Clustering in Wireless Sensor Network using K-MEANS and MAP REDUCE Algorithm
Jyoti R. Patole, Jibi Abraham · 2012
A wireless sensor network (WSN) consists of a large number of small sensors with limited energy. Prolonged network lifetime, scalability, node mobility and load balancing are important requirements for many WSN applications. Clustering the sensor nodes is an effective technique to achieve these goals. The different clustering algorithms also differ in their objectives. We have proposed a new method to achieve these goals and the proposed method depends on MAP-REDUCE programming model and K-MEANS clustering algorithm. So, new clustering algorithm has been proposed to cluster the sensor nodes of a network. It uses MAP REDUCE and K MEANS algorithm for clustering. Network is divided into number of clusters, which we have taken as 5% of the total number of nodes of a network. Nodes are assigned to the cluster having minimum distance to the cluster head having maximum energy. The distance is calculated using Euclidean Distance Formula. We have also calculated the intra cluster and inter cluster distance for the cluster. We also found the end to end delay of packet transmission,energy consumption for the transmission. Initial simulations are performed to check how much we can lower the energy consumption by placing the cluster heads over the grid. We have considered two ways with which cluster heads can be placed over the grid, either place them randomly or keep some distance among them. For this results are found and checked. These results show that placing the cluster heads using some minimal distance performs well than placing them randomly.