scholarly journals Exploitation of Mobile Edge Computing in 5G Distributed Mission-Critical Push-to-Talk Service Deployment

IEEE Access ◽  
2018 ◽  
Vol 6 ◽  
pp. 37665-37675 ◽  
Author(s):  
Ruben Solozabal ◽  
Aitor Sanchoyerto ◽  
Eneko Atxutegi ◽  
Bego Blanco ◽  
Jose Oscar Fajardo ◽  
...  
Keyword(s):  
Edge Computing ◽  
Mobile Edge Computing ◽  
Service Deployment ◽  
Mission Critical

scholarly journals Cross-Layer Design for Mission-Critical IoT in Mobile Edge Computing Systems

2019 ◽  
Vol 6 (6) ◽  
pp. 9360-9374 ◽  
Author(s):  
Changyang She ◽  
Yifan Duan ◽  
Guodong Zhao ◽  
Tony Q. S. Quek ◽  
Yonghui Li ◽  
...  
Keyword(s):  
Edge Computing ◽  
Cross Layer ◽  
Cross Layer Design ◽  
Mobile Edge Computing ◽  
Computing Systems ◽  
Mission Critical

scholarly journals Energy-efficient offloading and resource allocation for mobile edge computing enabled mission-critical internet-of-things systems

2021 ◽  
Vol 2021 (1) ◽  
Author(s):  
Yaru Fu ◽  
Xiaolong Yang ◽  
Peng Yang ◽  
Angus K. Y. Wong ◽  
Zheng Shi ◽  
...  
Keyword(s):  
Resource Allocation ◽  
Internet Of Things ◽  
Energy Saving ◽  
Energy Cost ◽  
Cost Minimization ◽  
Edge Computing ◽  
Mixed Integer ◽  
Mobile Edge Computing ◽  
Mission Critical ◽  
Iot Devices

AbstractThe energy cost minimization for mission-critical internet-of-things (IoT) in mobile edge computing (MEC) system is investigated in this work. Therein, short data packets are transmitted between the IoT devices and the access points (APs) to reduce transmission latency and prolong the battery life of the IoT devices. The effects of short-packet transmission on the radio resource allocation is explicitly revealed. We mathematically formulate the energy cost minimization problem as a mixed-integer non-linear programming (MINLP) problem, which is difficult to solve in an optimal way. More specifically, the difficulty is essentially derived from the coupling of the binary offloading variables and the resource management among all the IoT devices. For analytical tractability, we decouple the mixed-integer and non-convex optimization problem into two sub-problems, namely, the task offloading decision-making and the resource optimization problems, respectively. It is proved that the resource allocation problem for IoT devices under the fixed offloading strategy is convex. On this basis, an iterative algorithm is designed, whose performance is comparable to the best solution for exhaustive search, and aims to jointly optimize the offloading strategy and resource allocation. Simulation results verify the convergence performance and energy-saving function of the designed joint optimization algorithm. Compared with the extensive baselines under comprehensive parameter settings, the algorithm has better energy-saving effects.

scholarly journals Energy-Efficient Offloading and Resource Allocation for Mobile Edge Computing Enabled Mission-Critical Internet-of-Things Systems

2020 ◽  
Author(s):  
Yaru Fu ◽  
Xiaolong Yang ◽  
Peng Yang ◽  
Angus K. Y. Wong ◽  
Zheng Shi ◽  
...  
Keyword(s):  
Resource Allocation ◽  
Internet Of Things ◽  
Energy Saving ◽  
Energy Cost ◽  
Cost Minimization ◽  
Edge Computing ◽  
Mixed Integer ◽  
Mobile Edge Computing ◽  
Mission Critical ◽  
Iot Devices

Abstract The energy cost minimization for mission-critical internet-of-things (IoT) in mobile edge computing (MEC) system is investigated in this work. Therein, short data packets are transmitted between the IoT devices and the access points (APs) to reduce transmission latency and prolong the battery life of the IoT devices. The effects of short-packet transmission on the radio resource allocation is explicitly revealed. We mathematically formulate the energy cost minimization problem as a mixed-integer non-linear programming (MINLP) problem, which is difficult to solve in an optimal way. More specifically, the difficulty is essentially derived from the coupling of the binary offloading variables and the resource management among all the IoT devices. For analytical tractability, we decouple the mixed-integer and non-convex optimization problem into two sub-problems, namely, the task offloading decision-making and the resource optimization problems, respectively. It is proved that the resource allocation problem for IoT devices under the fixed offloading strategy is convex. On this basis, an iterative algorithm is designed, whose performance is comparable to the best solution for exhaustive search, and aims to jointly optimize the offloading strategy and resource allocation. Simulation results verify the convergence performance and energy-saving function of the designed joint optimization algorithm. Compared with the extensive baselines under comprehensive parameter settings, the algorithm has better energy-saving effects.

Toward Reinforcement-Learning-Based Service Deployment of 5G Mobile Edge Computing with Request-Aware Scheduling

2020 ◽  
Vol 27 (1) ◽  
pp. 84-91 ◽  
Author(s):  
Yanlong Zhai ◽  
Tianhong Bao ◽  
Liehuang Zhu ◽  
Meng Shen ◽  
Xiaojiang Du ◽  
...  
Keyword(s):  
Reinforcement Learning ◽  
Edge Computing ◽  
Mobile Edge Computing ◽  
Service Deployment

Efficient service deployment in mobile edge computing environment

2020 ◽  
Vol 16 (2) ◽  
pp. 126
Author(s):  
Jiawei Lu ◽  
Jinglin Li ◽  
Wei Liu ◽  
Qibo Sun ◽  
Ao Zhou
Keyword(s):  
Edge Computing ◽  
Mobile Edge Computing ◽  
Computing Environment ◽  
Service Deployment
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