A Low-Complexity Compression Architecture for Wireless Sensor Networks

Wireless Sensor Networks (WSNs) have become an essential component of modern monitoring and communication systems, supporting applications such as environmental monitoring, industrial automation, healthcare, and smart agriculture. Sensor nodes continuously generate large volumes of data that must be transmitted over bandwidth-constrained and energy-limited wireless links. Data compression is widely employed to reduce transmission overhead and improve network efficiency. However, conventional compression techniques often introduce computational complexity that is unsuitable for resource-constrained sensor nodes. This paper presents a Low-Complexity Compression Architecture (LCCA) for Wireless Sensor Networks. The proposed architecture combines lightweight statistical analysis, adaptive parameter selection, and efficient lossless encoding to reduce communication overhead while maintaining low computational requirements. Experimental evaluation demonstrates improvements in compression ratio, energy consumption, and transmission latency when compared with traditional Huffman, LZW, and Golomb-Rice compression techniques. The proposed architecture is particularly suitable for battery-powered sensor nodes and real-time monitoring applications.