关键词： Mobile edge computing   Privacy of vehicle location   Computational collaborative scheduling strategy   Kalman filter   Double DQN algorithm  

摘要： This paper proposes a collaborative scheduling strategy for computing resources of the Internet of vehicles considering location privacy protection in the mobile edge computing environment. Firstly, a multi area multi-user multi MEC server system is designed, in which a MEC server is deployed in each area, and multiple vehicle user equipment in an area can offload computing tasks to MEC servers in different areas by a wireless channel. Then, considering the mobility of users in Internet of vehicles, a vehicle distance prediction based on Kalman filter is proposed to improve the accuracy of vehicle-to-vehicle distance. However, when the vehicle performs the task, it needs to submit the real location, which causes the problem of the location privacy disclosure of vehicle users. Finally, the total cost of communication delay, location privacy of vehicles and energy consumption of all users is formulated as the optimization goal, which take into account the system state, action strategy, reward and punishment function and other factors. Moreover, Double DQN algorithm is used to solve the optimal scheduling strategy for minimizing the total consumption cost of system. Simulation results show that proposed algorithm has the highest computing task completion rate and converges to about 80% after 8000 iterations, and its performance is more ideal compared with other algorithms in terms of system energy cost and task completion rate, which demonstrates the effectiveness of our proposed scheduling strategy.

关键词： cloud computing   integer programming   cellular radio   nonlinear programming   autonomous aerial vehicles   mobile computing   learning (artificial intelligence)   relay networks (telecommunication)   multi-robot systems   optimisation problem   complexity reduction   QCOA   quality of experience   MUs-Edge-Cloud three-layer network architecture   MU energy consumption   two-layer network architecture   traversal offloading   random offloading   efficient Q-learning based computation offloading algorithm   mixed-integer nonlinear programming problem   edge servers   remote cloud centre   computation resources   multiUAV-enabled MEC system   controllability   mobile edge computing   mobile users   relaying services   flying platform   UAV   unmanned aerial vehicles   multiUAV-enabled cloud-edge computing networks  

摘要： Unmanned aerial vehicles (UAVs) have been recently considered as a flying platform to provide wide coverage and relaying services for mobile users (MUs). Mobile edge computing (MEC) is developed as a new paradigm to improve quality of experience of MUs in future networks. Motivated by the high flexibility and controllability of UAVs, in this study, the authors study a multi-UAV-enabled MEC system, in which UAVs have computation resources to offer computation offloading opportunities for MUs, aiming to reduce MUs' total consumptions in terms of time and energy. Considering the rich computation resource in the remote cloud centre, they propose the MUs-Edge-Cloud three-layer network architecture, where UAVs play the role of flying edge servers. Based on this framework, they formulate the computation offloading issue as a mixed-integer non-linear programming problem, which is difficult to obtain an optimal solution in general. To address this, they propose an efficientQ-learning based computation offloading algorithm (QCOA) to reduce the complexity of optimisation problem. Numerical results show that the proposed QCOA outperforms benchmark offloading policies (e.g. random offloading, traversal offloading). Furthermore, the proposed three-layer network architecture achieves a 5% benefits compared with the traditional two-layer network architecture in terms of MUs' energy and time consumptions.

关键词： game theory   autonomous aerial vehicles   mobile computing   relatively stable state   MEC   computing services   energy consumption   unmanned aerial vehicle   mobility   flexibility   edge server   instantaneous computing surge   resources costs   offloading model   mobile user   computing resources competition   UAV-enabled computation offloading algorithm   lower computing cost   cost-efficient computation   UAV-enabled edge computing   computationally intensive applications   mobile edge computing   increasing computing demands  

摘要： With the popularity of computationally intensive applications, more and more computing resources are required. Mobile edge computing (MEC) is widely applied as an effective method to meet the increasing computing demands. In a relatively stable state, MEC can provide computing services with low latency and energy consumption. However, in special cases such as communication traffic, the unmanned aerial vehicle (UAV), by taking advantage of its mobility and flexibility, can assist the edge server to cope with the challenge of instantaneous computing surge. In this study, the authors consider a UAV-enabled edge computing system. In addition to delay and energy consumption, the authors also consider computing resources costs in the offloading model. Besides, in order to minimise the computing cost of each mobile user (MU), they apply the non-cooperative game method to model the channel and computing resources competition among MUs. Then, the authors prove that the proposed game is an ordinal potential game and the existence of Nash equilibrium in the game. The authors propose the UAV-enabled computation offloading (UECO) algorithm to obtain the equilibrium strategy. Finally, the authors show that the UECO algorithm can quickly converge through iterative experiments, and it can achieve lower computing cost through comparative experiments.

