Abstract
Abstract. Increasing the lifespan of a group of distributed wireless sensors is one of the major challenges in research. This is especially important for distributed wireless sensor nodes used in harsh environments since it is not feasible to replace or recharge their batteries. Thus, the popular low-energy adaptive clustering hierarchy (LEACH) algorithm uses the “computation and communication energy model” to increase the lifespan of distributed wireless sensor nodes. As an improved method, we present here that a combination of three clustering algorithms performs better than the LEACH algorithm. The clustering algorithms included in the combination are the k-means+ + , k-means, and gap statistics algorithms. These three algorithms are used selectively in the following manner: the k-means+ + algorithm initializes the center for the k-means algorithm, the k-means algorithm computes the optimal center of the clusters, and the gap statistics algorithm selects the optimal number of clusters in a distributed wireless sensor network. Our simulation shows that the approach of using a combination of clustering algorithms increases the lifespan of the wireless sensor nodes by 15 % compared with the LEACH algorithm. This paper reports the details of the clustering algorithms selected for use in the combination approach and, based on the simulation results, compares the performance of the combination approach with that of the LEACH algorithm.
Highlights
Wireless sensor networks are being used for many different applications, such as monitoring chemical spills, detecting and assessing the extent of environmental contamination, and monitoring the movement of soldiers and weapons on the battlefield
Many different techniques have been introduced in an effort to maximize their lifespan, but these techniques have focused on having the nodes in a cluster send their data to a selected cluster head node that, in turn, reports the data to the base station
Algorithms were developed for this purpose by the energy efficient heterogeneous clustered scheme (EEHC) (Kumar et al, 2009) by the design of a distributed energy efficient clustering (DEEC)(Qing et al, 2006), and by the lowenergy adaptive clustering hierarchy (LEACH) (Heinzelman et al, 2000)
Summary
Wireless sensor networks are being used for many different applications, such as monitoring chemical spills, detecting and assessing the extent of environmental contamination, and monitoring the movement of soldiers and weapons on the battlefield. ‖2The k-meanis a=lgor1ith,m...is, na maetnhodd ojf =grou1p,in...g ,olr classifying sensor nodes into k numbers of groups/clusters (Zhong et al, 2012) This technique selects an optimal center location of a cluster from which the sum of the squared distances to. And x is the center of the cluster, n is the number of sensor nodes, and di,i is the distance between two nodes (i and i ), k is the number of clusters, method is to compare the curve of the observed weight (log(Wk)) to the curve that represents the expectation of a referenced weight (En∗ {log (Wkb)}) to determine the optimal number of clusters based on the maximum gap between the two curves. Phase, first, the sensor no1d0es identify their locations and positions and transmit the information to a base station
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