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： DDQN Model   Internet of Vehicles   Markov Decision Model   Mobile Edge Computing   System Model  

摘要： An efficient and reasonable resource allocation strategy can greatly improve the service quality of Internet of Vehicles (IoV). However, most of the current allocation methods have overestimation problem, and it is difficult to provide high-performance IoV network services. To solve this problem, this paper proposes a network resource allocation strategy based on deep learning network model DDQN. Firstly, the method implements the refined modeling of IoV model, including communication model, user layer computing model, edge layer offloading model, mobile model, etc., similar to the actual complex IoV application scenario. Then, the DDQN network model is used to calculate and solve the mathematical model of resource allocation. By decoupling the selection of target Q value action and the calculation of target Q value, the phenomenon of overestimation is avoided. It can provide higher-quality network services and ensure superior computing and processing performance in actual complex scenarios. Finally, simulation results show that the proposed method can maintain the network delay within 65 ms and show excellent network performance in high concurrency and complex scenes with task data volume of 500 kbits.

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： Mobile edge computing   power control   invex   joint heterogeneous resource allocation  

摘要： Energy saving for mobile devices is considered to be one of prospective benefits of mobile edge computing (MEC) networks, where computation-intensive tasks can be offloaded from the mobile devices to their associated MEC servers for execution. Extra energy consumption for data migration should therefore be less than the energy consumption for local execution. However, in multi-cell MEC-assisted networks, due to both the presence of co-channel interference and the latency requirement of each offloading task, power control is tightly coupled with computation offloading, which becomes an obstacle to achieve the aim of energy' saving. In this paper, we develop an analytic model to decouple power control and computation resource allocation from each other, in which the transmission power can he considered as a solution to a set of linear equations with a coefficient matrix depending on the computation resource budget. Based on this analytic foundation, we show that with a fixed offloading decision, the joint power control and computation resource allocation problem is invex, which ensures that every KKT (Karush-Kuhn-Tucker) stationary point of the problem must be a global minimizer. Moreover, we deduce a criterion for energy-efficient offloading decision making from the partial derivative of the total energy consumption of mobile devices with respect to the computation resource budget. Finally, we propose a heuristic algorithms to jointly optimizing power and computation resource allocation, and offloading decision. The numerical results demonstrate the optimality and efficiency of our proposed algorithm.

关键词： Mobile edge computing   Early-warning of financial risks   Internet of things   Service preloading   Back propagation neural network   Cash flow  

摘要： The present work aims to apply the Internet of Things (IoT) to help enterprises improve financial management, timely discover hidden financial risks, and enhance the ability to resist risks. Firstly, the traditional financial risk early-warning model based on accounting information is analyzed. Accordingly, a corporate risk early-warning model is put forward from the perspective of cash flow, and the financial risk early-warning indicator system is established. Secondly, the Backpropagation neural network (BPNN) is employed to mine the financial data of the enterprise. After pre-processing, the data are input into the model according to the type of financial risk early-warning indicators to obtain the financial risk prediction result of the company. Thirdly, the mobile edge computing (MEC) service is introduced into the corporate financial management to improve the calculation performance of the corporate financial information processing and strengthen the timeliness of cash flow information and risk warning cooperating with IoT. Finally, an edge service preloading optimization model is established based on the Geographic Point of Interest Information and BPNN. According to the feature vector of the user location, the next service probability of the user is predicted, and the service preloading is carried out on the accessed edge server. Finally, the effect of this model is verified by experiments. The experimental results indicate that the prediction accuracy of the risk early-warning model designed here for financial health and crisis is 91.6% and 75%, respectively, and the response rate of the service preloading optimization model is between 2.1% and 5%. Therefore, service preloading can improve the response speed of corporate financial risk early-warning to a certain extent. This exploration provides a reference for the study of the corporate financial risk early-warning model based on MEC and IoT.

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： 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%.

关键词： mobile edge computing   computational offloading   optimal stopping theory   structured tasks   time optimization   sequential decision making  

摘要： Vehicular edge computing is a new computing paradigm. By introducing edge computing into the Internet of Vehicles (IoV), service providers are able to serve users with low-latency services, as edge computing deploys resources (e.g., computation, storage, and bandwidth) at the side close to the IoV users. When mobile nodes are moving and generating structured tasks, they can connect with the roadside units (RSUs) and then choose a proper time and several suitable Mobile Edge Computing (MEC) servers to offload the tasks. However, how to offload tasks in sequence efficiently is challenging. In response to this problem, in this paper, we propose a time-optimized, multi-task-offloading model adopting the principles of Optimal Stopping Theory (OST) with the objective of maximizing the probability of offloading to the optimal servers. When the server utilization is close to uniformly distributed, we propose another OST-based model with the objective of minimizing the total offloading delay. The proposed models are experimentally compared and evaluated with related OST models using simulated data sets and real data sets, and sensitivity analysis is performed. The results show that the proposed offloading models can be efficiently implemented in the mobile nodes and significantly reduce the total expected processing time of the tasks.

关键词： mobile edge computing   ultra-dense network   computation offloading   deep reinforcement learning  

摘要： Mobile edge computing (MEC) has the potential to realize intensive applications in 5G networks. Through migrating intensive tasks to edge servers, MEC can expand the computing power of wireless networks. Fifth generation networks need to meet service requirements, such as wide coverage, high capacity, low latency and low power consumption. Therefore, the network architecture of MEC combined with ultra-dense networks (UDNs) will become a typical model in the future. This paper designs a MEC architecture in a UDN, which is our research background. First, the system model is established in the UDN, and the optimization problems is proposed. Second, the action classification (AC) algorithm is utilized to filter the effective action in Q-learning. Then, the optimal computation offloading strategy and resource allocation scheme are obtained using a deep reinforcement learning-based AC algorithm, which is known as the DQN-AC algorithm. Finally, the simulation experiments show that the proposed DQN-AC algorithm can effectively reduce the system weighted cost compared with the full local computation algorithm, full offloading computation algorithm and Q-learning algorithm.

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.