关键词： Unmanned aerial vehicle   Mobile edge computing   Non-orthogonal multiple access   Fair resource allocation design   Computation efficiency  

摘要： In this paper, we study the issue of fair resource optimization for an unmanned aerial vehicle (UAV)-enabled mobile edge computing (MEC) system with multi-carrier non-orthogonal multiple access (MC-NOMA). A computation efficiency (CE) optimization problem based on the max-min fairness principle under the partial offloading mode is formulated by optimizing the subchannel assignment, the local CPU frequency, and the transmission power jointly. The formulated problem belongs to the non-convex mixed integer nonlinear programming (MINLP), that is NP-hard to find the global optimal solution. Therefore, we design a polynomial-time algorithm based on the big-M reformulation, the penalized sequential convex programming, and the general Dinkelbach's method, which can choose an arbitrary point as the initial point and eventually converge to a feasible suboptimal solution. The proposed algorithm framework can be also applied to computation offloading only mode. Additionally, we derive the closed-form optimal solution under the local computing only mode. Simulation results validate the convergence performance of the proposed algorithm. Moreover, the proposed partial offloading mode with the CE maximization scheme outperforms that with the computation bits (CB) maximization scheme with respect to CE, and it can achieve higher CE than the benchmark computing modes. Furthermore, the proposed MC-NOMA scheme can attain better CE performance than the conventional OFDMA scheme.

关键词： Mobile edge computing  

摘要： The combination of non-orthogonal multiple access (NOMA) and mobile edge computing (MEC) can significantly improve the spectrum efficiency beyond the fifth-generation network. In this paper, we mainly focus on energy-efficient resource allocation for a multi-user, multi-BS NOMA assisted MEC network with imperfect channel state information (CSI), in which each user can upload its tasks to multiple base stations (BSs) for remote executions. To minimize the energy consumption, we consider jointly optimizing the task assignment, power allocation and user association. As the main contribution, with imperfect CSI, the optimal closed-form expressions of task assignment and power allocation are analytically derived for the two-BS case. Specifically, the original formulated problem is nonconvex. We first transform the probabilistic problem into a non-probabilistic one. Subsequently, a bilevel programming method is proposed to derive the optimal solution. In addition, by incorporating the matching algorithm with the optimal task and power allocation, we propose a low complexity algorithm to efficiently optimize user association for the multi-user and multi-BS case. Simulations demonstrate that the proposed algorithm can yield much better performance than the conventional OMA scheme but also the identical results with lower complexity from the exhaustive search with the small number of BSs. Copyright © 2020, The Authors. All rights reserved.

关键词： Mobile edge computing   Partial computation offloading   Game theory   NOMA  

摘要： Mobile edge computing provides powerful computing for mobile device users to broaden their computation capability. However, due to the limited bandwidth of the wireless channel, both the energy consumption for mobile device's computation offloading and the service latency maybe increase. In this paper, we focus on the partial computation offloading problem for multi-user in mobile edge computing environment with multi-wireless channel. The computation overhead model is built based on game theory. Then, the existence of Nash equilibrium is proven. Furthermore, the partial computation offloading algorithm with low time complexity is given to achieve the Nash equilibrium. In addition, another partial computation task offloading mechanism for mobile device users, who has enough energy and only pay attention to the computation time overhead, is given to reduce the computation overhead. Finally, extensively numerical experiments are conducted. The experimental results show that our proposed algorithm can achieve better performance than the compared algorithms in the mobile edge computing environment.

关键词： Unmanned aerial vehicle   Mobile edge computing   Channel estimation error   Computation task offloading   Resource allocation  

摘要： In this paper, we develop task offloading and resource allocation scheme for unmanned aerial vehicle (UAV)-assisted mobile edge computing (MEC) system with channel estimation errors over Rician fading channels. The objective is to maximize the system utility with constrained network stability, transmit power, and data arrival rate. We consider a general multi-user UAV-assisted MEC system based on frequency division multiple access (FDMA), and we assume that the computation tasks are split into separate tasks and offloaded to the server for computing. We study stochastic computational resource management based on the Lyapunov optimization algorithm. The optimal transmit power and bandwidth allocation for computation offloading are obtained alternately, and the optimal computation task admission at each time slot and the optimal value of the auxiliary variable are derived. Simulation results verify the effectiveness of the proposed scheme in the paper and evaluate the influence of various parameters to the system performance.

