Dynamic data compression in multi-hop wireless networks

Abstract

Data compression can save energy and increase network capacity in wireless sensor networks. However, the decision of whether and when to compress data can depend upon platform hardware, topology, wireless channel conditions, and application data rates. Using Lyapunov optimization theory, we design an algorithm called SEEC that makes joint compression and transmission decisions with the goal of minimizing energy consumption. A practical distributed variant, DSEEC, is able to achieve more than 30% energy savings and adapts seamlessly across a wide range of conditions, without explicitly taking topology, application data rates, and link quality changes into account.