scholarly journals Distributed Edge Computing to Assist Ultra Low Latency VANET Applications

2018 ◽  
Author(s):  
Andrei Vladyko ◽  
Abdukodir Khakimov ◽  
Ammar Muthanna ◽  
Abdelhamied A. Ateya ◽  
Andrey Koucheryavy
Keyword(s):  
Base Station ◽  
Road Accident ◽  
Edge Computing ◽  
Transport Infrastructure ◽  
City Management ◽  
Mobile Edge Computing ◽  
General Will ◽  
Provision Of Services ◽  
Stationary Objects ◽  
Roadside Unit

VANET networks are a class of peer-to-peer wireless networks that are used to organize communication between cars (V2V), cars and infrastructure (V2I) and between cars and other types of nodes (V2X). These networks are based on the DSRC, 802.11 standards and are mainly intended for organizing the exchange of various types of messages, mainly emergency ones, to prevent road accidents or alert when road accident occur, or control the priority of the driveway. Initially it was assumed that cars would only interact with each other, but later, with the advent of the concept of Internet of things (IoT). Researchers began to analyze connectivity with other devices, which in general will allow to combine various road users and other devices that can used in the creation of intelligent transport infrastructure in a single smart city management system. Infrastructure is necessary for the provision of services, monitoring and management of the VANET network. As infrastructure objects it is proposed to use stationary objects of Roadside unit (RSU). The aim of this paper is to analyze the use of mobile edge computing to decrease the load to the base station and latency between RSU clouds and provide a real experiment using software defined networking and mobile edge computing for RSU.

scholarly journals Distributed Edge Computing to Assist Ultra Low Latency VANET Applications

2018 ◽  
Author(s):  
Andrei Vladyko ◽  
Abdukodir Khakimov ◽  
Ammar Muthanna ◽  
Abdelhamied A. Ateya ◽  
Andrey Koucheryavy
Keyword(s):  
Base Station ◽  
Road Accident ◽  
Edge Computing ◽  
Transport Infrastructure ◽  
City Management ◽  
Mobile Edge Computing ◽  
General Will ◽  
Provision Of Services ◽  
Stationary Objects ◽  
Roadside Unit

VANET networks are a class of peer-to-peer wireless networks that are used to organize communication between cars (V2V), cars and infrastructure (V2I) and between cars and other types of nodes (V2X). These networks are based on the DSRC, 802.11 standards and are mainly intended for organizing the exchange of various types of messages, mainly emergency ones, to prevent road accidents or alert when road accident occur, or control the priority of the driveway. Initially it was assumed that cars would only interact with each other, but later, with the advent of the concept of Internet of things (IoT). Researchers began to analyze connectivity with other devices, which in general will allow to combine various road users and other devices that can used in the creation of intelligent transport infrastructure in a single smart city management system. Infrastructure is necessary for the provision of services, monitoring and management of the VANET network. As infrastructure objects it is proposed to use stationary objects of Roadside unit (RSU). The aim of this paper is to analyze the use of mobile edge computing to decrease the load to the base station and latency between RSU clouds and provide a real experiment using software defined networking and mobile edge computing for RSU.

scholarly journals Dynamic Task Migration Combining Energy Efficiency and Load Balancing Optimization in Three-Tier UAV-Enabled Mobile Edge Computing System

Electronics ◽  
2021 ◽  
Vol 10 (2) ◽  
pp. 190
Author(s):  
Wu Ouyang ◽  
Zhigang Chen ◽  
Jia Wu ◽  
Genghua Yu ◽  
Heng Zhang
Keyword(s):  
Energy Consumption ◽  
Load Balancing ◽  
Markov Decision Process ◽  
Decision Process ◽  
Base Station ◽  
Edge Computing ◽  
Mobile Edge Computing ◽  
Task Migration ◽  
Migration Strategy ◽  
Markov Decision

As transportation becomes more convenient and efficient, users move faster and faster. When a user leaves the service range of the original edge server, the original edge server needs to migrate the tasks offloaded by the user to other edge servers. An effective task migration strategy needs to fully consider the location of users, the load status of edge servers, and energy consumption, which make designing an effective task migration strategy a challenge. In this paper, we innovatively proposed a mobile edge computing (MEC) system architecture consisting of multiple smart mobile devices (SMDs), multiple unmanned aerial vehicle (UAV), and a base station (BS). Moreover, we establish the model of the Markov decision process with unknown rewards (MDPUR) based on the traditional Markov decision process (MDP), which comprehensively considers the three aspects of the migration distance, the residual energy status of the UAVs, and the load status of the UAVs. Based on the MDPUR model, we propose a advantage-based value iteration (ABVI) algorithm to obtain the effective task migration strategy, which can help the UAV group to achieve load balancing and reduce the total energy consumption of the UAV group under the premise of ensuring user service quality. Finally, the results of simulation experiments show that the ABVI algorithm is effective. In particular, the ABVI algorithm has better performance than the traditional value iterative algorithm. And in a dynamic environment, the ABVI algorithm is also very robust.

