Performance Evaluation of IoT-Fog-Cloud Deployment for Healthcare Services

Kafhali, Said El; Alla, Said Ben

doi:10.1109/cloudtech.2018.8713355

Cited by 25 publications

(9 citation statements)

References 18 publications

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“…However, the framework does not support user's mobility, and also does not provide any learning technique to model and predict location related tasks. Kafhali et al 31 emphasize on personalized health monitoring and improving e-healthcare services using IoT-Fog-Cloud paradigm. The potentials of VGI in pervasive healthcare computing applications are presented in Reference 25, where the authors illustrate varied data sources using openstreetmap (OSM) in their case study.…”

Section: Time-critical Applicationmentioning

confidence: 99%

RESCUE: Enabling green healthcare services using integrated IoT‐edge‐fog‐cloud computing environments

Das

Ghosh

Mukherjee³

et al. 2022

Softw Pract Exp

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Internet of Things (IoT) has a pivotal role in developing intelligent and computational solutions to facilitate varied real-life applications. To execute high-end computations and data analytics, IoT and cloud-based solutions play the most significant role. However, frequent communication with long distant cloud servers is not a delay-aware and energy-efficient solution while providing time-critical applications such as healthcare. This article explores the possibilities and opportunities of integrating cloud technology with fog and edge-based computing to provide healthcare services to users in exigency. Here, we propose an end-to-end framework named RESCUE (enabling green healthcare services using integrated iot-edge-fog-cloud computing environments), consisting efficient spatio-temporal data analytics module for efficient information sharing, spatio-temporal data analysis to predict the path for users to reach the destination (healthcare center or relief camps) with minimum delay in the time of exigency (say, natural disaster). This module analyzes the collected information through crowd-sourcing and assists the user by extracting optimal path postdisaster when many regions are nonreachable. Our work is different from the existing literature in varied aspects: it analyses the context and semantics by augmenting real-time volunteered geographical information (VGI) and refines it. Furthermore, the novel path prediction module incorporates such VGI instances and predicts routes in emergencies avoiding all possible risks. Also, the design of development of a latency-aware, power-aware data-driven analytics system helps to resolve any spatio-temporal query more efficiently compared to the existing works for any time-critical application. The experimental and simulation results outperform the baselines in terms of accuracy, delay, and power consumption. K E Y W O R D Scloud computing, edge computing, geospatial query processing, green computing, healthcare service, internet of things, spatio-temporal data Jaydeep Das and Shreya Ghosh contributed equally to this study.

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Section: Time-critical Applicationmentioning

confidence: 99%

RESCUE: Enabling green healthcare services using integrated IoT‐edge‐fog‐cloud computing environments

Das

Ghosh

Mukherjee³

et al. 2022

Softw Pract Exp

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“…Task exchange between fog nodes and the cloud is only considered if the primary fog node and all its neighbors are congested, e.g., all their offloading thresholds reach the maximum threshold. We assume that cloud servers are much more efficient, and their queueing latency is ignored—i.e., tasks are processed immediately upon arrival at a cloud server [ 36 , 37 , 38 ]. The maximum offloading threshold is calculated as followed:

…”

Section: System Modelling and Constraintsmentioning

confidence: 99%

Dynamically Controlling Offloading Thresholds in Fog Systems

Alenizi

Rana

2021

Sensors

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Fog computing is a potential solution to overcome the shortcomings of cloud-based processing of IoT tasks. These drawbacks can include high latency, location awareness, and security—attributed to the distance between IoT devices and cloud-hosted servers. Although fog computing has evolved as a solution to address these challenges, it is known for having limited resources that need to be effectively utilized, or its advantages could be lost. Computational offloading and resource management are critical to be able to benefit from fog computing systems. We introduce a dynamic, online, offloading scheme that involves the execution of delay-sensitive tasks. This paper proposes an architecture of a fog node able to adjust its offloading threshold dynamically (i.e., the criteria by which a fog node decides whether tasks should be offloaded rather than executed locally) using two algorithms: dynamic task scheduling (DTS) and dynamic energy control (DEC). These algorithms seek to minimize overall delay, maximize throughput, and minimize energy consumption at the fog layer. Compared to other benchmarks, our approach could reduce latency by up to 95%, improve throughput by 71%, and reduce energy consumption by up to 67% in fog nodes.

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“…In the healthcare sector, Alsubaei et al evaluated security in the Internet of Medical Things (IoMT) [39]. In addition, Kafhali et al evaluated the response times for accessing medical data stored in a fog-based IoMT implementation framework [40]. They also proposed a queuing model to predict the minimum number of computing resources (both fog and cloud nodes) required to meet the service level agreement (SLA) for response time.…”

Section: Related Workmentioning

confidence: 99%

Examining the Performance of Fog-Aided, Cloud-Centered IoT in a Real-World Environment

Aleisa

Abuhussein

Alsubaei

et al. 2021

Sensors

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The fog layer provides substantial benefits in cloud-based IoT applications because it can serve as an aggregation layer and it moves the computation resources nearer to the IoT devices; however, it is important to ensure adequate performance is achieved in such applications, as the devices usually communicate frequently and authenticate with the cloud. This can cause performance and availability issues, which can be dangerous in critical applications such as in the healthcare sector. In this paper, we analyze the efficacy of the fog layer in different architectures in a real-world environment by examining performance metrics for the cloud and fog layers using different numbers of IoT devices. We also implement the fog layer using two methods to determine whether different fog implementation frameworks can affect the performance. The results show that including a fog layer with semi-heavyweight computation capability results in higher capital costs, although the in the long run resources, time, and money are saved. This study can serve as a reference for fundamental fog computing concepts. It can also be used to walk practitioners through different implementation frameworks of fog-aided IoT and to show tradeoffs in order to inform when to use each implementation framework based on one’s objectives.

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Performance Evaluation of IoT-Fog-Cloud Deployment for Healthcare Services

Cited by 25 publications

References 18 publications

RESCUE: Enabling green healthcare services using integrated IoT‐edge‐fog‐cloud computing environments

RESCUE: Enabling green healthcare services using integrated IoT‐edge‐fog‐cloud computing environments

Dynamically Controlling Offloading Thresholds in Fog Systems

Examining the Performance of Fog-Aided, Cloud-Centered IoT in a Real-World Environment

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