Distributed sensor placement with sequential particle swarm optimization

Ngatchou, P.N.; Fox, Warren L. J.; El-Sharkawi, M.A.

doi:10.1109/sis.2005.1501647

Cited by 27 publications

(18 citation statements)

References 5 publications

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“…3) Swarm Intelligence: Sequential PSO: A sequential form of PSO is presented for distributed placement of sensors for maritime surveillance application in [54]. Given fixed-size sets S Tx of N Tx transmitters and S Rx of N Rx receivers, the goal is to determine the optimal placement of sonar sensors so that detection coverage is maximized in a fixed volume V representing a maritime region.…”

Section: A Design and Deploymentmentioning

confidence: 99%

Computational Intelligence in Wireless Sensor Networks: A Survey

Kulkarni

Förster

Venayagamoorthy

2011

IEEE Commun. Surv. Tutorials

610

270

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Wireless sensor networks (WSNs) are networks of distributed autonomous devices that can sense or monitor physical or environmental conditions cooperatively. WSNs face many challenges, mainly caused by communication failures, storage and computational constraints and limited power supply. Paradigms of computational intelligence (CI) have been successfully used in recent years to address various challenges such as data aggregation and fusion, energy aware routing, task scheduling, security, optimal deployment and localization. CI provides adaptive mechanisms that exhibit intelligent behavior in complex and dynamic environments like WSNs. CI brings about flexibility, autonomous behavior, and robustness against topology changes, communication failures and scenario changes. However, WSN developers are usually not or not completely aware of the potential CI algorithms offer. On the other side, CI researchers are not familiar with all real problems and subtle requirements of WSNs. This mismatch makes collaboration and development difficult. This paper intends to close this gap and foster collaboration by offering a detailed introduction to WSNs and their properties. An extensive survey of CI applications to various problems in WSNs from various research areasand publication venues is presented in the paper. Besides, a discussion on advantages and disadvantages of CI algorithms over traditional WSN solutions is offered. In addition, a general evaluation of CI algorithms is presented, which will serve as a guide for using CI algorithms for WSNs.

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Section: A Design and Deploymentmentioning

confidence: 99%

Computational Intelligence in Wireless Sensor Networks: A Survey

Kulkarni

Förster

Venayagamoorthy

2011

IEEE Commun. Surv. Tutorials

610

270

View full text Add to dashboard Cite

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“…We assume that we start with a near optimum placement of transmitters and receivers [5]. For a large field of sensors it is difficult to know which sensor is the best choice to ping.…”

Section: Distributed Searchmentioning

confidence: 99%

Intelligent Ping Sequencing for Multistatic Sonar Systems

Krout

El-Sharkawi

Fox

et al. 2006

2006 9th International Conference on Information Fusion

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We study the problem of determining optimal pinging strategies in multistatic sonar systems with multiple sources. We are specifically investigating algorithms that determine optimal pinging strategies both for generalized search scenarios, and for holding confirmed target tracks with constraints related to maintaining search performance in the rest of the area. An important part of this work is the development of metrics to be used in the optimization procedures. For maintaining search coverage, we used a "probability of target presence" metric formulation. This formulation utilizes sonar performance prediction and a Bayesian update equation to incorporate negative information (i.e., searching an area but finding no targets). We also discuss strategies that can be used to increase the performance of a multistatic field, such as the use of bandwidth diversity.

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“…Kammer and Tinker 13 proposed a sensor configuration method containing triaxial accelerometers based on EI to carry out modal vibration tests. Ngatchou et al 14 presented a type of improved particle swarm optimization (PSO) algorithm named sequential-PSO to layout the sensors. Ferentinos and Tsiligiridis 15 proposed a multi-objective optimization strategy to implement in the wireless sensor networks via GA. Kang et al 16 investigated three-sensor configuration performance indexes and proposed a virus co-evolutionary partheno-genetic algorithm.…”

Section: Introductionmentioning

confidence: 99%

Optimal placement of sensors and piezoelectric friction dampers in the pipeline networks based on nonlinear dynamic analysis

Shokouhi

Yuan

Zhu

2017

Journal of Low Frequency Noise, Vibration and Active Control

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Experiences of past earthquakes demonstrate that pipeline systems have no proper performance when exposed to severe earthquakes. In this study, sensor and damper placement approaches are presented for doing reliable health monitoring and seismic retrofitting of the piping networks. Since most of the available sensor placement methods are based on modal analysis results, the authors propose a new scheme that relies on the nonlinearity which utilizes nonlinear time history analysis results, and genetic algorithm is selected to act as the methodology of optimization as well. The results demonstrate that the proposed optimal sensor configuration strategy is more accurate and efficient than the extended modal assurance criterion method. To assess the number of sensors, a sensitivity analysis is undertaken in which the number of sensors computed optimally by the proposed algorithm contains the least convergence error. In addition, the number of iterations and the time consumed in the proposed approach are considerably less than the extended modal assurance criterion method. Moreover, the efficiency of the proposed sensor placement scheme was compared with a new algorithm proposed by Sun and Büyükö ztürk, named discrete artificial bee colony, where the simulation result demonstrates high accuracy of the proposed sensor configuration approach. The initial time history analysis results show the vulnerable points of the system, which destroyed due to the applied seismic waves. Hence, to enhance the seismic performance of the system, piezoelectric friction dampers are optimally placed, where it can be clearly seen that the optimal arrangement of piezoelectric friction dampers in the piping system can significantly decrease the seismic response.

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Distributed sensor placement with sequential particle swarm optimization

Cited by 27 publications

References 5 publications

Computational Intelligence in Wireless Sensor Networks: A Survey

Computational Intelligence in Wireless Sensor Networks: A Survey

Intelligent Ping Sequencing for Multistatic Sonar Systems

Optimal placement of sensors and piezoelectric friction dampers in the pipeline networks based on nonlinear dynamic analysis

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