关键词： Asymptotic performance bound   Cloud-assisted edge server   Effective speed   Energy constraint   Heuristic algorithm   Mobile edge computing   Task scheduling  

摘要： In this paper, we study task scheduling with or without energy constraint in mobile edge computing with multiple cloud-assisted edge servers as combinatorial optimization problems within the framework of classical scheduling theory. The first problem is to schedule a list of independent tasks on a mobile device and several heterogeneous edge servers and cloud servers, such that the makespan is minimized. The second problem is to schedule a list of independent tasks and to determine the computation and communication speeds of a mobile device, such that the makespan is minimized and the energy consumption of the mobile device does not exceed certain energy budget. The paper makes the following tangible contributions. We design heuristic task scheduling algorithms for both problems by considering the heterogeneity of computation and communication speeds. We derive a lower bound for the optimal schedule and prove an asymptotic performance bound for our heuristic algorithms. We experimentally evaluate the performance of our heuristic algorithms and show that their performance is very close to that of an optimal algorithm. Our analysis employs three key techniques, namely, the method of communication unification (i.e., all tasks have the same communication to computation ratio), the concept of effective speed of an edge server or a cloud server (i.e., the perceived speed of a server by ignoring the details and differences of communication speed and computation speed, wireless communication time and wired communication time, a regular edge server and a cloud-assisted edge server, execution time and waiting time), and the construction of virtual tasks (i.e., imaginary tasks which do not exist). Such unique techniques make it possible to derive a lower bound for the optimal solution, to derive an upper bound for the heuristic solution, to prove an asymptotic performance bound, and to find the best edge server order. To the best of the author's knowledge, this is the first pa

关键词： Predictive models   Data models   Computational modeling   Manufacturing   Adaptation models   Predictive maintenance   Performance evaluation   Machine diagnosis   mobile edge computing   machine learning   predictive maintenance   explainable artificial intelligence  

摘要： As the number of Internet of Things edge devices in smart factories increasing, it is crucial to predict the lifetime of the equipment to keep the production running normally. Although predictive maintenance based on machine learning achieve a better performance, they still face the challenge of black box and time-efficient. This paper propose a stacking-based model called ERF-XGB for predictive maintenance in the edge computing environment. The model first performs initial prediction by integrating the Random Forest model and Extreme Gradient Boosting model, followed by further processing of the initial prediction results using the linear regression model to obtain the final prediction results. In addition, the model incorporates the Shapley Additive exPlanations method, which can enhance the interpretability of the model when performing predictive maintenance. An experimental evaluation of the Predictive and Health Management dataset shows that the ERF-XGB model has an RMSE of 18.271 and an MAE of 13.454, which are the two metrics that perform the best when compared to other comparison models, suggesting that the model has a better predictive performance. Meanwhile, the Shapley Additive exPlanations method visualizes the impact of each edge device on normal operation in the production process, facilitating precise equipment management and maintenance.

关键词： Autonomous aerial vehicles   NOMA   Semantic Web   Security   Blockchains   Resource management   Jamming   Mobile edge computing   resource allocation   unmanned aerial vehicle   blockchain   federated learning   WEB 3.0.  

摘要： The use of unmanned aerial vehicles (UAVs) in Internet-of-Things (IoT) has grown, but security for UAV communications still a challenge due to the distributed nature of line-of-sight communication networks. This paper analyses the security issues for UAV-assisted MEC systems, in which UAVs are used to support base stations (BSs) in computing offloaded workloads while also operate as jammers to inhibit malicious eavesdroppers. In this paper, a minimum secure computing capacity maximization issues is addressed by jointly optimizing communication link, computation resources, and UAVs trajectories. These issues are of a non-trivial nature and present a significant challenge to be resolved due to the presence of highly coupled variables. In order to effectively overcome these issues, we introduced the concept of WEB 3.0. Firstly, we introduced blockchain method to enhance security to the UAV assisted MEC system and then to train the data locally and to solve various wireless schemes problems, respectively we proposed a FL method. To contribute to the literature, we developed an effective resource allocation strategy that maximizes computational offloading using WEB 3.0, a Blockchain-based FRL method. Finally, numerical findings indicate that the suggested techniques improve the security computation capacity efficiency of the systems when compared to the standards.

