Optimal node placement in industrial Wireless Sensor Networks using adaptive mutation probability binary Particle Swarm Optimization algorithm

Wang, Sheng; Fu, Xiping; Fang, Jiating; Wang, Haikuan; Fei, Minrui

doi:10.1109/icnc.2011.6022417

Cited by 19 publications

(7 citation statements)

References 10 publications

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“…Yang et al [19] attempted to minimize the cost for the target coverage problem in a 3D space above a 3D terrain. However, the above studies did not consider network lifetime or energy consumption [20]. In real-world marine environment application, sensor nodes in WSNs have limited battery power.…”

Section: Literature Reviewmentioning

confidence: 99%

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3-D Multiobjective Deployment of an Industrial Wireless Sensor Network for Maritime Applications Utilizing a Distributed Parallel Algorithm

Cao

Zhao

Yang

et al. 2018

IEEE Trans. Ind. Inf.

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Abstract-Effective monitoring marine environment has become a vital problem in the marine applications. Traditionally, marine application mostly utilizes oceanographic research vessel methods to monitor the environment and human parameters. But these methods are usually expensive and time-consuming, also limited resolution in time and space. Due to easy deployment and cost-effective, WSNs have recently been considered as a promising alternative for next generation IMGs. This paper focuses on solving the issue of 3D WSN deployment in a 3D engine room space of a very large crude-oil carrier (VLCC), in which many power devices are also considered. To address this 3D WSN deployment problem for maritime applications, a 3D uncertain coverage model is proposed with a new 3D sensing model and an uncertain fusion operator, is presented. The deployment problem is converted into a multi-objective problems (MOP) in which three objectives are simultaneously considered: Coverage, Lifetime and Reliability. Our aim is to achieve extensive Coverage, long Lifetime and high Reliability. We also propose a distributed parallel cooperative co-evolutionary multi-objective large-scale evolutionary algorithm (DPCCMOLSEA) for maritime applications. In the simulation experiments, the effectiveness of this algorithm is verified in comparing with five state-of-the-art algorithms. The numerical outputs demonstrate that the proposed method performs the best with respect to both optimization performance and computation time.Index Terms-wireless sensor network deployment, 3D engine room, Very large crude-oil carrier (VLCC), Coverage, Lifetime, Reliability, Parallel, Multi-objective evolutionary algorithm.

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Section: Literature Reviewmentioning

confidence: 99%

“…For an IWSN, reliability is also crucial. In the work of [23], Wang et al guaranteed reliability by ensuring the associations of each node to multiple relay nodes. In this paper, we also consider the reliability of the IWSN.…”

Section: Literature Reviewmentioning

confidence: 99%

3-D Multiobjective Deployment of an Industrial Wireless Sensor Network for Maritime Applications Utilizing a Distributed Parallel Algorithm

Cao

Zhao

Yang

et al. 2018

IEEE Trans. Ind. Inf.

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“…The uniformity of the numbers of CHs that all CHs can communicate with should be optimized. The standard deviation can be utilized to measure the uniformity [36]:…”

Section: Network Connectivitymentioning

confidence: 99%

“…Connectivity is basic for the reliable data transmission, and it's basic of the topology control and routing protocol. Besides the basic coverage and lifetime, connection and reliability [21] also should be concerned to ensure the wireless networks performance. Li et al [22] proposed a deployment strategy for simultaneously considering coverage and connectivity based on the elitist non-dominated sorting genetic algorithm (NSGA-II) [19].…”

Section: Introductionmentioning

confidence: 99%

Differential Evolution-Based 3-D Directional Wireless Sensor Network Deployment Optimization

Cao

Kang

Zhao

et al. 2018

IEEE Internet Things J.

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Wireless sensor networks (WSNs) are applied more and more widely in real life. In actual scenarios, 3D directional wireless sensors (DWSs) are constantly employed, thus, research on the real-time deployment optimization problem of 3D directional wireless sensor networks (DWSNs) based on terrain big data has more practical significance. Based on this, we study the deployment optimization problem of DWSNs in the 3D terrain through comprehensive consideration of coverage, lifetime, connectivity of sensor nodes, connectivity of cluster headers and reliability of DWSNs. We propose a modified differential evolution (DE) algorithm by adopting CR-sort and polynomial-based mutation on the basis of the cooperative coevolutionary (CC) framework, and apply it to address deployment problem of 3D DWSNs. In addition, to reduce computation time, we realize implementation of message passing interface (MPI) parallelism. As is revealed by the experimentation results, the modified algorithm proposed in this paper achieves satisfying performance with respect to either optimization results or operation time.

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“…A bi-objective (user coverage and network connectivity) genetic-based optimization algorithm is considered in [9] for node placement in wireless mesh networks. In [31], a PSO algorithm is proposed to determine the best placement of nodes in industrial environments in terms of network reliability, load uniformity, total cost and convergence speed. For sensing coverage purposes in WSNs, the authors in [32] propose a PSO-based solution for minimizing the existing coverage holes through the use of a fitness function based on the computation of Voronoi regions.…”

Section: Related Workmentioning

confidence: 99%

Optimal relay placement in multi-hop wireless networks

et al. 2016

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Relay node placement in wireless environments is a research topic recurrently studied in the specialized literature. A variety of network performance goals, such as coverage, data rate and network lifetime, are considered as criteria to lead the placement of the nodes. In this work, a new relay placement approach to maximize network connectivity in a multi-hop wireless network is presented. Here, connectivity is defined as a combination of inter-node reachability and network throughput. The nodes are placed following a two-step procedure: (i) initial distribution, and (ii) solution selection. Additionally, a third stage for placement optimization is optionally proposed to maximize throughput. This tries to be a general approach for placement, and several initialization, selection and optimization algorithms can be used in each of the steps. For experimentation purposes, a leave-one-out selection procedure and a PSO related optimization algorithm are employed and evaluated for second and third stages, respectively. Other node placement solutions available in the literature are compared with the proposed one in realistic simulated scenarios. The results obtained through the properly devised experiments show the improvements achieved by the proposed approach.

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Optimal node placement in industrial Wireless Sensor Networks using adaptive mutation probability binary Particle Swarm Optimization algorithm

Cited by 19 publications

References 10 publications

3-D Multiobjective Deployment of an Industrial Wireless Sensor Network for Maritime Applications Utilizing a Distributed Parallel Algorithm

3-D Multiobjective Deployment of an Industrial Wireless Sensor Network for Maritime Applications Utilizing a Distributed Parallel Algorithm

Differential Evolution-Based 3-D Directional Wireless Sensor Network Deployment Optimization

Optimal relay placement in multi-hop wireless networks

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