RESCUE: Enabling green healthcare services using integrated IoT‐edge‐fog‐cloud computing environments. (24th February 2022)

• Record Type: Journal Article
• Authors: Das, Jaydeep; Ghosh, Shreya; Mukherjee, Anwesha; Ghosh, Soumya K.; Buyya, Rajkumar
• Is Part Of: Software, practice & experience. Volume 52:Number 7(2022)
• Page Start: 1615
• Page End: 1642
• Publication Date: 2022-02-24
• Subjects: cloud computing -- edge computing -- geospatial query processing -- green computing -- healthcare service -- internet of things -- spatio‐temporal data; Computer software -- Periodicals; Computer programming -- Periodicals; Computer programs -- Periodicals; 005.3
• DOI: 10.1002/spe.3078
• Languages: English
• ISSNs: 0038-0644

Abstract

Abstract: 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.