ENERGY-AWARE MOBILE AGENT-BASED MODEL FOR ROUTING IN WIRELESS SENSOR NETWORKS

Abstract

Mobile agent (MA)-based wireless sensor networks (SNs) offer a good alternative to the traditional client/server architecture. In this study, a routing itinerary scheme for multi- mobile agent itinerary planning for energy efficiency in wireless sensor networks by constructing the spanning tree of wireless sensor nodes is formulated. MA is a special type of software entity that migrates among Sensor Nodes (SNs) to gather data. An itinerary is the route that the MA follows during its migration among SNs. It is important to note that the itinerary planning for MA is the most challenging issue with this paradigm. In this study, an energy aware-mobile agent-based model is proposed to mitigate the substantial increase in cost of energy and time used in the data gathering processes. It is assumed that the sink node has all the required information about SNs so that the sink can calculate the weights between Cluster Heads (CHs). The sensor network is divided into several clusters. Agents are launched from the processing elements also known as the sink node. The cluster formation of the network consist of n sensor nodes divided into m disjoint clusters. The sequence of the sensed data is combined with a fusion factor. Data aggregation is performed using bottom-up approach. All sensor nodes have an environment installed for the mobile agent. When the sink node receives a task from the remote user, the sink node traverses the network topology to generate a spanning tree and assigns paths to mobile agents. The itinerary planning among the SNs is performed using Minimum Spanning Tree (MST). A network area of 500m x 500m with 1000 number of nodes was used and random mode of node distribution was used for the simulation. Extensive simulation experiments have been conducted to test the performance of the proposed approach against Leach Energy Adaptive Clustering Hierarchy, Local Closest First and Global Closest First Algorithms. The simulation results show that the proposed model is more appropriate for large-scale WSN and has better performance than existing clustering-based algorithms in terms of both life cycle and energy consumption.