Adaptive and Fault-Tolerant Data Processing in Healthcare IoT Based on Fog Computing

Abstract

In recent years, healthcare IoT have been helpful in mitigating pressures of hospital and medical resources caused by aging population to a large extent. As a safety-critical system, the rapid response from the health care system is extremely important. To fulfill the low latency requirement, fog computing is a competitive solution by deploying healthcare IoT devices on the edge of clouds. However, these fog devices generate huge amount of sensor data. Designing a specific framework for fog devices to ensure reliable data transmission and rapid data processing becomes a topic of utmost significance. In this paper, a Reduced Variable Neighborhood Search (RVNS)-based sEnsor Data Processing Framework (REDPF) is proposed to enhance reliability of data transmission and processing speed. Functionalities of REDPF include fault-tolerant data transmission, self-adaptive filtering and data-load-reduction processing. Specifically, a reliable transmission mechanism, managed by a self-adaptive filter, will recollect lost or inaccurate data automatically. Then, a new scheme is designed to evaluate the health status of the elderly people. Through extensive simulations, we show that our proposed scheme improves network reliability, and provides a faster processing speed.