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Article
Computer Science, Information Systems
Chen Chen et al.
Summary: Mobile-edge computing (MEC) combined with Internet of Vehicles (IoV) can improve task computing and offloading efficiency. However, MEC server coverage limits edge computing power, and utilizing the computing resources of idle vehicles is a primary issue. This article proposes a distributed multihop task offloading model and experimentally shows that the greedy algorithm or bat-based algorithm outperforms random and local offloading schemes in terms of latency performance.
IEEE INTERNET OF THINGS JOURNAL
(2023)
Article
Computer Science, Hardware & Architecture
Jian Wang et al.
Summary: This study presents a computation offloading scheme based on reinforcement learning and deep reinforcement learning for handling the workloads of wireless users. The proposed scheme can learn the optimal offloading decision without priori knowledge and outperforms other algorithms in terms of performance.
Article
Telecommunications
Changxiang Li et al.
Summary: Multi-access edge computing (MEC) is a computing paradigm that integrates computing capability into a wireless access network to enhance user experience. This letter proposes a novel computation offloading scheme using non-orthogonal multiple access (NOMA) and dual connectivity (DC) to minimize energy consumption. The scheme is optimized by jointly optimizing task segmentation and power allocation, and the optimization problem is solved using a heuristic algorithm and an online learning algorithm based on twin delayed deep deterministic policy gradient (TD3).
IEEE COMMUNICATIONS LETTERS
(2022)
Article
Computer Science, Information Systems
Kang Tian et al.
Summary: This paper investigates the optimization problem of IoT devices' offloading decisions, CPU frequencies, and transmit powers in a multi-mobile edge computing server and multi-IoT device cellular network. A deep reinforcement learning-based optimization algorithm is proposed to solve the nonconvex problem, and simulation results demonstrate the correctness and effectiveness of the algorithm.
Article
Computer Science, Information Systems
Junwen Lu et al.
Summary: Mobile cloud computing provides rich computational resources for mobile users, network operators, and cloud computing providers. Offloading applications to remote cloud resources can save energy in a dynamic mobile cloud computing environment. Our proposed algorithm outperforms other methods in energy consumption reduction and number of finished jobs.
JOURNAL OF KING SAUD UNIVERSITY-COMPUTER AND INFORMATION SCIENCES
(2022)
Article
Computer Science, Hardware & Architecture
Wenti Yang et al.
Summary: This article introduces the concept and current applications of federated learning (FL), highlighting the challenges of applying FL on mobile devices and potential solutions through the use of 5G networks. A novel cross-device FL framework is proposed to protect privacy and reduce computation overhead on mobile devices, along with a contribution-based incentive mechanism to encourage user participation in FL. The article also includes a case study on autonomous driving, performance evaluation of the proposed scheme, and discussion of open issues in FL.
IEEE WIRELESS COMMUNICATIONS
(2022)
Article
Engineering, Electrical & Electronic
Yanlin Guo et al.
Summary: This study improves the mobile cloud computing resource scheduling algorithm under the 5G network architecture and introduces mobile edge cloud computing into the financial 5G network system model. It proposes an offloading strategy based on the MEC load status and constructs a mobile cloud computing optimization system under the 5G network architecture. Experimental research shows that this system has good effects in the optimization of corporate financial management.
ADVANCES IN MULTIMEDIA
(2022)
Article
Engineering, Electrical & Electronic
Heng Yang et al.
Summary: This paper investigates the optimization of execution latency, processing accuracy, and energy consumption in the MEC-based cooperative vehicle infrastructure system. By designing machine learning task models for the vehicle side and RSU side, and employing a two-stage deep reinforcement learning strategy, the problem of task offloading and resource allocation is effectively addressed.
IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY
(2022)
Article
Computer Science, Information Systems
Fan Lu et al.
Summary: This paper proposes a novel method for 3D near-field direction of arrival (DOA) estimation based on large-scale uniform rectangular array (URA). The method divides the complete parameters set into subsets and estimates one subset in each iteration, achieving maximum convergence rate. Simulation results confirm the effectiveness and superiority of the proposed algorithm over the existing algorithm.
