Optimum sensor node placement in a monitored area is needed for cost-effective deployment. The positions of sensor nodes must be able to provide maximum coverage with longer lifetimes. This paper proposed a sensor node placement technique that utilizes a new biologically inspired optimization technique that imitates the behaviour of territorial predators in marking their territories with their odours, known as territorial predator scent marking algorithm (TPSMA). The TPSMA deployed in this paper uses the maximum coverage objective function. A performance study has been carried out by comparing the performance of the proposed technique with the minimax and lexicographic minimax (lexmin) sensor node placement schemes in terms of coverage ratio and uniformity. Uniformity is a performance metric that can be used to estimate a WSN lifetime. Simulation results show that the WSN deployed with the proposed sensor node placement scheme outperforms the other two schemes with larger coverage ratio and is expected to provide as long lifetime as possible. 1. Introduction One of the ways in provisioning maximum coverage with longer lifetime is through the use of some sort of wireless sensor network (WSN) deployment mechanism. This can be done by utilizing an effective planning mechanism in arranging the limited number of sensor nodes. WSN for target monitoring applications such as landslide monitoring, forest fire detection, and precision agriculture can be implemented with a fixed number of sensor nodes that are deployed to monitor one or more locations within a monitored area. For cost-effective deployment, it is critically important to determine optimal locations for these sensor nodes. The locations of the sensor nodes strongly affect the energy consumption, operational lifetime, and sensing coverage [1]. Thus, careful sensor node placement is needed. Romoozi et al. [2] stated that there is a tradeoff between energy consumption of sensor nodes and network coverage. Closer sensor nodes will reduce the energy consumption but the network coverage will become smaller. This scenario has attracted numerous research works on WSN sensor node deployment. Ingle and Bawane in [3] utilized a Voronoi diagram [4] as shown in Figure 1 in their technique known as Node Network Voronoi (NNV) and Edge Network Voronoi (ENV) as a solution for WSN coverage determination and optimization of energy. In Voronoi diagram, a partition of sensor nodes that are points inside a polygon are closer to the sensor node inside the polygon than any other sensor nodes. Thus, one of the polygon
References
[1]
F. Oldewurtel and P. M？h？nen, “Analysis of enhanced deployment models for sensor networks,” in Proceedings of the 71st IEEE Vehicular Technology Conference (VTC 2010-Spring), pp. 1–5, Taipei, Taiwan, May 2010.
[2]
M. Romoozi, M. Vahidipour, M. Romoozi, and S. Maghsoodi, “Genetic algorithm for energy efficient & coverage-preserved positioning in wireless sensor networks,” in Proceedings of the International Conference on Intelligent Computing and Cognitive Informatics (ICICCI '10), pp. 22–25, Kuala Lumpur, Malaysia, June 2010.
[3]
M. R. Ingle and N. Bawane, “An energy efficient deployment of nodes in wireless sensor network using Voronoi diagram,” in Proceedings of the 3rd International Conference on Electronics Computer Technology (ICECT '11), pp. 307–311, Kanyakumari, India, April 2011.
[4]
L. C. Shiu, C. Y. Lee, and C. S. Yang, “The divide-and-conquer deployment algorithm based on triangles for wireless sensor networks,” IEEE Sensors Journal, vol. 11, no. 3, pp. 781–790, 2011.
[5]
S. C. Huang, “Energy-aware, self-deploying approaches for wireless sensor networks,” in Proceedings of the 24th IEEE International Conference on Advanced Information Networking and Applications (AINA '10), pp. 896–901, Perth, Australia, April 2010.
[6]
S. M. A. Salehizadeh, A. Dirafzoon, M. B. Menhaj, and A. Afshar, “Coverage in wireless sensor networks based on individual particle optimization,” in Proceedings of the International Conference on Networking, Sensing and Control (ICNSC '10), pp. 501–506, Chicago, Ill, USA, April 2010.
[7]
D. Zorbas and T. Razafindralambo, “Wireless sensor network redeployment under the target coverage constraint,” in Proceedings of the 5th International Conference on New Technologies, Mobility and Security (NTMS '12), pp. 1–5, Istanbul, Turkey, May 2012.
