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Energy-Efficient Resource Allocation for Mobile Edge Computing With Multiple Relays
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DOI：10.1109/JIOT.2021.3125953.png)
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Abstract
En 中文
The merging of Internet of Things (IoT) and mobile edge computing (MEC) enables resource-limited IoT devices to complete computation-intensive or urgent task through offloading the task to the adjacent edge server, and is becoming popular recently. Due to blockage or deep fading, one IoT device may not be able to build direct link with the edge server. On the other hand, many IoT devices can serve as relay nodes as there may exist massive ones in the neighborhood. In this article, we study an MEC system with the IoT device aided by multiple relay nodes for task offloading. Specifically, the modes of decode-and-forward (DF) with time-division-multiple-access (TDMA) and frequency-division-multiple-access (FDMA), and the mode of amplify-and-forward (AF) are investigated, which are denoted as DF-TDMA, DF-FDMA, and AF, respectively. The allocation of computation and communication resources is optimized in order to minimize the weighted sum of energy consumption of all the IoT devices. Associated optimization problems are formulated but shown to be nonconvex, which are challenging to solve. For the DF-TDMA mode, we transform the original nonconvex problem to be convex and further develop a low complexity yet optimal solution. In DF-FDMA mode, with some transformation on the original problem, we prove the mathematical equivalence between the problems in DF-FDMA and DF-TDMA mode. In AF mode, the convergent solution is found by decomposing the associated optimization problem into two levels, with monotonic optimization and successive convex approximation (SCA) utilized for upper level and lower level, respectively. The numerical results prove the effectiveness of our proposed methods.
Keywords:
Relays
Task analysis
Optimization
Resource management
Internet of Things
Time division multiple access
Energy consumption
Internet of Things (IoT)
mobile edge computing (MEC)
relay communications
resource allocation for communication and computation
Journal
IF:
8.9
Papers:
1.4W
Citations:
7.8W
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