关键词： Vehicle dynamics   Heuristic algorithms   Optimization   Servers   Real-time systems   Delays   Resource management   Dynamic scheduling   Computational modeling   Costs   Mobile edge computing   vehicular edge computing   combinatorial optimization   deep reinforcement learning   task offloading  

摘要： Vehicular Edge Computing (VEC) is an emerging computing paradigm that serves as a specific application of Mobile Edge Computing (MEC) in intelligent transportation systems. As a core technology of VEC, task offloading improves computing efficiency and service quality by offloading computing tasks from vehicular devices to edge nodes. However, the high mobility of vehicles, heterogeneity of resources, and real-time requirements present significant challenges for task offloading. To address the issue of reducing overall system latency and increasing the offloading success rate in multi-task offloading, we propose a task offloading scheme combining Deep Reinforcement Learning (DRL) and Kuhn-Munkres (KM) Matching algorithm, named the PDQKM offloading scheme. Firstly, to mitigate the delay caused by frequent Roadside Unit (RSU) handovers, we propose a method to detect whether a vehicle is within the coverage area of the RSU. This method filters out unreasonable offloading decisions, avoiding the overhead associated with frequent RSU handovers. Secondly, the combination of DRL and the KM matching algorithm leverages the strengths of both approaches. DRL provides initial offloading strategies in highly dynamic and high-dimensional decision environment. Although DRL may get stuck in local optima, it can quickly adapt to environmental changes. The KM matching algorithm, a classic solution for perfect task-resource matching, performs global optimization on the initial strategies provided by DRL. This integration overcomes the limitations that a single algorithm might have. Finally, to effectively coordinate and manage the heterogeneous resources of RSU, we utilize an improved KM matching algorithm to update computational resources in real time, enhancing matching efficiency. Experimental results demonstrate that PDQKM outperforms comparable offloading schemes in terms of overall system latency and offloading success rate optimization.

关键词： Terahertz communications   Autonomous aerial vehicles   Optimization   Resource management   Servers   Computational modeling   Costs   Mobile edge computing   deep learning   unmanned aerial vehicles   intelligent reflective surface   terahertz communications  

摘要： Integrating wireless-powered Mobile Edge Computing (MEC) with Unmanned Aerial Vehicles (UAVs) leverages computation offloading services for mobile devices, significantly enhancing the mobility and control of MEC networks. However, current research has not focused on customizing system designs for Terahertz (THz) communication networks. When dealing with THz communication, one must account for blockage vulnerability due to severe THz wave propagation attenuation and insufficient diffraction. The Intelligent Reflecting Surface (IRS) can effectively address these limitations in the model, enhancing spectrum efficiency and coverage capabilities while reducing blockage vulnerability in THz networks. In this paper, we introduce an upgraded MEC system that integrates IRS and UAVs into THz communication networks, focusing on a binary offloading policy for studying the computation offloading problem. Our primary objective is to optimize the energy consumption of both UAVs and User Electronic Devices, alongside refining the phase shift of the IRS reflector. The problem is a Mixed Integer Non-Linear Programming problem known as NP-hard. To tackle this challenge, we propose a two-stage deep learning-based optimization framework named Iterative Order-Preserving Policy Optimization (IOPO). Unlike exhaustive search methods, IOPO continually updates offloading decisions through an order-preserving quantization method, thereby accelerating convergence and reducing computational complexity, especially when handling complex problems with extensive solution spaces. The numerical results demonstrate that the proposed algorithm significantly improves energy efficiency and achieves near-optimal performance compared to benchmark methods.

关键词： IoT   overbooking   application placement   UAV   mobile edge computing  

摘要： Due to the high mobility and low cost, unmanned aerial vehicle (UAV)-mounted edge computing (UMEC) provides an efficient way to provision computing offloading services for Internet of Forestry Things (IoFT) applications in forest areas without sufficient infrastructure. Multiple IoFT applications can be consolidated into fewer UAV-mounted servers to improve the resource utilization and reduce deployment costs with the precondition that all applications' Quality of Service (QoS) can be met. However, most existing application placement schemes in UMEC did not consider the dynamic nature of the aggregated computing resource demand. In this paper, the resource allocation and application placement problem based on fine-grained cooperative overbooking in UMEC is studied. First, for the two-tenant overbooking case, a Two-tenant Cooperative Resource Overbooking (2CROB) scheme is designed, which allows tenants to share resource demand violations (RDVs) in the cooperative overbooking region. In 2CROB, an aggregated-resource-demand minimization problem is modeled, and a bisection search algorithm is designed to obtain the minimized aggregated resource demand. Second, for the multiple-tenant overbooking case, a Proportional Fairness-based Cooperative Resource Overbooking (PF-MCROB) scheme is designed, and a bisection search algorithm is also designed to obtain the corresponding minimized aggregated resource demand. Then, on the basis of PF-MCROB, a First Fit Decreasing-based Cooperative Application Placement (FFD-CAP) scheme is proposed to accommodate applications in as few servers as possible. Simulation results verify that the proposed cooperative resource overbooking schemes can save more computing resource in cases including more tenants with higher or differentiated resource demand violation ratio (RDVR) thresholds, and the FFD-ACP scheme can reduce about one third of necessarily deployed UAVs compared with traditional overbooking. Thus, applying efficient cooperative over

