关键词： Auction   edge computing   cloudlet   fog computing   mobile edge computing   resource allocation   computing offloading   incentive   IoT   blockchain  

摘要： Edge computing as a promising technology provides lower latency, more efficient transmission, and faster speed of data processing since the edge servers are closer to the user devices. Each edge server with limited resources can offload latency-sensitive and computation-intensive tasks from nearby user devices. However, edge computing faces challenges such as resource allocation, energy consumption, security and privacy issues, etc. Auction mechanisms can well characterize bidirectional interactions between edge servers and user devices under the above constraints in edge computing. As demonstrated by the existing works, auction and mechanism design approaches are outstanding on achieving optimal allocation strategy while guaranteeing mutual satisfaction among edge servers and user devices, especially for scenarios with scarce resources. In this paper, we introduce a comprehensive survey of recent researches that apply auction approaches in edge computing. Firstly, a brief overview of edge computing including three common edge computing paradigms, i.e., cloudlet, fog computing and mobile edge computing, is presented. Then, we introduce fundamentals and backgrounds of auction schemes commonly used in edge computing systems. After then, a comprehensive survey of applications of auction-based approaches applied for edge computing is provided, which is categorized by different auction approaches. Finally, several open challenges and promising research directions are discussed.

关键词： Delays   Task analysis   Servers   Mobile handsets   Minimization   Processor scheduling   Wireless communication   Full-duplex   mobile edge computing   computation offloading   resource allocation  

摘要： Mobile edge computing (MEC) which enables communication, computation and storage capabilities at the network edge, is an emerging paradigm for 5G and beyond 5G networks. Resource-limited mobile devices can offload their tasks to the MEC server to reduce computation latency and save energy consumption. However, limited wireless spectrum resources make it hard for MEC to guarantee low latency when a large number of users offload the computation tasks or download the computed results. Full-duplex (FD) communication that allows simultaneous transmission and reception of signals over the same frequency band is a promising solution to the shortage of spectrum resources. In this paper, we study a multi-user FD-MEC system involving all the phases of uploading, computing, and downloading. With the help of an FD-capable base station (BS), two half-duplex (HD) users can form an FD pair and share the same time-frequency resource for task uploading and result downloading. We formulate a latency minimization problem considering power control, time scheduling, offloading decision, and user pairing strategy, which is a mixed-integer non-linear programming (MINLP) problem. The problem can be decomposed into two subproblems. The first subproblem is delay minimization for each possible FD pair, and the second one is to decide the offloading and user pairing strategy, and the user scheduling order. We analyze the problem in two cases: (1) the computing delay at the edge server is negligible and (2) the computing delay at the edge server is non-negligible. For both cases, the first subproblem is solved using a bisection algorithm. Then, we solve the second subproblem in the two cases by a relaxing-based algorithm and a concave-convex procedure (CCCP)-based algorithm, respectively. Finally, numerical results demonstrate that with our proposed strategies, the overall latency can be significantly reduced by integrating FD communication with the MEC system.

关键词： UAV   Dual-UAV trajectory   Mobile edge computing   Intelligent reflective surfaces (IRS)   Energy minimization   Full offloading   Secure communications  

摘要： In this paper, we focus on minimizing energy consumption under the premise of ensuring the secure offloading of ground users. We used dual UAVs and intelligent reflective surfaces (IRS) to assist ground users in offloading tasks. Specifically, one UAV is responsible for collecting task data from ground users, and the other UAV is responsible for sending interference noise to counter potential eavesdroppers. The IRS can not only improve the transmission capacity of ground users, but also reduce the acceptance of eavesdroppers. The original problem is strong non-convex, so we consider using the block coordinate descent method. For the proposed sub-problems, we use Lagrangian duality and first-order Taylor expansion to obtain the results, and finally achieve system design through alternate optimization. The simulation results show that our proposed scheme is significantly better than other existing schemes. (C) 2022 Elsevier B.V. All rights reserved.

关键词： Cognitive radio networks   delay optimization   gradient projection method   mobile edge computing   non-orthogonal multiple access   unmanned aerial vehicle  

摘要： Mobile edge computing (MEC) in cognitive radio networks is an optimistic technique for improving the computational capability and spectrum utilization efficiency. In this study, we developed an MEC system assisted by a cognitive unmanned aerial vehicle (CUAV), where a CUAV equipped with an MEC server can serve as a relay node and computing node. In such networks, a non-orthogonal multiple access scheme is considered to serve enhanced mobile broadband communication (eMBB) and massive machine-type communication (mMTC) users, in which the transmission delay for both users is derived. To optimize the delay in this system, we formulated an optimization problem aimed at minimizing the processing delay of eMBB and mMTC users by jointly optimizing the transmit power of the users' information, considering the constraints of the transmit power of the secondary network. The numerical results demonstrate that the proposed Rosen's gradient projection algorithm can considerably minimize the processing delay for a CUAV with a fixed position compared with a CUAV with a predetermined trajectory.

