Online Optimization of Animation Art Design User Virtual Perception in Mobile Edge Computing Environment

Article Engineering, Electrical & Electronic

Joint optimization of computing ratio and access points' density for mixed mobile edge/cloud computing

Tianqi Jing et al.

Summary: Collaboration between MEC and MCC can enhance QoS for UEs with computation-intensive tasks. This paper focuses on minimizing cost by optimizing offloading and AP density. A proposed algorithm, ICONP, addresses the resulting mixed-integer and non-convex optimization problem. Numerical results show the algorithm's improved performance in computing ratios and AP density allocation for cost savings.

EURASIP JOURNAL ON WIRELESS COMMUNICATIONS AND NETWORKING (2021)

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Article Engineering, Electrical & Electronic

Research on task-offloading decision mechanism in mobile edge computing-based Internet of Vehicle

Jun Cheng et al.

Summary: Mobile edge computing (MEC) technology can improve resource utilization and computation performance in Internet of Vehicles (IoV), but uncertainties in interconnection need to be addressed. This paper proposes a task-offloading decision mechanism with particle swarm optimization to reduce energy consumption and ensure service quality of users.

EURASIP JOURNAL ON WIRELESS COMMUNICATIONS AND NETWORKING (2021)

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Article Computer Science, Artificial Intelligence

Unified Optimization for Multiple Active Object Recognition Tasks with Feature Decision Tree

Haibo Sun et al.

Summary: This research focuses on the importance of active object recognition in reducing image ambiguity, proposing a multi-task AOR framework that utilizes feature decision tree and prior feature distribution table to determine the next best viewpoint through optimization problem and tree dynamic programming algorithm, while also improving the evaluation method.

JOURNAL OF INTELLIGENT & ROBOTIC SYSTEMS (2021)

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Article Engineering, Electrical & Electronic

Computing resource allocation scheme of IOV using deep reinforcement learning in edge computing environment

Yiwei Zhang et al.

Summary: This paper proposes a computing resource allocation scheme based on deep reinforcement learning network for mobile edge computing scenarios in Internet of Vehicles, which aims to minimize total computing cost with mathematical modeling and deep Q-learning network. Experience replay method is used to improve algorithm robustness and avoid dimension disaster, achieving excellent network performance with low latency.

EURASIP JOURNAL ON ADVANCES IN SIGNAL PROCESSING (2021)

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Article Computer Science, Information Systems

Meta-heuristic-based offloading task optimization in mobile edge computing

Aamir Abbas et al.

Summary: With the recent advancements in communication technologies, computation-intensive applications on mobile devices are now possible. Mobile edge computing has evolved as a solution to offload complex tasks, but optimal task offloading is necessary to minimize response time and energy consumption. The Grey wolf optimization algorithm has been shown to outperform others in optimizing energy consumption and execution latency.

INTERNATIONAL JOURNAL OF DISTRIBUTED SENSOR NETWORKS (2021)

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Article Engineering, Multidisciplinary

Task offloading and resource allocation algorithm based on mobile edge computing in Internet of Things environment

Junwei Liu

Summary: This paper proposes a task offloading and resource allocation algorithm based on mobile edge computing. By establishing system model and introducing energy consumption deficit queue and game theory, the long-term performance optimization of small base stations is achieved, showing better performance in simulation experiments.

JOURNAL OF ENGINEERING-JOE (2021)

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Article Computer Science, Information Systems