Autonomic energy management with Fog Computing

Sampaio, Hugo Vaz; Westphall, Carlos Becker; Koch, Fernando; Boing, Ricardo do Nascimento; Cruz, Rene Nolio Santa

doi:10.1016/j.compeleceng.2021.107246

Cited by 10 publications

(2 citation statements)

References 10 publications

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“…Continuous progress in terms of storage capacity and development power is critical to the implementation of a real-time management system [7,19,24]. Similarly, the use of fog and edge computing can foster the better real-time control and optimization of energy usage [8,62,63]. The presence of real-time information sharing also supports the continuous collection of improvement suggestions (kaizen) to drive continuous improvement.…”

Section: Digital Twin and Simulationmentioning

confidence: 99%

Integrating Industry 4.0 and 5.0 Innovations for Enhanced Energy Management Systems

Introna,

Santolamazza,

Cesarotti

2024

Energies

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Industry 4.0 and Industry 5.0 have introduced a lot of innovative technologies in industrial plants, transforming them into complex digital systems. On the other hand, the importance of Energy Management Systems in industrial plants is growing for both sustainability and economic reasons, but the opportunity of Industry 4.0/5.0 technologies in enhancing energy management systems is not fully understood. Thus, this paper analyzes how Industry 4.0/5.0 technologies can be applied to meet the requirements of Energy Management Systems, focusing on each aspect such as design, monitoring, control, and budget planning. It identifies additional opportunities that arise with different levels of technological implementation, suggesting organic implementation steps. The final aim is to provide a comprehensive framework for fostering a strategic and conscious implementation approach of these tools in the Energy Management Systems of industrial plants, giving clear and comprehensive suggestions.

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Section: Digital Twin and Simulationmentioning

confidence: 99%

Integrating Industry 4.0 and 5.0 Innovations for Enhanced Energy Management Systems

Introna,

Santolamazza,

Cesarotti

2024

Energies

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“…For example, autonomic controllers dynamically change the processing between fog and centralized servers to improve response time [58] has been proposed. In addition to network and computing, researchers have also attempted to use the autonomic management approach [59] to make buildings energy efficient by proactively adjusting duty cycles based on contextual data. An autonomic approach for a fully distributed service placement for fog computing was considered in [60] to avoid central coordination for virtual machine (VM) placement usually deployed in the cloud environment.…”

Section: B Fog Computingmentioning

confidence: 99%

Toward Autonomic Internet of Things: Recent Advances, Evaluation Criteria, and Future Research Directions

Ashraf

Tahir

Habaebi

et al. 2023

IEEE Internet Things J.

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With the rise of the Internet of Things (IoT), tiny devices capable of computation and data transmission are being deployed across various technological domains. Due to the wide deployment of these devices, manual setup and management are infeasible and inefficient. To address this inefficiency, intelligent procedures must be established to enable autonomy that allows devices and networks to operate efficiently with minimal human intervention. In the traditional client-server paradigm, autonomic computing has been proven effective in minimizing user intervention in computer systems management and will benefit IoT networks. However, IoT networks tend to be heterogeneous, distributed, and resource constrained, mandating the need for new approaches to implement autonomic principles compared to traditional approaches. We begin by introducing the basic principles of autonomic computing and its significance in IoT. We then discuss the self-* paradigm and monitor, analyze, plan, and execute (MAPE) loop from an IoT perspective, followed by recent works in IoT and key enabling technologies for enabling autonomic properties in IoT. Based on the self-* paradigm and MAPE loop analysis from the existing literature, we propose a set of qualitative characteristics for evaluating the autonomy of the IoT network. Finally, we provide a comprehensive list of challenges associated with achieving autonomic IoT and directions for future research.

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Enhancing smart home device identification in WiFi environments for futuristic smart networks-based IoT

Fakhruldeen,

Saadh,

Khan

et al. 2024

Int J Data Sci Anal

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Autonomic energy management with Fog Computing

Cited by 10 publications

References 10 publications

Integrating Industry 4.0 and 5.0 Innovations for Enhanced Energy Management Systems

Integrating Industry 4.0 and 5.0 Innovations for Enhanced Energy Management Systems

Toward Autonomic Internet of Things: Recent Advances, Evaluation Criteria, and Future Research Directions

Enhancing smart home device identification in WiFi environments for futuristic smart networks-based IoT

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