Online Anticipatory Proactive Network Association in Mobile Edge Computing for IoT

• Published in: IEEE transactions on wireless communications Vol. 19; no. 7; pp. 4519 - 4534
• Main Authors: Cui, Qimei, Zhang, Jian, Zhang, Xuefei, Chen, Kwang-Cheng, Tao, Xiaofeng, Zhang, Ping
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.07.2020; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Algorithms; Associations; Computational modeling; Computer networks; Computer simulation; Delay; Delays; distributed computing; Distributed processing; Edge computing; Energy conservation; Energy consumption; event-triggered; Internet of Things; Internet of Things (IoT); Lyapunov optimization; Machine learning; Markov decision process (MDP); Markov processes; Mobile communication systems; Mobile computing; mobile edge computing; network association; Network latency; Network reliability; Optimization; Servers; System reliability; Task analysis; Wireless communication; Wireless networks
• ISSN: 1536-1276; 1558-2248
• DOI: 10.1109/TWC.2020.2984599

Ultra-low latency communication for mobile intelligent machines, such as autonomous vehicles and robots, is a central technology in Internet of Things (IoT) to achieve system reliability. Proactive network association and communication has been suggested to achieve ultra-low latency under the assistance of mobile edge computing. Highly dynamic and stochastic nature of IoT mobile machines suggests applying machine learning methodology to effectively enhance the proactive network association. In this paper, an online proactive network association is proposed for this distributed computing and networking scenario, in order to minimize the average task delay subject to time-average energy consumption. We first formulate an event-triggered delay model for mobility-aware anticipatory network association mechanism that takes future possible handovers into account. Based on the Markov decision processes (MDP) and Lyapunov optimization, a two-stage online decision algorithm for proactive network association is innovated for individual mobile machine without the statistical knowledge of random events that may lack of enough prior data. Theoretical analysis proves that the delay performance of proposed algorithm attains asymptotic optimality within the bounded deviation. Furthermore, an asynchronous online distributed association decision algorithm based on the nonlinear problem transformation is proposed to support more general scenarios of multi-machine event-triggered associations. Simulations verify the effectiveness of the proposed methodology.