Iterative Sensor Clustering and Mobile Sink Trajectory Optimization for Wireless Sensor Network with Nonuniform Density

Abstract

Sensor clustering and trajectory optimization are a hot topic for last decade to improve energy efficiency of wireless sensor network (WSN). Most of existing studies assume that the sensor is uniformly deployed or all regions in the WSN coverage have the same level of interest. However, even in the same WSN, areas with high probability of disaster will have to form a “hotspot” with more sensors densely placed in order to be sensitive to environmental changes. The energy hole can be serious if sensor clustering and trajectory optimization are formulated without considering the hotspot. Therefore, we need to devise a sensor clustering and trajectory optimization algorithm considering the hotspots of WSN. In this paper, we propose an iterative algorithm to minimize the amount of energy consumed by components of WSN named ISCTO. The ISCTO algorithm consists of two phases. The first phase is a sensor clustering phase used to find the suitable number of clusters and cluster headers by considering the density of sensor and residual battery of sensors. The second phase is a trajectory optimization phase used to formulate suitable trajectory of multiple mobile sinks to minimize the amount of energy consumed by mobile sinks. The ISCTO algorithm performs two phases repeatedly until the amount of energy consumed by the WSN is not reduced. In addition, we show the performance of the proposed algorithm in terms of the total amount of energy consumed by sensors and mobile sinks.