JMIR Publications

ABSTRACT

Background:

The burgeoning use of telehealth Internet of Things (IoT) devices has generated concerns surrounding energy utilization and data processing efficacy in healthcare informatics. This paper presents an innovative, energy-conserving model that amalgamates telehealth IoT devices with a fog and cloud computing-based platform, furnishing a durable and eco-friendly solution to address these issues. The proposed model employs fog computing for local data processing while utilizing cloud computing for tasks demanding extensive resources, substantially diminishing energy consumption. Our design incorporates adaptive energy-saving strategies, and simulation analyses substantiate its effectiveness in improving energy efficiency for telehealth IoT systems combined with localized fog nodes and both private and public cloud frameworks. Subsequent research will refine the energy conservation model, examine additional functional improvements, and evaluate its wider applicability in diverse healthcare and industrial contexts.

Objective:

The primary goal of this model is to minimize energy consumption through intelligent task allocation between fog nodes and cloud servers, by considering their computational capacity and proximity to IoT devices. This task allocation process also considers various sensitivity and priority levels within the healthcare context, ensuring prompt responses to critical and high-sensitivity requests. Our ground-breaking model synergistically combines the strengths of fog and cloud computing, creating an energy-efficient telehealth IoT system that effectively manages data processing and delivers real-time healthcare services in accordance with various levels of sensitivity and priorities.

Methods:

Our novel energy-efficient model integrates fog and cloud computing paradigms to optimize data processing for telehealth IoT devices without compromising real-time healthcare services. The model enables localized data processing by incorporating fog computing as an intermediary layer between IoT devices and public or private cloud servers, effectively reducing latency and data transfer overhead. Simultaneously, public and private cloud computing provides a robust infrastructure for handling large data volumes and performing resource-intensive computations.

Results:

This paper provides a compelling model for the use of fog and cloud computing-based platforms in telehealth IoT deployments to reduce energy consumption, improve data processing efficiency, and maintain high-quality healthcare services. The model leverages the strengths of both fog and cloud computing paradigms to address the challenges associated with large-scale telehealth IoT deployments, such as energy consumption, data processing efficiency, latency, security, and privacy. The simulation results show that the proposed fog-based model significantly reduces energy consumption compared to the cloud-only model while maintaining high-quality data processing and transmission. Moreover, the methodology described in this paper provides a comprehensive approach to analyzing network performance and energy consumption, which includes examining the impact of various parameters, such as the number of devices, fog node deployment, task allocation algorithm, energy consumption metrics, and performance metrics. Sensitivity analyses were conducted with respect to energy cost, latency, idle power, and transmit power, consistently showing that IoT devices with fog nodes had higher mean energy remaining compared to devices without fog nodes.

Conclusions:

This approach allows for a more detailed understanding of the network behavior and potential bottlenecks and provides insights into how to optimize the model to be more resilient and efficient. The simulation results and methodology demonstrate the effectiveness of the proposed model and provide a roadmap for future research in this area. we demonstrated the effectiveness of the proposed model in reducing energy consumption while, more importantly, ensuring efficient data processing and maintaining the quality of healthcare services. The proposed model can help healthcare providers and stakeholders improve patient care and outcomes while reducing costs and energy consumption.