关键词： Autonomous vehicular network   Data security and privacy   Mobile edge computing   User authentication   User quality of experience   Quality of service  

摘要： Cloud computing has widely been used for applications that require huge computational and data storage resources. Unfortunately, with the advent of new technologies such as fifth generation of cellular networks that provide new applications like IoT, cloud computing presents many limits among which the End-To-End (E2E) latency is the main challenge. These applications generally degrade scenarios that require low latency. Mobile Edge Computing (MEC) has been proposed to solve this issue. MEC brings computing and storage resources from cloud data center to edge data center, closer to end-user equipment to reduce the E2E latency for request processing. However, MEC is vulnerable to security, data privacy, and authentication that affect the end-user Quality of Experience (QoE). It is therefore fundamental that these challenges are addressed to avoid poor user experience due to the lack of security or data privacy. In this paper, we propose a hybrid cryptographic system that uses the symmetric and asymmetric cryptographic systems, to improve data security, privacy, and user authentication in a MEC-based network. We show that our proposed scheme is secured by validating it with the Automated Validation of Internet Security Protocol and Application tool. Simulation results show that our solution consumes less computing resources.

关键词： Blockchain   Classification   Mobile edge computing   Meta-heuristics   Henry gas algorithm   Resource allocation  

摘要： This paper investigates a wireless blockchain network with mobile edge computing in which Internet of Things (IoT) devices can behave as blockchain users (BUs). This blockchain network's ultimate goal is to increase the overall profits of all BUs. Because not all BUs join in the mining process, using traditional swarm and evolution algorithms to solve this problem results in a high level of redundancy in the search space. To solve this problem, a modified chaotic Henry single gas solubility optimization algorithm, called CHSGSO, has been proposed. In CHSGSO, the allocation of resources to BUs who decide to engage in mining as an individual is encoded. This results in a different size for each individual in the entire population, which leads to the elimination of unnecessary search space regions. Because the individual size equals the number of participating BUs, we devise an adaptive strategy to fine-tune each individual size. In addition, a chaotic map was incorporated into the original Henry gas solubility optimization to improve resource allocation and accelerate the convergence rate. Extensive experiments on a set of instances were carried out to validate the superiority of the proposed CHSGSO. Its efficiency is demonstrated by comparing it to four well-known meta-heuristic algorithms.

关键词： space-air-ground integrated   mobile edge computing   UAV deployment   device access   computation offloading  

摘要： Computation-intensive applications offloading is challenging, especially in the designated regions where communication infrastructure is absent or compromised. In this paper, we present a Space-Air-Ground integrated Mobile Edge Computing (SAGiMEC) system for these regions to provide quality computational services, where the unmanned aerial vehicles (UAVs) act as in-fight edge servers to provide low-latency edge computing and the satellite provides resident cloud computing. A joint optimization problem is formulated considering UAV deployment, ground device (GD) access, and computation offloading to minimize the system average response latency. To cope with the problem's complexity, we propose a Particle Swarm Optimization (PSO) and Greedy Strategy (GS)-based algorithm (PSO & GS) to obtain an approximate optimal solution. Extensive simulations validate the convergence of the proposed algorithm. Numerical results show that the proposed approach has excellent convergence, and the system average response latency is about 0.65x-0.85x that of the benchmark algorithm.

关键词： artificial intelligence   Internet of Things   machine learning   mobile edge computing   network Management  

关键词： Context-aware   Task scheduling   Mobile edge computing   Time series data prediction   Cloud-edge collaborative architecture  

摘要： Context-aware time-series data prediction is crucial and ubiquitous in cloud-edge collaborative storage in Mobile Edge Computing. Most existing studies aim to reduce energy consumption or improve performance in different aspects by computation offloading among the MEC nodes. In the MEC scenario, the mobile edge devices generate and send time series data at a high frequency. The edge nodes will process the collected data to support real-time tasks and periodically migrate the compressed local data to Cloud Data Center(CDC). However, when it comes to reality, it is crucial to locate the data and decide the task execution strategy. In this paper, we first design a management system to manage the metadata and the data collected from mobile edge devices. Based on the management system, we propose a context-aware approach to task scheduling for time-series data prediction in MEC, using the task contexts to locate the data sources, generate the task execution strategy, and choose the targets of result forwarding. For the data prediction, we adopted the LSTM model to predict future time-series data considering the performance. We evaluate the feasibility and effectiveness of the proposed approach on an Automatic Identification System (AIS) dataset. The results illustrate that the proposed approach can effectively schedule the context-aware time-series data prediction tasks in MEC.

