Energy-Efficient Data Compression in Clustered Wireless Sensor Networks Using Adaptive Arithmetic Coding With Low Updating Cost
This paper presents a study of energy reduction technique using data compression based on arithmetic coding in clustered wireless sensor networks to maximize the network's lifetime. Initially, a simulation approach is used to investigate the effect of multiple data types found in environmental monitoring application on data compression and the effect of cluster's parameters on their energy consumption. This paper points out the important of probability models of multiple sensor data such as temperature and relative humidity on the arithmetic coding's performance. The investigation results provide insights for designing an energy-efficient arithmetic coding framework that is suitable for compressing multiple data types in clustered multi-hop wireless sensor networks.