Abstract: The sending/receiving of data (data communication) is the most power consuming in wireless communication, wireless sensor networks (WSN), bio medical devices and data storage since the electronic components at transmitter end are depending on batteries not generally rechargeable characterized by limited capacity. Data compression is among the techniques that can help to reduce the amount of the exchanged data between wireless sensor nodes in bio medical devices resulting in power saving. Nevertheless, there is a lack of effective methods to improve the efficiency of data compression algorithms and to increase transmission reception energy efficiency. In this paper, we proposed a novel lossless compression approach for ECG data compression using Transition Inversion based Run Length Encoding algorithms. TIE -RLE is an optimization of the RLE algorithm, which aims to improve the compression ratio. This method will lead to less storage cost and less bandwidth to transmit the data, which positively affects the sensor nodes’ lifetime and the network lifetime in general. The proposed scheme increases run length of number zeroes and reduces reduced number of one's transmission which reduces power consumption and increases compression ratio of ECG transmission and storage. The proposed architecture is implemented using verilog HDL and simulation/synthesize was done in Modelsim and Xylinx vivado tools.
Keywords: renewable and non-renewable, walking or jogging, generate power, piezoelectric sensor, noiseless and pollution-free.
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DOI:
10.17148/IJARCCE.2024.13576
