Optimized Distributive Cross-Layer and Thermal-Aware Convergecast Protocol for Wireless Body Area Network

Abstract

The Internet of Healthcare Things has significantly altered traditional patient-doctor relationships by allowing essential healthcare treatment from the comfort of one’s home. The wireless body area network is an IEEE 802.15.6 standard that focuses on healthcare data, necessitating various cross-layer and thermal-aware protocols. However, most cross-layer protocols have long convergence delays and a single failure point. Moreover, these protocols exploit excessive broadcasts and handshake acknowledgments, causing communication and processing overheads. Furthermore, thermal-aware protocols focus on thermal variations, disperse data collection, and do not support cross-layer techniques. To address these limitations, this study proposes an optimal distributive cross-layer and thermal-aware convergecast protocol. The proposed protocol enforces a novel hybrid convergecast using probability and both minimum attenuation strategies to collect data from leaf nodes to the root to improve data flow and adaptability in the network. In addition, it accelerates the convergence process by reducing recurrent broadcasts and unnecessary acknowledgments, resulting in improved energy efficiency and thermal control. The proposed protocol supports a distributive hierarchy by establishing multiple parent-child relationships to avoid a single root point failure. A multi-parameter maximum benefit-cost function calculates the next hop according to the extracted weights. Packet loss probability validates the number and sequence of packets received at the sink node. The simulation results demonstrate that cross-layer and thermal-aware protocols can coexist effectively. The proposed protocol reduces delays to 19.4%, improves throughput from 8% to 13.75%, and retains a packet loss probability of 0.3% by keeping the thermal rise within bounds.