Optimizing Sensory Data Accumulation in IoMT Framework Using Energy-Efficient Compressed Sensing Techniques

Concurrent sensory data gathering is essential for the Internet-of-Medical-Things (IoMT) networks, since detecting and addressing anomalies is vital in the health-care domain. Given the energy and computational constraints of sensor nodes, it is essential to have an efficient method for gathering sensory data. In this context, the range of sensory data, rather than precise values, is more relevant to health-care industry. This range is expressed through the categories of sensed data. To minimize power utilization in IoMT networks, This research proposes an energy-efficient data accumulation mechanism. This approach leverages a compressed sensing algorithm to process different categories of sensory data. Moreover, a novel mechanism is introduced to propagate data over the cloud for further investigation. Data is only transmitted to the cloud if a sensor reading falls outside the acceptable threshold range. Experimental results demonstrate that this method outperforms in reconstructing original data with above 98% reliability, minimizing network traffic and energy usages less than 8.91% compared to traditional methods.