The Mobile Sensor Deployment Problem and the Target Coverage Problem in Mobile Wireless Sensor Networks are NP-Hard

Nguyen, Ngoc‐Tu; Liu, Bing-Hong

doi:10.1109/jsyst.2018.2828879

Cited by 110 publications

(33 citation statements)

References 12 publications

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“…First, all the rendezvous point was selected among all the other sensor nodes based upon the energy. The main disadvantages of this routing protocol are to find the optimal routes for data transmission at NP-hard problems [5]. In order to overcome these issues, the heuristic algorithms are used.…”

Section: Related Workmentioning

confidence: 99%

Energy-eﬀicient virtual infrastructure based geo-nested routing protocol for wireless sensor networks

Varadharajan¹,

Gopalakrishnan²,

Varadharajan³

et al. 2021

Turk J Elec Eng & Comp Sci

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Section: Related Workmentioning

confidence: 99%

Energy-eﬀicient virtual infrastructure based geo-nested routing protocol for wireless sensor networks

Varadharajan¹,

Gopalakrishnan²,

Varadharajan³

et al. 2021

Turk J Elec Eng & Comp Sci

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“…In this paper, a maximum-coverage-based algorithm and a Steiner-treebased algorithm were proposed. In Reference [ 37 ], they also showed the hardness of some related topic on target coverage problem and rectified the incorrectness of the proof in Reference [ 17 ] by reducing MMTC problem to the minimum geometric disk cover problem. No approximation algorithm for the MMTC problem has been presented until now.…”

Section: Related Workmentioning

confidence: 99%

“…We prove the MTCLM problem is NP-hard by reducing the minimum geometric disk cover (MGDC) problem to it as in Reference [37]. The MGDC problem is NP-hard and its definition is showed as following: Definition 2.…”

Section: Hardness Of the Problemmentioning

confidence: 99%

Maximum Target Coverage Problem in Mobile Wireless Sensor Networks

Liang

Shen

Chen

2020

Sensors

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We formulate and analyze a generic coverage optimization problem arising in wireless sensor networks with sensors of limited mobility. Given a set of targets to be covered and a set of mobile sensors, we seek a sensor dispatch algorithm maximizing the covered targets under the constraint that the maximal moving distance for each sensor is upper-bounded by a given threshold. We prove that the problem is NP-hard. Given its hardness, we devise four algorithms to solve it heuristically or approximately. Among the approximate algorithms, we first develop randomized (1−1/e)-optimal algorithm. We then employ a derandomization technique to devise a deterministic (1−1/e)-approximation algorithm. We also design a deterministic approximation algorithm with nearly ▵−1 approximation ratio by using a colouring technique, where ▵ denotes the maximal number of subsets covering the same target. Experiments are also conducted to validate the effectiveness of the algorithms in a variety of parameter settings.

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“…The coverage range of a charging base station is a circle with radius R. One sensor node can be covered by multiple charging base stations according to its power consumption. Building some base stations to cover many discrete sensor nodes in a plane region [41] is a nonlinear programming problem, which are basically NP-hard problems [49,50]. The NP-hard problems can be solved only by approximation methods.…”

Section: Problem Definitionmentioning

confidence: 99%

An algorithm to optimize deployment of charging base stations for WRSN

Wan

Cheng

et al. 2019

J Wireless Com Network

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Optimizing deployment of charging base stations in wireless rechargeable sensor networks can considerably reduce the cost. Previously, the charging base stations are simply installed at some fixed special points (e.g., the grid points) after partitioning the area distributed by sensor nodes. In this paper, a new algorithm of planning the charging base stations is proposed based on the greedy algorithm and the location relationship of the sensor nodes. The proposed algorithm exploits the local search ability and avoids falling into an exponential increase of the number of the charging base stations (i.e., combinatorial explosion). The simulated results show that the proposed algorithm can result in less number and flexible deployed locations of the charging base stations. In addition, this algorithm provides a novel solution for the point coverage problems.

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The Mobile Sensor Deployment Problem and the Target Coverage Problem in Mobile Wireless Sensor Networks are NP-Hard

Cited by 110 publications

References 12 publications

Energy-eﬀicient virtual infrastructure based geo-nested routing protocol for wireless sensor networks

Energy-eﬀicient virtual infrastructure based geo-nested routing protocol for wireless sensor networks

Maximum Target Coverage Problem in Mobile Wireless Sensor Networks

An algorithm to optimize deployment of charging base stations for WRSN

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