关键词： Delays   Resource management   Energy consumption   Optimization   Autonomous aerial vehicles   Quality of experience   Processor scheduling   Heuristic algorithms   Base stations   Convergence   Dependent task scheduling   mobile edge computing   multi-objective evolutionary algorithms based on decomposition   resource allocation   service caching   uncrewed aerial vehicles  

摘要： This paper studies the joint optimization of multi-objective dependent task scheduling, resource allocation, and service caching in an aerial-ground integrated mobile edge computing system that includes multiple uncrewed aerial vehicles (UAVs). These UAVs, in coordination with a high-altitude platform, work together to process numerous dependent tasks collected by the UAVs. The optimization problem involves two conflicting objectives that need to be minimized simultaneously: the average execution delay of all dependent tasks and the average energy consumption of all UAVs. The conflict between the two objectives makes the problem quite challenging. Recently, some multi-objective approaches, such as multi-objective evolutionary algorithms (MOEAs), have been introduced to address dependent task scheduling. However, these approaches often suffer from premature convergence and tend to fall into local optima. To address these issues, we propose a modified MOEA based on decomposition that incorporates two performance-improving strategies. The first one is a probability-based neighborhood search strategy that selects two individuals to update neighborhood individuals based on the neighborhoods and external population, thereby improving population updating efficiency. The second one is a dynamic voltage and frequency scaling-based energy reduction strategy that further enhances the quality of solutions by adjusting the computing frequencies. Experimental results verify that the proposed algorithm obtains a number of outstanding nondominated solutions and achieves a better balance between objectives compared with several algorithms.

关键词： Industrial Internet of Things   Federated learning   Resource allocation   Mobile edge computing  

摘要： The study explores resource allocation in Federated Machine Learning (FedML) for the Industrial Internet of Things (IIoT), focusing on efficient and privacy-conscious data processing. It proposes optimizing the FedML training process to enhance system performance and ensure data privacy. The research also presents a long-term average unified cost minimization problem considering energy constraints, device heterogeneity, and limited bandwidth in federated edge learning contexts. To address these issues, a novel Lyapunov-driven optimization algorithm for device selection and bandwidth allocation is introduced. This algorithm adeptly balances resource expenditures with model quality, employing Lyapunov-driven optimization theory to convert long-term stochastic challenges into short-term deterministic resolutions. Furthermore, the study presents a multi-tier federated edge learning architecture that integrates cloud collaboration with edge servers to manage the increasing number of industrial devices and the demand for timely local model training. Simulations confirm the methods' low complexity and superior efficacy, highlighting reduced system delay and enhanced model accuracy. The proposed method reduced system delay by up to 30%, achieved a model accuracy of 98% on the MNIST dataset and 91% on CIFAR-10, and improved convergence speed, with training loss decreasing by 25% within the first 10 rounds. The method also achieved a 40.5% improvement in computational efficiency and a 30-50% reduction in system costs, demonstrating its practicality and scalability. These results augment the performance of federated machine learning applications in practical IIoT settings. The insights garnered facilitate the development of intelligent industrial systems that prioritize efficiency and data privacy.

关键词： Mobile edge computing   Spatio-temporal optimization   Double deep Q -learning   Latency and energy efficiency   Mobile edge computing   Spatio-temporal optimization   Double deep Q -learning   Latency and energy efficiency  

摘要： The rapid increase in Mobile Internet of Things (IoT) devices requires novel computational frameworks. These frameworks must meet strict latency and energy efficiency requirements in Edge and Mobile Edge Computing (MEC) systems. Spatio-temporal dynamics, which include the position of edge servers and the timing of task schedules, pose a complex optimization problem. These challenges are further exacerbated by the heterogeneity of IoT workloads and the constraints imposed by device mobility. The balance between computational overhead and communication challenges is also a problem. To solve these issues, advanced methods are needed for resource management and dynamic task scheduling in mobile IoT and edge computing environments. In this paper, we propose a Deep Reinforcement Learning (DRL) multi-objective algorithm, called a Double Deep Q-Learning (DDQN) framework enhanced with Spatio-temporal mobility prediction, latency-aware task offloading, and energy-constrained IoT device trajectory optimization for federated edge computing networks. DDQN was chosen for its optimize stability and reduced overestimation in Q-values. The framework employs a reward-driven optimization model that dynamically prioritizes latency-sensitive tasks, minimizes task migration overhead, and balances energy efficiency across devices and edge servers. It integrates dynamic resource allocation algorithms to address random task arrival patterns and real-time computational demands. Simulations demonstrate up to a 35 % reduction in end-to-end latency, a 28 % improvement in energy efficiency, and a 20 % decrease in the deadline-miss ratio compared to benchmark algorithms.

