Optimizing Wireless Sensor Network Communication through Differential Encoding-Enhanced Compressed Sensing Protocol

Wireless sensor networks (WSNs) face inherent constraints in resources, such as power supply, processing speed, memory requirements, and bandwidth for communication. Given their limited power supply, energy consumption is a critical challenge in the development of protocols and algorithms for WSNs. Various operations in WSNs, including data sensing, computation, node switching, and transmission, necessitate a focus on energy efficiency. Extensive literature reveals that a substantial portion of energy in WSNs is consumed during radio communications. To address this issue, reducing the number of transmitted data bits has been identified as an effective strategy to minimize energy consumption.

Therefore, employing data compression techniques becomes imperative in order to reduce the overall number of bits transmitted. Although researchers have explored numerous energy-efficient lightweight compression algorithms tailored for WSN data, there remains a need for more efficient compression techniques that not only compress data but also minimize the mean square error (MSE) of the received data. In this paper, we propose a novel approach using differential encoding-based compressed sensing (CS) to achieve this goal. Simulation results demonstrate a notable improvement in the compression ratio compared to the standard compressed sensing technique, highlighting the efficacy of the suggested protocol in enhancing the existing WSN system.