关键词： Uncrewed aerial vehicle   mobile edge computing   reliability optimization   reliability optimization   motion control   motion control   resource allocation   resource allocation   resource allocation  

摘要： Uncrewed aerial vehicles (UAVs) play a crucial role in mobile edge computing (MEC) within space-air-ground integrated networks. They serve as aerial cloudlets, enabling task processing in close proximity to ground users. While numerous joint trajectory design and resource allocation schemes aim to enhance energy efficiency or computation rate, few focus on improving system reliability, which is often challenged by stochastic channels and node mobility. This paper presents a stochastic modeling perspective to derive a system reliability expression. Our reliability formulation incorporates the impacts of stochastic Line-of-Sight (LoS) and Non-Line-of-Sight (NLoS) air-to-ground communication channels, application data load, available bandwidth, offloading time, and transmission power. This comprehensive approach leads to a reliability-oriented joint optimization model that considers not only resource allocation and user data transmission scheduling but also the motion of UAVs. To solve this problem, we propose a low-complexity algorithm. By utilizing augmented Lagrangian multipliers, the algorithm transforms nonlinear constraints into a tractable formulation, enabling the utilization of legacy unconstrained optimization techniques. We provide a proof of convergence for this algorithm. Through simulations, we demonstrate that our proposed method guarantees convergence within finite iterations and improves the average communication reliability in comparison with several other joint optimization schemes.

关键词： Federated learning   Incentive mechanism   Mobile edge computing   Stackelberg game   Yardstick pricing rule  

摘要： Federated Learning (FL) enables collaborative model training across multiple participants without sharing original data, making it a valuable tool for preserving privacy in Mobile Edge Computing (MEC) environment. However, due to users' varying levels of motivation and commitment, it is challenging to incentivize effective participation in FL. To address this, we propose a pricing-based incentive mechanism that enhances FL efficiency and energy sustainability in MEC. To be specific, we firstly develop the formula of incentive mechanism based on the yardstick pricing rule. Subsequently, we determine the optimal hyperparameters of the utility function aiming to maximize model accuracy. Additionally, we formulate a Stackelberg game to derive optimal reward strategies, balancing users' transmission power allocation and the server's reward distribution. Simulation results show that our proposed scheme outperforms other existing schemes with over 98.2% accuracy, 0.7% server utility enhancement, and 14.6% server loss decrease compared with static incentives. Moreover, our proposed scheme contributes to faster growth in both server and users utilities when compared with the advanced schemes by varying user numbers, which demonstrates its better scalability and adaptability.

关键词： Mobile edge computing   privacy preservation   privacy preservation   user behavior analysis   user behavior analysis   federated learning   federated learning   blockchain   blockchain   blockchain  

摘要： In mobile edge computing, user behavior analysis can provide a more clear understanding of edge user behavior, which is crucial for providing more accurate recommendation services to mobile users. However, privacy leakage is inevitable in the process of edge user behavior data collection, storage and analysis. How to protect edge user privacy is very urgent for the development of mobile edge computing. Existing schemes dealing with these problems still suffer from 1) high computational overhead, 2) low model accuracy and poor convergence under privacy-preserving, and 3) poor model robustness when edge nodes fail. Therefore, we propose a novel privacy-preserving model in edge user behavioral analysis termed Safe-FL to protect the behavioral privacy of all edge participants by introducing secure data aggregation and encryption in the federated learning (FL) framework. To reduce the privacy leakage of data uploading in edge user behavior analysis, we first construct a federated learning-based edge user behavior analysis framework. Secondly, we utilize a hybrid encryption scheme to encrypt the local model hierarchically to protect the gradient privacy in edge user behavior analysis and reduce the encryption computation overhead. And finally, to improve the robustness of the model, we propose a decentralized secure gradient aggregation algorithm based on blockchain. Extensive experiments are conducted on two public datasets, whose results illustrate that our model has 1) strong privacy preservation and small computational overhead;2) high accuracy and convergence speed;and 3) good robustness, compared to other models.

关键词： Computational modeling   Mobile handsets   Energy consumption   Servers   Optimization   Artificial neural networks   Adaptation models   Inference algorithms   Costs   Data models   Split computing   deep neural network   mobile edge computing   mixed-integer nonlinear programming (MINLP)   joint optimization   sorting policy  

摘要： To tackle the difficulties of running sophisticated deep neural network (DNN) models on mobile devices, split computing presents a viable solution by offloading computations to the edge server. Current split computing schemes typically aim to lower either inference latency or energy use separately;however, optimizing both simultaneously is quite challenging due to numerous shifting factors, such as intensive continuous DNN model inferences, DNN model traits, and device/network conditions. Moreover, in practical applications, edge server overload might lead to substantial queuing delays, adding complexity to the optimization process. This article outlines a joint optimization problem that simultaneously seeks to minimize both inference latency and energy consumption, with a distinct inclusion of queue clearance latency for an accurate analysis of the continuously generated DNN model inferences. To address this intricate optimization challenge, we introduce a low-complexity heuristic algorithm that sets split point decisions based on the residual energy of mobile devices for each DNN inference cycle. Upon evaluation, our proposed algorithm demonstrates notable improvements by reducing inference latency by between 73.37% and 99.39%, and cutting down energy usage by between 39.97% and 94.67% compared to fully local processing on mobile devices.

