关键词： Internet of vehicles (IoV)   Road side unit   Mobile edge computing   Reinforcement learning   (Analytic Hierarchy Process-Deep Q Network) AHP-DQN   Task allocation   Offloading strategy  

摘要： Mobile edge computing has been deeply integrated with internet of vehicles (IoV) due to its efficient computing capabilities close to devices. However, the inefficiency of storage and computing capabilities for vehicle terminals is in conflict with the diversification of network application services, which poses a huge challenge to the high-performance computing in IoV. In response to the high-performance computing requirements, a mobile edge computing task distribution and offloading algorithm based on deep reinforcement learning is proposed in order to solve low terminal storage capacity and diversified network service problems. Firstly, taking the energy consumption and transmission bandwidth of vehicle terminals as constraints, this paper establishes a task offloading and resource allocation model based on mathematical model using in-vehicle communication network. Besides, the model takes the maximum task processing rate as the objective function. Secondly, the AHP-DQN framework is used to solve the model, and the optimization variables are allocated according to the real-time state of the network to ensure the better performance of the task allocation algorithm in the multi-user scenario of IoV. Finally, simulation experiments show that the proposed algorithm can effectively realize the effective distribution of computing tasks in IoV.

关键词： Mobile edge computing  

摘要： We consider a mobile edge computing scenario where users want to perform a linear inference operation Wx on local data x for some network-side matrix W. The inference is performed in a distributed fashion over multiple servers at the network edge. For this scenario, we propose a coding scheme that combines a rateless code to provide resiliency against straggling servers-hence reducing the computation latency-and an irregular-repetition code to provide spatial diversity-hence reducing the communication latency. We further derive a lower bound on the total latency-comprising computation latency, communication latency, and decoding latency. The proposed scheme performs remarkably close to the bound and yields significantly lower latency than the scheme based on maximum distance separable codes recently proposed by Zhang and Simeone. Copyright © 2021, The Authors. All rights reserved.

关键词： Mobile edge computing   Popularity prediction  

摘要： In this paper, we study the cache prediction problem for mobile edge networks where there exist one base station (BS) and multiple relays. For the proposed mobile edge computing (MEC) network, we propose a cache prediction framework to solve the problem of contents prediction and caching based on neural networks and relay selection, by exploiting users' history request data and channels between the relays and users. The proposed framework is then trained to learn users' preferences by using the users' history requested data, and several caching policies are proposed based on the channel conditions. The cache hit rate and latency are used to measure the performance of the proposed framework. Simulation results demonstrate the effectiveness of the proposed framework, which can maximize the cache hit rate and meanwhile minimize the latency for the considered MEC networks.

关键词： Mobile edge computing  

摘要： An intelligent reflecting surface (IRS)-aided wireless powered mobile edge computing (WP-MEC) system is conceived, where each device's computational task can be divided into two parts for local computing and offloading to mobile edge computing (MEC) servers, respectively. Both time division multiple access (TDMA) and non-orthogonal multiple access (NOMA) schemes are considered for uplink (UL) offloading. Given the capability of IRSs in intelligently reconfiguring wireless channels over time, it is fundamentally unknown which multiple access scheme is superior for MEC UL offloading. To answer this question, we first investigate the impact of three different dynamic IRS beamforming (DIBF) schemes on the computation rate of both offloading schemes, based on the flexibility for the IRS in adjusting its beamforming (BF) vector in each transmission frame. Under the DIBF framework, computation rate maximization problems are formulated for both the NOMA and TDMA schemes, respectively, by jointly optimizing the IRS passive BF and the resource allocation. We rigorously prove that offloading adopting TDMA can achieve the same computation rate as that of NOMA, when all the devices share the same IRS BF vector during the UL offloading. By contrast, offloading exploiting TDMA outperforms NOMA, when the IRS BF vector can be flexibly adapted for UL offloading. Despite the non-convexity of the computation rate maximization problems for each DIBF scheme associated with highly coupled optimization variables, we conceive computationally efficient algorithms by invoking alternating optimization. Our numerical results demonstrate the significant performance gains achieved by the proposed designs over various benchmark schemes and also unveil that the optimal time allocated to downlink wireless power transfer can be effectively reduced with the aid of IRSs, which is beneficial for both the system's spectral efficiency and its energy efficiency. Copyright © 2021, The Authors. All rights re

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： 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   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： 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： Task analysis   Delays   Edge computing   Optimization   Unmanned aerial vehicles   Cloud computing   Time division multiple access   Mobile edge computing   tethered UAV   delay minimization   UAV placement   resource allocation  

摘要： In this correspondence, we study a tethered unmanned aerial vehicle (TUAV)-assisted mobile edge computing (MEC) system, in which tasks of several ground mobile users (MUs) are served by ground edge cloud (GEC) with superior computing capability and poor communication links and TUAV-mounted cloudlet with lower computing capability and better communication links. We aim to minimize weighted-sum system delay of MUs by jointly optimizing TUAV position, time slot allocation, and task splitting ratio. We first decompose the optimization problem into three subproblems, and then to solve non-convex subproblem related to TUAV position, we reformulate it by the first-order Taylor series approximation and apply the successive convex approximation (SCA) method. Then, we tackle the overall problem by applying the alternative optimization algorithm. Numerical results verify that our proposed cooperative sky-ground mobile edge computing system (CoSMoS) algorithm can achieve a better performance than other benchmark schemes.