Learning-Based Computation Offloading for IoT Devices With Energy Harvesting

By Minghui Min, Liang Xiao, Ye Chen, Peng Cheng, Di Wu, Weihua Zhuang. IEEE Transactions on Vehicular Technology, 2017

493 citations recorded by the source corpus.

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.

Subfields: Computer Networks and Communications, Electrical and Electronic Engineering

View Paper: https://www.semanticscholar.org/paper/a685e230d7e6f1a77055426734d13b01afa25ccc

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Citations in the corpus

Edge Computing with Artificial Intelligence: A Machine Learning Perspective (2022, methodology): “Computing offloading optimization Reduce both energy consumption and latency [93] DRL-based offloading scheme No prior knowledge of transmission delay and energy consumption model; compress the state space dimension through DRL to further improve the learning rate; additionally consider the EC scena”
Convergence of Edge Computing and Deep Learning: A Comprehensive Survey (2019, methodology): “Hence, CNN is used to compress the state space in the learning process [254].”
Applications of Deep Reinforcement Learning in Communications and Networking: A Survey (2018, background): “The authors in [113] view the MEC-enabled BSs as different physical machines constituting a part of the cloud resources.”
A Survey of Multi-Access Edge Computing in 5G and Beyond: Fundamentals, Technology Integration, and State-of-the-Art (2019, unknown): “● EH/WPT techniques can power the edge devices in the MEC systems to enlarge the set of options for computation offloading which will result in improving the network performance [107].”
Multi-Agent Reinforcement Learning Based Resource Management in MEC- and UAV-Assisted Vehicular Networks (2021, background): “The MEC server, as an extension of the cloud computing server, shifts computing and caching capabilities close to user devices [16]–[19], and allows vehicles to ofﬂoad tasks to it via multiple wireless communication technologies.”
Deep Reinforcement Learning for Online Computation Offloading in Wireless Powered Mobile-Edge Computing Networks (2018, methodology): “There exists limited work on deep reinforcement learning-based ofﬂoading for MEC networks [18], [19], [20], [21], [22].”