Prediction-Based Data Transmission for Energy Conservation in Wireless Body Sensors

TL;DR

This paper proposes a prediction-based data transmission method for wireless body sensors that reduces energy consumption by leveraging a dual prediction framework and a PID-based algorithm, tailored for healthcare applications.

Contribution

It introduces a novel, generic prediction framework and algorithm specifically designed for energy-efficient data transmission in wireless body sensor networks.

Findings

Significant energy savings demonstrated in simulations.

Effective data transmission with low complexity.

Applicable to real-world health monitoring datasets.

Abstract

Wireless body sensors are becoming popular in healthcare applications. Since they are either worn or implanted into human body, these sensors must be very small in size and light in weight. The energy consequently becomes an extremely scarce resource, and energy conservation turns into a first class design issue for body sensor networks (BSNs). This paper deals with this issue by taking into account the unique characteristics of BSNs in contrast to conventional wireless sensor networks (WSNs) for e.g. environment monitoring. A prediction-based data transmission approach suitable for BSNs is presented, which combines a dual prediction framework and a low-complexity prediction algorithm that takes advantage of PID (proportional-integral-derivative) control. Both the framework and the algorithm are generic, making the proposed approach widely applicable. The effectiveness of the approach is verified through simulations using real-world health monitoring datasets.