关键词： Task analysis   Satellites   Delays   Servers   Computer architecture   Cloud computing   Computational modeling   Satellite-terrestrial networks   mobile edge computing   computation offloading   delay optimization  

摘要： The integrated satellite-terrestrial network is promising to provide global broadband communication service. However, the long propagation delay of satellite-terrestrial links will lead to high communication delay when users access the Internet via satellites. In this paper, we investigate the cooperative multi-tier computing in the integrated satellite-terrestrial network, in which the computation tasks of users are processed by leveraging the cooperation of devices, edge nodes, and cloud servers. Based on the proposed three-tier computing framework, we formulate the cooperative edge-cloud offloading problem to minimize the total delay of the network. Considering the computation task is dividable, we propose the optimal task splitting strategy based on the partial offloading model, in which the closed-form solution is derived for each computation task. With the optimal task splitting strategy obtained, the original optimization problem is reformulated as the problem of the time slot allocation strategy and the computation capacity allocation strategy. Then, we further propose the cooperative edge-cloud computing strategy to optimize the delay performance of the network. Finally, numerical results are presented to demonstrate the performance of the proposed three-tier computing architecture and the offloading strategies.

关键词： Mobile edge computing   Task offloading   Location prediction   Machine learning  

摘要： Several applications have emerged with the proliferation of mobile devices to provide communication, learning, social networking, entertainment, and community computing services. Such applications include augmented reality, online gaming, and other real-time applications that need higher computational resources. These applications, executing on mobile devices, often need to access external computing resources and offload the application tasks to the cloud or mobile edge computing (MEC) servers. However, delivering task offloading results to the users in the MEC environment is a challenge, certainly when user mobility is high. Sub-optimal server selection at the offloading stage increases latency, energy consumption and deteriorates both quality of experience and quality of service. Existing techniques proposed in the literature handle computation offloading and mobility management separately. Without considering the real-time mobility factors, the solutions produced are sub-optimal. Some solutions exist to manage mobility, but they involve higher time complexity. We consider the user mobility in offloading decisions and present a lightweight mobility prediction and offloading (LiMPO) framework that offloads the compute-intensive tasks to the predicted user location using artificial neural networks with less complexity. In addition, we propose a multi-objective genetic algorithm based server selection technique that jointly optimizes latency and energy consumption while improving the resource utilization of MEC servers. The performance of the proposed framework is compared with two other techniques task-assignment with optimized mobility and dynamic mobility-aware offloading algorithm for edge computing. The simulation results show that LiMPO outperforms the others by latency reduction, energy efficiency, and enhanced resource utilization.

关键词： mobile edge computing   computation offloading strategy   multi-agent system   deep reinforcement learning   federated learning  

摘要： With the rise of latency-sensitive and computationally intensive applications in mobile edge computing (MEC) environments, the computation offloading strategy has been widely studied to meet the low-latency demands of these applications. However, the uncertainty of various tasks and the time-varying conditions of wireless networks make it difficult for mobile devices to make efficient decisions. The existing methods also face the problems of long-delay decisions and user data privacy disclosures. In this paper, we present the FDRT, a federated learning and deep reinforcement learning-based method with two types of agents for computation offload, to minimize the system latency. FDRT uses a multi-agent collaborative computation offloading strategy, namely, DRT. DRT divides the offloading decision into whether to compute tasks locally and whether to offload tasks to MEC servers. The designed DDQN agent considers the task information, its own resources, and the network status conditions of mobile devices, and the designed D3QN agent considers these conditions of all MEC servers in the collaborative cloud-side end MEC system;both jointly learn the optimal decision. FDRT also applies federated learning to reduce communication overhead and optimize the model training of DRT by designing a new parameter aggregation method, while protecting user data privacy. The simulation results showed that DRT effectively reduced the average task execution delay by up to 50% compared with several baselines and state-of-the-art offloading strategies. FRDT also accelerates the convergence rate of multi-agent training and reduces the training time of DRT by 61.7%.

