关键词： mobile edge computing   task offloading   game theory   Nash equilibrium  

摘要： Various types of service applications increase the amount of computing in vehicular networks. The lack of computing resources of the vehicle itself will hinder the improvement of network performance. Mobile edge computing (MEC) technology is an effective computing method that is used to solve this problem at the edge of network for multiple mobile users. In this paper, we propose the multi-user task offloading strategy based on game theory to reduce the computational complexity and improve system performance. The task offloading decision making as a multi-user task offloading game is formulated to demonstrate how to achieve the Nash equilibrium (NE). Additionally, a task offloading algorithm is designed to achieve a NE, which represents an optimal or sub-optimal system overhead. In addition, the vehicular communication simulation frameworks Veins, SUMO model and OMNeT++ are adopted to run the proposed task offloading strategy. Numerical results show that the system overhead of the proposed task offloading strategy can degrade about 24.19% and 33.76%, respectively, in different scenarios.

关键词： Mechanism design   task offloading   resource allocation   mobile edge computing   network economics  

摘要： With the rapid popularity of IoT devices and smart city technologies, mobile edge computing considered as a promising technology appears, which can support real-time applications at the edge of the network. In this paper, we focus on the auction mechanism in mobile edge computing to allocate resources between IoT devices regarded as buyers and edge servers regarded as sellers and establish a two-sided relationship based on network economics to depict the process of resource allocation. Next, two double auction mechanisms named TMF and EMF are proposed, which aim at optimizing the rationality of resource allocation by considering successful trades and social welfare. Both TMF and EMF are applied to the scenario that each seller services to multi-buyers and each buyer also requests for multi-sellers with different sizes of resources requirements. The TMF mechanism we proposed is a truthful mechanism for both buyers and sellers and EMF is an efficient mechanism with high successful trades and social welfare. Then, a theoretical analysis is given and our proposed mechanisms are proved to be computational efficiency, individual rationality and incentive compatibility. Finally, the simulation results indicate that our mechanisms perform better than traditional mechanisms and improve successful trades and social welfare in mobile edge computing.

关键词： Servers   Costs   Computational modeling   Switches   Delays   Reinforcement learning   Vehicle dynamics   Mobile edge computing   edge server   user mobility   deep reinforcement learning  

摘要： With Mobile Edge Computing (MEC), computational intensive applications can be offloaded to the nearby edge servers to support latency-sensitive applications on mobile devices. Different from the cloud, edge servers usually have limited resources, and then selecting which edge server to run the offloaded computation becomes an important issue. Although server selection has received considerable attention, not much work has been done to consider the limited coverage of the edge server and the frequent user movement, which introduce many dynamic changing factors affecting the workload of the edge server and making it hard to achieve long-term optimum in the edge server selection. To deal with these challenges, we model the problem of continuous server selection as a Markov Decision Process (MDP). The difficulty of this problem is that achieving long-term optimum requires future knowledge, such as user mobility, server workload, etc, which is not known a priori. We do not have such knowledge and thus cannot find the optimal policy through traditional methods. To address this problem, we propose a Deep Reinforcement Learning (DRL) based algorithm to learn the selection policy based on the observed performance of past server selections. Specifically, a Long Short-Term Memory (LSTM) based neural network is exploited to encode the historical information which helps infer future knowledge of the dynamically changing factors. Then the DRL model selects the optimal server automatically based on the extracted system states. Extensive trace-driven evaluations demonstrate that the proposed DRL-based algorithm has the lowest overall cost compared to existing solutions.

关键词： Mobile edge computing   user association   hybrid beamforming   DC programming   power allocation  

摘要： Mobile edge computing (MEC) can provide computing and storage services to user equipments (UEs) by utilizing edge nodes known as the small base stations (SBS's) deployed at the edge of the network. The short-distance transmission nature between SBS's and UEs makes the millimeter-wave (mmWave) communication empowered with multiple-input multiple-output (MIMO) hybrid precoding techniques particularly attractive for MEC. In this work, we consider the UE-SBS association, precoding design and power allocation for MEC networks endowed with mmWave MIMO. More specifically, the user association problem is first formulated as a max-k-cut (M k C) problem and then, solved by a distributed local-search algorithm. Next, the joint optimization of precoding and power allocation is cast into the difference of two convex functions (D.C.) programming framework before an iterative rank-constrained D.C. programming algorithm is developed to maximize the weighted sum-rate (WSR) of all UEs while taking into account the quality of service (QoS) requirement of each UE. Furthermore, the monotonic convergence of the proposed iterative algorithm is analytically proven. Finally, extensive computer simulation is conducted to demonstrate the effectiveness of the proposed iterative algorithm.

