关键词： Emerging computing paradigms   IoT   Mobile cloud computing   Mobile edge computing   Mobile learning applications   Mobile learning architectures  

摘要： The emerging computing paradigms like mobile cloud, mobile edge computing, fog computing, AI, and 5G provide the scope to advance mobile learning (m-learning) performances across educational disciplines. Edge computing brings the computing power at the edge of the network and closer to mobile learning actors. This study investigates the potentials of emerging computing paradigms and proposes a novel mobile edge-based hierarchical architecture to optimize m-learning performance efficiency. The architecture's design follows the ETSI MEC ISG framework and ISG MEC technical requirements. An m-learning use case was executed to evaluate the architecture's performance efficiency in a university facility. The implementation results show reduced latencies and quick response time. The architecture handles data locally on-site, lowers demand on radio access bandwidth, increases confidential data privacy, and continues the application execution even the network is disrupted. The architecture is open to redesign, reconfigure, and integrate with other computing paradigms. IoT-based m-learning use cases should be considered by reflecting learners' scaling of resource needs. Execution of complex use cases will expand the architecture base, MEC-based learning models usage, and change learning-teaching dynamics across educational disciplines.

关键词： asymmetric group key agreement   intelligent information system   mobile edge computing   secure communication  

摘要： Security communication and information sharing among mobile devices are important application technologies of the intelligent information system (IIS). Because IIS is vulnerable to attacks, so the security of information sharing among mobile devices is seriously threatened. Thence, it is necessary to establish a secure channel for communication among mobile devices of IIS over an opening network. Group key agreement (GKA) can establish a secure channel among mobile devices of IIS by encryption technology. Due to the resource-constraints of mobile devices, such as weak computing power, small storage capacity, and limited communication range. To address these issues, an asymmetric GKA protocol among terminals of IIS for mobile edge computing (GKA-IIS-MEC) network is proposed in this paper. Adopted asymmetric GKA to achieve the group secure communication mechanisms that message sender unfettered in this protocol;the protocol also uses edge computing environment to migrate the computation and communication loads of mobile devices of IIS to edge nodes, thereby ensuring that mobile devices have lightweight computation and communication loads;and the members participating in the GKA can verify whether the group session keys they calculated are correctness. Under the hardness assumption of bilinear inverse Diffie-Hellman problem, the proposed protocol is proven that it can resist negative attacks. After evaluating the performance of the protocol, GKA-IIS-MEC has higher efficiency than the referred works in terms of time cost, communication consumption, and computation consumption.

关键词： Servers   Task analysis   Autonomous aerial vehicles   Energy consumption   Array signal processing   Time-frequency analysis   Resource management   Energy efficiency   UAV-mounted RIS   mobile edge computing   trajectory design   passive beamforming  

摘要： Unmanned aerial vehicle (UAV) and reconfigurable intelligent surface (RIS) have been recently applied in the field of mobile edge computing (MEC) to improve the data exchange environment by proactively changing the wireless channels through maneuverable location deployment and intelligent signals reflection, respectively. Nevertheless, they may suffer from inherent limitations, e.g., UAV's low endurance and RIS's finite coverage, which limit their wider applications. UAV-mounted RIS (U-RIS), as a promising integrated approach, can combine the advantages of UAV and RIS to break the limit. Inspired by this, we consider a novel U-RIS assisted MEC system, where a U-RIS is deployed to assist the communication between the ground users and an MEC server. The joint UAV trajectory, RIS passive beamforming and MEC resource allocation design is developed to maximize the energy efficiency (EE) of the system. To tackle the intractable non-convex problem, we divide it into two subproblems and solve them iteratively based on successive convex approximation (SCA) and the Dinkelbach method. Finally we obtain a high-performance suboptimal solution. Simulation results show that the proposed algorithm significantly improves the energy efficiency of the MEC system.

