Adaptive Mobility Models for Cluster-Based Wireless Sensor Network with Mobile Sink

Wireless Sensor Network is considered as an important technology in recent years, especially with introducing mobility to this kind of network. Mobility Control and management represent a challenge for Cluster-Based Wireless Sensor Network. For the aim of handling this challenge, many mobility models are proposed in the literature. In this paper, we propose two hybrid mobility models for the Mobile Sink with adaptive pause time in order to improve the data collection process in Wireless Sensor Network organized in clusters using LEACH protocol. The proposed mobility models are divided into two phases, which are the discovery phase of cluster heads in the network using Grid Mobility Model and data collection phase using mobility models based on metaheuristics algorithms in order to find the optimal trajectory of the Mobile Sink passing by all cluster heads discovered. The mobility models based on metaheuristics used in this paper are Tabu Search and Simulated Annealing algorithms, where we have adapted to Cluster-Based Wireless Sensor Network with adaptive pause time. To evaluate the efficiency of our proposal on data collection, we compare the proposed hybrid mobility models to Grid Mobility Model and Random Way Point Mobility Model. The simulation results show that our proposed hybrid mobility models perform better than other mobility models in data collection.