关键词： Semantics   Task analysis   Energy consumption   Consumer electronics   Optimization   Resource management   Bandwidth   Mobile edge computing   semantic communication   resilient optimization   deep reinforcement learning  

摘要： In this paper, we investigate a semantic-aware mobile edge computing (MEC) network for sustainable next-G consumer electronics, which leverages advanced semantic communication technology to overcome the limitations of available bandwidth and thereby improve communication efficiency. For this network, consumer electronic devices can offload the semantic information extracted from the task instead of transmitting the whole task in the conventional MEC networks, where the latency and energy consumption can be significantly reduced through proper semantic encoding, offloading, and bandwidth and computation allocation decisions. However, the non-convexity of the issue makes it difficult to obtain the optimal decision. To address this issue, a two-level optimization framework is proposed. Specifically, in the upper-level optimization, a resilient deep reinforcement learning (DRL) approach is utilized to enable adaptive offloading and semantic encoding decisions within a dynamic network. In the lower-level optimization, we design three criteria for allocating bandwidth and computation resources by carefully considering the trade-off between computational complexity and implementation efficiency. Finally, extensive simulations are conducted to validate the effectiveness of our proposed strategy. The findings in this paper can help reduce the energy consumption of consumer electronics, hence supporting the development of sustainable next-G consumer electronics.

关键词： Cloud computing   ensemble learning and resource scheduling   mobile edge computing  

摘要： An edge cloud architecture plays a key role in improving the user task computing service system by combining the powerful data processing capability of cloud centres with the low latency of edge computing. Existing methods for maximizing the efficiency of an edge cloud architecture take into account time and task parameters but ignore other factors such as load balancing, cost, and user satisfaction when scheduling resources. In this work, we propose a many-objective resource scheduling model for optimizing the performance of an edge cloud architecture, which takes into account the time spent on task, cost, load balance, user satisfaction, and trust measurement. The resource scheduling model converges to the optimal solution using a novel many-objective ensemble optimization algorithm based on a dynamic selection mechanism. The study also explores the support set convergence of eight evolutionary operators using the ensemble algorithm. The model solutions are dynamically updated with the help of the dynamic integration probability, and then a selection criteria is used to pick the best solutions from the pool of generated solutions. Two simulations on a benchmark dataset are used to verify the usefulness and performance of the designed algorithm. Our approach was able to locate more than half of the best solutions on the benchmark functions, and it also showed to be a better model solution than the some of the popular many-objective algorithms for dealing with the edge cloud resource scheduling problem, according to the results obtained from the simulations.

关键词： mobile edge computing   wireless sensor networks   semantic communications   attention mechanism   joint source channel coding   deep neural network  

摘要： Wireless Sensor Networks (WSNs) have emerged as an efficient solution for numerous real-time applications, attributable to their compactness, cost-effectiveness, and ease of deployment. The rapid advancement of 5G technology and mobile edge computing (MEC) in recent years has catalyzed the transition towards large-scale deployment of WSN devices. However, the resulting data proliferation and the dynamics of communication environments introduce new challenges for WSN communication: (1) ensuring robust communication in adverse environments and (2) effectively alleviating bandwidth pressure from massive data transmission. In response to the aforementioned challenges, this paper proposes a semantic communication solution. Specifically, considering the limited computational and storage resources of WSN devices, we propose a flexible Attention-based Adaptive Coding (AAC) module. This module integrates window and channel attention mechanisms, dynamically adjusts semantic information in response to the current channel state, and facilitates adaptation of a single model across various Signal-to-Noise Ratio (SNR) environments. Furthermore, to validate the effectiveness of this approach, the paper introduces an end-to-end Joint Source Channel Coding (JSCC) scheme for image semantic communication, employing the AAC module. Experimental results demonstrate that the proposed scheme surpasses existing deep JSCC schemes across datasets of varying resolutions;furthermore, they validate the efficacy of the proposed AAC module, which is capable of dynamically adjusting critical information according to the current channel state. This enables the model to be trained over a range of SNRs and obtain better results.

关键词： Task analysis   Energy consumption   Optimization   Servers   Reinforcement learning   Performance evaluation   Costs   Mobile edge computing   computation offloading   task characteristics   completion time estimation  

摘要： Edge computing extends computational capabilities to the physical edge of the world, providing faster and more efficient responses to consumer devices such as smart home appliances, smartphones, and wearable devices. However, it raises a critical question: How can we efficiently manage time-sensitive and computationally intensive tasks to meet device performance needs? To address this, we propose a computational cost-driven strategy for computation offloading based on reinforcement learning for consumer devices. Firstly, we convert the problem of minimizing computational costs, including latency and energy consumption, into maximizing the cumulative rewards problem of consumer devices. Second, we design a task completion time estimation method to ensure a favorable user experience. Third, by comprehensively considering the status of the network, computational demands, and task completion times, we employ reinforcement learning techniques to determine the optimal task offload strategy for consumer devices by maximizing cumulative rewards. This ensures efficient task completion with lower computational costs. The simulation results demonstrate that our method achieves the highest rewards in different scenarios. Our method reduces the task discard ratio by 25% compared to the PPO_KLP-based offload strategy.

