关键词： Mobile edge computing  

摘要： We introduce FEDQ-Trust, an innovative data-driven trust prediction approach designed for mobile edge-based Internet of Things (IoT) environments. The decentralized nature of mobile edge environments introduces challenges due to variations in data distribution, impacting the accuracy and training efficiency of existing distributed data-driven trust prediction models. FEDQ-Trust effectively tackles the statistical heterogeneity challenges by integrating Federated Expectation-Maximization with Deep Q Networks. Federated Expectation-Maximization’s robust handling of statistical heterogeneity significantly enhances trust prediction accuracy. Meanwhile, Deep Q Networks streamlines the model training process, efficiently reducing the number of training clients while maintaining model performance. We conducted a suite of experiments within simulated MEC-based IoT settings by leveraging two real-world IoT datasets. The experimental results demonstrate that our model achieved a significant convergence time reduction of 97% to 99% while ensuring a notable improvement of 8% to 14% in accuracy compared to state-of-the-art models. © 2024, CC BY-NC-ND.

关键词： Mobile edge computing   heterogeneous internet of things   big data analysis   subchannel   terminal  

摘要： The big data heterogeneous Internet of Things (IoT) requires mobile edge computing (MEC) to process some data, and the data analysis system of MEC often has the problem of excessive terminal energy consumption (ECS) or long delay. So this study designed an energy-saving optimization algorithm for the task offloading processing module in the big data heterogeneous IoT analysis system, and designed and conducted simulation experiments to verify the application performance of the algorithm. The experimental results show that the #04 scheme of the designed algorithm has the lowest terminal ECS under the same conditions. Choosing the #04 scheme to build the algorithm, comparative analysis shows that when the edge server (ES) computing rate is 10 cycles/s, the weighted sum values of terminal ECS for EOPU, MPCO, exhaustive search, and local computing methods are 23.6 J, 23.9 J, 28.5 J and 84.5 J, respectively. Moreover, the algorithm possesses a significantly higher percentage of remaining time under different conditions of total SMD devices and total subchannels compared to other methods. This indicates that the designed algorithm can markedly enhance the processing performance of the task offloading model of the big data heterogeneous IoT data analysis system, and can also effectively reduce terminal ECS and system latency. The research results can provide reference for improving the processing ability of heterogeneous IoT big data analysis systems. The contribution of this study to the academic field lies in providing a model that can effectively reduce the operational ECS and time consumption of heterogeneous IoT big data analysis systems containing mobile animal networking devices. Moreover, from an industrial perspective, the results of this study contribute to improving the efficiency of information exchange and processing in the field of IoT computing, thereby promoting the promotion of IoT technology.

关键词： Mobile edge computing  

摘要： Mobile Edge Computing (MEC) has emerged as a solution to the high latency and suboptimal Quality of Experience (QoE) associated with Mobile Cloud Computing (MCC). By processing data near the source, MEC reduces the need to send information to distant data centers, resulting in faster response times and lower latency. This paper explores the differences between MEC and traditional cloud computing, emphasizing architecture, data flow, and resource allocation. Key technologies like Network Function Virtualization (NFV) and Software-Defined Networking (SDN) are discussed for their role in achieving scalability and flexibility. Additionally, security and privacy challenges are addressed, underscoring the need for robust frameworks. We conclude with an examination of various edge computing applications and suggest future research directions to enhance the effectiveness and adoption of MEC in the evolving technological landscape. © 2024, CC BY.

关键词： Bilevel optimization   mobile edge computing   personalized service customization   resource allocation   task offloading  

摘要： This article addresses the pricing scheme in a wireless-powered cooperative mobile edge computing (WP-CoMEC) system, focusing on personalized service customization. Traditional pricing schemes in such systems often assume a passive mode, with the service provider leading, and the device owner following. However, with the rise of personalized requirements, this paper proposes a novel approach where the device owner becomes an active participant in the pricing scheme, leading to personally customized services. The proposed pricing model formulates a bilevel multi-objective optimization problem, considering task offloading, resource allocation, and energy harvesting. This comprehensive approach ensures a more holistic optimization process. To address the computational challenges posed by the bilevel pricing model, this article proposes a bilevel periodically interactive evolutionary algorithm (BL-PIEA), which efficiently handles mixed variables, complex objective conflicts, and the inner nested structure of the bilevel pricing model. The proposed BL-PIEA is tested on ten instances, and the results indicate that BL-PIEA can effectively solve the proposed pricing model, showcasing superior performance in terms of reduced run time and saved evaluation budgets compared to other algorithms. With the proposed bilevel pricing model solved by BL-PIEA, the service provider can make out better pricing schemes that satisfy the device owner's requirements, so as to achieve a good personalized service customization.

关键词： Mobile edge computing  

摘要： Task offloading plays a pivotal role in mobile edge computing, enabling terminal devices to enhance task execution efficiency and conserve energy. However, servers are reluctant to offer services without compensation. Currently, pricing mechanisms are commonly employed to incentivize servers to serve terminal devices, with servers earning revenue through payments from these devices. Given the rapid surge in terminal devices, determining the optimal number of servers placement for service providers (SPs) to maximize revenue is crucial. In this paper, we propose a server placement scheme based on an all-pay auction framework. Experimental simulations reveal that an optimal server-user ratio of approximately 25% maximizes SP profits. Copyright © 2024, The Authors. All rights reserved.

