Optimal Node Clustering and Scheduling in Wireless Sensor Networks
- 46 Downloads
Selection and rotation of cluster head (CH) is a well known optimization problem in hierarchical Wireless sensor networks (WSNs), which affects its overall network performance. Population-based metaheuristic particularly Artificial bee colony (ABC) has shown to be competitive over other metaheuristics for solving optimization problems in WSNs. However, its search equation contributes to its insufficiency due to poor exploitation phase and low convergence rate. This paper, presents an improved artificial bee colony (iABC) metaheuristic with an improved search equation, which will be able to search an optimal solution to improve its exploitation capabilities moreover, in order to increase the global convergence of the proposed metaheuristic, an improved approach for population sampling is introduced through Student’s-t distribution. The proposed metaheuristic maintain a balance between exploration and exploitation search abilities with least memory requirements, with the use of first of its kind compact Student’s-t distribution, which is particularly suitable for WSNs limited hardware environment. Further utilising the capabilities of the proposed metaheuristic, an improved artificial bee colony based clustering and scheduling (iABC-CS) scheme is introduced, to obtain optimal cluster heads (CHs) along with optimal CH scheduling in WSNs. Simulation results manifest that iABC-CS outperform over other well known clustering algorithms on the basis of packet delivery ratio, energy consumption, network lifetime and end to end delay.
KeywordsWireless sensor networks Clustering and scheduling algorithm Improved artificial bee colony (iABC) metaheuristic
The authors of the study acknowledge the contribution of I. K. Gujral Punjab Technical University, Kapurthala, Punjab, India.
- 9.Das, S., Abraham, A., & Konar, A. (2009). Metaheuristic clustering. In Studies in computational intelligence (1st ed., Vol. 178). Berlin: Springer.Google Scholar
- 10.Samrat, L., & Udgata, A. A. S. (2010). Artificial bee colony algorithm for small signal model parameter extraction of MESFET. Engineering Applications of Artificial Intelligence, 11, 1573–1592.Google Scholar
- 18.Yang, J., Xu, M., Zhao, W., & Xu, B. (2009). A multipath routing protocol based on clustering and ant colony optimization for wireless sensor networks. Sensors, 10(5), 4521–4540.Google Scholar
- 28.Neri, F., Iacca, G., & Mininno, E. (2013). Compact Optimization. In I. Zelinka, V. Snášel, & A. Abraham (Eds.), Handbook of Optimization. Intelligent Systems Reference Library (Vol. 38). Berlin: SpringerGoogle Scholar
- 30.Abro, A. G., & Mohamad-Saleh, J. (2012). Enhanced global-best artificial bee colony optimization algorithm. In Sixth UKSim-AMSS European symposium on computer modeling and simulation (pp. 95–100).Google Scholar
- 37.Walck, C. (2007). Statistical Distributions for experimentalists. Particle Physics Group.Google Scholar
- 40.Gonuguntla, V., Mallipeddi. R., & Veluvolu, K. C. (2015). Differential evolution with population and strategy parameter adaptation. Mathematical Problems in Engineering.Google Scholar