Enhanced Range Free Localization in Wireless Sensor Networks

Abstract

Node localization is one of the most crucial issues for wireless sensor networks (WSNs), as many applications depend on the precise location of the sensor nodes. In last two decades, a number of range-based and range-free localization algorithms have been proposed. Range-free techniques are relatively more efficient but have poor localization accuracy. One of the most widely used range-free techniques is Distance Vector Hop (DV-Hop) algorithm which attracted more attention of researchers due to its stability, feasibility and less hardware cost. To achieve higher accuracy in range free localization algorithms, an Improved DV-Hop algorithm based on Teaching Learning Based Optimization (IDV-Hop based on TLBO) is proposed. In the proposed algorithm, average hop size of the anchor node is modified by updating a correction factor in traditional DV-Hop algorithm. For further improvement of the accuracy, TLBO technique in IDV-Hop is
used. With the help of bounded population feasible region, IDV-Hop using TLBO locates the unknown nodes more accurately and achieves higher convergence rate relatively. Simulation results show that the proposed algorithm is efficient and more accurate in terms of localization accuracy compared
to DV-Hop, DV-Hop based on Genetic Algorithm (GADV-Hop) and DV-Hop using PSO (Particle Swarm optimization) algorithms.