On-Supporting Energy Balanced $k$ -Barrier Coverage in Wireless Sensor Networks

Weng, Chien-I; Chang, Chih‐Yung; Chang, Chao-Tsun; Chen, Hai-Bao

doi:10.1109/access.2018.2792678

Cited by 24 publications

(26 citation statements)

References 18 publications

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“…A greedy approach is adopted in [192] to discover barriers on the transformed flow graph of the network, whereas, in [185], the Kruskal minimum spanning tree algorithm is applied to generate connected barriers. An efficient distributed k-barrier construction algorithm is proposed in [186], which is a combination of grid and graph-based approaches. The primary goal of the algorithm is to construct disjoint k-barriers.…”

Section: ) the Graph-based Approachesmentioning

confidence: 99%

QoS-Aware Energy Management and Node Scheduling Schemes for Sensor Network-Based Surveillance Applications

et al. 2021

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Section: ) the Graph-based Approachesmentioning

confidence: 99%

QoS-Aware Energy Management and Node Scheduling Schemes for Sensor Network-Based Surveillance Applications

et al. 2021

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“…The difference is that the nodes are distributed in different locations. Representative networks mainly include flood routing [6], chat routing [7], SPIN routing protocol [8], DD protocol [9], Rumor protocol [10], and so on. However, clustered routing protocols are more widely used.…”

Section: Related Workmentioning

confidence: 99%

Analysis of Network Coverage Optimization Based on Feedback K-Means Clustering and Artificial Fish Swarm Algorithm

2020

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There is a certain energy loss in the process of wireless sensor network information collection. Moreover, the current network protocols and network coverage methods are not sufficient to effectively reduce system energy consumption. In order to improve the operating efficiency and service life of wireless sensor networks, this study analyzes the classic LEACH protocol, summarizes the advantages and disadvantages, and proposes a targeted clustering method based on the K-means algorithm. At the same time, in order to maximize the network coverage and minimize the energy consumption on the basis of ensuring the quality of service, a wireless sensor network coverage optimization method based on an improved artificial fish swarm algorithm was proposed. In addition, a controlled experiment is designed to analyze the effectiveness and practical effects of the proposed algorithm. The experimental results show that the method proposed in this paper has certain advantages over traditional methods and can provide theoretical references for subsequent related research. INDEX TERMS Feedback, K-means clustering, artificial fish swarm algorithm, network coverage, algorithm optimization.

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“…Based on the requirement, coverage can be classified into three main categories: area coverage, point coverage, and barrier coverage [1,[20][21][22].…”

Section: Wsn Coverage Issuementioning

confidence: 99%

The Coverage Issues in WSNs

Rani

Anand

2020

ARJOM

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Aim: Wireless Sensor Networks is one of the hot research topic in previous years due to its wide contribution in emerging industry that is Internet of Things. A wireless sensor network is a network of tiny devices, which can gather the information from surrounding and then successfully communicate it via wireless link. WSNs are widely used for monitoring targets in given field of interest. Most popular area of research in Wireless Sensor Networks is coverage. This paper focus on coverage issues in Wireless Sensor Networks. Study Design: Various Issues in WSNs being studied and analyzed. Place and Duration of Study: Swami Parmanand College Lalru, between May 2019 to December 2019. Methodology: It included following steps:- Survey of the literature related to the proposed work. Comparison of different types of WSNs and challenges and issues related to them. Detailed study of coverage issue involved in implementation of WSN. Results: Coverage selection depends on the system requirement. Each coverage has its own advantages and disadvantages related to its implementation. “Table 2: Comparison between Various WSN and challenges related to them” it discusses about various types of WSNs and the challenges related to each one of them. Conclusion: Coverage in WSNs can be defined on how well the sensor nodes monitor the targets in given field of interest. Each application comes with different set of coverage requirements; hence, deployment strategies are chosen based on the requirements.

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On-Supporting Energy Balanced $k$ -Barrier Coverage in Wireless Sensor Networks

Cited by 24 publications

References 18 publications

QoS-Aware Energy Management and Node Scheduling Schemes for Sensor Network-Based Surveillance Applications

QoS-Aware Energy Management and Node Scheduling Schemes for Sensor Network-Based Surveillance Applications

Analysis of Network Coverage Optimization Based on Feedback K-Means Clustering and Artificial Fish Swarm Algorithm

The Coverage Issues in WSNs

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