Polyhedral Approach for Lifetime Maximization of Target Coverage Problem

Pujari, Arun K.; Mini, S.; Padhi, Trideba; Sahoo, Prabhanjan

doi:10.1145/2684464.2684495

Cited by 6 publications

(8 citation statements)

References 20 publications

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“…Since, network dynamics getting changed over the time due to frequent node failure, learning automata-based energy-based heuristics [5][6][7] are proven to be better than many existing column generations and approximation-based schemes. Further, Pujari et al [8] proposed another heuristic based on a polyhedral approach to maximise the total network lifetime for the target coverage problem. None of the work discussed above [2][3][4][5][6][7][8] considers the coverage of least covered targets with the highest priority.…”

Section: Related Workmentioning

confidence: 99%

“…Further, Pujari et al [8] proposed another heuristic based on a polyhedral approach to maximise the total network lifetime for the target coverage problem. None of the work discussed above [2][3][4][5][6][7][8] considers the coverage of least covered targets with the highest priority. Since the least covered targets (called critical targets) are the one which becomes uncovered first in the network, the network lifetime can be extended by extending the coverage duration of such targets.…”

Section: Related Workmentioning

confidence: 99%

“…The target coverage problem is usually represented as a maximum network lifetime problem (MLP). In order to solve the MLP, various heuristic approaches have been discussed [2][3][4][5][6][7][8][9][10][11][12][13][14]. As the sensors are deployed densely; therefore, to monitor all the targets, a subset of sensor called cover set, is enough instead of activating all the sensors at once.…”

Section: Introductionmentioning

confidence: 99%

“…By activating only a subset of sensors, one can save unnecessary battery uses when compared with the scenario when all the sensors are activated. The basic idea adopted by most of the protocols [2][3][4][5][6][7][8][9][10][11][12][13][14] is to find maximum possible cover sets. These covers are activated for a fixed duration called working time.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Genetic algorithm‐based meta‐heuristic for target coverage problem

Manju

Chand

Kumar

2018

IET wirel. sens. syst.

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In wireless sensor networks (WSNs), network lifetime and energy consumption are two important parameters which directly impacts each other. In order to enhance the global network lifetime, one should need to utilise the available sensors' energy in an optimise way. There are several approaches discussed in the literature to maximise the network lifetime for wellknown target coverage problem in WSN. The target coverage problem is presented as a maximum network lifetime problem (MLP) and solved heuristically using various approaches. In this study, the authors propose a genetic algorithm (GA)-based meta-heuristic to solve the above said MLP. The GA is a non-linear optimisation solution method which is proven to be better as compared to the column generation or approximation schemes.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Genetic algorithm‐based meta‐heuristic for target coverage problem

Manju

Chand

Kumar

2018

IET wirel. sens. syst.

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“…There has been lots of research work [3][4][5][6][7][8][9][10][11][12][13][14][15][16] done on target coverage problem and its variant. The work done in [5,13] is an improvement over work done in [4] for target coverage problem, yet, they have not given enough attention to those targets which are least covered in the WSNs.…”

Section: Introductionmentioning

confidence: 99%

Maximising network lifetime for target coverage problem in wireless sensor networks

Manju¹,

Chand

Kumar³

2016

IET wirel. sens. syst.

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Target coverage problem is one of the important problems in wireless sensor network in which each target should be covered by at least one sensor. All the sensors are organised into different groups called sensor cover in such a way that each cover can monitor all the targets for a fixed duration. By keeping one sensor cover active at a time while others in sleep mode, sensors batteries can be utilised in efficient way. This helps in prolonging the network lifetime. In this study, the authors propose a new energy-efficient heuristic to schedule the sensors in different non-disjoint sensor covers which helps to maximise network lifetime. At first, the authors' heuristic identify all the critical targets (least covered) and the critical sensors (covering critical targets). The critical targets, covered by minimum number of sensors, will be the targets that become uncovered first. Utilising critical sensors efficiently will help to increase the network lifetime. In their method, they try to select minimum number of critical sensors in each sensor cover so that the critical targets can be covered for longer period. Simulation results shows that the proposed heuristic outperforms many existing approaches for solving target coverage problem.

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