关键词： Mobile edge computing  

摘要： The Internet of Vehicles (IoVs) is considered a critical technology for developing future intelligent transportation systems. The next-generation IoVs technology integrates Mobile Edge Computing (MEC) with V2X, 5G/6G, etc. applications, which can significantly improve the quality of vehicular services. With the emergence of 6G communication technologies, large-scale MEC facilities will be intensively deployed. While providing a wide range of computing services for vehicles, it also brings high energy consumption. To realize sustainable and green IoVs, introducing sleep mechanisms for MEC servers is a promising solution. In this paper, we study the dynamic sleep and service association problem for edge servers in MEC-assisted IoVs networks. Energy consumption is saved by switching the operating mode of the edge servers between sleep and active based on satisfying the quality of service. To tackle this problem, we propose the Latency-aware Iterative Self-organized Clustering algorithm (DISC), which enables self-organized cluster splitting and merging with latency awareness. DISC can efficiently determine the optimal number of edge servers to be activated and their locations based on the distribution of vehicular tasks. Sufficient simulation results show that the proposed algorithm can efficiently improve resource utility and reduce energy consumption, and is applicable to different scales of IoVs networks. © 2023, The Authors. All rights reserved.

关键词： Mobile edge computing  

摘要： Recently, Mobile Edge Computing (MEC) and service migration policies have shown promising results to improve the user experience and optimize infrastructure resources. In addition, Unmanned Aerial Vehicles (UAVs) appear as a promising solution to provide cloud service in collaboration with traditional MEC scenarios. However, in conjunction with the resources available on users’ devices, contextual information has been ignored by most policies. In this article, we propose a service migration strategy based on contextual information and evaluate the influence of user mobility on migration strategies, called DVSM. Simulation results highlight the superior performance of the DVSM compared to state-of-the-art algorithms and a performance equivalent to the optimal solution when thecollection and analysis of context information are carried out correctly. © 2023, Brazilian Computing Society. All rights reserved.

关键词： Task analysis   Distributed computing   Computational modeling   Security   Resilience   Bayes methods   Privacy   Auction theory   Bayesian methods   coded distributed computing   data privacy   deep learning   game theory   incomplete information   Internet of Things   machine learning   Markov processes   mobile edge computing   pricing   reinforcement learning   resiliency   resource allocation   security  

摘要： We design a novel encoding model based on Lagrange coded computing (LCC) for private, secure, and resilient distributed mobile edge computing (MEC) systems, where multiple base stations (BSs) act as "masters" offloading their computations to edge nodes acting as "workers". A two-fold objective of the scheme is: i) efficient allocation of computing tasks to the workers;ii) providing the workers with appropriate incentives to complete their tasks. As such, each master must decide on its offloading requests to the workers including the allocated tasks and service fees to be paid. This problem is complex due to the following reasons: i) masters can be privately-owned or managed by different operators, i.e., there is no communication and no coordination among them;ii) workers are heterogeneous non-dedicated nodes with limited and nondeterministic transmission and computing resources. As a result, the masters must compete for constrained resources of workers in a stochastic partially-observable environment. To address this problem, we define the interactions between masters and workers as a direct stochastic first-price-sealed-bid (FPSB) auction. To analyze the auction, we represent it as a stochastic Bayesian game and develop a Bayesian learning framework to perfect the auction solution.

关键词： Sensors   Intelligent sensors   Roads   Cloud computing   Servers   Sensor systems   Monitoring   Context-aware   edge intelligence   mobile edge computing   road monitoring system (RMS)   sensors   task offloading  

