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Comparative study of RLE K-RLE compression and decompression in WSN

机译:WSN中RLE和K-RLE压缩与解压缩的比较研究

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Wireless sensor network is very active research area. WSNs are very popular nowadays due to their wide range of application areas like in health monitoring, industrial monitoring, environmental monitoring, inventory location monitoring, surveillance, factory and process automation, object tracking, precision agriculture, disaster management, and equipment diagnostics etc. To perform a specific task sensor nodes in WSNs communicate with each other wirelessly and they are generally self-organized. Each node is equipped with sensors, battery, processor, wireless transceiver, and memory. Due to the limited capacity of the batteries, it is important to consider the energy (power) in the design and deployment of wireless sensor networks (WSNs). Energy is consumed during sensing, processing and communication. But the major power consumer is the communication unit in WSNs, one of possible solution that can help to reduce the amount of data transmitted between wireless sensor nodes resulting in power saving is the use of efficient data compression technique. In this paper we evaluate a K-RLE method which is inspired from existing Run Length Encoding algorithm. Method is designed in Matlab software. With creating effective GUI, we show here the compression ratios for different values of K with variable input temperature dataset values. We get higher compression ratios for long length of runs. It is found that as the values of K goes on increasing, the compression ratios are very high. K-RLE is efficient but lossy technique.
机译:无线传感器网络是非常活跃的研究领域。由于其广泛的应用领域,如健康监测,工业监测,环境监测,库存位置监测,监视,工厂和过程自动化,对象跟踪,精确农业,灾难管理和设备诊断等,WSN在当今很受欢迎。 WSN中执行特定任务的传感器节点彼此无线通信,并且它们通常是自组织的。每个节点都配备有传感器,电池,处理器,无线收发器和内存。由于电池的容量有限,因此在设计和部署无线传感器网络(WSN)时必须考虑能量(功率),这一点很重要。在传感,处理和通信过程中会消耗能量。但是主要的功耗设备是WSN中的通信单元,使用高效的数据压缩技术可以帮助减少无线传感器节点之间传输的数据量,从而实现节能的一种可能解决方案。在本文中,我们评估了一种K-RLE方法,该方法的灵感来自于现有的运行长度编码算法。方法是在Matlab软件中设计的。通过创建有效的GUI,我们在此处显示了具有可变输入温度数据集值的K的不同值的压缩率。对于较长的运行时间,我们获得了更高的压缩比。可以发现,随着K值的增加,压缩比非常高。 K-RLE是高效但有损的技术。

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