JoWUA

Abstract

About the last decade or so, Wireless Sensor Networks (WSNs) have found a place as an enabling technology in modern healthcare systems. Case in point is the real-time monitoring processes for chronic diseases like diabetes. To get at continuous monitoring of diabetic patients, reliable and energy-efficient communication between sensor nodes and healthcare must occur to guarantee uninterrupted data collection and be able to obtain timely medical intervention with these data. But insufficient energy in sensor nodes creates a real challenge, especially in dynamic environments that have mobility among both patients and healthcare providers. This research aims at developing and simulating an energy-efficient WSN framework for mobile diabetic patient treatment in association with the Low-Energy Adaptive Clustering Hierarchy (LEACH) Protocol with the First-Order Radio Model for consumption estimation. This research simulates diabetic patients as mobile sensor nodes moving within a defined room while representing the doctor as a mobile base station within a wider hospital environment. The simulation considers critical factors influencing communication performance, including cluster head (CH) selection, varying transmission distances, environmental loss factors, packet size variability, and adaptive data rates. Energy consumption for data transmission and reception is calculated using the First-Order Radio Model, considering both free-space and multipath propagation environments based on the distance threshold d0. Additionally, a realistic loss factor (ranging between 0.9 and 1.1) is applied to simulate the effects of environmental interference and signal attenuation on energy efficiency. Dynamic packet sizes and randomly selected data rates determine transmission latency. The experiments run in MATLAB for about 20 communication rounds would yield results demonstrating that the proposed framework balances the energy consumption of the sensor nodes, increases the lifetime of the network, and guarantees dependable data transmission under various environmental and mobility conditions. Nodes acting as CHs suffer from a greater energy depletion and reducing overall energy consumption can be aided by optimizing the selection of CHs and routing adaptation in the performance. An area further elaborated in this study describes the tradeoffs between increased data rates and decreased latencies at a cost of greater energy consumption on larger packet sizes. This study gives some useful perspectives for designing and deploying robust energy-aware WSNs for health solutions that will help in the monitoring of diabetic patients in naturalized mobile environments. Future research directions will look at machine learning-based methods of CH selection with dynamic transmission control to improve the performance of the network.