关键词： Mobile edge computing  

摘要： Platooning strategy is an important part of autonomous driving technology. Due to the limited resource of autonomous vehicles in platoons, mobile edge computing (MEC) is usually used to assist vehicles in platoons to obtain useful information, increasing its safety. Specifically, vehicles usually adopt the IEEE 802.11 distributed coordination function (DCF) mechanism to transmit large amount of data to the base station (BS) through vehicle-to-infrastructure (V2I) communications, where the useful information can be extracted by the edge server connected to the BS and then sent back to the vehicles to make correct decisions in time. However, vehicles may be moving on different lanes with different velocities, which incurs the unfair access due to the characteristics of platoons, i.e., vehicles on different lanes transmit different amount of data to the BS when they pass through the coverage of the BS, which also results in the different amount of useful information received by various vehicles. Moreover, age of information (AoI) is an important performance metric to measure the freshness of the data. Large average age of data implies not receiving the useful information in time. It is necessary to design an access scheme to jointly optimize the fairness and data freshness. In this paper, we formulate a joint optimization problem in the MEC-assisted V2I networks and present a multi-objective optimization scheme to solve the problem through adjusting the minimum contention window under the IEEE 802.11 DCF mode according to the velocities of vehicles. The effectiveness of the scheme has been demonstrated by simulation. Copyright © 2021, The Authors. All rights reserved.

关键词： electric vehicles   billing data interaction   blockchain   mobile edge computing   reinforcement learning  

摘要： Recently,electric vehicles(EVs)have been widely used under the call of green travel and environmental protection,and diverse requirements for charging are also increasing *** order to ensure the authenticity and privacy of charging information interaction,blockchain technology is proposed and applied in charging station billing ***,there are some issues in blockchain itself,including lower computing efficiency of the nodes and higher energy consumption in the consensus *** handle the above issues,in this paper,combining blockchain and mobile edge computing(MEC),we develop a reliable billing data transmission scheme to improve the computing capacity of nodes and reduce the energy consumption of the consensus *** jointly optimizing the primary and replica nodes offloading decisions,block size and block interval,the transaction throughput of the blockchain system is maximized,as well as the latency and energy consumption of the system are ***,we formulate the joint optimization problem as a Markov decision process(MDP).To tackle the dynamic and continuity of the system state,the reinforcement learning(RL)is introduced to solve the MDP ***,simulation results demonstrate that the performance improvement of the proposed scheme through comparison with other existing schemes.

关键词： Mobile edge computing  

摘要： In this paper, a multi-vehicle multi-task non-orthogonal multiple access (NOMA) assisted mobile edge computing (MEC) system with passive eavesdropping vehicles is investigated. To heighten the performance of edge vehicles, we propose a vehicle grouping pairing method, which utilizes vehicles near the MEC as full-duplex relays to assist edge vehicles. For promoting transmission security, we employ artificial noise to interrupt eavesdropping vehicles. Furthermore, we derive the approximate expression of secrecy outage probability of the system. The combined optimization of vehicle task division, power allocation, and transmit beamforming is formulated to minimize the total delay of task completion of edge vehicles. Then, we design a power allocation and task scheduling algorithm based on genetic algorithm to solve the mixed-integer nonlinear programming problem. Numerical results demonstrate the superiority of our proposed scheme in terms of system security and transmission delay. © 2021, CC BY.

关键词： 5G   computation offloading   deep reinforcement learning   mobile edge computing   security  

摘要： The internet of things (IoT) is permeating our daily lives through continuous environmental monitoring and data collection. The promise of low latency communication, enhanced security, and efficient bandwidth utilization lead to the shift from mobile cloud computing to mobile edge computing. In this study, we propose an advanced deep reinforcement resource allocation and security-aware data offloading model that considers the constrained computation and radio resources of industrial IoT devices to guarantee efficient sharing of resources between multiple users. This model is formulated as an optimization problem with the goal of decreasing energy consumption and computation delay. This type of problem is non-deterministic polynomial time-hard due to the curse-of-dimensionality challenge, thus, a deep learning optimization approach is presented to find an optimal solution. In addition, a 128-bit Advanced Encryption Standard-based cryptographic approach is proposed to satisfy the data security requirements. Experimental evaluation results show that the proposed model can reduce offloading overhead in terms of energy and time by up to 64.7% in comparison with the local execution approach. It also outperforms the full offloading scenario by up to 13.2%, where it can select some computation tasks to be offloaded while optimally rejecting others. Finally, it is adaptable and scalable for a large number of mobile devices.

