This paper presents a new centralized adaptive Energy Based Clustering protocol through the application of Self organizing map neural networks (called EBC-S) which can cluster sensor nodes, based on multi parameters; energy level and coordinates of sensor nodes. We applied some maximum energy nodes as weights of SOM map units; so that the nodes with higher energy attract the nearest nodes with lower energy levels. | International Journal of Computer Networks and Communications Security VOL. 3, NO. 4, APRIL 2015, 156–163 Available online at: E-ISSN 2308-9830 (Online) / ISSN 2410-0595 (Print) Increasing the lifetime of wireless sensor networks by SelfOrganizing Map algorithm Faramarz Ahmadnezhad1 and Ali Rezaee2 1 2 Master's students, Department of Computer Engineering, Information Technology, PNU, Asalooye Assistant Professor, Department of Computer Engineering and Information Technology, Payame Noor University, PO BOX 19395-3697 Tehran, IRAN E-mail: , 2a_rezaee@ ABSTRACT Wireless sensor networks are composed of a large number of sensor nodes with limited energy resources. One critical issue in wireless sensor networks is how to gather sensed information in an energy efficient way since the energy is limited. The clustering algorithm is a technique used to reduce energy consumption the performance of Wireless Sensor Networks strongly depends on their lifetime. As a result, Dynamic Power Management approaches with the purpose of reduction of energy consumption in sensor nodes, after deployment and designing of the network. Recently, there have been a strong interest to use intelligent tools especially Neural Networks in energy efficient approaches of Wireless Sensor Networks, due to their simple parallel distributed computation, distributed storage, data robustness, auto-classification of sensor nodes and sensor reading. This paper presents a new centralized adaptive Energy Based Clustering protocol through the application of Self organizing map neural networks (called EBC-S) which can cluster sensor nodes, based on multi parameters; energy level and coordinates of sensor nodes. We applied some maximum energy nodes as weights of SOM map units; so that the nodes with higher energy attract the nearest nodes with lower energy levels. Therefore, formed clusters may not necessarily contain adjacent nodes. The new algorithm
