Mishra Mukesh, Sen Gupta Gourab, Gui Xiang
Department of Mechanical and Electrical Engineering, School of Food and Advanced Technology, Massey University, Palmerston North 4442, New Zealand.
Sensors (Basel). 2022 Oct 10;22(19):7685. doi: 10.3390/s22197685.
The exponential growth in remote sensing, coupled with advancements in integrated circuits (IC) design and fabrication technology for communication, has prompted the progress of Wireless Sensor Networks (WSN). WSN comprises of sensor nodes and hubs fit for detecting, processing, and communicating remotely. Sensor nodes have limited resources such as memory, energy and computation capabilities restricting their ability to process large volume of data that is generated. Compressing the data before transmission will help alleviate the problem. Many data compression methods have been proposed but mainly for image processing and a vast majority of them are not pertinent on sensor nodes because of memory impediment, energy utilization and handling speed. To overcome this issue, authors in this research have chosen Run Length Encoding (RLE) and Adaptive Huffman Encoding (AHE) data compression techniques as they can be executed on sensor nodes. Both RLE and AHE are capable of balancing compression ratio and energy utilization. In this paper, a hybrid method comprising RLE and AHE, named as H-RLEAHE, is proposed and further investigated for sensor nodes. In order to verify the efficacy of the data compression algorithms, simulations were run, and the results compared with the compression techniques employing RLE, AHE, H-RLEAHE, and without the use of any compression approach for five distinct scenarios. The results demonstrate the RLE's efficiency, as it surpasses alternative data compression methods in terms of energy efficiency, network speed, packet delivery rate, and residual energy throughout all iterations.
遥感技术的指数级增长，再加上通信集成电路（IC）设计与制造技术的进步，推动了无线传感器网络（WSN）的发展。无线传感器网络由适合进行远程检测、处理和通信的传感器节点与集线器组成。传感器节点的资源有限，如内存、能量和计算能力，这限制了它们处理大量生成数据的能力。在传输前压缩数据将有助于缓解这一问题。已经提出了许多数据压缩方法，但主要用于图像处理，而且由于内存限制、能量利用和处理速度等原因，其中绝大多数不适用于传感器节点。为了克服这个问题，本研究的作者选择了游程编码（RLE）和自适应哈夫曼编码（AHE）数据压缩技术，因为它们可以在传感器节点上执行。游程编码和自适应哈夫曼编码都能够平衡压缩率和能量利用。本文提出了一种将游程编码和自适应哈夫曼编码相结合的混合方法，称为H-RLEAHE，并对传感器节点进行了进一步研究。为了验证数据压缩算法的有效性，进行了模拟，并将结果与在五种不同场景下采用游程编码、自适应哈夫曼编码、H-RLEAHE以及不使用任何压缩方法的压缩技术进行了比较。结果证明了游程编码的效率，因为在所有迭代中，它在能量效率、网络速度、数据包交付率和剩余能量方面都超过了其他数据压缩方法。