scholarly journals Mobility-Aware Service Caching in Mobile Edge Computing for Internet of Things

Sensors ◽  
2020 ◽  
Vol 20 (3) ◽  
pp. 610 ◽  
Author(s):  
Hua Wei ◽  
Hong Luo ◽  
Yan Sun
Keyword(s):  
Target Location ◽  
Geometric Model ◽  
Back Propagation ◽  
Base Station ◽  
Edge Computing ◽  
Computation Offloading ◽  
Target Area ◽  
Mobile Edge Computing ◽  
Trajectory Data ◽  
Practical Applications

The mobile edge computing architecture successfully solves the problem of high latency in cloud computing. However, current research focuses on computation offloading and lacks research on service caching issues. To solve the service caching problem, especially for scenarios with high mobility in the Sensor Networks environment, we study the mobility-aware service caching mechanism. Our goal is to maximize the number of users who are served by the local edge-cloud, and we need to make predictions about the user’s target location to avoid invalid service requests. First, we propose an idealized geometric model to predict the target area of a user’s movement. Since it is difficult to obtain all the data needed by the model in practical applications, we use frequent patterns to mine local moving track information. Then, by using the results of the trajectory data mining and the proposed geometric model, we make predictions about the user’s target location. Based on the prediction result and existing service cache, the service request is forwarded to the appropriate base station through the service allocation algorithm. Finally, to be able to train and predict the most popular services online, we propose a service cache selection algorithm based on back-propagation (BP) neural network. The simulation experiments show that our service cache algorithm reduces the service response time by about 13.21% on average compared to other algorithms, and increases the local service proportion by about 15.19% on average compared to the algorithm without mobility prediction.

scholarly journals International Trade Balance Algorithm Based on the Ownership Principle of Mobile Edge Computing

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Fangfang Du
Keyword(s):  
International Trade ◽  
Division Of Labor ◽  
Arrival Rate ◽  
Base Station ◽  
Edge Computing ◽  
Structural Var ◽  
Series Data ◽  
Mobile Edge Computing ◽  
International Division ◽  
International Division Of Labor

As an emerging mobile computing technology, mobile edge computing is an important key technology to improve the computing services of mobile devices. This paper mainly studies the balance of international trade algorithm based on the principle of moving edge computing ownership. In order to obtain all the data needed to perform the task, each mobile device can exchange data information with its connected base station through the wireless network. On the basis of satisfying the quality of service of users, including considering the user connection and service configuration, the network energy consumption is minimized in continuous t period by shutting down some servers whose resources are not fully utilized. At the same time, in order to reduce the switching cost of edge server and ensure the stability of service, frequent switching of edge server should be avoided. At the beginning, there is division of labor economy. With the development of specialized production, the degree of international division of labor is increasing due to the effect of experience accumulation. The trade efficiency is growing endogenously. The international division of labor is further deepened, and the types and quantity of products participating in the international division of labor are greatly increased, so as to realize the upgrading of trade structure. Before constructing the structural VAR model of Bti, R/W, K/L, and TFP, we need to test its stationarity. Using Eviews 5.0 software, ADF test and PP test were carried out on the unit root of BTI, r/ w , K/L, and TFP time series data. With the increase of user task arrival rate, the average time revenue increases continuously. However, when the arrival rate is greater than 3 kbit/slot, the average time revenue increases slowly. The results show that the research results in system model and resource optimization algorithm will provide reliable theoretical and technical support for the practical application of mobile edge computing.

scholarly journals Energy-Efficient Mobile Edge Computing: Three-Tier Computing under Heterogeneous Networks

2020 ◽  
Vol 2020 ◽  
pp. 1-17 ◽  
Author(s):  
Yongsheng Pei ◽  
Zhangyou Peng ◽  
Zhenling Wang ◽  
Haojia Wang
Keyword(s):  
Heterogeneous Networks ◽  
Energy Efficient ◽  
Base Station ◽  
Edge Computing ◽  
Mobile Edge Computing ◽  
Energy Efficiency Improvement ◽  
Efficient Resource ◽  
Energy Efficient Resource Allocation ◽  
Smart Mobile ◽  
Computing Structure

Mobile edge computing (MEC) is a promising technique to meet the demands of computing-intensive and delay-sensitive applications by providing computation and storage capabilities in close proximity to mobile users. In this paper, we study energy-efficient resource allocation (EERA) schemes for hierarchical MEC architecture in heterogeneous networks. In this architecture, both small base station (SBS) and macro base station (MBS) are equipped with MEC servers and help smart mobile devices (SMDs) to perform tasks. Each task can be partitioned into three parts. The SMD, SBS, and MBS each perform a part of the task and form a three-tier computing structure. Based on this computing structure, an optimization problem is formulated to minimize the energy consumption of all SMDs subject to the latency constraints, where radio and computation resources are considered jointly. Then, an EERA mechanism based on the variable substitution technique is designed to calculate the optimal workload distribution, edge computation capability allocation, and SMDs’ transmit power. Finally, numerical simulation results demonstrate the energy efficiency improvement of the proposed EERA mechanism over the baseline schemes.