摘要： Smart vehicles equipped with onboard sensors can detect the surrounding cars, cycles, pedestrians, and other objects in real time and can make control decisions to achieve road safety. The high dynamism of vehicular mobility switches base stations in their trajectory, and at an instant, one edge server services one base station only. So, making control decisions by processing and transmitting vehicular data in real time is challenging. In this article, we propose a road monitoring system (RMS) in which moving vehicles collect road data and offload collected data to the paired edge server. The edge devices and connected vehicular networks receive road-hindering alerts. It comes up with Road Ambient Intelligence that supports ubiquitous computing and generates context-aware alerts with low latency. It uses a deep learning model for the decision-making about road-hindering alerts and a K-nearest neighbor algorithm to form clusters for the distribution of alert messages. For intense occurrences, the data are sent to the cloud server for further notifications to remote-control offices. The proposed study is compared with social vehicle route selection (SVRS) and delay tolerant processing (DTP) schemes. The performance analysis shows that rms presents 12%-44% better performance compared to the SVRS scheme and 15%-53% better compared to the DTP scheme in terms of delay. The energy requirement analysis shows that compared to SVRS, RMS involves 50% less energy when processed at edge clouds, and DTP involves 51%-65% more energy requirements compared to the RMS scheme.

关键词： Energy consumption   Servers   Cellular networks   Trajectory   Optimization   Interference   Edge computing   Mobile edge computing   service migration   multi-user   Lyapunov optimization   particle swarm optimization  

摘要： Mobile edge computing (MEC) is a key enabler for ultra-low latency in heterogeneous dense cellular networks in the 5G era and beyond, by deploying services at the network edge. Due to high user mobility, the services are usually migrated to follow the users by predicting the user trajectory to achieve a balance between energy consumption and service latency. However, service migration for multi-user heterogeneous dense cellular networks is challenging because (1) the user trajectory prediction, which is crucial for service migration, becomes intractable with a large number of users, and (2) making service migration decisions for each user independently is subjected to interference among the users. Therefore, in this study, we formulated the service migration of all the users in MEC-enabled heterogeneous dense cellular networks as an optimization problem, with the objective of minimizing the average energy consumption while satisfying the service latency requirements, taking into account the interference among different users. Next, we developed an efficient energy-efficient online algorithm based on the Lyapunov and particle swarm optimizations, called EGO, to resolve the original problem without predicting the trajectories of the users. Finally, a series of simulations based on real-world mobility traces of vehicles in Bologna were conducted to establish the superiority of the EGO algorithm over state-of-the-art solutions.

关键词： mobile edge computing   video analytics   digital twin   6G   deep reinforcement learning  

摘要： Camera networks are essential to constructing fast and accurate mapping between virtual and physical space for digital *** this paper,with the aim of developing energy-efficient digital twin in 6G,we investigate real-time video analytics based on cameras mounted on mobile devices with edge *** problem is challenging because 1)mobile devices are with limited battery life and lightweight computation capability,and 2)the captured video frames of mobile devices are continuous changing,which makes the corresponding tasks arrival *** achieve energy-efficient video analytics in digital twin,by taking energy consumption,analytics accuracy,and latency into consideration,we formulate a deep reinforcement learning based mobile device and edge coordination video analytics framework,which can utilized digital twin models to achieve joint offloading decision and configuration *** edge nodes help to collect the information on network topology and task *** simulation results demonstrate that our proposed framework outperforms the benchmarks on accuracy improvement and energy and latency reduction.

关键词： Mobile edge computing   fused-layer   DNN inference   partial offloading   model parallelism  

摘要： With the rapid development of Internet-of-Things (IoT) and the explosive advance of deep learning, there is an urgent need to enable deep learning inference on IoT devices in Mobile Edge Computing (MEC). To address the computation limitation of IoT devices in processing complex Deep Neural Networks (DNNs), computation offloading is proposed as a promising approach. Recently, partial computation offloading is developed to dynamically adjust task assignment strategy in different channel conditions for better performance. In this paper, we take advantage of intrinsic DNN computation characteristics and propose a novel Fused-Layer-based (FL-based) DNN model parallelism method to accelerate inference. The key idea is that a DNN layer can be converted to several smaller layers in order to increase partial computation offloading flexibility, and thus further create the better computation offloading solution. However, there is a trade-off between computation offloading flexibility as well as model parallelism overhead. Then, we investigate the optimal DNN model parallelism and the corresponding scheduling and offloading strategies in partial computation offloading. In particular, we propose a Particle Swarm Optimization with Minimizing Waiting (PSOMW) method, which explores and updates the FL strategy, path scheduling strategy, and path offloading strategy to reduce time complexity and avoid invalid solutions. Finally, we validate the effectiveness of the proposed method in commonly used DNNs. The results show that the proposed method can reduce the DNN inference time by an average of 12.75 times compared to the legacy No FL (NFL) algorithm, and is very close to the optimal solution achieved by the Brute Force (BF) algorithm with the difference of less than 0.04%.

