Abstract
The biggest problems faced by the software defined wireless sensor network are energy conservation and load balancing techniques which impose a high level of constraint. In general clustering is used in a network in order to decrease the use of energy thereby enhancing lifetime of the network. Hot spot problems are a common issue caused due to drainage of battery when there are more multi-hop networks that are close to the base station. In order to overcome this restriction, we have proposed the use of multilayer clustering architecture that is used to choose the intra and inter-cluster communication, rotation of cluster head and forwarding node. Using the routing table, the proposed methodology will be able to efficiently handle the rotation of the forwarder node. The rotation takes place based on the residual energy's threshold levels and also exploits the non-forwarder node, backup forwarder node, forwarder node and decision maker node to enhance the routing strategy of the WSN. Testing and evaluation of the proposed work is done using C programming language and results show that this methodology has better results than EADUC and TLPER as far as hop count, communication and energy consumption are taken into consideration in the cluster formation.
References
- Sarkar, A., & Murugan, T. S. (2019). Cluster head selection for energy efficient and delay-less routing in wireless sensor network. Wireless Networks, 25(1), 303-320.
- Wang, J., Gao, Y., Liu, W., Sangaiah, A. K., & Kim, H. J. (2019). An improved routing schema with special clustering using PSO algorithm for heterogeneous wireless sensor network. Sensors, 19(3), 671.
- Diwakaran, S., Perumal, B., & Devi, K. V. (2019). A cluster prediction model-based data collection for energy efficient wireless sensor network. The Journal of Supercomputing, 75(6), 3302-3316.
- Robinson, Y. H., Julie, E. G., & Kumar, R. (2019). Probability-based cluster head selection and fuzzy multipath routing for prolonging lifetime of wireless sensor networks. Peer-to-Peer Networking and Applications, 12(5), 1061-1075.
- Raj, J. S. (2019). QoS optimization of energy efficient routing in IoT wireless sensor networks. Journal of ISMAC, 1(01), 12-23.
- Shalini, V. B., & Vasudevan, V. (2019). Achieving energy efficient wireless sensor network by choosing effective cluster head. Cluster Computing, 22(4), 7761-7768.
- Wan, Z., Liu, S., Ni, W., & Xu, Z. (2019). An energy-efficient multi-level adaptive clustering routing algorithm for underwater wireless sensor networks. Cluster Computing, 22(6), 14651-14660.
- Yan, Z., Mukherjee, A., Yang, L., Routray, S., & Palai, G. (2019). Energy-efficient node positioning in optical wireless sensor networks. Optik, 178, 461-466.
- Pacharaney, U. S., & Gupta, R. K. (2019). Clustering and compressive data gathering in wireless sensor network. Wireless Personal Communications, 109(2), 1311-1331.
- Narendran, M., & Prakasam, P. (2019). An energy aware competition based clustering for cluster head selection in wireless sensor network with mobility. Cluster Computing, 1-10.
- Yu J, Qi Y, Wang G, Guo Q and Gu X (2011) "an energy-aware distributed unequal clustering protocol for wireless sensor networks, " international journal of distributed sensor networks, vol. 2011
- RadiM, Dezfouli B, Bakar KA, LeeM(2012) Multipath routing in wireless sensor networks: survey and research challenges. Sensors 12(1):650–685.
- Raj, J. S. (2019). A comprehensive survey on the computational intelligence techniques and its applications. Journal of ISMAC, 1(03), 147-159.