Energy-Efficient Device-to-Device Edge Computing Network: An Approach Offloading Both Traffic and Computation

Wen, Jinming; Ren, Chao; Sangaiah, Arun Kumar

doi:10.1109/mcom.2018.1701054

Cited by 40 publications

(15 citation statements)

References 12 publications

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“…As MEC and D2D communication are both applications of the offloading concept [9], [291], [292], the authors in [293], [294] proposed different MEC architecture to further improve the network performance compared with the standard MEC. A D2D-based MEC architecture was proposed in [293], where each relay gateway can act as a local cloud.…”

Section: ) Lightweight Platforms For Edge Computingmentioning

confidence: 99%

“…Further, D2D communication is used to establish direct connections between a relay gateway and users so as to provide edge services and between two neighbor relay gateways to balance the traffic and computation demands among them. The work in [294] introduced a concept of "MEC D2D". Concretely, D2D MEC enables the direct link between users and the MEC server, neighboring D2D helps users to connect with the other server if they are not satisfied with the local MEC sever, cooperative relay can extend the MEC service, conventional MEC provides service to users via the collocated eNB, and remote cloud let all users with Internet access use cloud services.…”

Section: ) Lightweight Platforms For Edge Computingmentioning

confidence: 99%

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A Survey of Multi-Access Edge Computing in 5G and Beyond: Fundamentals, Technology Integration, and State-of-the-Art

et al. 2020

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Driven by the emergence of new compute-intensive applications and the vision of the Internet of Things (IoT), it is foreseen that the emerging 5G network will face an unprecedented increase in traffic volume and computation demands. However, end users mostly have limited storage capacities and finite processing capabilities, thus how to run compute-intensive applications on resource-constrained users has recently become a natural concern. Mobile edge computing (MEC), a key technology in the emerging fifth generation (5G) network, can optimize mobile resources by hosting compute-intensive applications, process large data before sending to the cloud, provide the cloud-computing capabilities within the radio access network (RAN) in close proximity to mobile users, and offer context-aware services with the help of RAN information. Therefore, MEC enables a wide variety of applications, where the real-time response is strictly required, e.g., driverless vehicles, augmented reality, robotics, and immerse media. Indeed, the paradigm shift from 4G to 5G could become a reality with the advent of new technological concepts. The successful realization of MEC in the 5G network is still in its infancy and demands for constant efforts from both academic and industry communities. In this survey, we first provide a holistic overview of MEC technology and its potential use cases and applications. Then, we outline up-to-date researches on the integration of MEC with the new technologies that will be deployed in 5G and beyond. We also summarize testbeds and experimental evaluations, and open source activities, for edge computing. We further summarize lessons learned from state-of-the-art research works as well as discuss challenges and potential future directions for MEC research.

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Section: ) Lightweight Platforms For Edge Computingmentioning

confidence: 99%

Section: ) Lightweight Platforms For Edge Computingmentioning

confidence: 99%

A Survey of Multi-Access Edge Computing in 5G and Beyond: Fundamentals, Technology Integration, and State-of-the-Art

et al. 2020

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“…In most papers, when authors talk about "the fog computing architecture", they mean something like the structure shown in Fig. 1; examples include [7,11,25,55,61,63,71,72]. There are some small variations: e.g., some authors also allow communication among end devices [63] or among fog nodes [11], Sharma et al differentiate between "edge nodes" and "fog nodes" [55], while Zhou et al [72] extend the three layers of Fig.…”

Section: Architecture In Fog Computingmentioning

confidence: 99%

Notions of architecture in fog computing

Mann

2020

Computing

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Fog computing is becoming a popular paradigm for bringing the advantages of the cloud nearer to the network edge. This way, computational tasks can be offloaded from end devices to nearby fog nodes, thus benefiting from high computational power and low latency at the same time. Architecture plays a central role in fog computing. Many papers on fog computing address architectural questions. However, a closer look reveals that different papers use the term “architecture” for very different concepts. This is rooted in the multi-disciplinary nature of the fog computing paradigm. The different communities involved in fog computing—network, hardware, system software, application software—all use the term “architecture,” but with different meaning. To facilitate the mutual understanding of architectural issues in fog computing, this paper introduces a conceptual framework for reasoning about architecture in fog computing. This conceptual framework uses three independent dimensions to describe architecture. Based on the three architecture dimensions, several architecture views can be defined to serve the different viewpoints of the involved disciplines, and to highlight different aspects of the architecture. The conceptual framework is validated using a literature mapping study.

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“…Although the above studies have demonstrated the effectiveness of the resource allocation strategy of RF energy harvesting combined with MEC to improve the computation performance of communication system, the limited computing resources of MEC server is not always adequate to support all SMDs under the coverage range of base station (BS). To improve this situation, there have been also several works [17]- [19] that investigate the device-to-device (D2D)assisted MEC system. In [17], the D2D-assisted and nonorthogonal multiple access (NOMA)-based MEC system was investigated to minimize the weighted sum of the energy consumption and delay of all SMDs.…”

Section: Introductionmentioning

confidence: 99%

“…A novel D2D-enabled multi-helper MEC system was developed in [18], where the local user could offload its computing tasks to multiple helpers and download the results from them over an orthogonal pre-scheduled time slot. J. Wen et al [19] attempted to further improve the energy-efficient communication in MEC by proposing a D2D offloading architecture. The aforementioned studies mainly focus on the communication system with energy harvesting from ambient or dedicated RF sources.…”

Section: Introductionmentioning

confidence: 99%

Resource Allocation Strategy for D2D-Assisted Edge Computing System With Hybrid Energy Harvesting

Chen

Zhao

et al. 2020

IEEE Access

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Due to the limited battery capacity and computing capability of mobile users, the resource allocation strategy in device-to-device (D2D)-assisted edge computing system with hybrid energy harvesting is investigated in this paper. By employing magnetic induction-based wireless reverse charging technology, mobile user can supplement extra energy from nearby users when the energy harvested from ambient radio frequency sources is about to be exhausted. Moreover, mobile user can not only perform local computation, but also offload computing tasks to nearby users for auxiliary computation through D2D communication links or mobile edge computing (MEC) server under base station (BS) for edge computation. Due to the limited computing resources of MEC server, when the computing capability of the MEC server reaches the maximum value, an adjacent user under another nearby BS can be considered as a relay node. The computing tasks of the remaining users under the previous BS can be transferred to the MEC server with sufficient resources under another nearby BS by establishing D2D relay links. The objective of the resource allocation strategy is to maximize the energy efficiency under the constraints of computation delay and energy harvesting. The resource allocation problem is formulated as a mixed-integer nonlinear programming problem, which is not easy to obtain the optimal solution at low computational complexity. A suboptimal solution is obtained by adopting the quantum-behaved particle swarm optimization (QPSO) algorithm. Simulation results show that the performance of the proposed strategy is superior to other benchmark strategies, and QPSO algorithm can achieve higher energy efficiency than the standard particle swarm optimization algorithm.

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Energy-Efficient Device-to-Device Edge Computing Network: An Approach Offloading Both Traffic and Computation

Cited by 40 publications

References 12 publications

A Survey of Multi-Access Edge Computing in 5G and Beyond: Fundamentals, Technology Integration, and State-of-the-Art

A Survey of Multi-Access Edge Computing in 5G and Beyond: Fundamentals, Technology Integration, and State-of-the-Art

Notions of architecture in fog computing

Resource Allocation Strategy for D2D-Assisted Edge Computing System With Hybrid Energy Harvesting

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