关键词： IoT   Futuristic smart vertical networks   Ultra-dense IoT networks   Mobile edge computing   IoT mobile devices   Latency-sensitive tasks   Energy utilization  

摘要： As the Internet of Things (IoT) evolves and is integrated into cutting-edge Smart Vertical Networks-based IoT, a plethora of IoT mobile devices (IMD) must contend with the increasing processing demands of time-critical tasks. The dynamic nature of the environment raises novel challenges for networks that use mobile edge computing. As a proactive response to these issues, the concept of ultra-dense IoT with Mobile Edge Computing has emerged. Within this architecture, Integrated Mobile Devices (IMDs) can save power and preserve their internal processing resources by offloading compute-intensive tasks to servers located at the network's periphery (the "edge"). Nevertheless, the increased efficiency comes at the cost of greater transmission overhead, leading to an elevated delay. To achieve an ideal equilibrium between energy preservation and latency reduction, we propose a new optimization problem that focuses on minimizing both energy utilization and latency in ultra-dense IoT networks with multiple users and tasks. This issue entails the complex optimization of concurrent user (IMD) associations, computation offloading decisions, and resource allocations. To achieve a fair distribution of network load and maximize the utilization of computational resources, we integrate multi-step computation offloading methodologies into the issue formulation. Finally, the Adaptive Particle Swarm Optimization (PSO) technique is utilized as an intelligent way of solving the problem. Significantly, our methodology exhibits a noteworthy improvement over traditional Particle Swarm Optimization (PSO) techniques, resulting in a substantial decrease in overall expenses, encompassing reductions that span from 20 to 65%.

关键词： Servers   Vehicle dynamics   Resource management   Delays   Dynamic scheduling   Processor scheduling   Computers   Heuristic algorithms   Computational modeling   Quality of service   Mobile edge computing   dependency application   resource allocation   task offloading   dynamic caching   deep reinforcement learning  

摘要： Mobile Edge Computing (MEC) is a distributed computing paradigm that provides computing capabilities at the periphery of mobile cellular networks. This architecture empowers Mobile Users (MUs) to offload computation-intensive applications to large-scale computing nodes near the edge side, reducing application latency for MUs. The resource allocation and task offloading in MEC has been widely studied. However, the burgeoning complexity inherent to modern applications, often represented as Directed Acyclic Graphs (DAGs) comprising a multitude of subtasks with interdependencies, poses huge challenges for application offloading and resource allocation. Meanwhile, previous work has neglected the impact of edge caching on the offloading execution of dependent tasks. Therefore, this paper introduces a novel dynamic caching dependency-aware task offloading (CachOf) scheme. First, to effectively enhance the rationality of cache and computing resource allocation, we develop a subtask priority computation scheme based on DAG dependencies. This scheme includes the execution sequence priority of subtasks on a single MU and the offloading sequence priority of subtasks from multiple MUs. Second, a dynamic caching scheme, designed to cater to dependent tasks, is proposed. This caching approach can not only assist offloading decisions, but also contribute to load balancing by harmonizing caching resources among edge servers. Finally, based on the task prioritization results and caching results, this paper presents a Deep Reinforcement Learning (DRL)-based offloading scheme to judiciously allocate resources and improve the execution efficiency of applications. Extensive simulation experiments demonstrate that CachOf outperforms other baseline schemes, achieving improved execution efficiency for applications.

关键词： Multi-cell association   Heterogeneous networks   Task offloading   Mobile edge computing   System latency   Service caching   Energy consumption   And load balancing  

摘要： In highly dense networks with huge computational requirements, mobile edge computing has been proposed to alleviate network traffic congestion and reduce system latency by offloading the intensive computational tasks to the network edges for execution. As a result, achieving low energy consumption and reduced system latency has become increasingly important under this paradigm. In this paper, we propose a delay-energy-aware algorithm for minimizing the overall system latency, energy consumption and balancing the load among base stations, particularly in the case of hybrid-task scenarios. A novel crafted weighted-sum objective function for the total system latency and energy consumption is designed to formulate a non-convex joint optimization problem. The Gibbs sampling algorithm is used to solve the formulated optimization problem through updating the caching and offloading decision variables. The proposed framework investigates the optimal multi-cell association, power allocation, service data caching, and computational task offloading for multi-tier communication and edge computing networks. The effect of limited quota on multi-tier heterogeneous networks is investigated under Rayleigh fading channels. Simulation results demonstrate the superiority of the proposed algorithms over the state-of-the-art works in terms of reducing the system latency and energy consumption.

