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

Abstract

Wireless sensor networks (WSNs) face inherent constraints in resources, such as power supply, processing speed, memory requirements, and bandwidth for communication [1]. 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 [2]. To address this issue, reducing the number of transmitted data bits has been identified as an effective strategy to minimize energy consumption [3].

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 [4] 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.