关键词： Mobile edge computing   Internet of Vehicle   Stackelberg game   Federal learning   Incentive mechanism  

摘要： With the development of Intelligent Transportation Systems (ITS), data on the Internet of Vehicles (IoV) is increasing day by day. To alleviate computing pressure in IoV, Vehicle Edge Computing (VEC) is being widely used as a new computing paradigm. Moreover, to address the serious problem of privacy leakage in VEC, Federated Learning (FL) is increasingly considered for analyzing big data in VEC. However, in actual VEC, problems such as data heterogeneity and poor training often occur. To address this set of problems, we introduce a two-stage Stackelberg game structure for FL training, choosing the Cloud Server (CS) as the leader and the Roadside Unit (RSU) as the follower. Then, we define the utility functions of CS and RSU and obtain the optimal reward rate and local accuracy for each iteration. To address the problem of inefficiency during learning process, we separate high-dimensional data features into global features and personalized features based on feature separation, and use them to capture historical information. Next, vehicle federated learning with historical information based on dynamic Stackelberg game (VFLHI-DSG) was proposed. Finally, we conducted a comprehensive comparative experiment, results show that VFLHI-DSG has excellent performance in different scenarios.

关键词： Federated learning   Cloud computing   Training   Resource management   Radio spectrum management   Optimization   Computational modeling   Servers   Data models   Costs   Mobile edge computing   hierarchical federated learning   adaptive aggregation   resource allocation  

摘要： Hierarchical federated learning shows excellent potential for communication-computation trade-offs and reliable data privacy protection by introducing edge-cloud collaboration. Considering non-independent and identically distributed data distribution among devices and edges, this article aims to minimize the final loss function under time and energy budget constraints by optimizing the aggregation frequency and resource allocation jointly. Although there is no closed-form expression relating the final loss function to optimization variables, we divide the hierarchical federated learning process into multiple cloud intervals and analyze the convergence bound for each cloud interval. Then, we transform the initial problem into one that can be adaptively optimized in each cloud interval. We propose an adaptive hierarchical federated learning process, termed as AHFLP, where we determine edge and cloud aggregation frequency for each cloud interval based on estimated parameters, and then the CPU frequency of devices and wireless channel bandwidth allocation can be optimized in each edge. Simulations are conducted under different models, datasets and data distributions, and the results demonstrate the superiority of our proposed AHFLP compared with existing schemes.

关键词： Server placement   Mobile edge computing   Deep reinforcement learning  

摘要： Mobile edge computing (MEC) is crucial in applications such as intelligent transportation, innovative healthcare, and smart cities. By deploying servers with computing and storage capabilities at the network edge, MEC enables low-latency services close to end users. However, the configuration of edge servers needs to meet the low-latency requirements and effectively balance the servers' workloads. This paper proposes an adaptive layout and dynamic optimization method, modeling the edge server layout problem as a Markov decision process. It introduces a workload-based server placement rule that adjusts the locations of edge servers according to the load of base stations, enabling the learning of low-latency and load-balanced server layout strategies. Experimental validation on areal dataset from Shanghai Telecom shows that the proposed algorithm improves average latency performance by about 40% compared to existing technologies, and enhances workload balancing performance by about 17%.

关键词： Cooperative caching   Mobile edge computing   Cost effective   Online algorithm  

摘要： The proliferation of technological advancement in the Internet of Things (IoT) and mobile devices accelerates the need for latency-sensitive multimedia applications. The network operator may charge application vendors to cache their content. Vendors risk losing their user base if users don't receive the expected Quality of Service (QoS), which includes meeting specific delay thresholds for services like video on demand. While multiple content copies can enhance QoS by delivering content quickly, maintaining redundant copies is costly. Therefore, deploying edge servers at edge nodes and caching content is a prominent solution for application vendors. This approach guarantees lower latency, fewer content replicas, and reduced strain on backhaul links. However, the limited cache capabilities of individual edge nodes present challenges, such as efficiently allocating scarce resources to meet user demands. This work addresses the earlier challenges by formulating a cooperative content caching and forwarding problem by placing the various latency-sensitive content to minimize the total cost with resource and deadline constraints. The problem is formulated as an integer linear programming problem for cooperative caching in mobile edge networks to mitigate the total cost to the application vendors. In the absence of content popularity information, an online cooperative caching mechanism is presented to solve the dynamic content request and forwarding problem. Extensive simulations have been performed to illustrate that the proposed online algorithm significantly enhances the performance in terms of the total cost, load on the cloud, hit ratio, time utility, and cache utilization.

