Abstract:
Large scale wireless sensor networks (WSNs) have emerged as the latest trend in revolutionizing the paradigm of collecting and processing data in diverse environments. Its advancement is fueled by development of tiny low cost sensor nodes (SNs) which are capable of sensing, processing and transmitting data. Due to the small size of SNs there are various resource constraints. It is the severe energy constraints and the limited computing resources that present the major challenge in converting the vision of WSNs to reality.
Power saving is a critical issue in wireless sensor networks (WSNs) since sensor nodes are powered by batteries which cannot be generally changed or recharged. As radio communication is often the main cause of energy consumption, extension of sensor node lifetime is generally achieved by reducing transmissions/receptions of data. It is useful to apply data compression to reduce the volume of data, and the associated energy consumption of transmission. Data compression is the process of encoding information using fewer bits than an unencoded representation would use, through use of specific encoding schemes. Due to limited processing and storage resources of sensor nodes, data compression in sensor nodes requires the use of ad-hoc algorithms.
In this dissertation, we propose a simple and efficient data compression algorithm which is lossless and particularly suited to the reduced memory and computational resources of a WSN node. The proposed data compression algorithm gives good compression ratio for highly correlated data. Simulations for the proposed data compression algorithm are performed on TOSSIM. Some experimental results and comparisons with the Marcelloni et al. data compression algorithm is shown and discussed
