Task-triggered Online Proactive Network Association for Mobile Machines in IoT
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Ultra-low latency communication for mobile machines emerges as a critical technology in Internet of Things (IoT). Proactive network association has been suggested to support ultra-low latency communication with the assistance of mobile edge computing. To resolve system dynamics and uncertainty, in this paper, an online proactive network association is proposed to minimize average task delay while considering time-average energy consumption constraints. Under distributed computing and networking environments, we formulate a task-triggered network association model by semi-Markov task states and independent identically distributed (i.i.d.) random events. Then we resolve the mobility-aware association problem to predictively consider handover effects caused by the mobility. Based on the Markov decision processes (MDP) and Lyapunov optimization, the two-stage online proactive network association (TOPNA) decision algorithm is proposed without the probability distribution knowledge of random events. Simulation results exhibit the effectiveness of the proposed algorithm.
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