Reducing Energy Consumption in Wireless Sensor Networks Using a Routing Protocol Based on Multi-level Clustering and Genetic Algorithm

Автор: Amin Rezaeipanah, Hamed Nazari, Mohammad Javad Abdollahi

Журнал: International Journal of Wireless and Microwave Technologies @ijwmt

Статья в выпуске: 3 Vol.10, 2020 года.

Бесплатный доступ

Wireless sensor networks (WSN) consist of a large number of sensor nodes with finite and limited energy levels distributed throughout a finite area. The energy of the nodes is mostly consumed to send information to a central station. Extending the network lifetime through decreasing the energy consumption of the nodes has always obtained attention, due to the energy limitations in WSNs. In this paper, a multi-level genetic based clustering algorithm is proposed to extend the lifetime of these types of networks. The proposed multi-level clustering algorithm divides the geographical area into three levels according to the radio range and the clustering of the nodes in each level is performed independently. Technically, Cluster Heads (CH) consumes more energy than other nodes to transmit data. So, the proposed algorithm aims to extend the network lifetime by reducing the number of CHs. Finally, a better energy consumption balance between the nodes is realized by altering the CHs in each routing round. The results of the experiments show the superiority of the proposed algorithm in terms of and the network lifetime over other analogous protocols.


Wireless Sensor Networks, Multi-level Clustering, Routing Protocol, Network Lifetime, Genetic Algorithm

Короткий адрес:

IDR: 15017635   |   DOI: 10.5815/ijwmt.2020.03.01

Список литературы Reducing Energy Consumption in Wireless Sensor Networks Using a Routing Protocol Based on Multi-level Clustering and Genetic Algorithm

