Jintian Hu

Bio: Jintian Hu is an academic researcher from Central South University. The author has contributed to research in topics: Cellular network & Mobile device. The author has co-authored 1 publications.

Resource Caching and Task Migration Strategy of Small Cellular Networks under Mobile Edge Computing

Abstract: As computing-intensive mobile applications become increasingly diversified, mobile devices’ computing power is hard to keep up with demand. Mobile devices migrate tasks to the Mobile Edge Computing (MEC) platform and improve the performance of task processing through reasonable allocation and caching of resources on the platform. Small cellular networks (SCN) have excellent short-distance communication capabilities, and the combination of MEC and SCN is a promising research direction. This paper focuses on minimizing energy consumption for task migration in small cellular networks and proposes a task migration energy optimization strategy with resource caching by combining optimal stopping theory with migration decision-making. Firstly, the process of device finding the MEC platform with the required task processing resources is formulated as the optimal stopping problem. Secondly, we prove an optimal stopping rule’s existence, obtain the optimal processing energy consumption threshold, and compare it with the device energy consumption. Finally, the platform with the best energy consumption is selected to process the task. In the simulation experiment, the optimization strategy has lower average migration energy consumption and higher average data execution energy efficiency and average distance execution energy efficiency, which improves task migration performance by 10% ∼ 60%.

Energy-Efficient Task Migration and Path Planning in UAV-Enabled Mobile Edge Computing System

Abstract: With the rapid development of unmanned aerial vehicles (UAVs) technology and the advent of the 5G era, the role of UAV-enabled mobile edge computing (MEC) system has attracted much attention, especially in the event of some emergencies. However, considering the limited battery life and computing capabilities of UAVs, it is challenging to provide energy-efficient services for mobile devices. To solve this challenge, we propose an energy-efficient dynamic task migration algorithm (EDTM) that minimizes the total energy consumption of the system while ensuring UAVs system load balance. Based on the improved ant colony algorithm and path elimination strategy, the proposed algorithm comprehensively considers task migration distance between UAVs, the load situation of UAVs, and environmental factors (e.g., wind speed and air density) and finally plans a reasonable task migration path. The simulation results show that the performance of the proposed EDTM is superior to the benchmark schemes.

An Optimized Multi-Layer Resource Management in Mobile Edge Computing Networks: A Joint Computation Offloading and Caching Solution

Abstract: Nowadays, data caching is being used as a high-speed data storage layer in mobile edge computing networks employing flow control methodologies at an exponential rate. This study shows how to discover the best architecture for backhaul networks with caching capability using a distributed offloading technique. This article used a continuous power flow analysis to achieve the optimum load constraints, wherein the power of macro base stations with various caching capacities is supplied by either an intelligent grid network or renewable energy systems. This work proposes ubiquitous connectivity between users at the cell edge and offloading the macro cells so as to provide features the macro cell itself cannot cope with, such as extreme changes in the required user data rate and energy efficiency. The offloading framework is then reformed into a neural weighted framework that considers convergence and Lyapunov instability requirements of mobile-edge computing under Karush Kuhn Tucker optimization restrictions in order to get accurate solutions. The cell-layer performance is analyzed in the boundary and in the center point of the cells. The analytical and simulation results show that the suggested method outperforms other energy-saving techniques. Also, compared to other solutions studied in the literature, the proposed approach shows a two to three times increase in both the throughput of the cell edge users and the aggregate throughput per cluster.