Joint computation offloading and multi-user scheduling using approximate dynamic programming in NB-IoT edge computing system

Lei, Lei, Xu, Huijuan, Xiong, Xiong, Zheng, Kan, and Xiang, Wei (2019) Joint computation offloading and multi-user scheduling using approximate dynamic programming in NB-IoT edge computing system. IEEE Internet of Things Journal. (In Press)

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The Internet of Things (IoT) connects a huge number of resource-constraint IoT devices to the Internet, which generate massive amount of data that can be offloaded to the cloud for computation. As some of the applications may require very low latency, the emerging mobile edge computing (MEC) architecture offers cloud services by deploying MEC servers at the mobile base stations (BSs). The IoT devices can transmit the offloaded data to the BS for computation at the MEC server. Narrowband Internet of Things (NB-IoT) is a new cellular technology for the transmission of IoT data to the BS. In this paper, we propose a joint computation offloading and multi-user scheduling algorithm in NB-IoT edge computing system that minimizes the long-term average weighted sum of delay and power consumption under stochastic traffic arrival. We formulate the dynamic optimization problem into an infinite-horizon average-reward continuous-time Markov decision process (CTMDP) model. In order to deal with the curse-of-dimensionality problem, we use the approximate dynamic programming techniques, i.e., the linear value-function approximation and TD learning with post-decision state and semi-gradient descent method, to derive a simple algorithm for the solution of the CTMDP model. The proposed algorithm is semi-distributed, where the offloading algorithm is performed locally at the IoT devices, while the scheduling algorithm is auction-based where the IoT devices submit bids to the BS to make the scheduling decision centrally. Simulation results show that the proposed algorithm provides significant performance improvement over the two baseline algorithms and the MUMTO algorithm which is designed based on the deterministic task model.

Item ID: 57451
Item Type: Article (Research - C1)
ISSN: 2327-4662
Keywords: Internet of Things; task analysis; servers; mobile edge computing; heuristic algorithms; computational modeling; approximation algorithms; computation offloading; approximate dynamic programming
Copyright Information: (c) 2018 IEEE
Date Deposited: 26 Mar 2019 06:57
FoR Codes: 10 TECHNOLOGY > 1005 Communications Technologies > 100510 Wireless Communications @ 100%
SEO Codes: 89 INFORMATION AND COMMUNICATION SERVICES > 8901 Communication Networks and Services > 890103 Mobile Data Networks and Services @ 100%
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