关键词： Mobile edge computing   computation offloading   blockchain   deep reinforcement learning  

摘要： With the widespread popularity of the Internet of Things and various intelligent medical devices, the amount of medical data is rising sharply, and thus medical data processing has become increasingly challenging. Mobile edge computing technology allows computing power to be allocated at the edge closer to users, which enables efficient data offloading for healthcare systems. However, existing studies on medical data offloading seldom guarantee effective data privacy and security. Moreover, the research equipping data offloading architectures with Blockchain neglect the delay and energy consumption costs incurred in using Blockchain technology for medical data offloading. Therefore, in this paper, we propose a data offloading scheme for healthcare based on Blockchain technology, which achieves optimal medical resource allocation and simultaneously minimizes the cost of offloading tasks. Specifically, we design a smart contract to ensure secure data offloading. And, we formulate the cost problem as a Markov Decision Process, solved by a policy search-based deep reinforcement learning (Asynchronous Advantage Actor-Critic) scheme, where we jointly consider offloading decisions, allocation of computing resources and radio transmission bandwidth, and Blockchain data security audits. The security of our smart-contract-based mechanism is theoretically and empirically proved, while extensive experimental results also show that our solution can obtain superior performance gains with lower cost than other baselines.

关键词： Mobile edge computing   federated continual learning   data selection and aggregator placement   approximation and online algorithms  

摘要： Federated continual learning (FCL) is emerging as a key technology for time-sensitive applications in highly adaptive environments including autonomous driving and industrial digital twin. Each FCL trains machine learning models using newly-generated datasets as soon as possible, to obtain a highly accurate machine learning model for new event predictions. The age of data, defined as the time difference between the generation time of a dataset and the current time, is widely adopted as a key criterion to evaluate both timeline and quality of training. In this paper, we study the problem of age-aware FCL in a mobile edge computing (MEC) network. We not only investigate optimization techniques that optimize the data selection and aggregator placement for FCL but also implement a real system as a prototype for age-aware FCL. Specifically, we first propose an approximation algorithm with a provable approximation ratio for the age-aware data selection and aggregator placement problem for FCL with a single request. In real application scenarios, there are usually multiple FCL requests that require to train models, and delays in the MEC network are usually uncertain. We then study the problem of age-aware data selection and aggregator placement problem for FCL with uncertain delays and multiple requests, by devising an online learning algorithm with a bounded regret based on contextual bandits. We finally implement a prototype for FCL in an MEC network, with various heterogeneous user equipments (UEs) and cloudlets with different computing capabilities in the network. Experiment results show that the performance of the proposed algorithms outperform existing studies, by achieving 47% lower age of data and 12% higher model accuracy.

关键词： Mobile edge computing   decentralized D2D collaboration   mean field graph   task offloading   dynamic pricing  

摘要： Mobile edge computing (MEC) facilitates computation offloading to edge server and task processing via device-to-device (D2D) collaboration. Existing works mainly focus on centralized network-assisted offloading solutions, which are unscalable to collaborations among massive users. In this paper, we propose a joint framework of decentralized D2D collaboration and task offloading for MEC systems with large populations. Specifically, we utilize the power of two choices for D2D collaboration, which enables users to assist each other in a decentralized manner. Due to short-range D2D communication and user movements, we formulate a mean field model on a finite-degree and dynamic graph to analyze the collaboration state evolution. We derive the existence, uniqueness and convergence of the state stationary point to provide a tractable collaboration performance. Complementing this D2D collaboration, we further build a Stackelberg game to model users' task offloading, where the provider, managing many servers, is the leader to determine service prices, while users are followers to make offloading decisions. By embedding Stackelberg game into Lyapunov optimization, we develop an online offloading and pricing scheme, which can optimize servers' service utility or fairness, and users' system cost simultaneously. Extensive evaluations show that D2D collaboration can mitigate users' workloads by 73.8% and fair pricing can promote servers' utility fairness by 15.87%.