Enhancement of Wireless Sensor Network Lifetime with Mobile Base Station Using Particle Swarm Optimization
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In wireless sensor networks, energy utilization is often treated as the highest priority optimization goal due to the fact that the nodes in these networks rely heavily on battery powers which are typically irreplaceable. Hence, the protocols and algorithms used in this network should operate with minimum possible energy in order to improve overall energy efficiency. Utilizing mobile base station to collect data from sensor nodes in the field is one of the approaches that can be used to minimize the energy consumption of the sensor nodes. This is because the base station is usually equipped with high storage capacity and rechargeable battery supply. This paper proposed a method to optimize the movement of mobile base station in randomly distributed wireless sensor networks using particle swarm optimization (PSO) method for the purpose of prolonging network lifetime. Based on simulation results, it is demonstrated that the proposed technique can improve the network lifetime, data delivery and energy consumption when compared to existing energy-efficient protocols developed for this network.
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A. Mainwaring, J. Polastre, R. Szewczyk, D. Chuller, J. Anderson, Wireless Sensor Networks For Habitat Monitoring, Proceedings of ACM International Workshop Wireless Sensor Networks and Applications (WSNA) (Page: 88 – 97 Year of Publication: 2002)
Z. M. Wang, S. Basagni, E. Melachrinoudis, C. Petrioli, Exploiting sink mobility for maximizing sensor networks lifetime, Proceeding of the 38th Hawaii International Conference on System Sciences, (Page: 287 Year of Publication: 2005 ISBN: 0-7695-2268-8)
S. R. Gandham, M. Dawande, R. Prakash, S. Venkatesan, Energy Efficient Schemes For Wireless Sensor Networks With Multiple Mobile Base Stations, Proceedings of IEEE GLOBECOM, (Page: 377 – 381 Year of Publication: 2003)
S. Basagni, A. Carosi, E. Melachrinoudis, C. Petrioli, Z. M. Wang, Controlled Sink Mobility For Prolonging Wireless Sensor Networks Lifetime, ACM/Springer Wireless Networks Journal, Vol. 14, n. 6, pp. 831– 858, 2008.
O. Jerew, K. Blackmore, W. Liang, Mobile Base Station and Clustering To Maximize Network Lifetime In Wireless Sensor Networks, Journal of Electrical and Computer Engineering, Vol. 2012, n. 902862, 2012.
R. V. Kulkarni, G. K. Venayagamoorthy, Particle Swarm Optimization in Wireless Sensor Networks: A Brief Survey. IEEE Transactions on Systems, Man, and Cybernetics - Part C: Applications and Reviews, Vol. 40, n. 60, pp. 1-7, 2010.
W. R. Heinzelman, A. P. Chandrakasan, H. Balakrishnan, An Application-Specific Protocol Architecture For Wireless Microsensor Networks, IEEE Transactions on Wireless Communications, Vol. 1, n. 4, pp. 660 – 670. 2002.
N. M. Abdul Latiff, C. C. Tsimenidis, B. S. Sharif, Energy-Aware Clustering For Wireless Sensor Networks Using Particle Swarm Optimization, Proceedings of Personal Indoor Mobile Radio Communication, (Page: 1 – 5, Year of Publication: 2007 ISBN: 978-1-4244-1144-3).
Shankar, T., Shanmugavel, S., Karthikeyan, A., Mohan Gupte, A., Sarkar, S., Load balancing and optimization of network lifetime by use of double cluster head clustering algorithm and its comparison with various extended leach versions, (2013) International Review on Computers and Software (IRECOS), 8 (3), pp. 795-803.
N. A. Abdul Latiff, N. M. Abdul Latiff, R. B Ahmad, Prolonging Lifetime Of Wireless Sensor Networks With Mobile Base Station Using Particle Swarm Optimization, Proceedings of International Conference on Modeling, Simulation and Applied Optimization (Page: 1-6 Year of Publication: 2011 ISBN: 978-1-4577-0003-3)
J. Kennedy, R. C. Eberhart, Particle Swarm Optimization, IEEE International Conference on Neural Networks (Page: 1942 – 1948 Year of Publication: 1995 ISBN: 0-7803-2768-3)
D. L. Applegate, R. E. Bixby, V. Chvatal, W. J. Cook, The Travelling Salesman Problem: A Computational Study (New Jersey: Princeton University Press, 2006).
G. Reinelt, The Travelling Salesman. Computational Solutions For TSP Applications (New York: Springer-Verlag Berlin Heidelberg, 1994).
P. Merz, B. Freisleben, Genetic Local Search For The TSP: New results. In IEEE International Conference on Evolutionary Computation, 1997, p. 159 – 164.
The Network Simulator - ns2. [Online] Available at: http://www.isi.edu/nsnam/ns/
I. S. Michael, P. P. Franco, Computational Geometry: An Introduction (New York: Springer-Verlag, 1995).
Salai Thillai Thilagam, J., Jawahar, P.K., Sivakumar, A., Rectangular microstrip patch antenna characteristic study for wireless communication applications, (2012) International Journal on Communications Antenna and Propagation (IRECAP), 2 (1), pp. 10-15.
Krief, F., Bennani, Y., Gomes, D., Neuman de Souza, J., LECSOM: A low-energy routing algorithm based on SOM clustering for static and mobile wireless sensor networks, (2011) International Journal on Communications Antenna and Propagation (IRECAP), 1 (1), pp. 55-63.
Azimiyan, F., Kheirkhah, E., Jalali, M., Classification of routing protocols in wireless sensor networks, (2012) International Review on Computers and Software (IRECOS), 7 (4), pp. 1614-1623.
Paramasivam, B., Chidambaram, I.A., Design of a load-frequency controller using craziness based PSO for an interconnected power system with SSSC and RFB, (2012) International Review of Automatic Control (IREACO), 5 (2), pp. 102-112.
Ibrahim, H.E.A., Elnady, M.A., A comparative study of PID, fuzzy, fuzzy-PID, PSO-PID, PSO-fuzzy, and PSO-fuzzy-PID controllers for speed control of DC motor drive, (2013) International Review of Automatic Control (IREACO), 6 (4), pp. 393-403.
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