关键词： Internet of Things   Mobile edge computing   Convergence computing   IoT data transmission  

摘要： The convergence of mobile edge computing (MEC) to the current Internet of Things (IoT) environment enables a great opportunity to enhance massive IoT data transmission. In the narrowband Internet of Things (NB-IoT), the huge number of IoT devices sends the information to the remote network. Simultaneously, the massive IoT devices from the heterogeneous locations access to the radio remote head (RRH) resources to communicate with the remote servers (e.g., mobile cloud computing), while there are limited resources in NB-IoT environments. Therefore, many IoT traffic may be discarded and may have too long queue times for each traffic where it can cause the battery life reduction of the IoT devices. To cope with the mentioned issues, this paper proposed a novel and efficient IoT data transmission scheme based on dynamic resource sharing of RRHs called efficient dynamic resource sharing scheme (E-DRSS). The proposed E-DRSS computes the heterogeneous IoT device with different resources into new groups, and then each group of IoT devices is selected for accessing its appropriate RRH. The evaluation of performance shows that the proposed scheme enhances quality of service (QoS) outperform on the current IoT scheme in terms of the jitters, end-to-end (E2E) latency, throughputs, and number of packets drop during transmission. Therefore, the proposed scheme is suitable to handle more IoT devices simultaneously and increase the communication reliability.

关键词： Mobile edge computing   Collaborative resource allocation   Optimized deployment of edge servers   Average access delay  

摘要： In order to realize the collaborative resource allocation optimization of mobile edge computing (MEC) and reduce the delay of edge server in the transmission process, based on software-defined network (SDN) technology, two optimal edge server deployment schemes of Enumeration-Based Optimal Edge Server Placement Algorithm (EOESPA) and Ranking-based Near-optimal Edge Server Placement Algorithm (RNOESPA) are proposed. Performance comparison experiment simulation is conducted with K-Means cluster algorithm (KMCA) to verify the minimum access delay of edge server under different conditions. After the deployment of edge servers, three collaborative resource allocation optimization algorithms of Optimal Enumeration Service Deployment Algorithm (OESDA), Latency Aware Heuristic Service Deployment Algorithm (LAHSDA), and Clustering Enhanced Heuristic Service Deployment Algorithm (CEHSDA) are proposed, and simulation experiments are carried out to verify the performance of the proposed algorithm under different conditions. The results show that, under different conditions, when the number of deployments increases from 1 to 4, the average access delay of EOESPA can be at least 1ms, and the average access delay obtained by RNOESPA is close to the best performance obtained by EOESPA and better than that obtained by KMCA. When the number of network nodes increases to 50, the minimum average access delay obtained by RNOESPA is closer to the optimal value, which is about 1.42ms. The same performance is shown in relation to the average number of requests, the number of mobile devices, and the average access delay. Among the three collaborative resource allocation optimization algorithms, the minimum average response delay obtained by LAHSDA is close to the optimal average response delay obtained by OESDA, but all of them are lower than CEHSDA, and CEHSDA has the best performance in minimizing the total allocation cost. When the number of service types increases to 8, the total servic

关键词： Artificial Intelligence   6G   Communication networks   Mobile edge computing   Intelligent transportation system  

摘要： With 6G flagship program launched by the University of Oulu, Finland, for full future adaptation of 6G by 2030, many institutes worldwide have started to explore various issues and challenges in 6G communication networks. 6G offers ultra high-reliable and massive ultra-low latency while opening the doors for many applications currently not viable by today's 4G and 5G communication standards. The current 5G technology has security and privacy issues which makes its usage in limited applications. In such an environment, we believe that AI can offer efficient solutions for the aforementioned issues having low communication overhead cost. Keeping focus on all these issues, in this paper, we presented a comprehensive survey on AI-enabled 6G communication technology, which can be used in wide range of future applications. In this article, we explore how AI can be integrated into different applications such as object localization, UAV communication, surveillance, security and privacy preservation etc. Finally, we discussed a use case that shows the adoption of AI techniques in intelligent transport system.