关键词： Task analysis   Servers   Mobile handsets   Costs   Resource management   Optimization   Internet of Things   Mobile edge computing   Internet of Things   task offloading   resource allocation   multiple knapsack problem  

摘要： With the rapid development of Artificial Intelligence (AI) and Internet of Things (IoT), we have to perform increasingly more resource-hungry and compute-intensive applications on IoT devices, where the available computing resources are insufficient. With the assistance of Mobile Edge Computing (MEC), offloading partial complex tasks from mobile devices to edge servers can achieve faster response time and lower energy consumption. However, it still suffers from finding the optimal offloading decision when the total amount of computations overflows the available computing resources in MEC systems. In this paper, we establish a multi-user and multi-task MEC model and design an offloading indicator, through which we analyze what the current environment belongs to. In the cases where the computational resources of devices are sufficient or partially sufficient, we utilize the relationship between the offloading indicator and the cost incurred by the tasks that are executed in the current workflow to find the optimal offloading decision. In the cases where the computation on local and edge are both insufficient, we propose a novel Offloading Algorithm based on K-means clustering and Genetic algorithm for solving Multiple knapsack problem (OAKGM), aiming not only to jointly optimize the time and energy incurred by the tasks that are executed in the current workflow, but also to penalize the overflowed computations so that the task pressure in the next workflow can be greatly reduced. In addition, a simplified Offloading Algorithm based on Multiple Knapsack Problem (OAMKP) is proposed to further cope with the environments with a large number of users or tasks. Experimental results demonstrate the effectiveness and superiority of the proposed algorithms when compared with several benchmark offloading algorithms, which can better exploit the computing capacities of IoT devices and the edge server, greatly avoid resource occupation in edge nodes and make sustainable MEC possi

关键词： Task analysis   Security   Wireless communication   Communication system security   Array signal processing   Optimization   Servers   Computation efficiency   mobile edge computing   reconfigurable intelligent surface   security  

摘要： Mobile edge computing (MEC) has been recognized as a viable technology to satisfy low-delay computation requirements for resource-constrained Internet of things (IoT) devices. Nevertheless, the broadcast feature of wireless electromagnetic communications may lead to the security threats to IoT devices. In order to enhance the task offloading security, this paper proposes a reconfigurable intelligent surface (RIS)-assisted secure MEC network framework. Furthermore, we investigate the max-min computation efficiency problem under the secure computation rate requirements, by jointly optimizing the local computing frequencies and transmission power of IoT devices, time-slot assignment, and phase beamforming of the RIS. To solve the formulated non-convex problem, we further develop an iterative algorithm, in which the Dinkelbach-type method and block coordinate descent (BCD) technique are utilized to tackle the fractional objective function and coupled optimization variables, respectively. In particular, the successive convex approximation (SCA) and penalty function-based methods are exploited to solve the transmit power control and reflecting beamforming optimization subproblems, respectively, and the closed-form expression for local computing frequencies optimization subproblem is derived. Numerical results quantify the performance gain achieved by the proposed RIS-assisted secure MEC networks, when compared to existing benchmark methods.

关键词： Computation offloading   Mobile edge computing   Resource allocation   Networks optimization  

摘要： Computation offloading is one of the key technologies in Mobile Edge Computing (MEC), which makes up for the deficiencies of mobile devices in terms of storage resource, computing capacity, and energy efficiency. On one hand, computation offloading of task requests not only relieves the communication pressure on the core networks but also reduces the delay caused by long-distance data transmission. On the other hand, emerging applications in 5/6G also rely on the computation offloading technology for efficient service provisioning to users. At present, the industry and academia have conducted a lot of researches on the computation offloading methods in MEC networks with a diversity of meaningful techniques and approaches. In this paper, we present a comprehensive survey of the computation offloading in MEC networks including applications, offloading objectives, and offloading approaches. Particularly, we discuss key issues on various offloading objectives, including delay minimization, energy consumption minimization, revenue maximization, and system utility maximization. The approaches to achieve these objectives mainly include mathematical solver, heuristic algorithms, Lyapunov optimization, game theory, and Markov Decision Process (MDP) and Reinforcement Learning (RL). We compare the approaches by characterizing their pros and cons as well as targeting applications. Finally, from the four aspects of subtasks dependency and online task requests, server selection, real-time environment perception, and security, we analyze the current challenges and future directions of computation offloading in MEC networks.

