期刊
IEEE INTERNET OF THINGS JOURNAL
卷 7, 期 2, 页码 1531-1547出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/JIOT.2019.2956409
关键词
Task analysis; Resource management; Delays; Servers; Internet of Things; Energy consumption; Optimization; Internet of Things (IoT) networks; millimeter-wave (mmWave); IoT; mobile edge computing; nonorthogonal multiple access (NOMA) technique; successive convex approximation (SCA); ultradense (UD)
资金
- Natural Sciences and Engineering Research Council of Canada
This article considers a two-tier heterogeneous network consisting of conventional sub-6-GHz macrocells along with millimeter-wave (mmWave) small cells, where mobile devices (MDs) can connect to either macrocell or small cells opportunistically via the nonorthogonal multiple access (NOMA) protocol. We employ the queuing theory in our network model to conduct an assessment on the execution delay, energy consumption and the total cost of offloading tasks in a mobile-edge computation offloading (MECO) system. The main goal is to design an energy-efficient MECO decision algorithm in an ultradense Internet of Thing (UD-IoT) network to analyze the tradeoff between execution delay and energy consumption. The proposed scheme jointly optimizes the communication and computation resource management, subject to the energy and delay constraints. Due to the mixed-integer nonlinear problem (MINLP) for resource allocation and computation offloading, an iterative algorithm along with the successive convex approximation (SCA) is proposed to achieve the optimum local frequency scheduling, power allocation, and computation offloading. The superior performance of the proposed MECO algorithm in our UD-IoT network is verified by the extensive numerical results.
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