Energy and lifetime analysis of compressed Wireless Sensor Network communication

Abstract

Improving the lifetime of Wireless Sensor Networks (WSNs) is directly related with the energy efficiency of computation and communication operations in the sensor nodes. By employing the concepts of Compressive Sensing (CS) theory it is possible to reconstruct a sparse signal with a certain number of random linear measurements, which is much less than the number of measurements necessary in conventional signal reconstruction techniques. In this study, we built an energy dissipation model to quantitatively compare the energy dissipation characteristics of CS and conventional signal processing techniques. This model is used to construct a Linear Programming (LP) framework that jointly captures the energy costs for computing and communication both for CS based techniques and conventional techniques. It is observed that CS prolongs the network lifetime for sparse signals.