D2D Fogging: An Energy-Efficient and Incentive-Aware Task Offloading Framework via Network-assisted D2D Collaboration

By Lingjun Pu, Xu Chen, Jingdong Xu, Xiaoming Fu. IEEE Journal on Selected Areas in Communications, 2016

340 citations recorded by the source corpus.

Subfields: Computer Networks and Communications

View Paper: https://www.semanticscholar.org/paper/c141e32b6b3b52583cc78091550548c7a6222f8f

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Networks and Communications

1. Jie Xu
2. Kaibin Huang
3. Rui Zhang
4. Changsheng You
5. Khaled Ben Letaief
6. Shuguang Cui
7. Jun Zhang
8. Feng Wang

Citations in the corpus

• Joint Computation and Communication Cooperation for Energy-Efficient Mobile Edge Computing (2018, background): “For example, in the so-called device-to-device (D2D) fogging [28] and peer-to-peer (P2P) cooperative computing [29], users with intensive computing tasks can offload all or part of the tasks to other idle users via D2D or P2P communications for execution.”
• Joint computation and communication cooperation for mobile edge computing (2017, background): “, [11]–[14]) and the computation cooperation [15], [16], respectively.”
• 5G D2D Networks: Techniques, Challenges, and Future Prospects (2018, neutral): “in information exchange [122].”
• Stochastic Joint Radio and Computational Resource Management for Multi-User Mobile-Edge Computing Systems (2017, background): “However, there are some limitations in the commonly adopted assumptions in [12]–[16] and [19]–[21]: It is typically assumed that the computation tasks have strict delay requirements, and no new task will be generated before the old tasks are completed/abandoned.”
• Survey of Fog Computing: Fundamental, Network Applications, and Research Challenges (2018, background): “Nevertheless, pay-as-you-go seems to be an essential aspect in terms of incentives for the users who share their computing and storage resources [117].”
• Toward Edge Intelligence: Multiaccess Edge Computing for 5G and Internet of Things (2020, neutral): “[80] proposed a mobile”
• A Survey on End-Edge-Cloud Orchestrated Network Computing Paradigms (2019, background): “Pu et al. [119] investigate energy-efficient computation task offloading among multiple MDs through cellular and D2D links.”
• UAV-Assisted Relaying and Edge Computing: Scheduling and Trajectory Optimization (2018, methodology): “In [9], a device-todevice (D2D) fogging was explored to achieve energy-efficient task completion by sharing computation and communication resources amongst mobile devices.”
• Mobile Cooperative Computing: Energy-Efficient Peer-to-Peer Computation Offloading (2017, methodology): “This direction is pursued in [18] where offloading based on D2D transmissions is controlled by a cooperation policy optimized using Lyapunov optimization theory.”