关键词： Virtual reality   Mobile edge computing   QoE   VR   QoS   XR  

摘要： In this paper, we explore delivering mobile edge Virtual Reality (VR) gaming services with comprehensive and satisfactory Quality of Experience (QoE) across a distributed edge network. Our goal is to meet the QoE needs of all users, addressing both latency and visual considerations. We systematically capture the unique attributes of mobile VR games, including numerous independent objects with distinct rendering pipelines, diverse rendering levels, tight end-to-end delay requirements, and high bandwidth usage. We demonstrate that this challenge can be formulated as a Mixed-Integer Quadratically Constrained Quadratic Programming (MIQCQP) problem. By leveraging reinforcement learning techniques, we introduce a flexible and adaptive QoE weight formulation that incorporates symbolic generalization and real-time feedback loops. Unlike existing studies that often focus on single-user scenarios, our paper comprehensively addresses visual and latency concerns across multiple users. We employ optimization techniques through eigenvalue analysis and Lagrange-constrained methods, providing a scalable and robust framework for dynamically adjusting QoE weights based on user interactions and environmental factors. This approach enables us to deliver QoE-centric edge-assisted mobile VR gaming services to a large user base, advancing beyond current limitations in the field by leveraging reinforcement learning (RL), specifically Q-Learning, to dynamically adapt to the non-linear, real-time demands of multi-user VR gaming on edge networks.

关键词： mobile edge computing   smart objects   Internet of Things   connected vehicles  

摘要： This paper proposes a collaborative framework to support task offloading in connected vehicular environments. The approach relies on the dynamic formation of temporary teams of connected vehicles in a mobile edge computing scenario. A novel trust model is introduced, which integrates both quality of service and quality of results into a unified reliability score, and combines this score with distributed reputation to build a comprehensive trust metric. This trust metric is then exploited to guide a decentralized team formation algorithm, ensuring lightweight, interpretable, and scalable decision-making processes. Simulation results demonstrate that the proposed framework improves task execution quality and fairness, especially for low-performing vehicles. These contributions highlight the novelty and strengths of our collaborative model, positioning it as a promising solution for enhancing cooperation in vehicular edge systems.

关键词： Space-Air-Ground-Sea Integrated Networks   QoS   Reinforcement learning   Resource allocation   Mobile edge computing  

摘要： With the rapid advancements in Space-Air-Ground-Sea Integrated Networks (SAGSIN), optimizing intelligent network resource allocation has become a pivotal research problem. Effective resource allocation can significantly enhance network performance, ensure quality of service (QoS) for users, and optimize resource utilization. To tackle this challenge, this paper proposes a network resource allocation optimization algorithm based on reinforcement learning, specifically designed to guarantee user QoS. Simulation results demonstrate that as the number of users increases, the proposed allocation algorithm, Q-DER, outperforms baseline algorithms, including random algorithms, greedy algorithms, the Q-DR algorithm and the Q-DE algorithm, in terms of average delay, access success rate, access energy consumption, and resource utilization rate. Furthermore, the proposed Q-DER algorithm significantly mitigates congestion in wireless resource-constrained scenarios. A comparative analysis of user satisfaction degree reveals that the proposed Q-DER algorithm performs better in guaranteeing user QoS in complex network environments.

关键词： Servers   Optimal scheduling   Processor scheduling   Vectors   Information age   Queueing analysis   Multi-access edge computing   Computational modeling   Real-time systems   Delays   Age of Information   multi-source   transmission-computation trade-off   mobile edge computing   service preemption  

摘要： Age of Information (AoI) is emerging as a novel metric for measuring information freshness in real-time monitoring systems. For computation-intensive status data, the information is not revealed until being processed. We consider a status update problem in a multi-source single-server system where the sources are scheduled to generate and transmit status data which are received and processed at the edge server. Generate-at-will sources with both random transmission time and process time are considered, introducing the joint optimization of source scheduling and status sampling on the basis of transmission-computation balancing. We show that a random scheduler is optimal for both non-preemptive and preemptive server settings, and the optimal sampler depends on the scheduling result and its structure remains consistent with the single-source system, i.e., threshold-based sampler for non-preemptive case and transmission-aware deterministic sampler for preemptive case. Then, the problem can be transformed to jointly optimizing the scheduling frequencies and the sampling thresholds/functions, which is non-convex. We proposed an alternation optimization algorithm to solve it. Numerical experiments show that the proposed algorithm can achieve the optimal in a wide range of settings.

