Hybrid Cluster Algorithm and Task Allocation Optimization for Improving Percolation of Multi-targets in Wireless Sensor Networks

Anjani Rai and Ashish Sharma

A Wireless Sensor Network (WSN) is composed of enormous spatially disseminated sensors, which are either homogeneous or heterogeneous. WSN has an important role to play in tracking the movement of target objects such as animals for habitat monitoring, traffic monitoring etc. A target tracking system comprises of two important functions: The deployed sensors are used for detecting and the tracking the target moving along its trajectory path. Few algorithms that are available make the nodes, which are along the target trajectory to become active when the remaining sensors have to be in inactive mode. The existing optimal dynamic clusters present on the target trajectory depending on a probabilistic model provide the solution to address the trade-off occurring between the percolations associated with the target. But, this tracking system needs to perform the tracking of numerous targets, and therefore there is an increase in the computation and communication loads. With the aim of solving this problem, this research work, introduces a Hybrid clustering algorithm for predicting the location of target along with the energy preservation and task allocation optimization methods for multi sensor and multi target tracking. Hybrid clustering comprises of the dynamic clustering algorithm integrated with the improved particle Kalman filter algorithm for predicting the multi target’s location. Also a task allocation optimization algorithm that depends on enhanced particle swarm optimization algorithm for reducing the computational complexity is proposed. In the next step, the comparison of the fitness function is done with the help of the change task allocation matrix to perform the allocation task. The results of simulation indicate that hybrid clustering efficiently identifies the multi target and the task allocation depending on enhanced particle swarm optimization is efficient with regard to reducing the energy consumed in communication compared to the available task allocation problem.

Volume 11 | 10-Special Issue

Pages: 655-665

DOI: 10.5373/JARDCS/V11SP10/20192855