Learning-Based Computation Offloading for IoT Devices With Energy Harvesting

2019/01/01 by Minghui Min, Liang Xiao, Ye Chen +3 · 551 citations
Computer Science · Engineering · #Age of Information Optimization #Artificial intelligence #Cloud computing #Computation #Computation offloading #Computer network #Computer science #Edge computing #Edge device #Electrical engineering #Embedded system #Energy (signal processing) #Energy Harvesting in Wireless Networks #Energy consumption #Energy harvesting #Engineering #Internet of Things #IoT and Edge/Fog Computing #Latency (audio) #Mobile device #Mobile edge computing #Real-time computing #Reinforcement learning #Server #Telecommunications #Wireless

paper · doi:10.1109/tvt.2018.2890685

published in IEEE Transactions on Vehicular Technology 68(2), 1930-1941 (Institute of Electrical and Electronics Engineers)

openalex publication_date 2019/01/01 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/01

Abstract

Internet of Things (IoT) devices can apply mobile edge computing (MEC) and energy harvesting (EH) to provide high-level experiences for computational intensive applications and concurrently to prolong the lifetime of the battery. In this paper, we propose a reinforcement learning (RL) based offloading scheme for an IoT device with EH to select the edge device and the offloading rate according to the current battery level, the previous radio transmission rate to each edge device, and the predicted amount of the harvested energy. This scheme enables the IoT device to optimize the offloading policy without knowledge of the MEC model, the energy consumption model, and the computation latency model. Further, we present a deep RL-based offloading scheme to further accelerate the learning speed. Their performance bounds in terms of the energy consumption, computation latency, and utility are provided for three typical offloading scenarios and verified via simulations for an IoT device that uses wireless power transfer for energy harvesting. Simulation results show that the proposed RL-based offloading scheme reduces the energy consumption, computation latency, and task drop rate, and thus increases the utility of the IoT device in the dynamic MEC in comparison with the benchmark offloading schemes.

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• PHyPO: Priority-based Hybrid task Partitioning and Offloading in mobile computing using automated machine learning. [europepmc]
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