Multiobjective Optimization Approach for Sensor Arrangement in A Complex Indoor Environment

Lee, Joon‐Yong; Seok, Joon-Hong; Lee, Ju-Jang

doi:10.1109/tsmcc.2010.2103310

Cited by 22 publications

(11 citation statements)

References 26 publications

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“…The maximum number of sensors is 50. The weighted factors 1 O , 2 O , 3 O and 4 O in (1) are defined as 8 10 , 4 10 , 100 and 1, respectively.…”

Section: Simulation Resultsmentioning

confidence: 99%

“…And hence, the less number of sensors and less overlap of cells could cause a shorter path. The following objectives of the sensor deployment are considered in this paper [8,9]. III.…”

Section: Problem Formulationmentioning

confidence: 99%

“…The sensor deployment problem asks how to deploy the sensors in the region to satisfy coverage requirements with minimum cost [6][7][8][9]. Several movement rules are proposed in [7] for the robot to deploy sensors for complete coverage.…”

Section: Introductionmentioning

confidence: 99%

“…Several movement rules are proposed in [7] for the robot to deploy sensors for complete coverage. The genetic algorithm based metaheuristic search methods are developed to maximize the coverage rate as well as reduce the number of deployed sensors and the overlap of the covered regions in [8,9].…”

Section: Introductionmentioning

confidence: 99%

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Path planning for unmanned vehicle searching based on sensor deployment and travelling salesman problem

Zhou

Che³

2014

Proceedings of 2014 IEEE Chinese Guidance, Navigation and Control Conference

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The path planning problem for unmanned vehicle searching over the region of interest with obstacles is investigated in this paper. The path planning problem is decomposed into the sensor deployment problem and the travelling salesman problem based on the fundamental finding that the vehicle could search the region that covered by the sensor if the vehicle pass through the position where the sensor is deployed. The positions of the sensors deployed to cover the region completely are obtained by solving the sensor deployment problem. Taking the positions of the deployed sensors as the waypoints that the vehicle has to travel through, the shortest path connecting all the waypoints can be obtained by solving the travelling salesman problem. The cooperatively coevolving particle swarm optimization algorithm with multiple update rules is newly developed for the sensor deployment problem. Numerical simulation results demonstrate the feasibility of the proposed methods.

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“…The maximum number of sensors is 50. The weighted factors 1 O , 2 O , 3 O and 4 O in (1) are defined as 8 10 , 4 10 , 100 and 1, respectively.…”

Section: Simulation Resultsmentioning

confidence: 99%

“…And hence, the less number of sensors and less overlap of cells could cause a shorter path. The following objectives of the sensor deployment are considered in this paper [8,9]. III.…”

Section: Problem Formulationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Path planning for unmanned vehicle searching based on sensor deployment and travelling salesman problem

Zhou

Che³

2014

Proceedings of 2014 IEEE Chinese Guidance, Navigation and Control Conference

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“…The environmental monitoring system [1,2] based on WSNs (Wireless Sensor Networks) is used to reduce the cost of the entire system, mostly using mass low-cost sensor nodes [3] for networking. Invulnerability of WSNs is the ability measurement metric of WSNs, which could be sustainable and stable to provide reliable services for the entire network system.…”

Section: Introductionmentioning

confidence: 99%

Opportunistic Cooperative Sensing for Indoor Complex Environment Monitoring

Jin¹,

Qian²,

Xing³

et al. 2017

Int. J. Onl. Eng.

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Abstract-Sensor failure have become a major bottleneck, which restricted the wide application of wireless sensor networks WSNs (Wireless Sensor Networks). In order to satisfy the needs of industrial production and daily living environment monitoring, it is important to improve the survivability of wireless sensor networks. This paper analyzed the damage types and causes of WSNs and the measurement methods of WSNs survivability. Then, based on the whole system model and sensor node circuit, we studied the fault detection method, which can improve the survivability of the scheme. Additionally, the robust guarantee scheme through hardware design and algorithm model was proposed. The performance results shown that the proposed scheme can realize the remote wireless communication services and prolong the network life cycle, so as to improve the survivability of WSNs. Through the combination of theoretical research and practical analysis system, the network survivability would be improved through soft and hard optimization combination strategy.Keywords-Wireless sensor networks; invulnerability; indoor Complex environment monitoring; hardware design; robust guarantee IntroductionThe environmental monitoring system [1, 2] based on WSNs (Wireless Sensor Networks) is used to reduce the cost of the entire system, mostly using mass low-cost sensor nodes [3] for networking. Invulnerability of WSNs is the ability measurement metric of WSNs, which could be sustainable and stable to provide reliable services for the entire network system. In the case of random or deterministic failures [4] in the network communication, the system is able to complete the key services.About the robust stability guarantees, the article [5] presented a new soft constrained model predictive control approach for tracking that provides stability guarantees even for unstable systems. The research of article [6] exploited the cubature rule in the unknown input observer structure to overcome nonlinear calculations in the presence of external disturbances for sensor fault detection purposes. In article [7], it is demonstrated that, under mild assumptions, the distributional robust optimization problems over Wasserstein balls can in fact be reformulated as finite convex programs---in many interesting cases even as tractable linear programs. A robust adaptive beamforming scheme based on two-component electromagnetic vector-sensor 4

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A Specific Heuristic Dedicated to a Coverage/Tracking Bi-objective Problem for Wireless Sensor Deployment

Berre

Rebai

Hnaien

et al. 2015

Wireless Pers Commun

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Multiobjective Optimization Approach for Sensor Arrangement in A Complex Indoor Environment

Cited by 22 publications

References 26 publications

Path planning for unmanned vehicle searching based on sensor deployment and travelling salesman problem

Path planning for unmanned vehicle searching based on sensor deployment and travelling salesman problem

Opportunistic Cooperative Sensing for Indoor Complex Environment Monitoring

A Specific Heuristic Dedicated to a Coverage/Tracking Bi-objective Problem for Wireless Sensor Deployment

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