Energy awareness and energy efficiency in internet of things middleware: a systematic literature review

Silva, Pedro Victor Borges Caldas Da; Taconet, Chantal; Chabridon, Sophie; Conan, Denis; Cavalcante, Everton; Batista, Thaı́s

doi:10.1007/s12243-022-00936-5

Cited by 6 publications

(2 citation statements)

References 54 publications

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“…In Reference 22, researchers conducted an SLR‐type review article focusing on energy‐aware algorithms in green computing with the aim of energy efficiency. They described their method of article selection, but they did not present a taxonomy.…”

Section: Related Workmentioning

confidence: 99%

Energy‐aware resource management in fog computing for IoT applications: A review, taxonomy, and future directions

Hashemi,

Sahafi,

Rahmani

et al. 2023

Softw Pract Exp

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The energy demand for Internet of Things (IoT) applications is increasing with a rise in IoT devices. Rising costs and energy demands can cause serious problems. Fog computing (FC) has recently emerged as a model for location‐aware tasks, data processing, fast computing, and energy consumption reduction. The Fog computing model assists cloud computing in fast processing at the network's edge, which also exerts a vital role in cloud computing. Due to the fast computing in fog servers, different quality of service (QoS) approaches have been proposed in various sections of the fog system, and several quality factors have been considered in this regard. Despite the significance of QoS in Fog computing, no extensive study has focused on QoS and energy consumption methods in this area. Therefore, this article investigates previous research on the use and guarantee of Fog computing. This article reviews six general approaches that discuss the published articles between 2015 and late May 2023. The focal point of this paper is evaluating Fog computing and the energy consumption strategy. This article further shows the advantages, disadvantages, tools, types of evaluation, and quality factors according to the selected approaches. Based on the reviewed studies, some open issues and challenges in Fog computing energy consumption management are suggested for further study.

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Section: Related Workmentioning

confidence: 99%

Energy‐aware resource management in fog computing for IoT applications: A review, taxonomy, and future directions

Hashemi,

Sahafi,

Rahmani

et al. 2023

Softw Pract Exp

View full text Add to dashboard Cite

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“…Current research trends within the Internet of Things (IoT) focus on specific characteristics, such as systems, networking, Cloud support and middleware [1][2][3]. Currently, research trends are moving towards the application development aspect of IoT [4][5][6].…”

Section: Introductionmentioning

confidence: 99%

Leveraging Real-World Data from IoT Devices in a Fog–Cloud Architecture for Resource Optimisation within a Smart Building

Lawal,

Olaniyi,

Gibson

2023

Applied Sciences

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It is estimated that over 125 billion heterogeneous and homogeneous Internet of Things (IoT) devices will be internet-connected by 2030. This significant increase will generate large data volumes, posing a global problem for Cloud–Fog computing infrastructures. The current literature uses synthetic data in the iFogSim2 simulation toolkit; however, this study bridges the gap using real-world data to reflect and address the real-world issue. Smart IoT device data are captured, compared, and evaluated in a fixed and scalable scenario at both the Cloud and Fog layers, demonstrating the improved benefits achievable in energy consumption, latency, and network bandwidth usage within a smart office building. Real-world IoT device data evaluation results demonstrate that Fog computing is more efficient than Cloud computing, with increased scalability and data volume in a fixed- and low-bandwidth smart building architecture. This indicates a direct correlation between the increase in devices and the increase in efficiency within a scalable scenario, while the fixed architecture overall shows the inverse due to the low device numbers used in this study. The results indicate improved energy savings and significant improvements of up to 84.41% and 38.95% in network latency and usage, respectively, within a fixed architecture, while scalability analysis demonstrates improvements up to 4%, 91.38% and 34.78% for energy, latency, and network usage, respectively. Fog computing improvements are limited within a fixed smart building architecture with relatively few IoT devices. However, the benefits of Fog computing are significant in a scalable scenario with many IoT devices.

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A Novel Social Collaboration Platform for Enhancing Energy Awareness

Martinopoulou,

Dimara,

Tsita

et al. 2023

IFIP Advances in Information and Communication Technology

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Energy awareness and energy efficiency in internet of things middleware: a systematic literature review

Cited by 6 publications

References 54 publications

Energy‐aware resource management in fog computing for IoT applications: A review, taxonomy, and future directions

Energy‐aware resource management in fog computing for IoT applications: A review, taxonomy, and future directions

Leveraging Real-World Data from IoT Devices in a Fog–Cloud Architecture for Resource Optimisation within a Smart Building

A Novel Social Collaboration Platform for Enhancing Energy Awareness

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