EPC: Energy-Aware Probability-Based Clustering Algorithm for Correlated Data Gathering in Wireless Sensor Networks

This paper addresses energy-efficient data gathering issues in wireless sensor networks (WSNs). Leveraging data correlation in densely-deployed sensor networks, we propose an Energy-aware Probability-based Clustering algorithm (EPC), featuring high scalability and flexibility particularly suitable for large-scale WSNs. Unlike most existing data gathering schemes that construct static routing structures or only consider spatial correlation among sensed data, EPC establishes energy-efficient routes on the fly during the data gathering process, and dynamically organizes sensor nodes into clusters based on a probability factor determined by both spatial and temporal data correlations. Redundant data transmissions are suppressed within a cluster and energy consumption is balanced to prolong network lifetime. To verify the effectiveness of EPC, extensive simulations are conducted on a network of 625 randomly deployed sensor nodes. Results show that EPC balances the energy consumption of the whole network and reduces up to 71% of the transmission costs with near negligible error rates for representative aggregation functions.