Node localization over small world WSNs using constrained average path length reduction

Small world characteristics have been observed and studied in social networks in the context of information dissemination in important applications like disease spread among communities. However, the significance of small world characteristics in wireless sensor network (WSN) applications has hitherto not been investigated. This work investigates the significance of introducing small world characteristics in a conventional WSN for improving the node localization accuracy. In this context, a novel constrained iterative average path length reduction algorithm is proposed to introduce small world characteristics into a conventional WSN. This method utilizes a novel frequency selective approach for the introduction of small world phenomena. The frequency selective approach utilizes two frequency bands for link addition and pruning to create an optimized small world WSN. Subsequently, a constrained least squares node localization method over the small world WSN is developed using both model-based and model-free signal propagation approaches. The proposed constrained iterative average path length reduction method provides reduced average path length and a high average clustering coefficient when compared to conventional WSN. Constrained least squares node localization over the small world WSN yields improved localization performance when compared to node localization over conventional WSN. Performance analysis in terms of localization error, power consumption, bandwidth and anchors required for node localization is also discussed. Cramer-Rao lower bound analysis for node location parameters is also used to provide insights into the error bounds obtained using the proposed method. The experimental results obtained are motivating enough for the introduction of small world phenomena in conventional WSN for applications like node localization and related applications.