Towards decision support systems for energy management in the smart industry and Internet of Things. (November 2021)

• Record Type: Journal Article
• Authors: Li, Jiwen; Dai, Jiapeng; Issakhov, Alibek; Almojil, Sattam Fahad; Souri, Alireza
• Is Part Of: Computers & industrial engineering. Volume 161(2021)
• Publication Date: 2021-11
• Subjects: Internet of Things -- Decision support systems -- Energy consumption -- Smart industrial; Engineering -- Data processing -- Periodicals; Industrial engineering -- Periodicals; 620.00285
• DOI: 10.1016/j.cie.2021.107671
• Languages: English
• ISSNs: 0360-8352

Abstract

Highlights: Providing a technical analysis of decision-making models for energy management. Illustrating a taxonomy for energy management approaches based on DSS. Discussing innovations and open issues on the DSS models for energy management. Abstract: Decision Support Systems (DSS) have widely provided on intelligent human–machine interactions like the Internet of Things (IoT). As the dimensions and complexity of the IoT communications between intelligent devices, industrial equipment, sensors, and mobile applications are continuously increasing, supporting Service Level Agreements (SLA) for a large amount of cloud data centers and a huge number of user requests is going to be more challenging. This challenge would be more complicated when the energy consumption of the industrial IoT ecosystems increases exponentially as well. Therefore, DSS models are required for automated decision-making in critical IoT environments like intelligent industrial systems and smart cities. This paper provides a comprehensive review of the DSS strategies for energy-efficient IoT applications in intelligent urban computing. The main objective of this review is (1) presenting a technical taxonomy to categorize existing energy-efficient DSS models for industrial and smart environments in IoT ecosystems; (2) recognizing significant research trends in the field of energy-aware DSS strategies for intelligent urban computing in IoT and (3) presenting a technical and statistical analysis of reviewed studies and evaluation factors. Finally, innovations as forthcoming issues and new challenges of energy-based DSS strategies for intelligent urban computing in IoT are presented.