关键词： user association   resource scheduling   stochastic gradient descent   two-timescale optimization   mobile edge computing  

摘要： For the mobile edge computing network consisting of multiple base stations and resourceconstrained user devices,network cost in terms of energy and delay will incur during task offloading from the user to the edge *** the limitations imposed on transmission capacity,computing resource,and connection capacity,the per-slot online learning algorithm is first proposed to minimize the time-averaged network *** particular,by leveraging the theories of stochastic gradient descent and minimum cost maximum flow,the user association is jointly optimized with resource scheduling in each time *** theoretical analysis proves that the proposed approach can achieve asymptotic optimality without any prior knowledge of the network ***,to alleviate the high network overhead incurred during user handover and task migration,a two-timescale optimization approach is proposed to avoid frequent changes in user *** user association executed on a large timescale and the resource scheduling decided on the single time slot,the asymptotic optimality is *** results verify the effectiveness of the proposed online learning algorithms.

关键词： 5G mobile communication   coordinated placement   virtual network functions   5G wireless networks   distributed processing   resource cost   data plane   Optimisation techniques   Communications computing   mobile computing   SDN-NFV-enabled MEC networks   Computer networks and techniques   wireless networks   Mobile radio systems   software defined networks   nonbandwidth link allocation algorithm   on-the-fly creation   Ubiquitous and pervasive computing   Computer communications   distributed NFV-enabled MEC nodes   SDN-NFV infrastructure   software defined networking   resource allocation   genetic nonbandwidth link allocation algorithm   genetic based heuristic algorithm   virtualisation   telecommunication computing   VNFPRA problem   wireless MEC networks   control plane   VNF placement   mobile edge computing   resource utilisation-single objective evolutionary algorithm   evolutionary computation   random-fit placement algorithm   first-fit placement algorithm   genetic algorithms   beyond wireless networks  

摘要： Network function virtualisation (NFV), software defined networks (SDNs), and mobile edge computing (MEC) are emerging as core technologies to satisfy increasing number of users' demands in 5G and beyond wireless networks. SDN provides clean separation of the control plane from the data plane while NFV enables the flexible and on-the-fly creation and placement of virtual network functions (VNFs) and are able to be executed within the various locations of a distributed system. In this paper, VNF placement and resource allocation (VNFPRA) problem is considered which involves placing VNFs optimally in distributed NFV-enabled MEC nodes and assigning MEC resources efficiently to these VNFs to satisfy users' requests in the network. Current solutions to this problem are slow and cannot handle real-time requests. To this end, an SDN-NFV infrastructure is proposed to tackle the VNFPRA problem in wireless MEC networks. Our aim is to minimise the overall placement and resource cost and also to minimise the total number of VNF migrations. A genetic based heuristic algorithm is proposed. The superior performance of the proposed solution is confirmed in comparison with four existing algorithms, i.e. resource utilisation-single objective evolutionary algorithm (RU-SOEA), genetic non-bandwidth link allocation algorithm (GA-NBA), random-fit placement algorithm (RFPA), and first-fit placement algorithm (FFPA). The results demonstrate that a coordinated placement of VNFs in SDN/NFV enabled MEC networks can satisfy the objective of overall reduced cost. Simulation results also reveal that the proposed scheme approximates well with the optimal solution returned by Gurobi and also achieves reduction on overall cost compared to other methods.

关键词： Trust and verifiable   Ultra-reliable applications   Baseline data   5G and beyond networks   Mobile edge computing  

