Optimizing Medical IoT Disaster Management with Data Compression

Abstract

In today's technological landscape, the convergence of the Internet of Things (IoT) with various industries showcases the march of progress. This coming together involves combining diverse data streams from different sources and transmitting processed data in real-time. This empowers stakeholders to make quick and informed decisions, especially in areas like smart cities, healthcare, and industrial automation, where efficiency gains are evident. However, with this convergence comes a challenge – the large amount of data generated by IoT devices. This data overload makes processing and transmitting information efficiently a significant hurdle, potentially undermining the benefits of this union. To tackle this issue, we introduce "Beyond Orion," a novel lossless compression method designed to optimize data compression in IoT systems. The algorithm employs advanced techniques such as Lempel Ziv-Welch and Huffman encoding, while also integrating strategies like pipelining, parallelism, and serialization for greater efficiency and lower resource usage. Through rigorous experimentation, we demonstrate the effectiveness of Beyond Orion. The results show impressive data reduction, with up to 99% across various datasets, and compression factors as high as 7694.13. Comparative tests highlight the algorithm's prowess, achieving savings of 72% and compression factor of 3.53. These findings have significant implications. They promise improved data handling, more effective decision-making, and optimized resource allocation across a range of IoT applications. By addressing the challenge of data volume, Beyond Orion emerges as a significant advancement in IoT data management, marking a substantial step towards realizing the full potential of IoT technology.

Keywords

• data compression
• internet of things (iot)
• disaster management, enhanced lempel ziv-welch and huffman
• pipelining
• parallelism
• serialization