关键词： Relays   6G mobile communication   NOMA   Unmanned aerial vehicles   Optimization   Jamming   Wireless communication   5G mobile communication   Mobile edge computing   UAV   relay transmission   physical layer security  

摘要： With the continuous development of B5G/6G cellular networks, the explosive growth of mobile Internet services and the resulting tremendous traffic demand have led to a significantly heavy pressure on how to efficiently satisfy the users' traffic demands with guaranteed quality of services, especially the coverage at the cell-edge. Thanks to the advantage in flexible deployment, the unmanned aerial vehicle (UAV) has been considered as a promising approach for providing efficient data access for cell-edge users (CEUs), which usually suffer from a poor connection to the cellular base stations (BSs). In this paper, we consider a scenario of a group of CEUs with each CEU having both the task of delivering data to the cellular BS (i.e., the data-transmission task) and the task of completing a given computation workload (i.e., the computation-task). Meanwhile, we consider that the UAV serves as a dual-role (DUAV), i.e., the relay for forwarding CEUs' data to the cellular BS, and the edge-server for processing the CEUs' offloaded computation workloads. Taking into account that a malicious node which overhears the DUAV's relay transmission, we investigate the secrecy driven UAV assisted relay transmission via cooperative computation offloading, in which we exploit the CEUs' computation-offloading transmission to provide cooperative jamming to the malicious node who overhears the DUAV's relay transmission. Specifically, we formulate a joint optimization of the DUAV's hovering position, transmit-power and computation-rate allocation as well as the CEUs' transmission duration and computation offloading, with the objective of minimizing the total energy consumption of the DUAV and all CEUs. Despite the non-convexity of the formulated joint optimization problem, we propose an efficient algorithm for finding the optimal solution. With the optimal offloading and transmission solutions provided by the polyblock approximation based algorithm (PA-Algorithm) under the given hovering po

关键词： Blockchains   Servers   Edge computing   Wireless communication   Resource management   Games   Bitcoin   Mobile edge computing   blockchain   IoT   nonce ordering   non-cooperative game  

摘要： Deploying a mobile edge computing (MEC) server in the mobile blockchain-enabled Internet of things (IoT) system is a promising approach to improve the system performance, however, it imposes a significant challenge on the trust of the MEC server. To address this problem, we first propose an untrusted MEC proof of work (PoW) scheme in mobile blockchain networks where plenty of nonce hash computing demands can be offloaded to the MEC server. Then, we design a nonce ordering algorithm for this scheme to provide fairer computing resource allocation for all mobile IoT devices/users. Specifically, we formulate the user's nonce selection strategy as a non-cooperative game, where utilities of the individual user are maximized in the untrusted MEC-aided mobile blockchain networks. We also prove the existence of Nash equilibrium and analyze that the cooperation behavior is unsuitable for blockchain-enabled IoT devices by using the repeated game. Finally, we design the blockchain's difficulty adjustment mechanism to ensure stable block times during a long period of time. Compared with the weighted round-robin algorithm, our proposed nonce ordering algorithm can provide fairer computation resources and optimal nonce selection strategies for all mobile users. Network stability is gained through the proposed blockchain's difficulty adjustment mechanism. The analysis and optimization results provide valuable design insights for practical mobile blockchain IoT systems.

关键词： Mobile edge computing   Overlap area user allocation   Multi-objective optimization   Pareto frontier   Principal component analysis  

摘要： The rapid development of mobile communication technology has promoted the emergence of mobile edge computing (MEC), which allows mobile users to transfer their computing tasks to nearby edge servers to reduce access latency. In the actual MEC environment, the signal coverage areas of edge servers usually overlap partially, and users in the overlapped areas can choose to connect to one of the edge servers that cover them. How to allocate these users will seriously affect MEC performance. To solve this issue, we focus on the overlapped area user allocation (OAUA) problem in the MEC environment and model it as a multi-objective optimization problem. The objective is to balance the workload among edge servers and minimize the access delay between users and edge servers. Pareto model is universal for solving multi-objective optimization problems. However, the traditional method has high computational complexity to find the Pareto boundary. Therefore, we propose a Pareto boundary search algorithm based on convex hull to reduce the complexity of the algorithm. Since the Pareto boundary is a set of optimal solutions, which contains multiple optimal solutions, we further propose to use the principal component analysis algorithm to find the most suitable solution from the Pareto boundary as the final user allocation strategy. Our experiments use real data sets and compare the performance with several other baseline methods to verify the effectiveness of our proposed solution.