  • Yu, Y., Li, K., Zhou, W., & Li, P. (2012). Trust mechanisms in wireless sensor networks: Attack analysis and countermeasures. Journal of Network and Computer Applications, 35(3), 867-880.
  • Dogan, G., & Brown, T. (2014). A Survey of Provenance Leveraged Trust in Wireless Sensor Networks. Computer Engineering and Intelligent Systems, 5(2), 1-11.
  • Akyildiz, I. F., Su, W., Sankarasubramaniam, Y., & Cayirci, E. (2002). Wireless sensor networks: a survey. Computer networks, 38(4), 393-422.
  • Bao, F., Chen, R., Chang, M., & Cho, J. H. (2012). Hierarchical trust management for wireless sensor networks and its applications to trust-based routing and intrusion detection. Network and Service Management, IEEE Transactions on, 9(2), 169-183.
  • Intanagonwiwat, C., Govindan, R., & Estrin, D. (2000, August). Directed diffusion: A scalable and robust communication paradigm for sensor networks. In Proceedings of the 6th annual international conference on Mobile computing and networking (pp. 56-67). ACM.
  • Gastpar, M., & Vetterli, M. (2003). Source-channel communication in sensor networks. In Information Processing in Sensor Networks (pp. 162-177). Springer Berlin Heidelberg.
  • Clausen, T., & Jacquet, P. (2003). Optimized Link State with genetic Routing Protocol (OLSR). IETF, RFC 3626.
  • Chiang, C. (1997). Routing in Clustered Multihop, Mobile Wireless Networks with Fading Channel. Proc. IEEE SICON’97, pp.197-211.
  • Nehra, N. K., Kumar, M., & Patel, R. B. (2009, December). Neural network based energy efficient clustering and routing in wireless sensor networks. In Networks and Communications, 2009. NETCOM'09. First International Conference on (pp. 34-39). IEEE.
  • Minhas, M. R., Gopalakrishnan, S., & Leung, V. C. (2008, November). Fuzzy algorithms for maximum lifetime routing in wireless sensor networks. In Global Telecommunications Conference, 2008. IEEE GLOBECOM 2008. IEEE (pp. 1-6). IEEE.
  • Younis, O., & Fahmy, S. (2004). HEED: a hybrid, energy-efficient, distributed clustering approach for ad hoc sensor networks. IEEE Transactions on mobile computing, 3(4), 366-379.
  • Moh’d Alia, O. (2017). Dynamic relocation of mobile base station in wireless sensor networks using a cluster-based harmony search algorithm. Information Sciences, 385, 76-95.
  • Yao, G. S., Dong, Z. X., Wen, W. M., & Ren, Q. (2016). A routing optimization strategy for wireless sensor networks based on improved genetic algorithm. 19(2), 221-228.
  • Dasarathan, D., & Kumar, P. N. (2016). Quality of Service Based Improved Dynamic Source Routing in MANETs. Indian Journal of Applied Research, 5(8).
  • Bouyer, A., Hatamlou, A., & Masdari, M. (2015). A new approach for decreasing energy in wireless sensor networks with hybrid LEACH protocol and fuzzy C-means algorithm. International Journal of Communication Networks and Distributed Systems, 14(4), 400-412.
  • Barzegari, S., & Masdari, M. (2016). A Novel Fuzzy CMeans-Based Clustering Scheme for Wireless Sensor Networks. International Journal of Grid and Distributed Computing, 9(2), 193-202.
  • Kaushik, A. K. (2016). A hybrid approach of fuzzy c-means clustering and neural network to make energy-efficient heterogeneous wireless sensor network. International Journal of Electrical and Computer Engineering, 6(2), 674.
  • Khan, M. Y., Javaid, N., Khan, M. A., Javaid, A., Khan, Z. A., & Qasim, U. (2013). Hybrid DEEC: Towards efficient energy utilization in wireless sensor networks. arXiv preprint arXiv:1303.4679.
  • Rezaeipanah, A., Nazari, H., & Ahmadi, G. (2019). A Hybrid Approach for Prolonging Lifetime of Wireless Sensor Networks Using Genetic Algorithm and Online Clustering. Journal of Computing Science and Engineering, 13(4), 163-174.
  • Mohamed-Lamine, M. (2013, May). New clustering scheme for wireless sensor networks. In Systems, Signal Processing and their Applications (WoSSPA), 2013 8th International Workshop on (pp. 487-491). IEEE.
  • Ducrocq, T., Mitton, N., & Hauspie, M. (2013, April). Energy-based clustering for wireless sensor network lifetime optimization. In Wireless Communications and Networking Conference (WCNC), 2013 IEEE (pp. 968-973). IEEE.
  • Hoang, D. C., Kumar, R., & Panda, S. K. (2010, July). Fuzzy C-means clustering protocol for wireless sensor networks. In Industrial Electronics (ISIE), 2010 IEEE International Symposium on (pp. 3477-3482). IEEE.
  • Sasikumar, P., & Khara, S. (2012, November). K-means clustering in wireless sensor networks. In Computational intelligence and communication networks (CICN), 2012 fourth international conference on (pp. 140-144). IEEE.
  • Rappaport, T. S. (1996). Wireless communications: principles and practice (Vol. 2). New Jersey: prentice hall PTR.
  • Smaragdakis, G., Matta, I., & Bestavros, A. (2004). SEP: A stable election protocol for clustered heterogeneous wireless sensor networks. Boston University Computer Science Department.
  • Naranjo, P. G. V., Shojafar, M., Mostafaei, H., Pooranian, Z., & Baccarelli, E. (2017). P-SEP: a prolong stable election routing algorithm for energy-limited heterogeneous fog-supported wireless sensor networks. The Journal of Supercomputing, 73(2), 733-755.
  • Malluh, A. A., Elleithy, K. M., Qawaqneh, Z., Mstafa, R. J., & Alanazi, A. (2014, April). Em-sep: an efficient modified stable election protocol. In American Society for Engineering Education (ASEE Zone 1), 2014 Zone 1 Conference of the (pp. 1-7). IEEE.
  • Singh, D., & Panda, C. K. (2015, January). Performance analysis of modified stable election protocol in heterogeneous wsn. In 2015 International Conference on Electrical, Electronics, Signals, Communication and Optimization (EESCO).
Статья научная