关键词： Bandwidth   Feature extraction   Downlink   Servers   Monitoring   Long short term memory   Wireless communication   Mobile edge computing   RAN monitoring information   CNN   LSTM   bandwidth prediction  

摘要： In this letter, we propose an edge-assisted bandwidth prediction scheme based on a hybird convolutional neural network (CNN) and long short-term memory (LSTM) architecture. To accurately predict available downlink bandwidth with limited and regulated monitoring information, we decouple feature extraction and sequence learning to capture the feature vector by CNN and resolve the historical feature collection issue by LSTM, respectively. A two-stage training strategy is further adopted to improve the prediction accuracy and generalization ability. In addition, we deploy and evaluate the proposed bandwidth prediction scheme in our prototype system. The test results show that our proposed scheme improves the mean absolute error (MAE) and root mean square error (RMSE) by 81.4% and 82.0%, respectively, compared with the LSTM only prediction scheme, and consumes less than 3 ms running time, which is sufficient for many real-time video transmission applications.

关键词： LEO satellites   mobile edge computing   deep learning   task offloading   bandwidth allocation  

摘要： Huge low earth orbit (LEO) satellite networks can achieve global coverage with low latency. In addition, mobile edge computing (MEC) servers can be mounted on LEO satellites to provide computing offloading services for users in remote areas. A multi-user multi-task system model is modeled and the problem of user's offloading decisions and bandwidth allocation is formulated as a mixed integer programming problem to minimize the system utility function expressed as the weighted sum of the system energy consumption and delay. However, it cannot be effectively solved by general optimizations. Thus, a deep learning-based offloading algorithm for LEO satellite edge computing networks is proposed to generate offloading decisions through multiple parallel deep neural networks (DNNs) and store the newly generated optimal offloading decisions in memory to improve all DNNs to obtain near-optimal offloading decisions. Moreover, the optimal bandwidth allocation scheme of the system is theoretically derived for the user's bandwidth allocation problem. The simulation results show that the proposed algorithm can achieve a good convergence effect within a small number of training steps, and obtain the optimal system utility function values compared with the comparative algorithms under different system parameters, and the time cost of the system and DNNs is very satisfactory.

关键词： Mobile edge computing   intelligent reflective surface   internet of vehicles   deep reinforcement learning   digital twin   6G  

摘要： Digital Twin (DT) and Intelligent Reflective Surface (IRS), the most two promising technologies of 6G make the Internet of Vehicles (IoV) more adaptive. However, future autonomous driving needs powerful networking resources and high-quality wireless communications to guarantee the Quality of Service (QoS). Especially considering the time-varying physical operating environments of IoV, it is extremely urgent to improve resource utilization and wireless channel quality. In this work, we propose a Digital Twin-Driven Vehicular Task Offloading and IRS Configuration Framework (DTVIF) to efficiently monitor, learn, and manage the IoV. Specifically, we adopt Mobile Edge Computing (MEC) and IRS to provide augmented computing capacities for vehicles and improve transmission performance when vehicles communicate to MEC servers. DT is employed to achieve real-time data collection and digital representation of physical operating environments of IoV to better support decisions making. In order to reduce the overall delay and energy consumption of DTVIF, we propose a Two-Stage Optimization for Jointly Optimizing Task Offloading and IRS Configuration (TSJTI) algorithm based on Deep Reinforcement Learning (DRL) and Transfer Learning (TFL). In the first stage, we introduce Double Deep Q-learning Networks (DDQN) to find the optimal offloading decision. In the second stage, based on the parameters learned from the first stage, we migrate the parameters from the first stage to find the optimal IRS configuration based on the Deep Deterministic Policy Gradient (DDPG) method. The simulations demonstrate that the proposed algorithm can effectively reduce the processing latency of task offloading and reduce the average energy consumption in DTVIF.

关键词： Mobile edge computing   wireless power transfer   reinforcement learning   partial offloading  

摘要： The advanced Internet of Things (IoT) enables more and more interactions between people and machines in the emerging applications, which rely on real-time communication and computing. However, the limited battery capacity and low computing capacity of IoT nodes can hardly support high-performance computing applications. The integration of wireless power transmission (WPT) and mobile edge computing (MEC) is a feasible and promising solution to address the energy shortage and computing capacity limitation of IoT nodes by harvesting radio frequency signal's energy and offloading the nodes' computation tasks to edge computing servers (ECSs). In this work, we focus on the wireless powered MEC network with an ECS and multiple edge devices (EDs), and study the joint optimization of WPT duration, transmission time allocation of each ED and partial offloading decision to maximize the sum computation rate. First, we formulate this as a non-convex problem which is hard to solve. Second, to conquer this problem, we decompose the original offloading problem into the sub-problem of optimizing the offloading time allocation among EDs and the proportion of harvested energy allocated for offloading at each ED under a given WPT duration and the top-problem of optimizing the WPT duration. Finally, we design an online DRL-based framework where one DNN together with its exploration strategy and training strategy is adopted to learn the near-optimal WPT duration and an efficient optimal algorithm is designed to solve the sub-problem. Numerical results show that the DRL-based offloading algorithm achieves the near-maximal sum computation rate while greatly reducing the processing time by at least three orders of magnitude compared with using the solver CVX for the sub-problem and the DNN for the top-problem.

