Search

Copyright of Journal of Computer Engineering & Applications is the property of Beijing Journal of Computer Engineering & Applications Journal Co Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The integrated satellite-terrestrial relay network (ISTRN) is a necessary part of the next-generation wireless communication system, and has important practical significance for accelerating the construction of my country's air-space-terrestrial integrated network system.In the traditional ISTRN architecture, a large amount of signaling needs to be forwarded to the ground control center for processing, which increases the delay of network control and management.A new cloud fog computing architecture was proposed, which constructs a sub-regional edge fog computing layer between the ground access and the central cloud to improve the flexibility of business flow management and control.Under the cloud network framework, a Q-learning based edge computing offloading strategy was designed, and the offloading performance was evaluated by time delay and energy consumption.Simulation results show that, compared with Min-min algorithm and backtracking algorithm, Q-learning based computational offload algorithm has better performance in terms of time delay and energy consumption, and can achieve a balance between the joint optimization of time delay and energy consumption.

Multi-access edge computing(MEC) technology sinks computing and storage resources to the edge of the network, which can greatly improve the computing power and real-time performance of the Internet of things (IoT) system. However, MEC is often constrained by the growth of computing demand and energy constraints. Thus, efficient computing offloading and energy consumption optimization mechanism is an important research direction in MEC technology. To ensure the computing efficiency while maximizing the energy efficiency in the computing process, a two-level edge nodes (ENs) relay network model is proposed, and an optimal energy consumption algorithm (OECA) for joint optimization of computing resources and channel resources is designed. Firstly, the energy efficiency in MEC is modeled as a 0-1 knapsack problem. Secondly, the system adaptively selects the computing mode and allocates wireless channel resources, aiming at minimizing the overall energy consumption of the system. Finally, the OECA algorithm is simulated and verified in Python environment. The simulation results show that OECA can increase the network capacity by 18.3% and reduce the energy consumption by 13.1% compared with the offloading strategy algorithm based on directed acyclic graph algorithm (DAGA). [ABSTRACT FROM AUTHOR]

Copyright of Piezoelectrics & Acoustooptics is the property of Piezoelectric & Acoustooptic and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)