IoT Sensor Data Stream Compression with Hybrid Compression Algorithms
Karthik Garikipati, Tejaswi Muppala, A Vinitha Chowdary, Apurvanand Sahay · 2024
The recent world advancement in technology especially in the use of IoT devices has posed some serious complications in terms of how effectively we can convey data, this is normally a result of the limited bandwidth and power capacity. In this paper, we look into the need for efficient data compression in IoT devices and come up with a new approach to compress the data stream needed for IoT sensors. In this paper, we propose to integrate Dynamic Huffman Coding with Run Length Encoding smoothly as one novel network coding strategy to decrease the load carried by the communication channels and at the same time ensure the originality of the transmitted data. The proposed method is based on the use of RLE to remove a lot of redundant data and dynamic Huffman coding for further data compression. When compared with other central algorithms like dynamic Huffman, static Huffman, RLE, LZW, ZLIB, LZMA, and BZ2 this hybrid approach provided more efficient compression ratios and consumed less power. Compression can be considered an essential paradigm for IoT devices because it can minimize the restrictions associated with the prevention of limited bandwidths and power sources that are characteristic of such devices, which results in improved data sending and a longer lifespan of devices. As substantiated by the findings from our experiments, this approach holds a high degree of effectiveness and, therefore, is suitable for the IoT domain with a scarcity of resources in particular. The contributions of this research support the promotion of compression strategies while considering the IoT-specific needs thus enhancing standard system outcomes and durability.