关键词： Epoch adjustment   mobile edge computing   model reliability   federated learning  

摘要： As a distributed learning paradigm, federated learning (FL) can be applied in mobile edge computing (MEC) to support real-time artificial intelligence by leveraging edge computation resources while preserving data privacy in the end devices. However, the unpredictable wireless connections between end devices and edge servers in MEC (e.g., frequent handovers and unstable wireless channels) may result in the loss of important model parameters, which slows down the FL training process and degrades the quality of the global model. In this paper, we propose an adaptive collaborative federated learning (ACFL) scheme to accelerate the convergence and improve model reliability by mitigating communication-based parameter loss under a three-layer MEC architecture. First, a dynamic epoch adjustment method is proposed to reduce communication rounds by dynamically adjusting the training epochs in end devices. In addition, to accelerate the FL convergence, we present an edge server collaborative training scheme by leveraging a multi-layer computing architecture, where edge servers utilize their maintained data to collaboratively train models with end devices. Finally, extensive simulations are conducted and show that ACFL can efficiently improve model reliability and accelerate the convergence of the FL process in MEC.

关键词： Digital twin placement and migration   Digital twin synchronization   Cost modeling of digital twin placement   Digital twin service cost minimization   Mobile Edge Computing  

摘要： In the past decades, explosive numbers of Internet of Things (IoT) devices (objects) have been connected to the Internet, which enable users to access, control, and monitor their surrounding phenomenons at anytime and anywhere. To provide seamless interactions between the cyber world and the real world, Digital twins (DTs) of objects (IoT devices) are key enablers for real time monitoring, behaviour simulations, and predictive decisions on objects. Compared to centralized cloud computing, mobile edge computing (MEC) has been envisioning as a promising paradigm for low latency IoT applications. Accelerating the usage of DTs in MEC networks will bring unprecedented benefits to diverse services, through the co-evolution between physical objects and their virtual DTs, and DT-assisted service provisioning has attracted increasing attention recently. In this article, we consider novel DT placement and migration problems in an MEC network with the mobility assumption of objects and users, by jointly considering the freshness of DT data and the service cost of users requesting for DT data. To this end, we first propose an algorithm for the DT placement problem with the aim to minimize the sum of the DT update cost of objects and the total service cost of users requesting for DT data, through efficient DT placements and resource allocation to process user requests. We then devise an approximation algorithm with a provable approximation ratio for a special case of the DT placement problem when each user requests the DT data of only one object. Meanwhile, considering the mobility of users and objects, we devise an online, two-layer scheduling algorithm for DT migrations to further reduce the total service cost of users within a given finite time horizon. We finally evaluate the performance of the proposed algorithms through experimental simulations. The simulation results show that the proposed algorithms are promising.

关键词： Mobile edge computing  

摘要： Unmanned aerial vehicles (UAVs) are widely applied in multiple fields, which emphasizes the challenge of obtaining UAV flight information to ensure the airspace safety. UAVs equipped with automatic dependent surveillance-broadcast (ADS-B) devices are capable of sending flight information to nearby aircrafts and ground stations (GSs). However, the saturation of limited frequency bands of ADS-B leads to interferences among UAVs and impairs the monitoring performance of GS to civil planes. To address this issue, the integration of the 5th generation mobile communication technology (5G) with ADS-B is proposed for UAV operations in this paper. Specifically, a hierarchical structure is proposed, in which the high-altitude central UAV is equipped with ADS-B and the low-altitude central UAV utilizes 5G modules to transmit flight information. Meanwhile, based on the mobile edge computing technique, the flight information of sub-UAVs is offloaded to the central UAV for further processing, and then transmitted to GS. We present the deterministic model and stochastic geometry based model to build the air-to-ground channel and air-to-air channel, respectively. The effectiveness of the proposed monitoring system is verified via simulations and experiments. This research contributes to improving the airspace safety and advancing the air traffic flow management. Copyright © 2024, The Authors. All rights reserved.

关键词： Mobile edge computing  

摘要： Fifth generation (5G) advancements rely heavily on integrating mobile edge computing (MEC) with ultra-dense networks (UDN). This not only boosts network capacity but also offers on-site processing power for future mobile devices. However, efficiently utilizing these resources requires well-designed scheduling policies within the MEC-UDN setup. This paper addresses this by proposing a resource scheduling scheme that considers both macro and small cells, minimizing task delay and device energy consumption. It employs an adaptive group learning whale optimization algorithm (AGLWOA)" to select the best offloading mode, target base station, and channel for each task. Simulation results show this approach outperforms previous methods in optimizing system performance and user experience. © 2024, The Authors. All rights reserved.

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  

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： 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   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： 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.