangsu, Peoples R China
[2] Soochow Univ, Sch Comp Sci & Technol, Suzhou 215006, Jiangsu, Peoples R China
[3] Peking Univ, Beijing 100080, Peoples R China
基金
中国国家自然科学基金;
关键词
Placement; directional charging; limited mobility; wireless power transfer; approximation algorithm; SENSOR NETWORKS; ENERGY REPLENISHMENT; COVERAGE;
D O I
10.1109/TMC.2021.3136967
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Several recent works have studied mobile charging under the "one-to-many" charging pattern where a single charger can charge multiple devices simultaneously. However, most of them focus on path planning and charging time allocation, but overlook the underlying dependence of the charging efficiency on initial deployment positions of chargers. This paper studies the problem of Placing directional wIreless chargers with Limited mObiliTy (PILOT), that is, maximize the overall charging utility for a set of static rechargeable devices on a 2D plane by determining deployment positions, stop positions and orientations, and portions of time for all deployed chargers that can move in a limited area after their deployment. To the best of our knowledge, we are the first to study placement of mobile directional chargers under the "one-to-many" pattern. To address PILOT, we propose a (1/2 - epsilon)-approximation algorithm. First, we present a method to approximate nonlinear charging power of chargers, and further propose an approach to construct Maximal Covered Set uniform subareas to reduce the infinite continuous search space for stop positions and orientations to a finite discrete one. Second, we present geometrical techniques to further reduce the infinite solution space for candidate deployment positions to a finite one without performance loss, and transform PILOT to a mixed integer nonlinear programming problem. Finally, we propose a linear programming based greedy algorithm to address it. Simulation and experimental results show that our algorithm outperforms six comparison algorithms by 19.74% similar to 500.01%.
引用
收藏
页码:3589 / 3603
页数:15
相关论文
共 58 条
  • [1] AN IMPLEMENTATION OF KARMARKAR ALGORITHM FOR LINEAR-PROGRAMMING
    ADLER, I
    RESENDE, MGC
    VEIGA, G
    KARMARKAR, N
    [J]. MATHEMATICAL PROGRAMMING, 1989, 44 (03) : 297 - 335
  • [2] Complexity and approximation for Travelling Salesman Problems with profits
    Angelelli, Enrico
    Bazgan, Cristina
    Speranza, M. Grazia
    Tuza, Zsolt
    [J]. THEORETICAL COMPUTER SCIENCE, 2014, 531 : 54 - 65
  • [3] [Anonymous], 2021, POW TRANSM
  • [4] [Anonymous], 2021, US
  • [5] Optimal Placement of Wireless Chargers in Rechargeable Sensor Networks
    Arivudainambi, D.
    Balaji, S.
    [J]. IEEE SENSORS JOURNAL, 2018, 18 (10) : 4212 - 4222
  • [6] Mobile Sensor Deployment Optimization for k-Coverage in Wireless Sensor Networks with a Limited Mobility Model
    Bai, Xingzhen
    Li, Shu
    Xu, Juan
    [J]. IETE TECHNICAL REVIEW, 2010, 27 (02) : 124 - 137
  • [7] Deploying wireless sensor networks under limited mobility constraints
    Chellappan, Sriram
    Gu, Wenjun
    Bai, Xiaole
    Xuan, Dong
    Ma, Bin
    Zhang, Kaizhong
    [J]. IEEE TRANSACTIONS ON MOBILE COMPUTING, 2007, 6 (10) : 1142 - 1157
  • [8] Charge Me If You Can: Charging Path Optimization and Scheduling in Mobile Networks
    Chen, Lin
    Lin, Shan
    Huang, Hua
    [J]. MOBIHOC '16: PROCEEDINGS OF THE 17TH ACM INTERNATIONAL SYMPOSIUM ON MOBILE AD HOC NETWORKING AND COMPUTING, 2016, : 101 - 110
  • [9] UNIT DISK GRAPHS
    CLARK, BN
    COLBOURN, CJ
    JOHNSON, DS
    [J]. DISCRETE MATHEMATICS, 1990, 86 (1-3) : 165 - 177
  • [10] Dai HP, 2016, IEEE INFOCOM SER