Leveraging Mobile Edge Computing on Smart Grids Using LTE Cellular Networks

Trajano, Alex F R; Sousa, Antonio Alex Monteiro de; Rodrigues, Emanuel B.; Souza, José Neuman de; Callado, Arthur de Castro; Coutinho, Emanuel Ferreira

doi:10.1109/iscc47284.2019.8969784

Cited by 14 publications

(7 citation statements)

References 17 publications

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“…In particular, Feng et al [167] presented a survey on applying MEC in smart grid in latency-sensitive, cognitive, and security aspects. In [162], the authors investigated deploying MEC nodes to 4G (LTE) cellular networks to meet the requirements of smart-grid applications like smart meters. The work [168] presented an MEC framework for realtime monitoring in smart grids.…”

Section: B Network Slice Orchestration and Managementmentioning

confidence: 99%

A Survey of Intelligent Network Slicing Management for Industrial IoT: Integrated Approaches for Smart Transportation, Smart Energy, and Smart Factory

Dai²,

Wang

et al. 2022

IEEE Commun. Surv. Tutorials

151

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Section: B Network Slice Orchestration and Managementmentioning

confidence: 99%

A Survey of Intelligent Network Slicing Management for Industrial IoT: Integrated Approaches for Smart Transportation, Smart Energy, and Smart Factory

Dai²,

Wang

et al. 2022

IEEE Commun. Surv. Tutorials

151

View full text Add to dashboard Cite

show abstract

“…Another architecture proposed in [125] comprises five layers, namely, IoT device layer, edge network layer, edge server layer, internet layer, and cloud layer. In the architecture proposed in [126], the MEC servers are located in the cellular base stations and leverage wireless cellular network to communicate with smart meters. In these architectures, MEC does not replace cloud computing but is rather deployed alongside to extend the capabilities of cloud-based smart grids.…”

Section: E Smart Gridmentioning

confidence: 99%

Integration of ICN and MEC in 5G and Beyond Networks: Mutual Benefits, Use Cases, Challenges, Standardization, and Future Research

Gûr

Kalla

Alwis

et al. 2022

IEEE Open J. Commun. Soc.

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Multi-access Edge Computing (MEC) is a novel edge computing paradigm that moves cloud-based processing and storage capabilities closer to the mobile users by implementing server resources in the access nodes. MEC helps fulfill the stringent requirements of 5G and beyond networks to offer anytime-anywhere connectivity for many devices with ultra-low delay and huge bandwidths. Information-Centric Networking (ICN) is another prominent network technology that builds on a content-centric network architecture to overcome host-centric routing/operation shortcomings and to realize efficient pervasive and ubiquitous networking. It is envisaged to be employed in Future Internet including Beyond 5G (B5G) networks. The consolidation of ICN with MEC technology offers new opportunities to realize that vision and serve advanced use cases. However, various integration challenges are yet to be addressed to enable the wide-scale co-deployment of ICN with MEC in future networks. In this paper, we discuss and elaborate on ICN MEC integration to provide a comprehensive survey with a forwardlooking perspective for Beyond 5G networks. In that regard, we deduce lessons learned from related works (for both 5G and Beyond 5G networks). We present ongoing standardization activities to highlight practical implications of such efforts. Moreover, we render key B5G use cases and highlight the role for ICN MEC integration for addressing their requirements. Finally, we layout research challenges and identify potential research

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“…Smart grids based on edge computing have recently triggered an unprecedented upsurge, changing the model of power management in the past. Different from some general designs on edge computing [4][5][6], Trajano designs an edge computing-based architecture to support the implementation of smart grid applications, which provides a stable and low latency communication network to achieve an effective end-to-end power management [7]. With a hardware-implemented architecture, Barik adopts the concept of edge computing in smart grids to migrate task loads from the cloud, resulting in improved performance metrics in power consumption, storage requirements, and analysis capabilities [8].…”

Section: Related Workmentioning

confidence: 99%

Power flow adjustment for smart microgrid based on edge computing and multi-agent deep reinforcement learning

Wang

Cao

et al. 2021

J Cloud Comp

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In current power grids, a massive amount of power equipment raises various emerging requirements, e.g., data perception, information transmission, and real-time control. The existing cloud computing paradigm is stubborn to address issues and challenges such as rapid response and local autonomy. Microgrids contain diverse and adjustable power components, making the power system complex and difficult to optimize. The existing traditional adjusting methods are manual and centralized, which requires many human resources with expert experience. The adjustment method based on edge intelligence can effectively leverage ubiquitous computing capacities to provide distributed intelligent solutions with lots of research issues to be reckoned with. To address this challenge, we consider a power control framework combining edge computing and reinforcement learning, which makes full use of edge nodes to sense network state and control power equipment to achieve the goal of fast response and local autonomy. Additionally, we focus on the non-convergence problem of power flow calculation, and combine deep reinforcement learning and multi-agent methods to realize intelligent decisions, with designing the model such as state, action, and reward. Our method improves the efficiency and scalability compared with baseline methods. The simulation results demonstrate the effectiveness of our method with intelligent adjusting and stable operation under various conditions.

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Leveraging Mobile Edge Computing on Smart Grids Using LTE Cellular Networks

Cited by 14 publications

References 17 publications

A Survey of Intelligent Network Slicing Management for Industrial IoT: Integrated Approaches for Smart Transportation, Smart Energy, and Smart Factory

A Survey of Intelligent Network Slicing Management for Industrial IoT: Integrated Approaches for Smart Transportation, Smart Energy, and Smart Factory

Integration of ICN and MEC in 5G and Beyond Networks: Mutual Benefits, Use Cases, Challenges, Standardization, and Future Research

Power flow adjustment for smart microgrid based on edge computing and multi-agent deep reinforcement learning

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