scholarly journals An LSTM-Method-Based Availability Prediction for Optimized Offloading in Mobile Edges

Sensors ◽  
2019 ◽  
Vol 19 (20) ◽  
pp. 4467 ◽  
Author(s):  
Chaoxiong Cui ◽  
Ming Zhao ◽  
Kelvin Wong
Keyword(s):  
Completion Time ◽  
Base Station ◽  
Edge Computing ◽  
Mobile Edge Computing ◽  
Cognitive Computing ◽  
Mobile Environment ◽  
Limited Mobility ◽  
Mode Decomposition ◽  
Mobility Problem ◽  
Save Energy

Mobile edge computing (MEC) can augment the computation capabilities of a vehicle terminal (VT) through offloading the computational tasks from the VT to the mobile edge computing-enabled base station (MEC-BS) covering them. However, due to the limited mobility of the vehicle and the capacity of the MEC-BS, the connection between the vehicle and the MEC-BS may be intermittent. If we can expect the availability of MEC-BS through cognitive computing, we can significantly improve the performance in a mobile environment. Based on this idea, we propose a offloading optimization algorithm based on availability prediction. We examine the admission control decision of MEC-BS and the mobility problem, in which we improve the accuracy of availability prediction based on Empirical Mode Decomposition(EMD) and LSTM in deep learning. Firstly, we calculate the availability of MEC, completion time, and energy consumption together to minimize the overall cost. Then, we use a game method to obtain the optimal offloading decision. Finally, the experimental results show that the algorithm can save energy and shorten the completion time more effectively than other existing algorithms in the mobile environment.

scholarly journals Mobility-Aware Personalized Service Recommendation in Mobile Edge Computing

2020 ◽  
Author(s):  
Hongxia Zhang ◽  
Yanhui Dong ◽  
Yongjin Yang
Keyword(s):  
Cloud Computing ◽  
Recommendation Systems ◽  
Base Station ◽  
Edge Computing ◽  
Mobile Edge Computing ◽  
Service Recommendation ◽  
Trajectory Prediction ◽  
Personalized Service ◽  
Significant Challenge ◽  
Short Memory

Abstract With the proliferation of smartphones and an increasing number of services provisioned by clouds, mobile edge computing (MEC) is emerging as a complementary technology of cloud computing. It could provide cloud resources and services by local mobile edge servers, which are normally nearby users. However, a significant challenge is aroused in MEC because of the mobility of users. User trajectory prediction technologies could be used to cope with this issue, which has already played important roles in service recommendation systems with MEC. Unfortunately, little attention and work have been given in service recommendation systems considering users\' mobility. Thus, in this paper, we propose a mobility-aware personalized service recommendation approach based on user trajectory and QoS predictions. In the proposed method, users' trajectory is firstly discovered by hybrid long-short memory networks. Then, given users\' trajectories, service QoSs are predicted, considering the similarity of different users and different edge servers. Finally, services are recommended by a center trajectory strategy based on the aforementioned information. Experimental results based on the real base station dataset show that our proposed approach can outperform the traditional recommendation approaches in terms of the accuracy in mobile edge computing.

scholarly journals Collaborative Task Offloading Strategy of UAV Cluster Using Improved Genetic Algorithm in Mobile Edge Computing

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Hong Wang
Keyword(s):  
Genetic Algorithm ◽  
Base Station ◽  
Edge Computing ◽  
Base Stations ◽  
Task Completion ◽  
Mobile Edge Computing ◽  
Improved Genetic Algorithm ◽  
Collaborative Task ◽  
Good Signal ◽  
Task Offloading

Aiming at the problem that traditional fixed base stations cannot provide good signal coverage due to geographical factors, which may reduce the efficiency of task offloading, a collaborate task offloading strategy using improved genetic algorithm in mobile edge computing (MEC) is proposed by introducing the unmanned aerial vehicle (UAV) cluster. First, for the scenario of the UAV cluster serving multiple ground terminals, a collaborative task offloading model is formulated to offload the tasks to UAVs or the base station selectively. Then, an objective function and related constraints are put forward to minimize the time delay and energy consumption by analysis of those in the communication and computing process in the system while considering many factors. Then, the improved genetic algorithm is introduced to solve the optimization problem, obtaining the optimal collaborative task offloading strategy. To verify the performance of the proposed method, simulations are conducted on MATLAB. Simulation results showed that the joint utilization of UAV and MEC improves the offloading efficiency of the proposed strategy. When the number of UAVs is 12, the total utility is up to 1.83 and the task completion time does not exceed 110 ms. In this case, the task can be reasonably offloaded to UAVs or accomplished locally.

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