关键词： Task analysis   Resource management   Delays   Energy consumption   Servers   Cloud computing   Optimization   Caching   computation offloading   mobile edge computing   resource allocation  

摘要： Utilizing the data caching technology to reduce data transmission is a promising technique for improving the performance of mobile edge computing (MEC), because the delay and energy consumption produced by data transmission constitute the dominant cost of task execution in MEC. Besides, computation tasks generally consist of input parameters, executive codes, and computation results. The executive codes are fixed and can output difference computation results under different input parameters. Motivated by this, we consider to proactively cache executive codes of tasks at the MEC server to reduce the weighted sum of task execution delay and users' energy consumption. Aiming at establishing optimal system design, we formulate the problem as a non-linear programming problem which involves jointly optimizing the executive code caching strategy, computation offloading decision, wireless resource allocation, and computing resource allocation. We propose to find the optimal solution by employing an alternating optimization framework. The optimal wireless resource and computing resource allocation problem are firstly addressed by utilizing convex optimization technology. Then, a dynamic programming-based algorithm has been developed to achieve the optimal executive code caching and computation offloading strategies. Extensive simulation results show that the proposed scheme operates well and can substantially reduce the system cost over other benchmark schemes.

关键词： Task analysis   Energy consumption   NOMA   Unmanned aerial vehicles   Uplink   Trajectory   Minimization   Computation capacity   mobile edge computing   NOMA   time allocation   UAV  

摘要： An unmanned aerial vehicle (UAV)-assisted mobile edge computing (MEC) networks fulfil the on-demand computation services for the mobile terminals (MTs) with its high mobility and easy deployment. Thus, reduction in the latency is observed but the energy efficiency is still a major issue in this network, as both the UAV and MTs have limited energy storage batteries. Also, to support the massive connectivity, and to handle the data traffic generated by the MTs in an uplink scenario, using nonorthogonal multiple access (NOMA) is accomodated. The NOMA-based MEC provides flexible computing services to MTs. In this article, our goal is to minimize the total energy consumption of the NOMA-based MEC networks underlaying UAV with time, computation capacity, and UAV trajectory. The formulated model is a nonlinear programming problem;for the solution, it is divided into two subproblems: joint time allocation and task computation capacity, and UAV trajectory optimization. To solve the joint time allocation and computation task capacity, we proposed an iterative algorithm with low complexity, where closed-form solutions are obtained in each step. Next, to optimize the trajectory of the UAV, we used the successive convex approximation technique. Numerical results demonstrated that the proposed scheme achieves better results as compared to the orthogonal multiple access and equal resource allocation schemes.

关键词： Blockchains   Delays   Task analysis   Servers   Games   Resource management   Iterative methods   Blockchain   mobile edge computing   computation offloading   mining delay   non-cooperative game  

摘要： The proof-of-work (PoW) mining process requires a large amount of intensive computing, which leads to some plights such as heavy equipment and fixed access nodes in traditional blockchain networks. A novel mobile blockchain network with the help of a mobile edge computing (MEC) server is presented, where all mobile users participate in the PoW mining process. The traditional Bitcoin network adjusts the target difficulty value to ensure a stable block time. However, for MEC-assisted mobile blockchain networks, the adjusted difficulty value needs to be broadcast to all mobile users, which results in expensive communication costs. To maintain a stable block time of mobile blockchain networks, we formulate the delay-limited computation offloading strategy of the PoW-based mining task as a non-cooperative game that maximizes an individual revenue in the MEC-assisted mobile blockchain network. Specifically, the non-cooperative game problem can be divided into multiple sub-game optimization problems to obtain final solutions for all users. We analyze the sub-game optimization problem and prove the existence of Nash equilibrium (NE) of the non-cooperative game. Moreover, we design an alternating iterative algorithm based on the continuous relaxation and greedy rounding (CRGR) to achieve the NE of this game. Given the sub-optimal delay-limited computation offloading results, we also derive the optimal transmit power for an individual user within the maximum mining delay range. From the analytical results, we can see that the proposed CRGR-based alternating iterative algorithm can efficiently attain the sub-optimal delay-limited computation offloading strategies of all mobile users in the polynomial time. The individual transmit power increases accordingly with the delay-limited computation offloading strategies of all users. Numerical results demonstrate that the proposed CRGR-based alternating iterative algorithm has fast convergence and good stability.