关键词： Content caching   Mobile edge computing   Popularity prediction  

摘要： Mobile edge computing (MEC) enables intelligent content caching at the network edge to reduce traffic and enhance content delivery efficiency. In MEC architecture, popular content can be deployed at the MEC server to improve users' quality of experience (QoE). Existing content caching techniques attempt to improve cache hits but do not consider users' preferences while estimating the popularity of content. Knowing users' preferences is beneficial and essential for efficient content caching. This paper proposes Content Popularity and User Preferences aware content caching (CoPUP) in MEC. The proposed scheme uses content-based collaborative filtering first to analyze the user-content matrix and identify the relationships between different contents. The convolution neural network model (CNN) is used to predict users' preferences. The CoPUP significantly improves cache performance, enhances cache hit ratio, and reduces response time. The simulation experiments are conducted based on the real dataset from Movielens. The proposed CoPUP technique is compared with three baseline techniques namely Least Frequently Used (LFU), Least Recently Used (LRU), First-In-First-Out (FIFO) and a state-of-the-art technique Mobility-Aware Proactive edge caching scheme based on federated learning (MPCF). The experimental results reveal that the proposed model achieves 2% higher cache hit ratio and faster response time compared with baseline and state-of-the-art techniques.

关键词： Marine vehicles   Collision avoidance   Rivers   Blockchains   Navigation   Computer architecture   Real-time systems   Water transportation systems   internet of ships   cooperative collision avoidance   deep federated learning   mobile edge computing   consortium blockchain  

摘要： Cooperative collision avoidance between inland waterway ships is among the envisioned services on the Internet of Ships. Such a service aims to support safe navigation while optimizing ships' trajectories. However, to deploy it, timely and accurate prediction of ships' positioning with real-time reactions is needed to anticipate collisions. In such a context, ships positions are usually predicted using advanced Machine Learning (ML) techniques. Traditionally, ML schemes require that the data be processed in a centralized way, e.g., a cloud data center managed by a third party. However, these schemes are not suitable for the collisions avoidance service due to the inaccessibility of ships' positioning data by this third party, and allowing connected ships to get access to sensitive information. Therefore, in this paper, we design a new cooperative collision avoidance system for inland ships, while ensuring data security and privacy. Our system is based on deep federated learning to collaboratively build a model of ship positioning prediction, while avoiding sharing their private data. In addition, it is deployed at multi-access edge computing (MEC) level to provide low-latency communication to ensure fast responses during collision detection. Furthermore, it relies on Blockchain and smart contracts to ensure trust and valid communications between ships and MEC nodes. We evaluate the proposed system using a generated dataset representing ships mobility in France. The results, which demonstrate the accuracy of our prediction model, prove the effectiveness of our cooperative collision avoidance system in ensuring timely and reliable communications and avoiding collisions between ships.

关键词： deep reinforcement learning   energy harvesting   mobile edge computing   service caching  

摘要： Mobile user terminals in 5G networks can generate massive computational workloads, which require sufficient computation and caching resources, and the processors of user terminals cannot tackle these workloads. Emerging mobile edge computing (MEC) has become the key to solving the computation problem by offloading computation-intensive workloads to the MEC server. To make full use of the limited resources on the MEC side, service caching can pre-store specific executable programs, databases, or libraries for executing offloaded workloads from user terminals. In this study, a service caching-assisted MEC model is designed. According to the proposed MEC model, a decentralized model-free deep reinforcement learning algorithm-based server caching optimization policy (DDSCOP) is proposed to minimize the long-term weighted average cost. Considering the time-varying request workloads from user terminals, the stochastic channel state, and the arrival of renewable resources of the MEC server, DDSCOP can find the near-optimal service caching decision-making policy by training the neural networks, that is, whether the service caching can be hosted by the MEC server, and which MEC server hosts the service caching. The numerical experimental results verify the convergence and effectiveness of DDSCOP through extensive parameters configuration, and DDSCOP outperforms the three baseline algorithms.

关键词： computation offloading   mobile edge computing   privacy   security   smart campus   virtual reality  

摘要： Nowadays, as the smart campus concept becomes a reality, virtual reality (VR) applications are being applied to connect students with the virtual teaching world via VR devices (VDs), enhancing learning efficiency. Nevertheless, VR applications are latency-sensitive while VDs are subjected to shortcomings to handle many VR applications simultaneously. Fortunately, mobile edge computing (MEC) has been recognized as a promising solution that can bring abundant resources to VDs to relieve hardware limits. However, the computing resources of edge nodes in MEC are limited, and thus how to allocate resources effectively is critical. Meanwhile, some sensitive information of students collected by VD needs to be protected. In view of this, we investigate computation offloading for educational VR applications in MEC-enabled smart campus. The aims of this issue are to optimize the motion-to-photon latency, energy consumption, and resource utilization while satisfying the privacy and security constraints. To this end, we propose a new multi-objective optimization method using MoCell. Eventually, the experimental evaluations are designed to illustrate the effectiveness and superiority our proposed method.

