دانلود رایگان مقاله انگلیسی بهینه سازی ژنتیکی الگوریتم خوشه بندی ترکیبی در شبکه های حسگر بی سیم موبایل - امرالد 2018

Abstract

Purpose – This paper aims to provide a prolonging network lifetime and optimizing energy consumption in mobile wireless sensor networks (MWSNs). MWSNs have characteristics of dynamic topology due to the factors such as energy consumption and node movement that lead to create a problem in lifetime of the sensor network. Node clustering in wireless sensor networks (WSNs) helps in extending the network life time by reducing the nodes’ communication energy and balancing their remaining energy. It is necessary to have an effective clustering algorithm for adapting the topology changes and improve the network lifetime. Design/methodology/approach – This work consists of two centralized dynamic genetic algorithm-constructed algorithms for achieving the objective in MWSNs. The first algorithm is based on improved Unequal Clustering-Genetic Algorithm, and the second algorithm is Hybrid K-means Clustering-Genetic Algorithm. Findings – Simulation results show that improved genetic centralized clustering algorithm helps to find the good cluster configuration and number of cluster heads to limit the node energy consumption and enhance network lifetime. Research limitations/implications – In this work, each node transmits and receives packets at the same energy level throughout the solution. The proposed approach was implemented in centralized clustering only. Practical implications – The main reason for the research efforts and rapid development of MWSNs occupies a broad range of circumstances in military operations. Social implications – The research highly gains impacts toward mobile-based applications. Originality/value – A new fitness function is proposed to improve the network lifetime, energy consumption and packet transmissions of MWSNs.

5. Conclusion

Certainly, it is a challenging task to handle the dynamic clustering problem in unstable network topology of MWSN. The proposed algorithms KC-GA and UC-GA are implemented in NS-2 for selecting optimal number of clusters and CHs in dynamic environment. The KC-GA and UC-GA algorithms are considered mobility factor, energy and distance metric for calculating fitness function and then applied genetic operation. So, the proposed GAs have more amount of stability in solving dynamic clustering problems and network-based optimization issues. Based on the simulation results, KC-GA performs better by reducing energy consumption and improving network lifetime than UC-GA and LEACH. Finally, KC-GA suits well for dynamic network environment by avoiding faster convergence and obtaining the optimum solution.