关键词： Resource management   Computational modeling   Servers   Numerical models   Microprocessors   Computer architecture   Task analysis   Network slicing   mobile edge computing   resource allocation   market model   game theory  

摘要： Edge computing has been recently introduced to bring computational capabilities closer to end-users of modern network-based services, supporting existing and future delay-sensitive applications by effectively addressing the high propagation delay issue that affects cloud computing. However, the problem of efficiently and fairly managing the system resources presents particular challenges due to the limited capacity of both edge nodes and wireless access networks and the heterogeneity of resources and services' requirements. To this end, we propose a techno-economic market where service providers act as buyers, securing both radio and computing resources to execute their associated end-users' jobs while being constrained by a budget limit. We design an allocation mechanism that employs convex programming to find the unique market equilibrium point that maximizes fairness while ensuring that all buyers receive their preferred resource bundle. Additionally, we derive theoretical properties that confirm how the market equilibrium approach strikes a balance between fairness and efficiency. We also propose alternative allocation mechanisms and give a comparison with the market-based mechanism. Finally, we conduct simulations to numerically analyze and compare the performance of the mechanisms and confirm the market model's theoretical properties.

关键词： Computation offloading   MEC-enabled vehicular networks   Mobile edge computing   Task scheduling  

摘要： With the development of the mobile communication technology,a wide variety of envisioned intelligent transportation systems have emerged and put forward more stringent requirements for vehicular *** of computation-intensive and power-hungry applications result in a large amount of energy consumption and computation costs,which bring great challenges to the on-board *** is necessary to exploit traffic offloading and scheduling in vehicular networks to ensure the Quality of Experience(QoE).In this paper,a joint offloading strategy based on quantum particle swarm optimization for the Mobile Edge Computing(MEC)enabled vehicular networks is *** minimize the delay cost and energy consumption,a task execution optimization model is formulated to assign the task to the available service nodes,which includes the service vehicles and the nearby Road Side Units(RSUs).For the task offloading process via Vehicle to Vehicle(V2V)communication,a vehicle selection algorithm is introduced to obtain an optimal offloading decision ***,an improved quantum particle swarm optimization algorithm for joint offloading is proposed to optimize the task delay and energy *** maintain the diversity of the population,the crossover operator is introduced to exchange information among ***,the crossover probability is defined to improve the search ability and convergence speed of the ***,an adaptive shrinkage expansion factor is designed to improve the local search accuracy in the later *** results show that the proposed joint offloading strategy can effectively reduce the system overhead and the task completion delay under different system parameters.

关键词： mobile edge computing   sequential tasks   computation offloading   dependency  

摘要： Mobile edge computing has shown its potential in serving emerging latency-sensitive mobile applications in ultra-dense 5G networks via offloading computation workloads from the remote cloud data center to the nearby network ***,current computation offloading studies in the heterogeneous edge environment face multifaceted challenges:Dependencies among computational tasks,resource competition among multiple users,and diverse long-term *** applications typically consist of several functionalities,and one huge category of the applications can be viewed as a series of sequential *** this study,we first proposed a novel multiuser computation offloading framework for long-term sequential ***,we presented a comprehensive analysis of the task offloading process in the framework and formally defined the multiuser sequential task offloading ***,we decoupled the long-term offloading problem into multiple single time slot offloading problems and proposed a novel adaptive method to solve *** further showed the substantial performance advantage of our proposed method on the basis of extensive experiments.