A Comprehensive Cross-Layer Framework for Optimization of Correlated Data Gathering in Wireless Sensor Networks

Wireless Sensor Networks are energy-constrained and thereby require energy-efficient protocols to maximize the network lifetime. Since many sensors have closely-related readings, correlated data gathering has emerged as an efficient approach for achieving energy conservation. Hence, the objective of this work is to study the existing literature on correlated data gathering in WSN and propose an improvement. Many existing approaches are based on traditional layered architecture which optimizes some layers' functions independently. This strict layered approach results in unnecessary overhead in the context of resource-scarce sensor networks. Therefore, we focus on adaptive cross-layer design that jointly optimizes the activities of various layers. Existing cross-layer solutions optimize the activities of Routing and MAC layers only. RMC is an energy-aware cross-layer protocol that considers the clustering activity in addition to Routing and MAC layer functions for joint optimization. This research work studies the performance of RMC protocol in terms of energy consumption and network lifetime. We have proposed an enhancement to the basic RMC protocol named Enhanced-RMC (E-RMC) and show that it improves the network lifetime significantly. A thorough simulative study is carried out using Avrora simulator on TinyOS platform.