scholarly journals Cost-Effective Resource Sharing in an Internet of Vehicles-Employed Mobile Edge Computing Environment

Symmetry ◽  
2018 ◽  
Vol 10 (11) ◽  
pp. 594 ◽  
Author(s):  
Tri Nguyen ◽  
Tien-Dung Nguyen ◽  
Van Nguyen ◽  
Xuan-Qui Pham ◽  
Eui-Nam Huh
Keyword(s):  
Video Streaming ◽  
Resource Sharing ◽  
Vehicular Network ◽  
Edge Computing ◽  
Mixed Integer ◽  
Mobile Users ◽  
Vehicle Speed ◽  
Mobile Edge Computing ◽  
Internet Of Vehicles ◽  
The Cost

By bringing the computation and storage resources close proximity to the mobile network edge, mobile edge computing (MEC) is a key enabling technology for satisfying the Internet of Vehicles (IoV) infotainment applications’ requirements, e.g., video streaming service (VSA). However, the explosive growth of mobile video traffic brings challenges for video streaming providers (VSPs). One known issue is that a huge traffic burden on the vehicular network leads to increasing VSP costs for providing VSA to mobile users (i.e., autonomous vehicles). To address this issue, an efficient resource sharing scheme between underutilized vehicular resources is a promising solution to reduce the cost of serving VSA in the vehicular network. Therefore, we propose a new VSA model based on the lower cost of obtaining data from vehicles and then minimize the VSP’s cost. By using existing data resources from nearby vehicles, our proposal can reduce the cost of providing video service to mobile users. Specifically, we formulate our problem as mixed integer nonlinear programming (MINP) in order to calculate the total payment of the VSP. In addition, we introduce an incentive mechanism to encourage users to rent its resources. Our solution represents a strategy to optimize the VSP serving cost under the quality of service (QoS) requirements. Simulation results demonstrate that our proposed mechanism is possible to achieve up to 21% and 11% cost-savings in terms of the request arrival rate and vehicle speed, in comparison with other existing schemes, respectively.

scholarly journals Mobility-Enabled Edge Server Selection for Multi-User Composite Services

2019 ◽  
Vol 11 (9) ◽  
pp. 184
Author(s):  
Wenming Zhang ◽  
Yiwen Zhang ◽  
Qilin Wu ◽  
Kai Peng
Keyword(s):  
Waiting Time ◽  
Edge Computing ◽  
Mobile Users ◽  
Mobile Edge Computing ◽  
Computation Model ◽  
Real World Data ◽  
Server Selection ◽  
Composite Services ◽  
Selection For ◽  
Traditional Approaches

In mobile edge computing, a set of edge servers is geographically deployed near the mobile users such that accessible computing capacities and services can be provided to users with low latency. Due to user’s mobility, one fundamental and critical problem in mobile edge computing is how to select edge servers for many mobile users so that the total waiting time is minimized. In this paper, we propose a multi-user waiting time computation model about composite services and show the resource contention of the edge server among mobile users. Then, we introduce a novel and optimal Multi-user Edge server Selection method based on Particle swarm optimization (MESP) in mobile edge computing, which selects edge servers for mobile uses in advance within polynomial time. Extensive simulations on a real-world data-trace show that the MESP algorithm can effectively reduce the total waiting time compared with traditional approaches.

A Scalable and Quick-Response Software Defined Vehicular Network Assisted by Mobile Edge Computing

2017 ◽  
Vol 55 (7) ◽  
pp. 94-100 ◽  
Author(s):  
Jianqi Liu ◽  
Jiafu Wan ◽  
Bi Zeng ◽  
Qinruo Wang ◽  
Houbing Song ◽  
...  
Keyword(s):  
Vehicular Network ◽  
Edge Computing ◽  
Mobile Edge Computing ◽  
Quick Response

scholarly journals Reinforcement Learning for Security-Aware Workflow Application Scheduling in Mobile Edge Computing

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Binbin Huang ◽  
Yuanyuan Xiang ◽  
Dongjin Yu ◽  
Jiaojiao Wang ◽  
Zhongjin Li ◽  
...  
Keyword(s):  
Reinforcement Learning ◽  
Edge Computing ◽  
Mobile Users ◽  
Workflow Scheduling ◽  
Scheduling Problem ◽  
Mobile Edge Computing ◽  
Malicious Attacks ◽  
Computing Environment ◽  
Risk Probability ◽  
Workflow Application

Mobile edge computing as a novel computing paradigm brings remote cloud resource to the edge servers nearby mobile users. Within one-hop communication range of mobile users, a number of edge servers equipped with enormous computation and storage resources are deployed. Mobile users can offload their partial or all computation tasks of a workflow application to the edge servers, thereby significantly reducing the completion time of the workflow application. However, due to the open nature of mobile edge computing environment, these tasks, offloaded to the edge servers, are susceptible to be intentionally overheard or tampered by malicious attackers. In addition, the edge computing environment is dynamical and time-variant, which results in the fact that the existing quasistatic workflow application scheduling scheme cannot be applied to the workflow scheduling problem in dynamical mobile edge computing with malicious attacks. To address these two problems, this paper formulates the workflow scheduling problem with risk probability constraint in the dynamic edge computing environment with malicious attacks to be a Markov Decision Process (MDP). To solve this problem, this paper designs a reinforcement learning-based security-aware workflow scheduling (SAWS) scheme. To demonstrate the effectiveness of our proposed SAWS scheme, this paper compares SAWS with MSAWS, AWM, Greedy, and HEFT baseline algorithms in terms of different performance parameters including risk probability, security service, and risk coefficient. The extensive experiments results show that, compared with the four baseline algorithms in workflows of different scales, the SAWS strategy can achieve better execution efficiency while satisfying the risk probability constraints.

scholarly journals Latency-aware secure transmission in MEC-assisted HSR communication systems

2021 ◽  
Vol 2021 (1) ◽  
Author(s):  
Hao Xu ◽  
Ke Li ◽  
Jianfeng Cheng ◽  
Bo Jiang ◽  
Huai Yu
Keyword(s):  
Energy Consumption ◽  
Communication Systems ◽  
High Speed ◽  
Wireless Channel ◽  
Edge Computing ◽  
Mobile Users ◽  
Mobile Edge Computing ◽  
Backhaul Link ◽  
Time Latency ◽  
Secure Transmission

AbstractMobile edge computing can provide short-range cloud computing capability for the mobile users, which is considered to be a promising technology in 5G communication. The mobile users offload some computing tasks to the edge server through the wireless backhaul link, which can reduce the energy consumption and the time latency. Meanwhile, due to the open characteristics of the wireless channel, the offloading tasks through the backhaul link may face the risk of eavesdropping. Therefore, the secure transmission based on physical layer security for the offloading tasks to the edge server is considered. The optimization problem of minimizing the energy consumption for the vehicular stations (VSs) in mobile edge computing-assisted high-speed railway communication system is studied in this paper. The energy consumption of the mobile users is generated by executing the local computing task and by transmitting the partial offloading task to the edge server. In this paper, a novel joint iterative optimization algorithm is proposed. By jointly optimizing the task scheduling, the task offloading and the transmission power, the energy consumption of all VSs is minimized under the constraint of the time latency. Numerical simulation results verify the effectiveness of the proposed algorithm.

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.

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