Article
Computer Science, Information Systems
Song Yang et al.
Summary: This article addresses the issues of computation offloading and caching strategies in multiple regions. By utilizing deep reinforcement learning and collaborative caching algorithms, a holistic efficient caching and offloading strategy is proposed, which greatly improves system performance.
IEEE INTERNET OF THINGS JOURNAL
(2022)
Article
Computer Science, Information Systems
Sardar Khaliq Uz Zaman et al.
Summary: The technological evolution of mobile devices has led to the emergence of various user applications in learning, social networking, entertainment, and community computing domains. However, delivering task offloading results in networks with high user mobility poses challenges such as increased latency and inefficient resource utilization. Researchers are currently focused on optimizing task offloading in edge networks and discussing future research directions in mobility, security, and scalability.
CLUSTER COMPUTING-THE JOURNAL OF NETWORKS SOFTWARE TOOLS AND APPLICATIONS
(2021)
Article
Computer Science, Hardware & Architecture
Junaid Shuja et al.
Summary: Edge networking is a computing paradigm that aims to bring cloud resources closer to end users to improve responsiveness, with user mobility, preferences, and content popularity being key features. In next generation edge networks, machine learning techniques can be applied to predict content popularity and optimize cache strategies.
JOURNAL OF NETWORK AND COMPUTER APPLICATIONS
(2021)
Article
Computer Science, Information Systems
Jan Plachy et al.
Summary: This paper proposes a low-complexity computing and communication resource allocation method for real-time computing tasks generated by mobile users, utilizing probabilistic modeling of user movement to achieve resource pre-allocation and selection of suitable communication paths, keeping the edge computing delay below 100 ms and enhancing support for real-time applications.
IEEE TRANSACTIONS ON NETWORK AND SERVICE MANAGEMENT
(2021)
Article
Computer Science, Information Systems
Fengjun Zhao et al.
Summary: Driven by IoT and 5G technologies, mobile computing has shifted from centralized cloud computing to distributed edge computing, aiming to bridge the gap between QoS requirements and limited computing resources. This paper proposes an online dynamic offloading and resource scheduling algorithm based on Lyapunov optimization theory, effectively balancing scheduling costs with MEC system performance.
IEEE TRANSACTIONS ON NETWORK AND SERVICE MANAGEMENT
(2021)
Article
Computer Science, Information Systems
Lu Zhang et al.
Summary: MEC utilizes UAV to provide computation support, but is restricted by limited computation resources and sensitive energy consumption, while traditional optimization methods cannot effectively optimize computation resources and energy consumption.
Article
Telecommunications
Yuhang Wu et al.
IEEE COMMUNICATIONS LETTERS
(2020)
Article
Engineering, Electrical & Electronic
Umber Saleem et al.
IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY
(2020)
Article
Computer Science, Information Systems
Xianfu Chen et al.
IEEE INTERNET OF THINGS JOURNAL
(2019)
Article
Computer Science, Information Systems
Zhufang Kuang et al.
IEEE INTERNET OF THINGS JOURNAL
(2019)
Article
Engineering, Electrical & Electronic
Zhikai Kuang et al.
IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY
(2019)
Article
Computer Science, Information Systems
Chenmeng Wang et al.
IEEE COMMUNICATIONS SURVEYS AND TUTORIALS
(2018)
Article
Computer Science, Information Systems
Nasir Abbas et al.
IEEE INTERNET OF THINGS JOURNAL
(2018)
Article
Computer Science, Artificial Intelligence
Vincent Francois-Lavet et al.
FOUNDATIONS AND TRENDS IN MACHINE LEARNING
(2018)
Article
Engineering, Electrical & Electronic
Kai Arulkumaran et al.
IEEE SIGNAL PROCESSING MAGAZINE
(2017)
Article
Computer Science, Software Engineering
Valeria Cardellini et al.
MATHEMATICAL PROGRAMMING
(2016)
Article
Multidisciplinary Sciences
Volodymyr Mnih et al.