关键词： Servers   Uncertainty   Cloud computing   Data centers   Memory   Robustness   Resource management   Optimization methods   Hardware   Distributed databases   Edge data deduplication   mobile edge computing   robust optimization   uncertainties  

摘要： The emergence of mobile edge computing (MEC) in distributed systems has sparked increased attention toward edge data management. A conflict arises from the disparity between limited edge resources and the continuously expanding data requests for data storage, making the reduction of data storage costs a critical objective. Despite the extensive studies of edge data deduplication as a data reduction technique, existing deduplication methods encounter numerous challenges in MEC environments. These challenges stem from disparities between edge servers and cloud data center edge servers, as well as uncertainties such as user mobility, leading to insufficient robustness in deduplication decision-making. Consequently, this paper presents a robust optimization-based approach for the edge data deduplication problem. By accounting for uncertainties including the number of data requirements and edge server failures, we propose two distinct solving algorithms: uEDDE-C, a two-stage algorithm based on column-and-constraint generation, and uEDDE-A, an approximation algorithm to address the high computation overhead of uEDDE-C. Our method facilitates efficient data deduplication in volatile edge network environments and maintains robustness across various uncertain scenarios. We validate the performance and robustness of uEDDE-C and uEDDE-A through theoretical analysis and experimental evaluations. The extensive experimental results demonstrate that our approach significantly reduces data storage cost and data retrieval latency while ensuring reliability in real-world MEC environments.

关键词： character extraction   character segmentation   FusionNet model   mobile edge computing   YOLO v8  

摘要： Wrong parking incidents pose a pervasive challenge in urban environments, disrupting the smooth flow of traffic, compromising safety and contributing to various logistical issues. Unauthorized parking occurs when vehicles are parked in locations not designated for such purposes, leading to a myriad of problems for both authorities and the general public. This research introduces a pioneering approach to confront the persistent challenge of unauthorized parking incidents in urban environments. The study focuses on harnessing the advanced capabilities of the FusionNet model to enhance the accuracy of license plate detection. This paper introduces the YOLO v8 Model, a deep learning architecture designed to enhance urban parking management by accurately detecting vehicles parked in unauthorized slots. The objective is to enhance parking management efficiency by accurately detecting vehicles and their occupancy status in designated parking areas. The methodology begins with data collection and preprocessing of images of parking spaces, followed by the training of YOLO v8 to identify vehicles and parking spaces in real time. Leveraging a diverse dataset encompassing various parking scenarios, including instances of unauthorized parking, the model achieves an accuracy of 98.50% in identifying vehicles outside designated areas. This model segments characters from detected license plates, enabling the accurate extraction of alphanumeric information associated with each vehicle. The integrated system provides timely identification of parking violations and facilitates effective enforcement actions through captured license plate data. Results demonstrate the model's effectiveness in real-world scenarios, showcasing its potential for improving urban safety and efficiency. The implementation of FusionNet in the Python programming language, the proposed solution aims to streamline parking management, improve compliance with parking regulations and enhance overall urban mobility.,

关键词： Authentication frameworks   Physical Unclonable Functions (PUF)   Communication security   Internet of Things   Mobile edge computing   Elliptic Curve Cryptography (ECC)   Scyther  

摘要： The Internet of Things (IoT) devices are becoming increasingly integral to daily life, with cloud computing platforms serving as essential hubs for managing and processing the vast data generated by distributed IoT devices and sensors. The advent of 6G-powered cloud services facilitates applications such as augmented reality, virtual reality, autonomous driving, and healthcare, all of which require rapid data processing. Mobile edge computing (MEC) extends cloud capabilities to the network's edge, enabling large-scale, real-time data processing. However, this transition introduces security challenges due to the open nature of MEC infrastructures, which increases the risk of data breaches and privacy violations. To address these challenges, RAAF-MEC is proposed as an innovative authentication framework designed specifically for IoT-enabled MEC environments. The framework incorporates hash functions, PUF, ECC, and GIFT-COFB. GIFT-COFB, a lightweight encryption mechanism, and NIST finalist, ensures data authenticity and integrity. PUF technology, integrated on the MEC server side, dynamically derives secret keys, mitigating the risk of privileged insider attacks by eliminating the need to store keys in the MEC server's database. This approach enhances security by preventing unauthorized access to sensitive key material. RAAF-MEC also supports single sign-on for seamless access across MEC servers. The effectiveness of RAAF-MEC has been validated through comprehensive formal and informal security assessments, as well as performance evaluations against existing authentication frameworks. Our results show that RAAF-MEC reduces computational costs by 27.3% to 52.12% and communication costs by 69.44% to 75%, while significantly enhancing security features.