Energy-efficient and Multi-stage Clustering Algorithm in Wireless Sensor Networks Using Cellular Learning Automata

Abstract

One of the main challenges in wireless sensor networks is the energy constraints of sensor nodes which must be considered precisely when designing algorithms for such networks. Clustering is known as one of the approaches which can be used for addressing this challenge. In this paper, an efficient method for clustering wireless sensor networks by means of cellular learning automata has been presented (LaClustering). Proposed method selects cluster head (CHs) through several stages; each considers one parameter affecting the overall performance of the clustering. Parameters considered in different stages of the proposed algorithm are energy levels of the sensor nodes, number of neighbors of each node, network connectivity, and formation of balanced clusters. To evaluate the performance of the proposed method, several experiments have been conducted using the J-sim simulator and the proposed method has been compared with some of the best clustering algorithms reported in literature. The simulation results have shown that the proposed algorithm can provide clustering infrastructure with higher overall quality than the existing algorithms, especially in balancing the number of sensor nodes in different clusters and selecting CHs with higher energy levels.

Keywords:

• Cellular learning automata
• Clustering
• Wireless sensor network

Additional information

Notes on contributors

Mohammad Ahmadinia

Mohammad Ahmadinia received the BS degree in software engineering from Ferdowsi University in Iran, in 2006 and MS degree from Islamic Azad University, Science & Research Branch in Iran, in 2009. Currently, He is a PhD student in Islamic Azad University, Science & Research Branch, in Iran, in Computer Engineering, and he also is a Lecturer in Computer Engineering Department, Islamic Azad University, Kerman Branch, Kerman, Iran. His research interests include Wireless Sensor Network, semantic web and learning Systems. E-mail: [email protected]

Mohammad Reza Meybodi

Mohammad Reza Meybodi received the BS and MS degrees in Economics from Shahid Beheshti University in Iran, in 1973 and 1977, respectively. He also received the MS and PhD degree from Oklahoma University, USA, in 1980 and 1983, respectively in Computer Science. Currently, he is a full professor in Computer Engineering Department, Amirkabir University of Technology, Tehran, Iran. Prior to his current position, he worked from 1983 to 1985 as an assistant professor at Western Michigan University,and from 1985 to 1991 as an associate professor at Ohio University, USA. His research interests include channel management in cellular networks, learning systems, parallel algorithms, soft computing and software development. E-mail: [email protected]

Mahdi Esnaashari

Mahdi Esnaashari received both the B.S. and M.S. Degrees in Computer Engineering from the Amirkabir University of Technology in Iran, in 2002 and 2005, respectively. Currently, he is a Ph.D. student in the Computer Engineering Department at the Amirkabir University of Technology, Tehran, Iran. His research interests include computer networks, learning systems and soft computing. E-mail: [email protected]

Hamid Alinejad-Rokny

Hamid Alinejad-Rokny is the author/co-author of more than 65 publications in technical journals and conferences. He served on the program committees of several national and international conferences. Also He is Deputy Editor-Chief at International Journal of Software Engineering and Computing and he is editorial board member at IJSEI, IJFIPM, IJSCIP, IJCSCS, JIDM, IJCDS, IJCNT and IJEIS. His research interests are in the areas of Data Mining, Bioinformatics and Artificial Intelligence and evolutionary computing. E-mail: [email protected]