A data compression application for wireless sensor networks using LTC algorithm

In this paper, the author represents energy efficient data compression application based on LTC (Lightweight Temporal Compression) algorithm in wireless sensor networks (WSNs). WSNs are essentially constrained by motes' limited battery power and networks bandwidth. The author focuses on data compression algorithm which effectively supports data compression for data gathering in WSNs. Data reduction before transmission such as by compression will significantly decrease the resource usage. Therefore, the main idea of this paper is to show how a data compression application such as collection tree protocol (CTP) is used for data collection from different sensor nodes into the root node in order to increase the network lifetime. LTC algorithm is used to minimize the amount of error in each reading. In the context of the use of wireless sensor network technology for environmental monitoring, the two main elementary activities of wireless sensor network are data acquisition and transmission. However, transmitting/receiving data are power consuming task in order to reduce transmission associated power consumption; we explore data compression by processing information locally. The inception of sensor networks, in-network processing has been touted as enabling technology for long-lived deployments. Radio communication is the overriding consumer of energy in such networks. Therefore, data reduction before transmission, either by compression or feature extraction, will directly & significantly increase network lifetime. In many applications where all data must transport out of network, data may be compressed before transport, so chosen compression technique can operate under stringent resource constraints of low-power nodes and induces tolerable errors. This paper evaluates temporal compression scheme designed specially to be used by mica motes. By using LTC, it is possible to compress data up to -20 to -1. Furthermore this algorithm is simple and requires little storage as compared to other compression techniques. The proposed application is implemented on the tinyOS platform using the nesC programming language. To evaluate their work, the author conducts simulation via TOSSIM or a real-world testbed FlockLab. The result demonstrates the significance of the application.