A Robust Harmony Search Algorithm Based Markov Model for Node Deployment in Hybrid Wireless Sensor Networks

Mohsen, Abdulqader M.; Aljoby, Walid; Alenezi, Khaled; Alenezi, Abdulrahman

doi:10.21660/2016.27.1333

Cited by 4 publications

(2 citation statements)

References 24 publications

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“…Also, the same author in [13] proposed HSA based on dynamic deployment to maximize the network coverage and connectivity. Authors in [43] have proposed the combination of the Markov chain model and HSA to solve the coverage hole problem by redistribution mobile nodes after the initial deployment of the static nodes. The HSA was applied to set the optimal positions of the added mobile nodes, and the Markov chain model was applied to illustrate the network schemes.…”

Section: Literature Reviewmentioning

confidence: 99%

An Efficient Deployment Model for Maximizing Coverage of Heterogeneous Wireless Sensor Network Based on Harmony Search Algorithm

et al. 2020

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Due to the increase of Wireless Sensor Network (WSN) technologies demand, the optimal sensor node deployment is considered as one of the most important factors that directly affect the network coverage. Most researches in WSNs that solved the problem of coverage in homogeneous and heterogeneous cases are suffering from many drawbacks such as consumed energy and high cost. In this paper, we propose an efficient deployment model based on probabilistic sensing model (PSM) and harmony search algorithm (HSA) to achieve the balance between the network coverage performance and the network cost in a heterogeneous wireless sensor network (HEWSN). The HSA is used for deployment optimization of HEWSN nodes which makes a balance between the coverage and financial cost. The PSM is used to solve the overlapping problem among the sensors. The performance of the proposed model is analyzed in terms of coverage ratio and cost evaluations. The simulation results showed the capability of the proposed heterogeneous deployment model to achieve maximum coverage and a minimum number of sensors compared to homogeneous deployment. Furthermore, a comparative study with a meta-heuristic genetic-based algorithm in HEWSN has also been conducted, and its results confirm the superiority of the proposed model.

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Section: Literature Reviewmentioning

confidence: 99%

An Efficient Deployment Model for Maximizing Coverage of Heterogeneous Wireless Sensor Network Based on Harmony Search Algorithm

et al. 2020

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“…Combining HS and local search, a novel sensor localization approach is proposed by Manjarres et al [26]. Minimizing energy consumption and maximizing the network lifetime of a wireless sensor network (WSN) problem using the HS algorithm was closely studied [27][28][29][30]. Degertekin [31] optimized the frame size of truss structures by harmony search algorithm.…”

Section: Introductionmentioning

confidence: 99%

A New Differential Mutation Based Adaptive Harmony Search Algorithm for Global Optimization

Zhao

Hao

et al. 2020

Applied Sciences

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The canonical harmony search (HS) algorithm generates a new solution by using random adjustment. However, the beneficial effects of harmony memory are not well considered. In order to make full use of harmony memory to generate new solutions, this paper proposes a new adaptive harmony search algorithm (aHSDE) with a differential mutation, periodic learning and linear population size reduction strategy for global optimization. Differential mutation is used for pitch adjustment, which provides a promising direction guidance to adjust the bandwidth. To balance the diversity and convergence of harmony memory, a linear reducing strategy of harmony memory is proposed with iterations. Meanwhile, periodic learning is used to adaptively modify the pitch adjusting rate and the scaling factor to improve the adaptability of the algorithm. The effects and the cooperation of the proposed strategies and the key parameters are analyzed in detail. Experimental comparison among well-known HS variants and several state-of-the-art evolutionary algorithms on CEC 2014 benchmark indicates that the aHSDE has a very competitive performance.

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Maximizing Wireless Sensor Network Coverage With Minimum Cost Using Harmony Search Algorithm

2017

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A Robust Harmony Search Algorithm Based Markov Model for Node Deployment in Hybrid Wireless Sensor Networks

Cited by 4 publications

References 24 publications

An Efficient Deployment Model for Maximizing Coverage of Heterogeneous Wireless Sensor Network Based on Harmony Search Algorithm

An Efficient Deployment Model for Maximizing Coverage of Heterogeneous Wireless Sensor Network Based on Harmony Search Algorithm

A New Differential Mutation Based Adaptive Harmony Search Algorithm for Global Optimization

Maximizing Wireless Sensor Network Coverage With Minimum Cost Using Harmony Search Algorithm

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