关键词： Mobile edge computing   Machine learning   Deep learning   Data offloading   Cyberattack detection   QoE  

摘要： Due to the advancements of high-speed networks, mobile edge computing (MEC) has received significant attention to bring processing and storage resources in client's proximity. The MEC is also a form of Edge Network or In-network computing where the resources are brought closer to the user end (edge) of the network while increasing QoE. On the other hand, the increase in the utilization of the internet of things (IoT) gadgets results in the generation of cybersecurity issues. In recent times, the advent of machine learning (ML) and deep learning (DL) techniques paves way in the detection of existing traffic conditions, data offloading, and cyberattacks in MEC. With this motivation, this study designs an effective deep learning based data offloading and cyberattack detection (DL-DOCAD) technique for MEC. The goal of the DL-DOCAD technique is to enhance the QoE in MEC systems. The proposed DL-DOCAD technique comprises traffic prediction, data offloading, and attack detection. The DL-DOCAD model applies a gated recurrent unit (GRU) based predictive model for traffic detection. In addition, an adaptive sampling cross entropy (ASCE) approach is employed for the maximization of throughput and decision making for offloading users. Moreover, the birds swarm algorithm based feed forward neural network (BSA-FFNN) model is used as a detector for cyberattacks in MEC. The utilization of BSA to appropriately tune the parameters of the FFNN helps to boost the classification performance to a maximum extent. A comprehensive set of simulations are performed and the resultant experimental values highlight the improved performance of the DL-DOCAD technique with the maximum detection accuracy of 0.992.

关键词： Backscatter communication   Mobile edge computing   WSCB  

摘要： This article considers a backscatter-aided wireless powered mobile edge computing (BC-aided WPMEC) network, in which the tasks data of each Internet of Things (IoT) device can be computed locally or offloaded to the MEC server via backscatter communications, and design a resource allocation scheme regarding the weighted sum computation bits (WSCB) maximization of all the IoT devices. Towards this end, by optimizing the mobile edge computing (MEC) server's transmit power, IoT devices' power reflection coefficients, local computing frequencies and time, the time allocation between the energy harvesting and task offloading, as well as the binary offloading decision at each IoT device, we built a WSCB maximization problem, which belongs to a non-convex mixed integer programming problem. For solving this, the proof by contradiction and the objective function's monotonicity are considered to determine the optimal local computing time of each IoT device and the optimal transmit power of the MEC server, and the time-sharing relaxation (TSR) is adopted to tackle the integer variables, which are used to simplify the original problem. Then, we decouple the simplified problem into two sub-problems by means of the block coordinate decent (BCD) technology, and each of the sub-problems is transformed to a convex one by introducing auxiliary variables. Based on this, we design a two-stage alternative (TSA) optimization algorithm to solve the formulated WSCB problem. Computer simulations validate that the TSA algorithm has a fast convergent rate and also demonstrate that the proposed scheme achieves a higher WSCB than the existing schemes.(c) 2022 Elsevier B.V. All rights reserved.

关键词： Mobile edge computing   Big data analytics   Outlier detection   Machine learning   Outlier detection   Internet of Things  

摘要： In recent times, the Internet of Things and big data analytics have become a hot research topic in mobile edge computing (MEC) desires wide-ranging research works for intelligent decision making. In this view, this paper designs an intelligent outlier detection with machine learning empowered big data analytics (IODML-BDA) for MEC. The proposed model involves an adaptive synthetic sampling-based outlier detection technique to eradicate the existence of outliers. Besides, the oppositional swallow swarm optimization (OSSO) based feature selection technique is used to choose an effective set of features. Finally, long short-term memory based classification model is employed to identify different class labels. The design of OSSO algorithm for feature selection with ADASYN technique for big data analytics show the novelty of the work. A comprehensive experimental analysis is carried out on GPS trajectories, movement prediction, water treatment plant, hepatitis, and Twitter datasets to confirm the experimental results. The experimentation outcomes pointed out that the proposed IODML-BDA model achieves the higher accuracy of 0.9735, 0.9816, 0.9798, 0.9896, and 0.9912 on the applied datasets.