Exact and heuristic methods to maximize network lifetime in wireless sensor networks with adjustable sensing ranges

Cerulli, Raffaele; Donato, Renato De; Raiconi, Andrea

doi:10.1016/j.ejor.2012.01.046

Cited by 73 publications

(50 citation statements)

References 15 publications

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“…The third one is energy control by adjusting the sensing and communication ranges of sensors to reduce energy consumption as much as possible. [20][21][22] In this article, we consider the sensor scheduling.…”

Section: Introductionmentioning

confidence: 99%

“…1 How to assign monitoring tasks to sensor nodes to make them effectively accomplished and how to reflect how well a sensor field is monitored are the fundamental problems in wireless sensor network, which are considered as coverage problems. In general, coverage problems can be classified into four categories: area coverage, [2][3][4][5][6][7][8][9] target coverage, [10][11][12][13][14][15][16][17][18][19][20][21][22] barrier coverage, [23][24][25] and coverage problem aiming at determining the maximal breach path (MBP) and the maximal support path (MSP). 26 In this article, we consider the target coverage problem.…”

Section: Introductionmentioning

confidence: 99%

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Sensor scheduling for target coverage in directional sensor networks

Jia

Dong

et al. 2017

International Journal of Distributed Sensor Networks

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Directional sensor networks have a lot of practical applications, and target coverage is one of the most important issues. In this article, we study the target coverage problem in directional sensor network, where directional sensors can rotate freely around their centers and targets are attached with differentiated coverage quality requirements. Our goal is to maximize the lifetime of sensor network, satisfying the coverage quality requirements of all targets. We model it as the maximum cover sets problem and address it by organizing the directions of sensors into a group of non-disjoint cover sets, which can cover all targets, satisfying their coverage quality requirements, and then schedule them alternately to extend the network lifetime. It consists of two parts. First, since directional sensor has infinite directions by rotating continuously, we propose sensing direction partition algorithm to find all non-redundancy directions for each sensor according to the targets deployed within the sensing region of the sensor. Then, based on the result of the sensing direction partition algorithm, we propose an efficient heuristic algorithm for the maximum cover sets problem to organize the directions of sensors into a number of non-disjoint cover sets and allocate work time for each cover set. Besides, we get an upper bound of the optimal solution for the problem. Finally, simulation results are presented to demonstrate the performance of our algorithm.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Sensor scheduling for target coverage in directional sensor networks

Jia

Dong

et al. 2017

International Journal of Distributed Sensor Networks

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“…10 Therefore, scheduling wireless sensors is a simple and effective method for extending the network lifetime. 11 The omnidirectional wireless sensor has a circular monitoring area.…”

Section: Introductionmentioning

confidence: 99%

A novel multi-objective coverage optimization memetic algorithm for directional sensor networks

Wang

Gao

et al. 2016

International Journal of Distributed Sensor Networks

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For the target problem of a directional wireless sensor network, the greedy algorithm can easily fall into the local optimal solution, whereas the genetic algorithm must forecast the lifetime of the upper bound of the network. We propose a novel multi-objective coverage optimization memetic algorithm that encodes the solutions as chromosomes and simulates the biological evolution process in search for a favourable solution to address the aforementioned problems. Experimental results show that the proposed algorithm can prolong the network lifetime more effectively than similar heuristic algorithms in other studies.

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“…As explained by Ting and Liao [4], this approach basically divides all sensors in the network into disjoint sensor subsets, or sensor covers (or just covers) each of which must satisfy the full coverage constraint. At any time of the network lifespan, only one sensor cover is in active mode providing network functionality, and the other sensor covers are inactive to save energy.…”

mentioning

confidence: 99%

“…network is how to save the overall energy consumption of the sensors in order to maximize the lifetime of the network under the constraint of full coverage of the targets being monitored [4] [5].…”

mentioning

confidence: 99%