[8]
H. M. Ammari, “On the problem of k-coverage in mission-oriented mobile wireless sensor networks,” Computer Networks, vol. 56, no. 7, pp. 1935–1950, 2012.
[9]
X. Wang and S. Wang, “Hierarchical deployment optimization for wireless sensor networks,” IEEE Transactions on Mobile Computing, vol. 10, no. 7, pp. 1028–1041, 2011.
[10]
L. Zhiming and L. Lin, “Sensor node deployment in wireless sensor networks based on improved particle swarm optimization,” in Proceedings of the International Conference on Applied Superconductivity and Electromagnetic Devices (ASEMD '09), pp. 215–217, Chengdu, China, September 2009.
[11]
K. S. S. Rani and N. Devarajan, “Optimization model for sensor node deployment,” European Journal of Scientific Research, vol. 70, no. 4, pp. 491–498, 2012.
[12]
J. Zhao and H. Sun, “Intelligent single particle optimizer based wireless sensor networks adaptive coverage,” Journal of Convergence Information Technology, vol. 7, no. 2, pp. 153–159, 2012.
[13]
N. A. B. A. Aziz, A. W. Mohemmed, and M. Y. Alias, “A wireless sensor network coverage optimization algorithm based on particle swarm optimization and voronoi diagram,” in Proceedings of the IEEE International Conference on Networking, Sensing and Control (ICNSC '09), pp. 602–607, Okayama, Japan, March 2009.
[14]
N. A. B. A. Aziz, A. W. Mohemmed, and B. S. D. Sagar, “Particle swarm optimization and Voronoi diagram for wireless sensor networks coverage optimization,” in Proceedings of the International Conference on Intelligent and Advanced Systems (ICIAS '07), pp. 961–965, Kuala Lumpur, Malaysia, November 2007.
[15]
W. Yiyue, L. Hongmei, and H. Hengyang, “Wireless sensor network deployment using an optimized artificial fish swarm algorithm,” in Proceedings of the International Conference on Computer Science and Electronics Engineering (ICCSEE '12), vol. 2, pp. 90–94, Hangzhou, China, March 2012.
[16]
L. Zhang, D. Li, H. Zhu, and L. Cui, “OPEN: an optimisation scheme of N-node coverage in wireless sensor networks,” IET Wireless Sensor Systems, vol. 2, no. 1, pp. 40–51, 2012.
[17]
C. M. Begg, K. S. Begg, J. T. Du Toit, and M. G. L. Mills, “Scent-marking behaviour of the honey badger, Mellivora capensis (Mustelidae), in the Southern Kalahari,” Animal Behaviour, vol. 66, no. 5, pp. 917–929, 2003.
[18]
K. A. Descovich, A. T. Lisle, S. Johnston, V. Nicolson, and C. J. C. Phillips, “Differential responses of captive southern hairy-nosed wombats (Lasiorhinus latifrons) to the presence of faeces from different species and male and female conspecifics,” Applied Animal Behaviour Science, vol. 138, no. 1, pp. 110–117, 2012.
[19]
R. Kershner, “The number of circles covering a set,” The American Journal of Mathematics, vol. 61, no. 3, pp. 665–671, 1939.
[20]
H. Z. Abidin and N. M. Din, “Sensor node placement based on minimax for effective surveillance,” in Proceedings of the IEEE Symposium on Industrial Electronics and Applications (ISIEA '12), pp. 7–11, Bandung, Indonesia, September 2012.
[21]
H. Z. Abidin and N. M. Din, “Sensor node placement based on lexicographic minimax,” in Proceedings of the International Symposium on Telecommunication Technologies (ISTT '12), pp. 82–87, Kuala Lumpur, Malaysia, November 2012.
[22]
Y. Zhang and L. Wang, “A distributed sensor deployment algorithm of mobile sensor network,” in Proceedings of the 8th World Congress on Intelligent Control and Automation (WCICA '10), pp. 6963–6968, Jinan, China, July 2010.