摘要： With the development of Internet of Thing (IoT) joint 5G and Beyond Networks, Mobile Edge Users (MEUs) can act as mobile data collectors to collect data for various applications. However, some malicious MEUs reporting false or malicious data can cause serious harm to applications, especially for ultra-reliable applications. A novel Verifiable Trust Evaluation joint UAV (VTE-UAV) mechanism is proposed to select trustworthy MEUs to conduct the task for ultra-reliable applications. The VTE-UAV strategy adopts two novel trust evaluation methods, one is the aggregation-based MEU trust evaluation mechanism, when malicious evaluation objects are in the minority, the mechanism takes most evaluation results as baseline data. The other is an active trust acquisition mechanism, it takes the data obtained by Unmanned Aerial Vehicles (UAVs) as baseline data to actively validate the authenticity of the data. Through these two cross evaluation strategies, we obtain more accurate trust evaluation results. Finally, this paper transforms the trust evaluation optimization problem into the optimization of the accuracy of trust evaluation with reducing the cloud payment and the dispatch cost of UAVs. Extensive experiments have verified the validity of the VTE-UAV strategy. Compared with the previous strategies, the VTE-UAV improves the cloud recruitment performance by 7.74%-25.91%, increases the accuracy of trust evaluation of IoT devices by 2.24%-11.72%, and reduces the cloud payment and the cost of UAVs by 3.11%-10.20% and 58.23%, respectively.

关键词： Mobile edge computing   Path planning   Football training   Particle swarm algorithm  

摘要： In order to assist college football training, an edge computing-based path planning algorithm for the football robot, and an improved PSO (particle swarm optimization) algorithm are proposed. First, the cartesian coordinate system is transformed into the polar coordinate system, and the coordinates of the robots' path coding are calculated and displayed in the polar coordinate system. Then, the robots' coordinates in the current, the next, and the obstacle point are measured and compared to determine whether path planning is needed. Consequently, the PSO, optimized by the "concentration selection" strategy in the artificial immune algorithm, is chosen for path planning. The experiments show that with the improved PSO, five robots of 0.4 m in radius and moving less than 2 m/s can be avoided easily. The static obstacle avoidance experiment shows that obstacles can be avoided more easily, using the path planned through improved PSO. The dynamic obstacle avoidance experiments indicate that the robot can avoid all moving obstacles, blocking in front, intercepting from all sides, and tracking from behind timely. The experiments prove that the proposed path planning is effective, which has reference value for promoting the application of path planning algorithms in college football training.

关键词： Cloud computing   Energy-latency tradeoff   Internet of vehicles   Mobile edge computing   Workload allocation  

摘要： With the increasing number of vehicles, the generating vehicular data exceeds the capacity of mobile edge computing (MEC). Therefore, studying the interaction and collaboration of edge computing and cloud computing is of significance to provide vehicular users with low-latency high-rate services. This paper first proposes a MEC-cloud computing collaboration architecture for Internet of vehicles, then designs the interconnection/interaction framework between MEC and cloud computing. We consider reducing computation delay and power consumption, and formulate an energy-efficient workload allocation problem with load balancing and dynamic voltage frequency scaling technology, to obtain the optimal workload allocations of MEC and cloud computing. We then present the overall distribution optimization algorithm to solve this problem. The simulation and numerical results show that by saving communication bandwidth and reducing transmission delay, MEC significantly enhances the performance of cloud computing. Besides, the proposed workload balance scheme is better than the benchmark schemes in terms of power consumption and latency.

关键词： UAV   Federated learning   Latency   Energy consumption   Mobile edge computing  

摘要： In this paper, we investigate how to optimize the federated edge learning (FEEL) in unmanned aerial vehicle (UAV)-enabled Internet of Things (IoT) for B5G/6G networks. Federated learning is an effective framework to train a shared model between decentralized edge devices and servers without exchanging raw data, which helps protecting data privacy. In UAV-enabled IoT networks, latency and energy consumption are two important metrics limiting the performance of FEEL. Although most of the existing works have studied how to reduce latency and improve energy efficiency, only few of them have investigated the impact of the devices' limited batteries on FEEL. Motivated by this, we study the battery-constrained FEEL, where the UAVs can adjust their operating CPU-frequencies to prolong battery life and avoid dropping from federated learning training untimely. We optimize the system by jointly allocating the computational resources and wireless bandwidth in time-varying environments based on a deep deterministic policy gradient (DDPG) based strategy, where a linear combination of latency and energy consumption is used to evaluate the system cost. In this end, simulation results are demonstrated to show that the proposed strategy outperforms the conventional ones. In particular, it enables all the devices to complete all rounds of FEEL with limited batteries, and reduce the system cost effectively in the meantime. (C) 2021 Elsevier B.V. All rights reserved.