EERP-DPM: Energy Efficient Routing Protocol Using Dual Prediction Model for Healthcare Using IoT

Area of Science:

• Biomedical Engineering
• Computer Science
• Telecommunications

Background:

• The Internet of Things (IoT) offers significant potential in healthcare, enabling remote patient monitoring via wearable or implanted sensors.
• Real-time patient data access for healthcare professionals is a key benefit of IoT in healthcare.
• Limited battery life and computational power in IoT devices, coupled with spatio-temporal data redundancy, pose challenges to energy efficiency and device longevity.

Purpose of the Study:

• To propose a novel routing protocol designed to enhance energy efficiency in healthcare IoT systems.
• To extend the operational lifetime of sensor nodes through optimized data transmission strategies.
• To address the issue of redundant data transmission impacting energy consumption and battery life.

Main Methods:

• Development of the Energy Efficient Routing Protocol using Dual Prediction Model (EERP-DPM).
• Implementation of a Dual-Prediction Mechanism to intelligently reduce data transmission based on predicted readings or critical threshold breaches.
• System validation using MATLAB simulations and the MySignals HW V2 hardware platform.

Main Results:

• The proposed EERP-DPM protocol demonstrated superior energy consumption compared to existing routing protocols.
• Significant improvements in network lifetime were observed with the EERP-DPM protocol.
• The protocol successfully maintained high reliability, throughput, and minimal end-to-end delay.

Conclusions:

• EERP-DPM is a highly effective routing protocol for healthcare IoT applications, significantly improving energy efficiency and sensor network lifespan.
• The Dual-Prediction Mechanism is crucial for reducing unnecessary data transmission, thereby conserving energy.
• The protocol offers a robust solution for reliable and efficient remote patient monitoring systems.