Wireless Sensor Networks Optimization with Localization-Based Clustering using Game Theory Algorithm

Abstract

Wireless sensor networks, as remote monitoring system support or Internet of Things subsystems, require reliability and stability, which greatly influence sensor life spans. Optimization efforts were a breakthrough to obtain these requirements. The optimization carried out in this study is related to the clustering-based localization process. The optimization algorithm used was game theory. Simultaneously, clustering information in an energy availability based sensor node configuration helped the sensor node’s tracking process. Optimization in the localization process determined the coalition of anchor nodes, where the selection of nodes as coalition members was conducted through geometric approaches with game theory. This proposed concept was validated using a simulator built on the MATLAB platform. Root Mean Square Error (RMSE) was chosen as a measurement to show accuracy. The simulation results indicated that the number of dead nodes could be delayed by about 1,000 rounds if there are improvements in clustering localization using game theory. The experimental results showed that network performance increased after this cluster-based localization process, which indicated an increase in the number of data packets sent and lifetime of the sensor node. The simulation results for the data delivery test showed a 20% increase in data packets sent.

Keywords

• clustering
• game theory
• localization
• rmse
• wireless sensor networks