Towards energy-aware fog-enabled cloud of things for healthcare. (April 2018)

• Record Type: Journal Article
• Authors: Mahmoud, Mukhtar M.E.; Rodrigues, Joel J.P.C.; Saleem, Kashif; Al-Muhtadi, Jalal; Kumar, Neeraj; Korotaev, Valery
• Is Part Of: Computers & electrical engineering. Volume 67(2018)
• Page Start: 58
• Page End: 69
• Publication Date: 2018-04
• Subjects: Application allocation -- Cloud of things -- Energy efficiency -- Fog Computing -- Internet of Things -- Healthcare; Computer engineering -- Periodicals; Electrical engineering -- Periodicals; Electrical engineering -- Data processing -- Periodicals; Ordinateurs -- Conception et construction -- Périodiques; Électrotechnique -- Périodiques; Électrotechnique -- Informatique -- Périodiques; Computer engineering; Electrical engineering; Electrical engineering -- Data processing; Periodicals; Electronic journals; 621.302854
• DOI: 10.1016/j.compeleceng.2018.02.047
• Languages: English
• ISSNs: 0045-7906

Abstract

Abstract: The Internet-of-Things (IoT) represents the next groundbreaking change in information and communication technology (ICT) after the Internet. IoT is concerned with making everything connected and accessible through the Internet. However, IoT objects (things) are characterized by constrained computing and storage resources. Therefore, the Cloud of Things (CoT) paradigm that integrates the Cloud with IoT is proposed to meet the IoT requirements. In CoT, the IoT capabilities (e.g., sensing) are provisioned as services. Unfortunately, the two-tier CoT model is not efficient in the use cases sensitive to delays and energy consumption (e.g., in healthcare). Consequently, Fog Computing is proposed to support such IoT services and applications. This paper reviews the most relevant Fog-enabled CoT system models and proposes an energy-aware allocation strategy for placing application modules (tasks) on Fog devices. Finally, the performance of the proposed strategy is evaluated in comparison with the default allocation and Cloud-only policies, using the iFogSim simulator. The proposed solution was observed to be more energy-efficient, saving approximately 2.72% of the energy compared to Cloud-only and approximately 1.6% of the energy compared to the Fog-default.