Coverage in wireless sensor networks based on individual particle optimization

Effective sensor coverage is one of the key topics addressed in wireless sensor networks (WSNs) study, which refers to the deployment and detection probability of WSNs. This paper proposes a novel deployment algorithm for mobile sensor networks, based on individual particle optimization (IPO). The algorithm is designed for real-time online deployment for the purpose of maximum coverage in the environment. The mobile nodes will relocate themselves to find the best deployment under various kinds of situations in order to cover the largest area. The new locations of the mobile nodes are determined by IPO. Here, the remarkable point is the capability to be applied in a real-time manner due to considerable higher convergence rate of IPO algorithm. The experimental results verify that the deployment of the mobile sensors with IPO outperforms the previous deployment algorithms such as GA and PSO based approaches with respect to effective coverage area and computation time.

[1]  J. Freyd,et al.  A velocity effect for representational momentum , 1985 .

[2]  R A Finke,et al.  Implied velocity and acceleration induce transformations of visual memory. , 1986, Journal of experimental psychology. General.

[3]  J. Bharucha,et al.  Judged displacement in apparent vertical and horizontal motion , 1988, Perception & psychophysics.

[4]  T. Hubbard.  Cognitive representation of linear motion: Possible direction and gravity effects in judged displacement , 1990, Memory & cognition.

[5]  M. H. Kelly,et al.  Memory biases in left versus right implied motion. , 1993, Journal of experimental psychology. Learning, memory, and cognition.

[6]  Russell C. Eberhart,et al.  A new optimizer using particle swarm theory , 1995, MHS'95. Proceedings of the Sixth International Symposium on Micro Machine and Human Science.

[7]  James Kennedy,et al.  Particle swarm optimization , 2002, Proceedings of ICNN'95 - International Conference on Neural Networks.

[8]  Timothy L. Hubbard,et al.  Target size and displacement along the axis of implied gravitational attraction : Effects of implied weight and evidence of representational gravity , 1997 .

[9]  M P Munger,et al.  Representational momentum for rotations in depth: effects of shadings and axis. , 1999, Journal of experimental psychology. Learning, memory, and cognition.

[10]  S. Sitharama Iyengar,et al.  Grid Coverage for Surveillance and Target Location in Distributed Sensor Networks , 2002, IEEE Trans. Computers.

[11]  Gaurav S. Sukhatme,et al.  Mobile Sensor Network Deployment using Potential Fields : A Distributed , Scalable Solution to the Area Coverage Problem , 2002 .

[12]  Marco Locatelli,et al.  Packing equal circles in a square: a deterministic global optimization approach , 2002, Discret. Appl. Math..

[13]  Ian M. Thornton,et al.  Representational momentum: New findings, new directions , 2002 .

[14]  Krishnendu Chakrabarty,et al.  Sensor placement for effective coverage and surveillance in distributed sensor networks , 2003, 2003 IEEE Wireless Communications and Networking, 2003. WCNC 2003..

[15]  Pramod K. Varshney,et al.  A distributed self spreading algorithm for mobile wireless sensor networks , 2003, 2003 IEEE Wireless Communications and Networking, 2003. WCNC 2003..

[16]  Krishnendu Chakrabarty,et al.  Sensor deployment and target localization based on virtual forces , 2003, IEEE INFOCOM 2003. Twenty-second Annual Joint Conference of the IEEE Computer and Communications Societies (IEEE Cat. No.03CH37428).

[17]  Tatsuhiro Tsuchiya,et al.  A self-organizing technique for sensor placement in wireless micro-sensor networks , 2004, 18th International Conference on Advanced Information Networking and Applications, 2004. AINA 2004..

[18]  Congfu Xu,et al.  Sensor deployment optimization for detecting maneuvering targets , 2005, 2005 7th International Conference on Information Fusion.

[19]  Xue Wang,et al.  Dynamic Deployment Optimization in Wireless Sensor Networks , 2006 .

[20]  Xue Wang,et al.  Virtual Force-Directed Particle Swarm Optimization for Dynamic Deployment in Wireless Sensor Networks , 2007, ICIC.

[21]  Xue Wang,et al.  Prediction-based Dynamic Energy Management in Wireless Sensor Networks , 2007, Sensors (Basel, Switzerland).

[22]  Xue Wang,et al.  Collaborative signal processing for target tracking in distributed wireless sensor networks , 2007, J. Parallel Distributed Comput..

[23]  Patricia J. Bauer,et al.  Memory and Early Brain Development , 2008 .

[24]  Peyman Yadmellat,et al.  A Hybrid Algorithm for Training Recurrent Fuzzy Neural Network , 2008, Artificial Intelligence and Pattern Recognition.

[25]  Mohammad Bagher Menhaj,et al.  Transition from particle swarm optimization to individual particle optimization , 2009, 2009 IEEE Control Applications, (CCA) & Intelligent Control, (ISIC).