Optimizing Task, Memory and Energy of Wireless Sensor Grids using Grid Cluster based Multilevel Priority Scheduling

Abstract

With rapid growth of advancements in the field of Internet of Things (IoT), wireless sensor networks are integrated as collaborative constituent for developing insidious smart technologies. Researches based on incorporating grid computing with the wireless sensor network to establish wireless sensor grid are flourishing in this decade to meet the real time requirements. Scheduling is a very vital task in the sensor grid to effectively reduce end to end delay and to control the overhead. In this research work, Multi Level Priority Scheduling (MLPS) scheme is implemented in the sensor grid environment. In order to reduce the energy consumption in the grid, the sensor nodes are grouped as clusters and data communication within the same cluster, across different clusters and from cluster to base station is established based on a novel clustering algorithm called as Grid Cluster. Grid Cluster algorithm combined with Multi Level Priority Scheduling (GCMLPS) is implemented in the sensor grid to optimize the resources including memory awareness, task awareness and energy awareness. Simulation results show the proposed scheme outperforms the existing scheduling mechanisms like First Come First Served Algorithm and the traditional Multi Level Priority scheme in terms of Packet Delivery Rate, Control Message Overhead, Efficiency, Energy Consumption and Routing Tree Cost