关键词： Mobile edge computing  

摘要： The freshness of information in real-time monitoring systems has received increasing attention, with Age of Information (AoI) emerging as a novel metric for measuring information freshness. In many applications, update packets need to be computed before being delivered to a destination. Mobile edge computing (MEC) is a promising approach for efficiently accomplishing the computing process, where the transmission process and computation process are coupled, jointly affecting freshness. In this paper, we aim to minimize the average peak AoI (PAoI) in an MEC system. We consider the generate-at-will source model and study when to generate a new update in two edge server setups: 1) computing preemption, where the packet in the computing process will be preempted by the newly arrived one, and 2) non-preemption, where the newly arrived packet will wait in the queue until the current one completes computing. We prove that the fixed threshold policy is optimal in a non-preemptive system for arbitrary transmission time and computation time distributions. In a preemptive system, we show that the transmission-aware threshold policy is optimal when the computing time follows an exponential distribution. Our numerical simulation results not only validate the theoretical findings but also demonstrate that: 1) in our problem, preemptive systems are not always superior to non-preemptive systems, even with exponential distribution, and 2) as the ratio of the mean transmission time to the mean computation time increases, the optimal threshold increases in preemptive systems but decreases in non-preemptive systems. Copyright © 2024, The Authors. All rights reserved.

关键词： mobile edge computing   resource management   satellite-terrestrial networks   self-attention  

摘要： The satellite-terrestrial networks possess the ability to transcend geographical constraints inherent in traditional communication networks,enabling global coverage and offering users ubiquitous computing power support,which is an important development direction of future *** this paper,we take into account a multi-scenario network model under the coverage of low earth orbit(LEO)satellite,which can provide computing resources to users in faraway areas to improve task processing ***,LEO satellites experience limitations in computing and communication resources and the channels are time-varying and complex,which makes the extraction of state information a daunting ***,we explore the dynamic resource management issue pertaining to joint computing,communication resource allocation and power control for multi-access edge computing(MEC).In order to tackle this formidable issue,we undertake the task of transforming the issue into a Markov decision process(MDP)problem and propose the self-attention based dynamic resource management(SABDRM)algorithm,which effectively extracts state information features to enhance the training *** results show that the proposed algorithm is capable of effectively reducing the long-term average delay and energy consumption of the tasks.

关键词： deep reinforcement learning   mobile edge computing   service caching   service migrating  

摘要： Emerging mobile edge computing(MEC)is considered a feasible solution for offloading the computation-intensive request tasks generated from mobile wireless equipment(MWE)with limited computational resources and *** to the homogeneity of request tasks from one MWE during a longterm time period,it is vital to predeploy the particular service cachings required by the request tasks at the MEC *** this paper,we model a service caching-assisted MEC framework that takes into account the constraint on the number of service cachings hosted by each edge server and the migration of request tasks from the current edge server to another edge server with service caching required by ***,we propose a multiagent deep reinforcement learning-based computation offloading and task migrating decision-making scheme(MBOMS)to minimize the long-term average weighted *** proposed MBOMS can learn the near-optimal offloading and migrating decision-making policy by centralized training and decentralized *** and comprehensive simulation results reveal that our proposed MBOMS can converge well after training and outperforms the other five baseline algorithms.

关键词： Mobile edge computing   Dung beetle optimizer   Sequential game   Deep reinforcement learning  

摘要： The current technologies such as computer vision and artificial intelligence are continuously being applied to disaster rescue robots, enabling their adaptability to different environments. However, the challenge arising from this is that these robots will generate computationally intensive tasks, constrained by the size and energy supply of the robots. This limitation leads to a reduction in device endurance and an increase in processing latency, becoming a bottleneck for the application of artificial intelligence technologies. To address this concern, we propose a novel edge offloading system based on unmanned aerial vehicles (UAVs) and mobile edge service vehicles (MESVs) to assist with communication and computation tasks, aiming to minimize the overall computational burden of rescue robots in extreme scenarios where communication infrastructure is unavailable due to sudden natural disasters or accidents. Leveraging the high maneuverability of UAVs, we utilize an improved dung beetle optimization (DBO) algorithm to optimize the UAV's flight positions and assist robots in task processing and data relay. Moreover, we address the optimization problem of robot offloading strategies by employing a deep reinforcement learning-based Twin Delayed Deep Deterministic Policy Gradient (TD3) algorithm, which effectively reduces processing latency and energy consumption. Through simulation experiments and real -world simulation tests, our proposed method, compared to other UAV position optimization algorithms and computational strategy offloading methods, can effectively reduce the computational energy consumption of robots. In comparison with other methods presented in this paper, it can also reduce latency by nearly 40%.

关键词： caching   decentralization   mobile edge computing   blockchain   smart contract  

摘要： In the mobile edge computing (MEC) environment, the edge caching can provide the timely data response service for the intelligent scenarios. However, due to the limited storage capacity of edge nodes and the malicious node behavior, the question of how to select the cached contents and realize the decentralized security data caching faces challenges. In this paper, a blockchain-based decentralized and proactive caching strategy is proposed in an MEC environment to address this problem. The novelty is that the blockchain was adopted in an MEC environment with a proactive caching strategy based on node utility, and the corresponding optimization problem was built. The blockchain was adopted to build a secure and reliable service environment. The employed methodology is that the optimal caching strategy was achieved based on the linear relaxation technology and the interior point method. Additionally, in a content caching system, there is a trade-off between cache space and node utility, and the caching strategy was proposed to solve this problem. There was also a trade-off between the consensus process delay of blockchain and the caching latency of content. An offline consensus authentication method was adopted to reduce the influence of the consensus process delay on the content caching. The key finding was that the proposed algorithm can reduce latency and can ensure the security data caching in an IoT environment. Finally, the simulation experiment showed that the proposed algorithm can achieve up to 49.32%, 43.11%, and 34.85% improvements on the cache hit rate, the average content response latency, and the average system utility, respectively, compared to the random content caching algorithm, and it achieved up to 9.67%, 8.11%, and 5.95% increases, successively, compared to the greedy content caching algorithm.