Abstract
Abstract Retraction "The authors would like to retract this article as several portions of this manuscript were published previously by Gu, Wu, and Rao ("Optimizing Cluster Heads for Energy Efficiency in Large-Scale Heterogeneous Wireless Sensor Networks, International Journal of Distributed Sensor Networks 2010, 961591). The authors would like to apologise to Editors and readers." This article proposes a novel moment-based local cluster division optimization method in wireless sensor networks, and improves the energy efficiency of local cluster area with uneven nodes distribution. In the proposed method, first, each node estimates the higher moment of local sensors' coordinates. Second, the current cluster zone is divided into four quadrant zones with cluster head's (CH) coordinates as central point. Finally, among the divided quadrant zones, the slave CH is selected according to the higher moment to help the master CH optimize data transmission in the local area. To use the higher moment effectively in segmentation of zone, we present a hybrid higher moment method by computing the kurtosis coefficient of the sensors' coordinates. When the coordinates of sensors in a quadrant zone have higher kurtosis coefficient than a threshold, the quadrant zone is considered to be a zone needed to be a slave CH. The simulation results show that the proposed method can increase system throughput, decrease delay and packet loss rate, and enhance the energy efficiency.
Highlights
Wireless sensor network (WSN) [1] has found many applications in different areas, such as environmental surveillance, intelligent building, health monitoring, etc
There are many important aspects which need to be taken into consideration when we are dealing with the overall network design problem, energy efficiency should be considered as the key design objective among them
Minimizing the total energy consumption (TEC) [2] for sensor data gathering is critical to ensuring sustained operations of these large-scale WSNs, even though minimizing TEC does not necessarily maximize network lifetime, which depends on the balance of residual energy across the network
Summary
Wireless sensor network (WSN) [1] has found many applications in different areas, such as environmental surveillance, intelligent building, health monitoring, etc. Suitable cluster number will prolong the lifetime of WSN and reduce energy consumption in CH selection per round To solve those disadvantages, a distance-based crowdedness clustering (DCC) algorithm [3] to determine the CHs in sensor networks under general node distribution is presented, in which the number of CHs in sensor networks under uniform node distribution is optimized through deriving an analytical formula. In the MLCD algorithm, we first calculate the LNs coordinates’ kurtosis coefficient and mean value, each of which is used as the metric Using this metric, we designate the sensor with the comparatively large number of neighbor nodes as a CH and form a slave cluster of the master cluster with its crowded LNs. Namely, MLCD designates the sensor with the comparative large number of neighbor nodes as a CH and forms a cluster of this CH with all its neighbors considering the energy consumption of the CHs idle time. 17: Designate a node with the absolute value of y coordinates above [yrandom, |ymean|] as slave CH; 18: end if 19: Designate the LNs in the same quadrants with slave CH to form slave cluster; 20: end for 21: return slave CH and coordinates
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