MedGini: Gini index based sustainable health monitoring system using dew computing

Abstract

Monitoring biosignals is crucial for intelligent health applications. Internet of Health Things (IoHT) provides a new path for monitoring the biosignals. Environment adaptive data dissemination is the primary requirement for the deployment of time and space-efficient monitoring systems. Existing dew-based systems lack an opportunistic architecture of data-synchronization with the cloud. This paper proposes a model that makes efficient use of IoT and cloud-dew architecture for a sustainable health monitoring system. Wireless sensor nodes are used to monitor the biosignals dynamically. All accrued data is temporarily stored in the dew layer. It is synchronized with the cloud at a subsequent phase to achieve seamless accessibility and optimal scalability of the data. Data synchronization plays an essential role in the cloud dew framework. We have used the Gini index and Shannon entropy to ensure intelligent data synchronization with the cloud. Sometimes sensors produce erroneous data, which poses a significant threat to the sustainable health monitoring system. Fuzzy normal distribution with a triangular membership function has been used to clean up the data and filter out the outliers. Further, we compared the proposed MedGini model with the existing models and analyzed the system performance. MedGini is found to outperform others concerning cost and power consumption.

Keywords

• Dew computing
• Gini index
• Internet of health things (IoHT)
• Sustainable
• IoT