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： Federated learning   communication efficiency   mobile edge computing   Internet of Things  

摘要： Federated learning (FL) enables resource-constrained edge nodes to collaboratively learn a global model under the orchestration of a central server while keeping privacy-sensitive data locally. The non-independent-and-identically-distributed (non-IID) data samples across participating nodes slow model training and impose additional communication rounds for FL to converge. In this paper, we propose Federated Adaptive Weighting (FedAdp) algorithm that aims to accelerate model convergence under the presence of nodes with non-IID dataset. We observe the implicit connection between the node contribution to the global model aggregation and data distribution on the local node through theoretical and empirical analysis. We then propose to assign different weights for updating the global model based on node contribution adaptively through each training round. The contribution of participating nodes is first measured by the angle between the local gradient vector and the global gradient vector, and then, weight is quantified by a designed non-linear mapping function subsequently. The simple yet effective strategy can reinforce positive (suppress negative) node contribution dynamically, resulting in communication round reduction drastically. Its superiority over the commonly adopted Federated Averaging (FedAvg) is verified both theoretically and experimentally. With extensive experiments performed in Pytorch and PySyft, we show that FL training with FedAdp can reduce the number of communication rounds by up to 54.1% on MNIST dataset and up to 45.4% on FashionMNIST dataset, as compared to FedAvg algorithm.

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.

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： message transmission   mobile edge computing   secure messages   task offloading   vehicle communication  

摘要： With the rapid development of intelligent transportation, vehicle terminals generate a large number of data messages that need to be processed in real time, and the required computing and storage resources far exceed the load capacity of vehicle terminals. Mobile edge computing enables data resources to be processed near device terminals, and provides low-latency and high-reliability computing services to meet the power and service quality requirements of terminal devices. Therefore, in order to achieve better data resource management, this paper introduces mobile edge computing technology, and mainly researches secure message transmission optimization algorithms based on mobile edge computing. Firstly, we prioritize secure messages through the analytic hierarchy process. This can guarantee that the most urgent messages get the highest transmission level. Secondly, we establish an optimal task offloading model of delay and energy loss by assigning different weight factors to delay and energy loss. The Lagrangian relaxation method is used to transform the nonconvex problem into a convex problem. We use greedy algorithm to solve the main problem. Finally, the vehicle transmits secure messages through the topology of the local network within its defined communication range. Performance evaluation results show that the scheme not only reduces the redundant transmission of messages, but also improves the performance of end-to-end delay and message deliver success ratio of secure messages.

关键词： Optimization strategy   Computation offloading   Reinforcement learning   Mobile edge computing   Smart collaborative  

摘要： With the arrival of the 5th generation mobile networks (5 G) era, the data needed by mobile devices (MDs) is explosively growing. High-consumption, low-latency applications are huge challenges for resource-constrained Internet of things (IoT) devices. Mobile edge computing overcomes the limitations of computing resources on MDs by offloading tasks generated by MDs and assigning them to nearby MEC servers. Therefore, mobile edge computing (MEC) becomes important. This paper presents a task offloading strategy for the multi-device multi-server system. To meet the task requirements of different MDs, we formulate an overhead minimization problem to optimize the delay and energy consumption of the system. We propose the Double Deep Q Network (Double-DQN) algorithm to perform location selection strategies for tasks generated on the mobile devices and allocate respective computing resources. Simulation results show that the algorithm can allocate resources reasonably and reduce the overhead of the entire system.

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： Servers   Energy consumption   Edge computing   Resource management   Unmanned aerial vehicles   Trajectory   Wireless communication   Computation efficiency   mobile edge computing   trajectory optimization   unmanned aerial vehicle  

摘要： With the high altitude and flexible mobility, unmanned aerial vehicle (UAV) assisted mobile edge computing (MEC) is becoming a promising technology to cope with the computation-intensive and latency-critical task in prospective Internet of Things. In this paper, we propose a novel MEC system with several ground servers at access points and one aerial server carried by UAV. To balance the vital metrics of the MEC system, computation bits and energy consumption, we aim to maximize the weighted computation efficiency of the system, subject to the constraints on communication and computation resources, minimum computation requirement and UAV's mobility. To this end, a joint optimization problem with the goal of weighted computation efficiency maximization is formulated. First, we analyze the problem and transform it into an equivalent tractable form. Then, we solve the challenging non-convex problem by jointly optimizing the computation task assignment, time slot partition, transmission bandwidth and CPU frequency allocation, transmit power allocation, and UAV's trajectory, based on the Dinkelbach's method, Lagrange duality and successive convex approximation technique. Furthermore, we propose an alternative computation efficiency maximization algorithm, followed by the convergence and complexity analysis. Finally, numerical simulations show that our proposed algorithm significantly improves the computation efficiency compared to benchmark schemes. It is also validated that the proposed algorithm effectively obtains a good tradeoff between the computation task bits and energy consumption of the system.

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.