关键词： Mobile edge computing  

摘要： The digital twin edge network (DITEN) aims to integrate mobile edge computing (MEC) and digital twin (DT) to provide real-time system configuration and flexible resource allocation for the sixth-generation network. This paper investigates an intelligent reflecting surface (IRS)-aided multi-tier hybrid computing system that can achieve mutual benefits for DT and MEC in the DITEN. For the first time, this paper presents the opportunity to realize the network-wide convergence of DT and MEC. In the considered system, specifically, over-the-air computation (AirComp) is employed to monitor the status of the DT system, while MEC is performed with the assistance of DT to provide low-latency computing services. Besides, the IRS is utilized to enhance signal transmission and mitigate interference among heterogeneous nodes. We propose a framework for designing the hybrid computing system, aiming to maximize the sum computation rate under communication and computation resources constraints. To tackle the non-convex optimization problem, alternative optimization and successive convex approximation techniques are leveraged to decouple variables and then transform the problem into a more tractable form. Simulation results verify the effectiveness of the proposed algorithm and demonstrate the IRS can significantly improve the system performance with appropriate phase shift configurations. Moreover, the results indicate that the DT assisted MEC system can precisely achieve the balance between local computing and task offloading since real-time system status can be obtained with the help of DT. Copyright © 2023, The Authors. All rights reserved.

关键词： Mobile edge computing  

摘要： With the increasing demand for mobile computing, the requirement for intelligent resource management has also increased. Cloud computing lessens the energy consumption of user equipment, but it increases the latency of the system. Whereas edge computing reduces the latency along with the energy consumption, but it has limited resources and cannot process bigger tasks. To resolve these issues, a Priority-based Hybrid task Partitioning and Offloading (PHyPO) scheme is introduced in this paper, which prioritizes the tasks with high time sensitivity and offloads them intelligently. It also calculates the optimal number of partitions a task can be divided into. The utility of resources is maximized along with increasing the processing capability of the model by using a hybrid architecture, consisting of mobile devices, edge servers, and cloud servers. Automated machine learning is used to identify the optimal classification models, along with tuning their hyperparameters, which results in adaptive boosting ensemble learning-based models to reduce the time complexity of the system to O(1). The results of the proposed algorithm show a significant improvement over benchmark techniques along with achieving an accuracy of 96.1% for the optimal partitioning model and 94.3% for the optimal offloading model, with both the results being achieved in significantly less or equal time as compared to the benchmark techniques. © 2023, The Authors. All rights reserved.

关键词： connected and automated vehicles   cooperative perception   mobile edge computing   motion planning   surrogate safety assessment model   vehicle-to-everything communication  

摘要： This paper reports the implementation and results of a simulation-based analysis of the impact of cloud/edge-enabled cooperative perception on the performance of automated driving in unsignalized roundabouts. This is achieved by comparing the performance of automated driving assisted by cooperative perception to that of a baseline system, where the automated vehicle relies only on its onboard sensing and perception for motion planning and control. The paper first provides the descriptions of the implemented simulation model, which integrates the SUMO road traffic generator and CARLA simulator. This includes descriptions of both the baseline and cooperative perception-assisted automated driving systems. We then define a set of relevant key performance indicators for traffic efficiency, safety, and ride comfort, as well as simulation scenarios to collect relevant data for our analysis. This is followed by the description of simulation scenarios, presentation of the results, and discussions of the insights learned from the results.

关键词： Collaborative Tasks Scheduling   Geo Clustering   Mobile Edge Computing   Particle Swarm Optimization   Resource Allocation  

摘要： Multi-access Edge Computing (MEC) has recently been suggested as an addition to cloud computing. MEC servers are placed close to the network's edge to reduce latency and relieve demand on cloud data centres. The cloud is more resourceful than the MEC server, which has less resource. Each MEC server cannot satisfy all computational and large data needs from user devices when operating independently. Collaborative edge computing (CEC) is a popular new concept in which devices on the edge work together by sharing data and computer resources. CEC must determine when and where each task is executed, making task offloading an important problem to address. However, it is challenging to solve task offloading in CEC because tasks can be offloaded to neighbouring devices, resulting in bandwidth contention among network flows. Most existing works do not jointly consider network flow scheduling, which can result in network congestion and inefficient completion time performance. This paper suggests a unique approach for planning, executing, and clustering MEC servers in a resource allocation model. Belief rule Geo clustering method used to cluster the MEC server based on real-time data of intensive tasks. For the simple sake, real-time monitoring application based on video surveillance is taken. The unsupervised cluster algorithm used to distribute the software components optimally among mobile devices and reduce cluster traffic in relation to the data center. The distributed MEC channel task allocation model is used to share tasks when the MEC server is busy receiving data, and a particle swarm-based joint optimization architecture is used to optimize clustered data. Extensive experiments have been performed on three datasets concerning energy utilization, delay, computational burden, throughput, and network Quality of service (QoS). The proposed technique achieved better results than benchmark solutions, which do not make a joint decision. © 2023 Little Lion Scientific.

关键词： Aerial edge computing (AEC)   mobile edge computing   Quality of Service (QoS)   wireless communication network  

摘要： In the beyond 5G/6G era, aerial edge computing (AEC) is expected to be used as significant components in Internet of Things. AEC brings flexible deployment with Line-of-Sight communication links for task offloading and computing services. In this article, we present a comprehensive survey of the AEC technology. First, we introduce a three-layer architecture of AEC, which includes the satellite, unmanned aircraft vehicles, and ground terminals. Second, we illustrate challenges in AEC, and summarize recent studies in terms of AEC performance metrics that include energy efficiency, latency, and operation cost to address challenges in AEC. Further, we introduce the advanced technologies applied in AEC management, e.g., artificial intelligence (AI) and the distributed optimization. Finally, the applications and the open issues with AEC are summarized and classified in the study.