Context-aware Edge Computing and Internet of Things in Smart Grids: A systematic mapping study

Aranda, Jorge; Costa, Ricardo dos Santos; Pereira, Paulo; Barbosa, Jorge Luís Victória; Vianna, Marcelo Pinto

doi:10.1016/j.compeleceng.2022.107826

Cited by 24 publications

(10 citation statements)

References 20 publications

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“…The decision-making and analytics viewpoint, which is widely acknowledged as a key component of fog computing [6], [51], [52], is in charge of analyzing the stored data in order to produce various analytics and detect particular scenarios. The set of short-term analytics in the fog and long-term analytics in the cloud can enable both proactive and reactive decision-making in ubiquitous environments, enhancing system scalability and encompassing a wider range of IoT applications.…”

Section: Decision-making and Analyticsmentioning

confidence: 99%

The fog computing for internet of things: review, characteristics and challenges, and open issues

Al-Shareeda,

Alsadhan,

H. Qasim

et al. 2024

Bulletin EEI

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The internet of things (IoT) research envisions a world in which common place objects are linked to the internet and trade, store, process, and gather data from their surroundings. Due to their inherent resource limitations, IoT devices are typically unable to directly host application services, despite their increasing importance for facilitating the supply of data to enable electronic services. Since it can survive and work in tandem with centralized cloud systems and extends the latter toward the network edge, fog computing (FC) may be an appropriate paradigm to get around these restrictions. This paper reviews the overview of the IoT in terms of application and design parameters and FC. Meanwhile, this paper presents the architecture of fog computing for IoT (FC-IoT) in terms of communication, security, data quality, sensing and actuation management, codification, analysis, and decision-making. Additionally, this review provides several characteristics and challenges of FC-IoT. Finally, open issues for this paper have been discussed.

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Section: Decision-making and Analyticsmentioning

confidence: 99%

The fog computing for internet of things: review, characteristics and challenges, and open issues

Al-Shareeda,

Alsadhan,

H. Qasim

et al. 2024

Bulletin EEI

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“…When the network condition improves in a short time, the idea of reducing the multiple of the contention window will make it take a long time for the value of CW to drop to a reasonable range. It may not be possible to find this reasonable value [10]. The LoRa wireless communication technology is adopted in the system.…”

Section: An Improve Slotted Csma/ca Algorithmmentioning

confidence: 99%

Intelligent monitoring method of electronic equipment operation status based on Internet of Things technology

Shen

2023

International Conference on Internet of Things and Machine Learning (IoTML 2022)

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The new power system can strengthen the resource allocation ability of the distributed Internet of Things, and can also make the intelligent coordinated control of a large number of decentralized power generation and supply objects. It is an urgent task to realize the two-way interaction of various market players and establish a corresponding power communication network for the realization of a new power system. Aiming at the quality problem of communication data transmission of power Internet of Things (IOT) equipment, this paper proposes a unified data model of IOT agent. It can encapsulate the collected data into a user-defined binary data packet format for transparent transmission. This avoids complex data parsing and format conversion and improves the efficiency of data transmission. To better reflect the feasibility and effectiveness of the IOT agent information bearing model, this paper applies different network scenarios for simulation analysis. The experimental results indicate that the model can reduce the data transmission delay and the packet loss rate.

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“…By utilizing an enhanced particle swarm optimization algorithm, the authors [18] have taken advantage of fog to schedule and balance a smart manufacturing load over to production cluster centers. In [19] studies, the smart energy savings in public work establish an edge-IoT framework.…”

Section: Related Workmentioning

confidence: 99%

Edge-Cloud Computing for Scheduling the Energy Consumption in Smart Grid

Alorf¹

2023

Computer Systems Science and Engineering

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Nowadays, smart electricity grids are managed through advanced tools and techniques. The advent of Artificial Intelligence (AI) and network technology helps to control the energy demand. These advanced technologies can resolve common issues such as blackouts, optimal energy generation costs, and peakhours congestion. In this paper, the residential energy demand has been investigated and optimized to enhance the Quality of Service (QoS) to consumers. The energy consumption is distributed throughout the day to fulfill the demand in peak hours. Therefore, an Edge-Cloud computing-based model is proposed to schedule the energy demand with reward-based energy consumption. This model gives priority to consumer preferences while planning the operation of appliances. A distributed system using non-cooperative game theory has been designed to minimize the communication overhead between the edge nodes. Furthermore, the allotment mechanism has been designed to manage the grid appliances through the edge node. The proposed model helps to improve the latency in the grid appliances scheduling process.

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Context-aware Edge Computing and Internet of Things in Smart Grids: A systematic mapping study

Cited by 24 publications

References 20 publications

The fog computing for internet of things: review, characteristics and challenges, and open issues

The fog computing for internet of things: review, characteristics and challenges, and open issues

Intelligent monitoring method of electronic equipment operation status based on Internet of Things technology

Edge-Cloud Computing for Scheduling the Energy Consumption in Smart Grid

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