Task Offloading for UAV-based Mobile Edge Computing via Deep Reinforcement Learning

Li, Jun; Liu, Qian; Wu, Pingyang; Shu, Feng; Shi, Jin Wei

doi:10.1109/iccchina.2018.8641189

Cited by 50 publications

(23 citation statements)

References 10 publications

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“…The joint optimization considered the trajectory optimization, computing and communication resources allocated at the UAV, and task offloading decisions made at the IoT devices. UAV-assisted MECs for solving task offloading were discussed in detail by using different techniques [68][69][70][71][72][73][74].…”

Section: Uav Computingmentioning

confidence: 99%

Computing in the Sky: A Survey on Intelligent Ubiquitous Computing for UAV-Assisted 6G Networks and Industry 4.0/5.0

Alsamhi

Shvetsov

Kumar

et al. 2022

Drones

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Unmanned Aerial Vehicles (UAVs) are increasingly being used in a high-computation paradigm enabled with smart applications in the Beyond Fifth Generation (B5G) wireless communication networks. These networks have an avenue for generating a considerable amount of heterogeneous data by the expanding number of Internet of Things (IoT) devices in smart environments. However, storing and processing massive data with limited computational capability and energy availability at local nodes in the IoT network has been a significant difficulty, mainly when deploying Artificial Intelligence (AI) techniques to extract discriminatory information from the massive amount of data for different tasks.Therefore, Mobile Edge Computing (MEC) has evolved as a promising computing paradigm leveraged with efficient technology to improve the quality of services of edge devices and network performance better than cloud computing networks, addressing challenging problems of latency and computation-intensive offloading in a UAV-assisted framework. This paper provides a comprehensive review of intelligent UAV computing technology to enable 6G networks over smart environments. We highlight the utility of UAV computing and the critical role of Federated Learning (FL) in meeting the challenges related to energy, security, task offloading, and latency of IoT data in smart environments. We present the reader with an insight into UAV computing, advantages, applications, and challenges that can provide helpful guidance for future research.

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Section: Uav Computingmentioning

confidence: 99%

Computing in the Sky: A Survey on Intelligent Ubiquitous Computing for UAV-Assisted 6G Networks and Industry 4.0/5.0

Alsamhi

Shvetsov

Kumar

et al. 2022

Drones

View full text Add to dashboard Cite

show abstract

“…Deep Reinforcement Learning (DRL) has recently attracted much attention due to its model-free feature and high learning ability for non-linear approximation feature. Paper [24] aimed at maximizing migration throughput for user by DRL-based scheme with limited UAV energy, however which was only feasible to the single UAV scenario. Similarly, the authors in [25] extented Q-learning to multi-UAV enabled system, in which UAVs can effectively reduce users' total consumptions in terms of time and energy.…”

Section: Related Workmentioning

confidence: 99%

Joint Power and QoE Optimization Scheme for Multi-UAV Assisted Offloading in Mobile Computing

Wang

Gao

2021

IEEE Access

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Recent years, unmanned aerial vehicles (UAVs) have attracted much attention for providing intermediate relay to ground mobile user equipments (UEs) for their flexible mobility. UEs can offload computing-intensive task to mobile cloud computing (MCC) or mobile edge computing (MEC) for fast processing. However, with multi-UAV and ground mobile UEs in the system, heterogeneous performance requirement as well as fast-changing communication condition make the system more complicated. Meanwhile, both UEs and UAVs are battery-driven. How to optimize the energy efficiency for UEs' transmission and UAVs' position should be carefully considered. Since this is a non-convex and mixedinteger optimization problem, a heuristic joint power and quality of experience (HJPQ) algorithm is proposed in this paper, where the UEs' offloading delay, MIMO channel, transmission power, as well as UAVs' placement are jointly optimized. The numeral simulations not only reveal the effectiveness of HJPQ, but also guarantee the great quality of experience (QoE) performance for UEs with different priorities. Furthermore, the comparison experiments with random assignment and deep deterministic policy gradient (DDPG) show the superiority of HJPQ in lower complexity, faster convergence, shorter offloading delay as well as higher energy efficiency.

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“…DRL-based task offloading has also been considered in many works. Li et al [15] research the service provision of task offloading in the UAV-based MEC environment, which is modeled as a semi-Markov decision process, and then is solved by the DRL method to maximize the system throughput. However, the size of the action space is the number of perceptual access points, which can only solve the problem with a small action space.…”

Section: Drl-based Approaches For Task Offloadingmentioning

confidence: 99%

“…Hence, DRL has made a breakthrough in model-free learning. It has recently become a promising approach to implement task offloading in MEC and works well in practice [14][15][16][17][18][19].…”

mentioning

confidence: 99%

Energy-aware task offloading with deadline constraint in mobile edge computing

Chang

et al. 2021

J Wireless Com Network

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With the development of the wireless network, increasing mobile applications are emerging and receiving great popularity. These applications cover a wide area, such as traffic monitoring, smart homes, real-time vision processing, objective tracking, and so on, and typically require computation-intensive resources to achieve a high quality of experience. Although the performance of mobile devices (MDs) has been continuously enhanced, running all the applications on a single MD still causes high energy consumption and latency. Fortunately, mobile edge computing (MEC) allows MDs to offload their computation-intensive tasks to proximal eNodeBs (eNBs) to augment computational capabilities. However, the current task offloading schemes mainly concentrate on average-based performance metrics, failing to meet the deadline constraint of the tasks. Based on the deep reinforcement learning (DRL) approach, this paper proposes an Energy-aware Task Offloading with Deadline constraint (DRL-E2D) algorithm for a multi-eNB MEC environment, which is to maximize the reward under the deadline constraint of the tasks. In terms of the actor-critic framework, we integrate the action representation into DRL-E2D to handle the large discrete action space problem, i.e., using the low-complexity k-nearest neighbor as an approximate approach to extract optimal discrete actions from the continuous action space. The extensive experimental results show that DRL-E2D achieves better performance than the comparison algorithms on all parameter settings, indicating that DRL-E2D is robust to the state changes in the MEC environment.

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Task Offloading for UAV-based Mobile Edge Computing via Deep Reinforcement Learning

Cited by 50 publications

References 10 publications

Computing in the Sky: A Survey on Intelligent Ubiquitous Computing for UAV-Assisted 6G Networks and Industry 4.0/5.0

Computing in the Sky: A Survey on Intelligent Ubiquitous Computing for UAV-Assisted 6G Networks and Industry 4.0/5.0

Joint Power and QoE Optimization Scheme for Multi-UAV Assisted Offloading in Mobile Computing

Energy-aware task offloading with deadline constraint in mobile edge computing

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