Development of Energy Efficient and Optimized Coverage Area Network Configuration to Achieve Reliable WSN Network Using Meta-Heuristic Approaches

Banerjee, A.; Das, Victor; Biswas, Arindam; Chattopadhyay, Samiran; Biswas, Utpal

doi:10.4018/ijamc.2021070101

Cited by 12 publications

(15 citation statements)

References 18 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…From Tab. 2 we can see that the obtained result using SMOWA algorithm to minimize consumed energy in case of random deployment for 320 nodes using Random Deployment is much better than the obtained result using modified ACO algorithm to minimize consumed energy in case of random deployment 217 nodes using Random Deployment [20]. The compared result has made bold for easy understanding.…”

Section: Different Input Parametersmentioning

confidence: 77%

“…The compared result has made bold for easy understanding. Ultimately, we can conclude that the total number of day-life is bigger in the case of the proposed algorithm than that of the existing literature [20]. Here the day-life refer to the lifetime of the total WSN.…”

Section: Different Input Parametersmentioning

confidence: 78%

“…When the DC value is greater than the optimal value, it will increase the delay, because more than one node is in the awake situation. Banerjee et al [20] designed an optimized path with the help of Ant Colony Optimization for reducing energy. Maximization of the Coverage Area is also considered using hybrid Differential Evolution-Quantum behaved Particle Swarm Optimization (DE-QPSO)".…”

Section: Literature Surveymentioning

confidence: 99%

“…Here, Population1 represents the generated population for the SMOWA algorithm to minimize consumed energy in the case of random deployment for 320 nodes using Random Deployment. Population2 represents the generated population for SMOWA algorithm to minimize consumed energy in case of S-pattern deployment for 400 nodes using Random Deployment and Population 3 represents the generated population for ACO algorithm [20] to minimize consumed energy in case of random deployment for 217 nodes using Random Deployment. In Tab.…”

Section: Wilcoxon Rank-sum Test For the Multi-objective Reliability O...mentioning

confidence: 99%

See 3 more Smart Citations

Design of Energy Efficient WSN Using a Noble SMOWA Algorithm

Banerjee¹,

Garg²,

Das³

et al. 2022

Computers, Materials &Amp; Continua

Self Cite

View full text Add to dashboard Cite

In this paper, the establishment of efficient Wireless Sensor Network (WSN) networks has been projected to minimize the consumption of energy using a new Self-adaptive Multi-Objective Weighted Approach (SMOWA) algorithm for solving a multi-objective problem. The Different WSN nodes deployment policies have been proposed and applied in this paper to design an efficient Wireless Sensor Network to minimize energy consumption. After that, the cluster head for each cluster has been selected with the help of the duty cycle. After configuring the WSN networks, the SMOWA algorithms have been developed to obtain the minimum energy consumption for the networks. Energy minimization, as well as the amount of day-saving, has been calculated for the different WSNs which has been configured through different deployment policies. The major finding of the research paper is to improve the durability of Wireless Sensor Network (i) applying different deployment strategies: (Random, S pattern and nautilus shell pattern), and (ii) using a new Meta-heuristic algorithm (SMOWA Algorithm). In this research, the lifetime of WSN has been increased to a significant level. To choose the best result set from all the obtained results set some constraints such as "equivalent distribution", "number of repetitions", "maximum amount energy storage by a node" has been set to an allowable range.

show abstract

Section: Different Input Parametersmentioning

confidence: 77%

Section: Different Input Parametersmentioning

confidence: 78%

Section: Literature Surveymentioning

confidence: 99%

Section: Wilcoxon Rank-sum Test For the Multi-objective Reliability O...mentioning

confidence: 99%

See 2 more Smart Citations

Design of Energy Efficient WSN Using a Noble SMOWA Algorithm

Banerjee¹,

Garg²,

Das³

et al. 2022

Computers, Materials &Amp; Continua

Self Cite

View full text Add to dashboard Cite

show abstract

“…The proposed algorithm partitions the sensor set into non-disjoint subsets with varying sensing range such that each cover provides kcoverage in the network. In [74], the authors describe how these challenges can be resolved by designing an efficient WSN with the help of meta-heuristic algorithms. The authors have configured an optimized route/path using ACO algorithm and deployed static WSN nodes.…”

Section: Coverage Solutions Based Ai Techniques In Wsnsmentioning

confidence: 99%

Coverage, Deployment and Localization Challenges in Wireless Sensor Networks Based on Artificial Intelligence Techniques: A Review

et al. 2022

View full text Add to dashboard Cite

The growing importance and widespread adoption of Wireless Sensor Network (WSN) technologies have helped the enhancement of smart environments in various fields such as manufacturing, smart city, transport, health and the Internet of Things, by providing pervasive real-time applications. In this paper, we analyze the existing research trends of Coverage, Deployment and Localization challenges in WSN with respect to Artificial Intelligence (AI) methods for WSN enhancement. We present a comprehensive discussion on the recent studies that utilized various AI methods to meet specific objectives of WSN, during the span of 2010 to 2021. This would guide the reader towards an understanding on upto-date applications of AI methods with respect to different WSN challenges. Then, we provide a general evaluation and comparison of different AI methods used in WSNs, which will be a guide for research community in identifying the mostly adapted methods and the benefits of using various AI methods for solving the Coverage, Deployment and Localization challenges related to WSNs. Finally, we conclude the paper stating the open research issues and new directions for future research.

show abstract