Space/Aerial-Assisted Computing Offloading for IoT Applications: A Learning-Based Approach

By Nan Cheng, Feng Lyu, Wei Quan, Conghao Zhou, Hongli He, Weisen Shi, Xuemin Shen. IEEE Journal on Selected Areas in Communications, 2019

764 citations recorded by the source corpus.

Internet of Things (IoT) computing offloading is a challenging issue, especially in remote areas where common edge/cloud infrastructure is unavailable. In this paper, we present a space-air-ground integrated network (SAGIN) edge/cloud computing architecture for offloading the computation-intensive applications considering remote energy and computation constraints, where flying unmanned aerial vehicles (UAVs) provide near-user edge computing and satellites provide access to the cloud computing. First, for UAV edge servers, we propose a joint resource allocation and task scheduling approach to efficiently allocate the computing resources to virtual machines (VMs) and schedule the offloaded tasks. Second, we investigate the computing offloading problem in SAGIN and propose a learning-based approach to learn the optimal offloading policy from the dynamic SAGIN environments. Specifically, we formulate the offloading decision making as a Markov decision process where the system state considers the network dynamics. To cope with the system dynamics and complexity, we propose a deep reinforcement learning-based computing offloading approach to learn the optimal offloading policy on-the-fly, where we adopt the policy gradient method to handle the large action space and actor-critic method to accelerate the learning process. Simulation results show that the proposed edge VM allocation and task scheduling approach can achieve near-optimal performance with very low complexity and the proposed learning-based computing offloading algorithm not only converges fast but also achieves a lower total cost compared with other offloading approaches.

Subfields: Aerospace Engineering, Computer Networks and Communications, Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/7de203968af7e2e6d0552a389b2b1390745709ce

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Citations in the corpus

A Survey on Resource Allocation for 5G Heterogeneous Networks: Current Research, Future Trends, and Challenges (2020, background): “With the development of machine learning, deep learning, and artificial intelligence, many learning-based algorithms have been proposed to solve channel estimation [183], automatic modulation recognition [184], network traffic control [185], computing offloading [186], congestion control [187], dire”
Integrated Satellite-Terrestrial Networks Toward 6G: Architectures, Applications, and Challenges (2022, neutral): “Also, for less interactions with the distant cloud, which are of long latency, the technique of mobile edge computing (MEC) can be applied in the integrated satellite-terrestrial network to reduce the latency [149], [150].”
A Full Dive Into Realizing the Edge-Enabled Metaverse: Visions, Enabling Technologies, and Challenges (2022, neutral): “In [169], the authors set up a DT network in the virtual space for SAGIN to enable secure vehicle communication in SAGIN.”
UAV assistance paradigm: State-of-the-art in applications and challenges (2020, background): “Designing security mechanisms that consider all the challenges aforementioned in this manuscript.”
Evolution of Non-Terrestrial Networks From 5G to 6G: A Survey (2021, background): “offers significant performance gains and interesting use-cases, as discussed earlier in this section, further improvements and applications can be realized by having a multi-segment network with integrated GAS communications [124], [125], [126], [127].”
Enabling Massive IoT Toward 6G: A Comprehensive Survey (2021, methodology): “Considering this fact, the authors of [94] proposed a reinforcement learning (RL)based algorithm for task offloading, which was proven to achieve a near-optimal solution with low complexity.”
Edge Artificial Intelligence for 6G: Vision, Enabling Technologies, and Applications (2021, background): “In the scenario without abundant edge and cloud computing infrastructures, SAGIN provides an ubiquitous computing platform for the multi-layer hierarchical edge learning system, where the flying UAVs serve as the proximal edge computing, and the low earth orbit satellites serve as the relays to the ”
Holistic Network Virtualization and Pervasive Network Intelligence for 6G (2023, methodology): “DRL has been widely used for network optimization [134], resource allocation [19], [118], [135], and user association [116], [121], [123] in wireless networks.”
A Survey on Green 6G Network: Architecture and Technologies (2019, methodology): “In [30], a learning-based computing offloading approach was pre-”
Edge Computing with Artificial Intelligence: A Machine Learning Perspective (2022, methodology): “Therefore, it is an important research direction to design and adopt model-free methods to obtain efficient strategies [94].”
Edge Learning for B5G Networks With Distributed Signal Processing: Semantic Communication, Edge Computing, and Wireless Sensing (2022, neutral): “In [267], the task offloading decision in a satellite-UAV-served IoT network was formulated as an MDP with network dynamics,”
Energy-Efficient UAV-Assisted Mobile Edge Computing: Resource Allocation and Trajectory Optimization (2020, background): “Moreover, recently, more works utilize UAV as an aerial cloudlet to provide edge computing service [25]–[27].”