Energy-Saving in Wireless Sensor Networks Based on Optimization Sink Movement Control

الموضوعات : مهندسی هوشمند برق

Mozhgan Toulabi ¹ (Electrical Engineering Department, Islamic Azad University Central Tehran Branch, Tehran, Iran,)
Shahram Javadi ² (Electrical Engineering Department, Islamic Azad University Central Tehran Branch, Tehran, Iran,)

تاريخ الإرسال : 24 الإثنين , جمادى الثانية, 1436 تاريخ التأكيد : 24 الإثنين , جمادى الثانية, 1436 تاريخ الإصدار : 14 السبت , شوال, 1433

الکلمات المفتاحية: Genetic Algorithm, Wireless Sensor Network, lifetime, fuzzy method, mobile sink,

ملخص المقالة :

A sensor network is made up of a large number of sensors with limited energy. Sensors collect environmental data then send them to the sink. Energy efficiency and thereby increasing the lifetime of sensor networks is important. Direct transfer of the data from each node to the central station will increase energy consumption. Previous research has shown that the organization of nodes in clusters and selection the appropriate cluster head increases the network lifetime. In this study, clustering, determine to cluster heads and the sink movement on the predefined paths has been done with fuzzy method. There are two inputs for the fuzzy model; residual energy of the node and distance from the sink. The output is priority of cluster heads. Sink moves base on the highest priorities on the predefined paths. Then by using genetic algorithm, the number of clusters, shape type and area is optimized. Fitness function is based on network lifetime.

المصادر:

[1] I. F. Akyildiz, W. Su, Y. Sankarasubramaniam, and E. Cayirci, “Wireless Sensor Networks: A Survey”, The International Journal of Computer and Telecommunications Networking, Vol.38, No.4, pp.393–422, 2002.

[2] D. Puccinelli and M. Haenggi, “Wireless Sensor Networks: Applications and Challenges of Ubiquitous Sensing”, IEEE Circuits and Systems Magazine, Vol.5, No.3, pp.19–31, 2005.

[3] M. Haenggi, “Mobile Sensor-Actuator Networks: Opportunities and Challenges”, In Proceedings of the 7th IEEE International Workshop on Cellular Neural Networks and Their Applications (CNNA ’02), pp.283–290, Frankfurt, Germany, July 2002.

[4] Yanzhong Bi, Limin Sun, Jian Ma,3 Na Li, Imran Ali Khan, and Canfeng Chen, “An Autonomous Moving Strategy for Mobile Sinks in Data-Gathering Sensor Networks”, Hindawi Publishing Corporation, EURASIP Journal on Wireless Communications and Networking, Vol. 2007.

[5] K. Sohrabi, J. Gao, V. Ailawadhi, and G. J. Pottie, “Protocols for Self-Organization of a Wireless Sensor Network”, IEEE Personal Communications, Vol.7, No.5, pp.16–27, 2000.

[6] A. Boukerche and S. Nikoletseas, “Protocols for Data Propagation in Wireless Sensor Networks: A Survey”, Wireless Communications Systems and Networks, pp.23–51, Kluwer Academic Publishers, Boston, Mass, USA, 2004.

[7] J. N. Al-Karaki and A. E. Kamal, “Routing techniques in wireless sensor networks: a survey”, IEEEWireless Communications, Vol.11, No.6, pp.6–28, 2004.

[8] D. Niculescu, “Communication Paradigms for Sensor networks”, IEEE Communications, Vol.43, No.3, pp.116-122, 2005. [9] Liliana M.C. Arboleda, “Comparsion of Clustering Algorithm and Protocols for Wireless Sensor network“, Canadian Conference on Electrical and Computer Engineering, pp.1787-1792, May 2006.

[10] Heinzelman W. R., A. P. Chandrakasan and H. Balakrishnan, “Energy-Efficient Communication Protocol for Wireless Microsensor Networks”, Proc. of the 33rd IEEE Int. Conf. on System Sciences Honolulu, USA, pp.1–10, Jan. 2000.

[11] Heinzelman W. R., A. P. Chandrakasan and H. Balakrishnan, “An Application-Specific Protocol Architecture for Wireless Microsensor Networks”, IEEE Trans. on Wireless Communications, Vol.1, No.4, pp. 660-670, Oct.2002.

[12] D. Turgut, S. K. Das, R. Elmasri, and B. Turgut, “Optimizing Clustering Algorithm in Mobile Adhoc Networks Using Genetic Algorithmic Approach”, In Proceedings of the Global Telecommunications Conference (GLOBECOM), November 2002.

[13] S. Gandham, M. Dawande, R. Prakash, and S. Venkatesan, “Energy Efficient Schemes for Wireless Sensor Networks with Multiple Mobile Base Stations”, In Proceedings of IEEE Globecom, Vol.1, pp.377–381, Dec. 2003. [14] Singh, A.K., Alkesh, A., Purohit, N., ”Minimization of Energy Consumption of Wireless Sensor Networks Using Fuzzy”, International Conference on Computational Intelligence and Communication Systems, pp.519-521, 2011.