Multi-objective fractional gravitational search algorithm for energy efficient routing in IoT
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Nowadays, the Internet of Things (IoT) plays a significant role in the Internet world. The IoT is a system which integrates the computing devices, digital machines provided with unique identifiers which have the ability to transfer the data over the network via the better route. IoT is also expected to generate large amounts of data, the consequent necessity for quick aggregation of the data and process such data more effectively. In this paper, a multi-objective fractional gravitational search algorithm is proposed to find the optimal cluster head for energy efficient routing protocol in IoT network. To extend the lifetime of the node, the Fractional Gravitational Search Algorithm (FGSA) is proposed to find out the optimal cluster head node iteratively in the IoT network model. The cluster head node is selected in FGSA that is evaluated by the fitness function using multiple objectives such as distance, delay, link lifetime and energy, termed as multi-objective FGSA (MOFGSA). The simulation results and performance is analyzed using MATLAB implementation. The performance is compared with existing algorithms like Artificial Bee Colony, Gravitational Search Algorithm and multi-particle swarm immune cooperative algorithm. Thus, the proposed MOFGSA algorithm ensures to prolong the lifetime of IoT nodes.
KeywordsInternet of Things Fractional theory Gravitational search algorithm (GSA) Cluster head selection Multiple objectives
The authors would like to thank to Dr. Arvind V. Deshpande, Principal, Smt. Kashibai Navale College of Engineering, Pune, Dr. Parikshit N. Mahalle, Head of Computer Engineering Department, Smt. Kashibai Navale College of Engineering, Pune and Dr. Mrs. Jayashree R. Prasad, Professor, Computer Engineering Department, Sinhgad College of Engineering, Pune, India for their constant support and motivation in our work.
- 12.Christin, D., Reinhardt, A., Mogre, P. S., & Steinmetz, R. (2009). Wireless sensor networks and the internet of things: selected challenges. In Proceedings of the 8th GI/ITG KuVS Fachgesprach “Drahtlose Sensornetze” (pp. 31–34).Google Scholar
- 13.Gururaja, N, & Dr. Brahmananda, S. H. (2014). Lifetime maximization in heterogeneous wireless sensor networks using multipath routing technique. Scientific and Research Publications, 4(5).Google Scholar
- 25.Karlof, C. & Wagner, D. (2003). Secure routing in sensor networks: Attacks and countermeasures. In Proceedings of the IEEE 1st international workshop sensor network protocols applications, Vol 1, (pp. 113–127).Google Scholar
- 27.Li, L. & Zuo, M. (2009). A dynamic adaptive routing protocol for heterogeneous wireless sensor network. In Proceedings of international conference on networks security, wireless communications and trusted computing, vol. 1 (pp. 666–669).Google Scholar
- 38.Chen, R. -C., Chang, W. -L., Shieh, C. -F., Zou, C. C. (2012). Using hybrid artificial bee colony algorithm to extend wireless sensor network lifetime. In Proceedings of third international conference on innovations in bio-inspired computing and applications.Google Scholar
- 39.Singh, B., & Lobiyal, D. K., (2012). A novel energy-aware cluster head selection based on particle swarm optimization for wireless sensor networks. Human-Centric Computing and Information Sciences. doi: 10.1186/2192-1962-2-13.
- 43.Zhong, C., Mo, Y., Zhao, J., Lin, C., & Lu, X. (2014). Secure clustering and reliable multi-path route discovering in wireless sensor networks. In Proceedings of 2014 sixth international symposium on parallel architectures, algorithms and programming (PAAP), (pp. 130–134).Google Scholar
- 49.Leo, M., Battisti, F., Carli, M., & Neri, A. (2014). A federated architecture approach for Internet of Things security. In Proceedings of Euro med telco conference (EMTC) (pp. 1–5).Google Scholar
- 50.Kothmay, T., Schmitt, C., Hu, W., Brunig M., & Carle, G. (2012). A DTLS based end-to-end security architecture for the Internet of Things with two-way authentication. In Proceedings of IEEE 37th conference on local computer networks workshops (LCN workshops), (pp. 956–963).Google Scholar
- 52.Shafagh, H., Hithnawi, A., Dröscher, A., Duquennoy, S., & Hu, W. (2015). Poster: Towards encrypted query processing for the Internet of Things. In Proceedings of the 21st annual international conference on mobile computing and networking, (pp. 251–253).Google Scholar
- 53.Fan, K., Liang, C., Li, H., & Yang, Y. (2014). LRMAPC: A lightweight RFID mutual authentication protocol with cache in the reader for IoT. In Proceedings of IEEE international conference on computer and information technology (pp. 276–280).Google Scholar
- 54.Dhumane, A., Prasad, R., & Prasad, J. (2016). Routing issues in Internet of Things: A aurvey. In Proceedings of international multi conference of engineers and computer scientists, vol. 1, (pp. 1–9).Google Scholar
- 55.Dey, A. K. (2001). Understanding and using context (pp. 1–10). Atlanta: Georgia Institute of Technology.Google Scholar
- 56.Heinzelman, W. R., Chandrakasan, A., & Balakrishnan, H. (2000). Energy-efficient communication protocol for wireless microsensor networks. In Proceedings of the 33rd annual Hawaii international conference on system sciences, IEEE, vol. 2, (pp. 1–10).Google Scholar
- 57.Handy, M. J., Haase, M. & Timmermann, D. (2002). Low energy adaptive clustering hierarchy with deterministic cluster-head selection. In 4th international workshop on mobile and wireless communications network, (pp. 368–372).Google Scholar
- 58.Farooq, M. O., Dogar, A. B., & Shah, G. A. (2010). MR-LEACH: Multi-hop routing with low energy adaptive clustering hierarchy. In Proceedings of fourth international conference on sensor technologies and applications, Venice (pp. 262–268).Google Scholar