Hybrid Compression Techniques for Energy Efficient Wireless Sensor Networks

In today's Wireless Sensor Networks, energy efficiency is the key interest as these sensors are small in size and have no refillable battery. These sensors are deployed to accomplish various tasks e.g. to measure the environmental attributes such as temperature, humidity, pollution level of sea water; to monitor some ongoing activities such as surveillance, tracking of suspects; and etc. Also their batteries don't last very long since it requires continues transmission of data towards the user/customer/sink. Our main goal here is how to provide simple and effective approach to provide hybrid data compression (spatial and temporal both) in the wireless sensor network in order to improve energy efficiency of the network. WSNs are deployed in great amount over the detected environment and it has been observed that the data from neighboring sensors usually have large correlation so sometimes it is waste of energy for the transmission of this duplicate information from neighboring sensors rather we can exploit this spatial correlation to reduce the heavy burden of data in transmission. Besides it has been seen that on a particular sensor, their readings are also correlated for a limited duration so we can exploit this temporal correlation to reduce the amount of data. Our scheme is not suitable to real time data monitoring rather it is quite better for delay tolerant services as it requires a little time for processing of data before transmitting it to the sink. Neighboring correlated sensors forms a cluster and their data are collectively stored in two dimensional Matrix and afterwards, Compression techniques are applied in both domain (Space and Time). That's why it is termed as Hybrid Compression. After applying these compression methods the data are transmitted towards the sink. Our result shows that our proposed scheme gives satisfactory approximation of received data at the sink.