Target Coverage Heuristics in Wireless Sensor Networks

Manju, .; Singh, Deepak; Chand, Satish; Kumar, Bijendra

doi:10.1007/978-981-10-4603-2_25

Cited by 6 publications

(16 citation statements)

References 12 publications

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“…From now on, we skip the initial CAIA in notation given at the end of Section In the experimental part, we used the benchmark SCP1 proposed in our earlier publications but also did tests with two external benchmarks: one from [7,8] and the other from [9], selected due to compatible problem defnitions and optimization criteria but diferently defned test cases.…”

Section: Methodsmentioning

confidence: 99%

“…Again, boxplot graphs confrm conclusions from mean values. Manju et al in [9]. Te authors of the minimal-heuristic approach [9] proposed the following set of benchmarks for experiments with algorithms solving MLCP.…”

Section: Te Benchmark Proposed Bymentioning

confidence: 99%

“…Manju et al in [9]. Te authors of the minimal-heuristic approach [9] proposed the following set of benchmarks for experiments with algorithms solving MLCP. Te monitored area is a square with a side size of 150 abstract units.…”

Section: Te Benchmark Proposed Bymentioning

confidence: 99%

“…Te main contribution of this paper lies in generating nine heuristics, based on the building blocks originating from three other existing approaches, and experimentally verifying their efciency. We used three benchmarks for testing the performance of the heuristics: SCP1, provided in our earlier publications [3][4][5][6]; the benchmark from [7,8]; and the benchmark from [9].…”

Section: Introductionmentioning

confidence: 99%

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Wireless Sensor Network Coverage Optimization: Comparison of Local Search-Based Heuristics

Trojanowski

Mikitiuk

2022

Applied Computational Intelligence and Soft Computing

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The Maximum Lifetime Coverage Problem (MLCP) requires heuristic optimization methods due to its complexity. A real-world problem model determines how a solution is represented and the operators applied in these heuristics. Our paper describes adapting a local search scheme and its operators to MLCP optimization. The operators originate from three local search algorithms we proposed earlier: LSHMA, LSCAIA, and LSRFTA. Two steps of the LS scheme’s main loop can be executed in three different ways each. Hence, nine versions of the LS approach can be obtained. In experimental research, we verified their effectiveness. Test cases come from three benchmarks: SCP1, proposed and used in our earlier research on the three LS algorithms mentioned above, and two others found in the literature. The results obtained with SCP1 showed that the algorithm based on the hypergraph model approach (HMA) is the most effective. The remaining results of the other algorithms divide them into two groups: effective ones and weak ones. However, other benchmarks showed that the more redundant the coverage of points of interest (POIs) by sensors, the more effective the perturbation method from the approach inspired by cellular automata (CAIA). The findings expose the strengths and weaknesses of the problem-specific steps applied in the LS algorithms.

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Section: Methodsmentioning

confidence: 99%

Section: Te Benchmark Proposed Bymentioning

confidence: 99%

Section: Te Benchmark Proposed Bymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Wireless Sensor Network Coverage Optimization: Comparison of Local Search-Based Heuristics

Trojanowski

Mikitiuk

2022

Applied Computational Intelligence and Soft Computing

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“…Q-Coverage) [38]. Since lots of research has been done to extend network lifetime by applying various methodologies [39]- [42] for other variants of coverage, this section gives deep insight into the Q-Coverage only, addressed in [43]- [46].…”

Section: Target Q-coveragementioning

confidence: 99%

Proficient QoS-Based Target Coverage Problem in Wireless Sensor Networks

et al. 2020

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Assuring the coverage towards the predefined set of targets, power-constrained wireless sensor networks (WSNs) consist of sensing devices (i.e., sensor nodes) that are associated with limited battery life and fixed sensing range. All the sensors are collectively responsible for covering these sets of objects. The standard target coverage problem is the one where continuous coverage is provided over a predefined set of targets for the maximum possible duration so that the scarce resource (battery power) can be optimally utilized. Therefore, in order to incorporate quality of service (QoS) in the network and ensure smooth monitoring of the desired target set, the paper addresses target Q-Coverage, which is one of the variants of standard target coverage problem where a target is covered by at least Q-sensors (pre-defined number) in every cover set. A cover set is a subset of sensors which cover whole targets in a single iteration. In this paper, a greedy heuristic based technique, i.e., maximum coverage small lifetime (MCSL) has been proposed, which restricts the usages of those sensors that poorly cover targets and promotes the usage of those sensors that have maximum coverage and energy. Simulations are performed on static wireless sensor network with varying Q values to test the efficiency of the proposed method. The performance of the proposed heuristic is compared with optimal upper bound based on network lifetime, and results prove that performance is improvised by 94%. The obtained results are further compared with the existing approaches to prove the superiority of the proposed work via extensive experimentations.INDEX TERMS Coverage constraint, critical target, energy-efficient network, maximum coverage small lifetime, maximum lifetime target coverage, q-coverage.

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A Novel Meta-heuristic Differential Evolution Algorithm for Optimal Target Coverage in Wireless Sensor Networks

Naik

Shetty

2019

Advances in Intelligent Systems and Computing

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Target Coverage Heuristics in Wireless Sensor Networks

Cited by 6 publications

References 12 publications

Wireless Sensor Network Coverage Optimization: Comparison of Local Search-Based Heuristics

Wireless Sensor Network Coverage Optimization: Comparison of Local Search-Based Heuristics

Proficient QoS-Based Target Coverage Problem in Wireless Sensor Networks

A Novel Meta-heuristic Differential Evolution Algorithm for Optimal Target Coverage in Wireless Sensor Networks

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