4.7 Article

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

Journal

IEEE JOURNAL ON SELECTED AREAS IN COMMUNICATIONS
Volume 34, Issue 12, Pages 3887-3901

Publisher

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/JSAC.2016.2624118

Keywords

Network-assisted D2D collaboration; energy efficiency; task offloading; incentive awareness

Funding

  1. National Key Research and Development Program of China [2016YFB0201900]
  2. Start-Up Fund from Sun Yat-sen University
  3. EU FP7 IRSES MobileCloud Project [612212]
  4. EU-Japan Horizon [EU 723014, NICT 184]
  5. Alexander Humboldt Foundation
  6. Natural Science Foundation of Tianjin, China [16JCQNJC00700]

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In this paper, we propose device-to-device (D2D) Fogging, a novel mobile task offloading framework based on network-assisted D2D collaboration, where mobile users can dynamically and beneficially share the computation and communication resources among each other via the control assistance by the network operators. The purpose of D2D Fogging is to achieve energy efficient task executions for network wide users. To this end, we propose an optimization problem formulation that aims at minimizing the time-average energy consumption for task executions of all users, meanwhile taking into account the incentive constraints of preventing the over-exploiting and free-riding behaviors which harm user's motivation for collaboration. To overcome the challenge that future system information such as user resource availability is difficult to predict, we develop an online task offloading algorithm, which leverages Lyapunov optimization methods and utilizes the current system information only. As the critical building block, we devise corresponding efficient task scheduling policies in terms of three kinds of system settings in a time frame. Extensive simulation results demonstrate that the proposed online algorithm not only achieves superior performance (e.g., it reduces approximately 30% similar to 40% energy consumption compared with user local execution), but also adapts to various situations in terms of task type, user amount, and task frequency.

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