关键词： wireless sensor networks   mobile edge computing   migration strategy   optimization coverage,networks lifetime  

摘要： The traditional network coverage mode with the cost of deploying a large number of sensor nodes has poor coverage effect. Aiming at this problem, this paper proposes a Novel Optimized Coverage Algorithm with Mobile Edge Computing of Migration Strategy (MEC-MS). First, the algorithm uses the network coverage model to give the expression method of the distance measurement and the judgment conditions of the best and worst paths. Secondly, it analyzes the necessary conditions for improving the coverage quality and the prerequisite for the existence of redundant coverage for adjacent the redundant coverage nodes by the theory of probability. Thirdly, using the precondition of redundant coverage, we give the calculation process of the sensor nodes own redundant coverage and the calculation method of the redundant node coverage expectation. Finally, the algorithm compares the number of working sensor nodes with the other two algorithms under different parameters. The experimental results show that the average number of working sensor nodes in the MEC-MS algorithm is 9.74% lower than that of the other two algorithms, and the average value of network coverage is 9.92% higher than that of the other two algorithms, which verify the effectiveness of the algorithm in this paper.

关键词： mobile edge computing   blockchain   proof of learning   resource allocation   asynchronous advantage Actor-Critic   temporal convolutional network  

摘要： Currently, the concept of Mobile Edge Computing (MEC) has been applied as a solution against the plethora of demands for high-quality computing services. It comprises several essential processes, such as resource allocation, data transmission, and task processing. Furthermore, researchers applied blockchain technology, aiming to enhance the robustness of the MEC system. At present, resource allocation in the MEC system is a very hot field, but there are still some problems in the resource allocation process under the traditional MEC architecture, such as privacy disclosure and so on. Moreover, the resource allocation problem in a blockchain-enabled MEC system will be more complicated, while the mining process may have an impact on resource allocation policy. To address this issue, this paper investigates the resource allocation problem with blockchain-based MEC system architecture. A brand new consensus mechanism: proof of learning (PoL), is applied to the system, which does not waste the computing resources of edge computing servers. Based on this, we modeled the system mathematically, focusing on server processing latency, mining latency, rewards under the new consensus, and total cost. The asynchronous advantage Actor-Critic (A3C) algorithm is used to optimize resource allocation policy. To better capture the long-time trend of the system, the temporal convolutional network (TCN) is implemented to represent the policy function and state-value function in the reinforcement learning model. The results show that the A3C algorithm based on TCN not only converges faster but also is more stable.

关键词： Computing offloading   Non-orthogonal multiple access   Deep learning   Internet of medical things   Offloading decision   Mobile edge computing  

摘要： Aiming at the problem that it is quite hard to guarantee the real-time requirements of medical users with high efficiency and low latency in the current Internet of Medical Things (IoMT), we investigate the task offloading for collaborative cloud-edge-end computing in mobile networks. Non-orthogonal multiple access (NOMA) is suitable for wireless networks with higher spectral efficiency, faster speed, and larger capacity, while the existing cloud-edge-end cooperative computing ignores the advantages of NOMA. Therefore, by exploiting NOMA for improving the efficiency of radio transmission, we integrate collaborative cloud-edge-end computing and NOMA to propose a novel network communication model, which can provide medical users with energy-efficient and low latency services. Specifically, considering the energy consumption, transmission delay, and quality of service, we jointly optimize the offloading decision and its radio resource allocations for NOMA-transmission to reduce the system cost (the weighted sum of consumed energy and delay) in the IoMT of cloud-edge-end computing networks supported by NOMA. Although the joint optimization problem is non-convex, we use its hierarchical structure and propose a collaborative computing offloading algorithm based on deep learning to find the optimal offloading solution. Through extensive simulations, it is shown that the proposed algorithm stably converges to its optimal value, provides approximately 25.2% and 79.2% lower system cost than schemes such as only using edge computing and fully local processing, respectively. In addition, compared with the traditional orthogonal multiple access(OMA), our proposed NOMA-enabled multi-access computation offloading can reduce the system cost by approximately 93.4%.