关键词： Authentication key agreement   Lightweight   Anonymity   Identity-based   BAN logic   Mobile edge computing  

摘要： Due to the significance of mobile edge computing (MEC) in people's daily lives, both its security and efficiency have drawn a lot of attention. In the MEC environment, Internet of Things (IoT) terminal equipment (TE) are typically resource-constrained devices. It becomes crucial to design a provably secure lightweight authentication key agreement (AKA) protocol to guarantee data security. However, existing AKA protocols struggle to meet lightweight requirements due to their reliance on bilinear pairings. Recently, Xie et al. construct an AKA protocol by using bilinear pairings for MEC infrastructure. Unfortunately, we find that their AKA protocol is vulnerable to known session-specific temporary information (KSSTI) attack and temporary key leakage impersonation attack. To resolve these issues, an improved lightweight anonymous AKA protocol without bilinear pairing for MEC infrastructure is proposed in this paper. We conduct a comprehensive security analysis of the new protocol by utilizing BAN logic. In additional, the new protocol is untraceable, offline register center, perfect forward security, resistant to replay attacks, insider attacks, etc. Compared to previous protocols, our solution offers higher security and lower computational and communication overheads.

关键词： Resource optimization   Device-to-device   Task ratio   Mobile edge computing   Dynamic flux balance analysis  

摘要： 5G and 6G development aim to fulfil very low latency, low energy consumption, and great computation ability. In the present era, the number of devices is increasing daily, which requires more communication and computation. Device-to-device (D2D), relay server, and mobile edge computing (MEC) systems were developed to meet these objectives. By employing the Constraint Based Multi-Dimensional Flux Balance Analysis (CBMDFBA) approach, this work aims to provide optimized overall energy consumption. Each device in this system can partially offload tasks to the Relay Edge Server (RES), Edge Server (ES), and adjacent mobile helper. Reinforcement learning is utilized to optimize the Signal's signal-to-noise ratio (SNR), decreasing transmission power between the helper, relay, and edge server. By employing CBMDFBA for path optimization, the approach reduces overall energy usage. In comparison to (1) Resource allocations using Q learning with considering parameter throughput-RA(QL-Munkres-TH), (2) Resource allocations using Q learning with considering parameter distance-RA(QL-Munkres-Dist), and (3) Resource allocation with considering maximum power-RA(Max-Power), the suggested method reduces energy consumption by 83.26%, 86.01%, and 88.34%, respectively. A comparison of energy efficiency and fairness index is also evaluated. According to simulation findings, the suggested algorithm outperforms baseline systems in terms of system performance and energy efficiency.

关键词： Fog computing   Mobile edge computing   Big data reduction   Data cleaning service   Data warehouse   Data quality  

摘要： With the rapid growth of the Internet of Things (IoT) and the emergence of big data, handling massive amounts of data has become a major challenge. Traditional approaches involve sending raw data to cloud data centers for cleaning, processing, and interpretation using data warehouse tools. However, this study introduces BlueEdge, a fog edge mobile application that aims to shift the cleaning and preprocessing tasks from the cloud to the edge. We compare BlueEdge with four popular data cleaning tools (WinPure, DoubleTake, WizSame, and DQGlobal) that operate within data warehouse architectures, such as Hadoop servers. The comparison considers criteria such as time consumption, resource utilization (memory and CPU), and tool performance. BlueEdge utilizes Natural Language Processing (NLP) techniques, including those from the Natural Language Toolkit (NLTK) and Python packages, to connect with a real-time database. As shown in our results, the accuracy values that BlueEdge showed ranged between 72 and 95% across 6 categories of name-based duplicate detection tasks, proving its competitive performance in mobile edge environments. The validation of the framework was done using a larger dataset of 146 error cases with statistically significant values having confidence interval of between 3.4% to 5.8. Statistical comparison indicates consistently significant changes ( p < 0.05) compared to baseline settings of four commercial tools with large effect sizes ( Cohen d: 0.89- 1.34). BlueEdge takes care of data duplication elimination services such as using different spelling and pronunciation (78.4%, CI: 73.1-83.7%), misspellings (72.0%, CI: 66.2-77.8%), name abbreviations (90.5%, CI: 86.1-94.9%), honorific prefixes (95.2%, CI: 91.8-98.6%), common nicknames (76.2%, C The reliable performance of edge-based data cleaning is verified through cross-validation analysis (81.7% +/- 2.3%), the results of which prove the consistency of its activity. Additionally, BlueEdge utilizes a mini