关键词： Mobile edge computing   Internet of Things   Unmanned aerial vehicle   Multiobjective trajectory optimization  

摘要： Mobile edge computing (MEC) is used to provide IT services environment and cloud computing capabilities at the edge of the network. As the technology of unmanned aerial vehicles (UAVs) matures, the growing attempts have been made to use UAVs to replace fixed ground stations for MEC due to their flexibility. In this work, we study the multiobjective trajectory optimization for mobile edge computing system assisted by a single UAV, where the UAV is used to provide computing services for Internet of Things (IoT) devices located on the ground. A multiobjective trajectory optimization problem is formulated, which not only needs to minimize the energy consumption of the MEC system to provide computing services to all IoT devices, but also minimize the task urgency indicator by optimizing the UAV's flight trajectory. In this problem, the number and the locations of hover points (HPs) of UAVs have been taken into consideration. To solve this problem, a multiobjective trajectory optimization algorithm with a cutting and padding encoding strategy is proposed, where the cutting and padding encoding strategy is used to help optimize the population whose individuals may have different lengths. The verification experiments are carried out on a set of instances with up to 400 IoT devices and the experimental results demonstrate the promising performance of the proposed algorithm for trajectory optimization problems in a single-UAV-assisted MEC system.

关键词： Internet of vehicles   Mobile edge computing   Computation offloading   Task segmentation   Constrained multi-objective optimization   NSGS  

摘要： As a new technology, Internet of Vehicles (IoV) needs high bandwidth and low delay. However, the current on-board mobile terminal equipment cannot meet the needs of the IoV. Therefore, using mobile edge computing (MEC) can solve the problems of energy consumption and time delay in the IoV. In the MEC, task offloading can solve the problem of resource constraint on mobile devices effectively, but it is not optimal to offload all tasks to edge servers. In this paper, the vehicle computation task is regarded as a directed acyclic graph (DAG), and task nodes' execution location and scheduling order are optimized. Considering the energy consumption and delay of the system, the vehicle computation offloading is considered as a constrained multi-objective optimization problem (CMOP), and then a Non-dominated Sorting Genetic Strategy(NSGS) is proposed to solve the CMOP. The proposed algorithm can realize local and edge parallel processing to reduce delay and energy consumption. Finally, a large number of experiments are carried to prove the performance of the algorithm. The experimental results show that the algorithm can make the optimal decision in practical applications.

关键词： Mobile edge computing   Internet of Vehicle   Offloading   Deep reinforcement learning  

摘要： This article examines a multi-user mobile edge computing (MEC) system for the Internet of Vehicle (IoV), where one edge point (EP) nearby the vehicles can help assist in processing the compute-intensive tasks. For the MEC networks, the majority of existing works concentrate on the minimization of system cost of task offloading under the perfect channel estimation, which however fails to consider the practical limitation of imperfect channel estimation (CSI) because of vehicles' high-mobility. Therefore, the goal of our study is to reduce the delay as well as energy consumption (EC) of computation and communication with imperfect CSI, which are the two significant performance metrics of MEC network. With this aim, we first express the system cost as a form of the linear combination of the delay and EC, and then formulate the optimization problem for the system cost. Moreover, a novel deep approach is proposed, which is integrated by deep reinforcement learning (DRL) with the Lagrange multiplier to jointly minimize the system cost. In particular, the DRL algorithm is employed to obtain the capable offloading strategy, while the Lagrange multiplier is used to obtain the bandwidth allocation. The simulated results are finally presented to show that the devised approach outperforms the traditional ones. (C) 2022 Elsevier B.V. All rights reserved.