Energy-efficient Data Aggregation Using Realistic Delay Model in Wireless Sensor Networks

Abstract

Data aggregation is an important technique in wireless sensor networks. The data are gathered together by data fusion routines along the routing path, which is called data-centralized routing. We propose a localized, Delay-bounded and Energy-efficient Data Aggregation framework(DEDA) based on the novel concept of DEsired Progress (DEP). This framework works under request-driven networks with realistic MAC layer protocols. It is based on localized minimal spanning tree (LMST) which is an energy-efficient structure. Besides the energy consideration, delay reliability is also considered by means of the DEP. A node’s DEP reflects its desired progress in LMST which should be largely satisfied. Hence, the LMST edges might be replaced by unit disk graph (UDG) edges which can progress further in LMST. The DEP metric is rooted on realistic degree-based delay model so that DEDA increases the delay reliability to a large extent compared to other hop-based algorithms. We also combine our DEDA framework with area coverage and localized connected dominating set algorithms to achieve two more resilient DEDA implementations: A-DEDA and AC-DEDA. The simulation results confirm that our original DEDA and its two enhanced variants save more energy and attain a higher delay reliability ratio than existing protocols.