关键词： Ultra reliable low latency communication   Resource management   Safety   Optimization   Transportation   Vehicle-to-everything   Delays   Multi-access edge computing   Cloud computing   Uncertainty   Digital twin   mobile edge computing   C-V2X networks   vehicular safety prediction   robust optimization  

摘要： The development of intelligent transportation has generated many ultra reliable low latency communication (URLLC) tasks, which require sufficient communication and computation resources for task offloading and processing. Although mobile edge computing (MEC) provides a promising solution, its efficiency is subject to the limited knowledge and analysis capability on the physical networks. Therefore, in this paper, we propose a digital twin (DT) empowered MEC framework to strengthen the MEC task offloading efficiency in cellular vehicle-to-everything (C-V2X) networks. Our proposed DT is constructed through a hybrid data-driven and model-driven approach to capture the realistic transportation network features. Then, DT leverages the metric of time to collision to predict vehicular safety levels and estimates the corresponding URLLC task requirements of future time slots. The prediction results are further utilized to make decisions on the URLLC resource reservation. Different from conventional studies, we consider the influence of DT's inaccurate predictions (i.e., the prediction with error) on the resource allocations. Specifically, the inaccurate DT prediction results are considered as uncertain constraints of the resource reservation problem. A robust parameter from the robust optimization is adopted to adjust the tradeoff between the problem uncertainty and solution optimality degree. Further, we leverage the optimized resource reservation results to construct the task offloading problem. The problem is decoupled into two sub-problems of channel resource allocation and computation resource allocation, respectively. And a two-stage matching algorithm is developed to solve each sub-problem based on the resource reservation constraints. Finally, realistic road information is mapped into DT for simulations. Simulation results validate the advantages of our proposed approach by comparing with existing schemes.

关键词： Servers   Cloud computing   Feature extraction   Consumer electronics   Human activity recognition   Internet of Things   Hidden Markov models   Mobile edge computing   digital twin   deep reinforcement learning   human activity recognition   sensor inference  

摘要： The presence of digital twins (DTs) has expanded within the consumer electronics area due to its inherent benefits of high-fidelity modeling and predictive insight. However, DT deployment remains costly and constrained in providing the fundamental functionalities required by consumer electronics systems due to massive computation and communication. The emergence of mobile edge computing (MEC) has made DT deployment feasible to achieve data-driven modeling and consumer-centric concurrently with low communication costs. This article proposes an MEC-based DT deployment scheme in the smart home domain. The cloud service can predict subsequent sensor updates leveraging the MEC platform's human activity recognition result of residential environments. In addition, with deep reinforcement learning (DRL), it can only track essential data to maintain sensor update consistency between both physical and virtual sides. We implement experimental evaluation with two real-world residential datasets, and the results demonstrate that our scheme maintains high-level sensor update consistency while being energy-efficient in different residential environments, which illuminates the promising prospects of DT implementation in MEC and consumer electronics.

关键词： Optimization   Internet of Things   Resource management   Heuristic algorithms   Costs   Vehicle dynamics   Servers   Cloud computing   Quality of service   Minimization   Bursty traffic   Lyapunov optimization   mobile edge computing   online control   virtual network functions  

摘要： Mobile edge computing (MEC) integrated with Network Functions Virtualization (NFV) helps run a wide range of services implemented by Virtual Network Functions (VNFs) deployed at MEC networks. This emerging paradigm offers flexible edge resource management for Internet Service Providers (ISPs) and improves service satisfaction of diverse applications. However, a critical challenge lies in processing extremely unpredictable and bursty traffic flow, especially in event-trigger sensing tasks. In this paper, we study queue-aware service migration and resource allocation in an MEC network, where different devices with different arrival patterns request different network services. To this end, we first formulate a novel long-term operation cost minimization problem subject to the stability of multiple queues by adopting a simultaneous migration scheme and determining resource allocation dynamically. To address the problem efficiently, we propose an online control framework to make decisions without any prior system information. We first adopt the Lyapunov optimization technique to decompose the long-term optimization problem into multi-per-slot subproblems. Then, we adopt a decoupled optimization algorithm based on the coordinate descent method and augmented Lagrangian method to solve the offline mixed integer non-linear programming (MINLP) subproblems. Extensive results reveal that the proposed online optimization framework can efficiently balance queue stability and cost consumption.