Efficient Algorithms for Flexible Sweep Coverage in Crowdsensing

Huang, Peihuang; Wang, Zihao; Liao, Kewen; Sellis, Timos; Yu, Zhiyong; Guo, Longkun

doi:10.1109/access.2018.2868931

Cited by 7 publications

(1 citation statement)

References 28 publications

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“…The sweep coverage problem can be applied to scenarios where the targets do not need longterm coverage, but only need periodic coverage, such as the periodic patrol of the security system, the periodic collection of information, and so on. In addition, the models for the sweep coverage problem can also be used in the applications in the emerging mobile crowd sensing [25], [16]. Considering that the energy consumption of moving is far greater than the energy consumption of sensing and communication, mobile sensors would consume a lot of energy in sweep coverage.…”

Section: Introductionmentioning

confidence: 99%

Changeable Sweep Coverage Problem

Liang,

Shen

2021

Preprint

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Sweep coverage is to realize the periodic coverage of targets by planning the periodic sweeping paths of mobile sensors. It is difficult for sensors to reduce energy consumption by reducing the moving distances. Therefore, charging technology is the best way to extend the lifetime of the sweep coverage network. This paper studies the sweep coverage of rechargeable sensors: the sensors are rechargeable, constantly sweep between the target points and the charging stations not only tp meet the periodic coverage requirement of the target points, but also need to return to the charging stations during the charging period to avoid running out of energy. This paper proposes the general definition of Chargeable Sweep Coverage (CSC) problem for the first time, and studies the complexity of the CSC problem by analyzing CSC problems under different constraints, and then proposes two kinds of CSC problems under special constraints: 1) The sensors need to return to their original charging stations for charging; 2) The sensors can go to different charging stations for charging, and the number of charging stations is 2. Both of these problems are NP-hard. In this paper, these two problems are modeled as the maximum set coverage problem, and the approximation algorithms are obtained by reducing the number of candidate paths to polynomials. The validity and scalability of the proposed algorithms is proved by theoretical proof and experimental simulation.

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Section: Introductionmentioning

confidence: 99%