关键词： Delays   Industrial Internet of Things   Optimization   Heuristic algorithms   Energy consumption   Dynamic scheduling   Resource management   Industrial IoT   mobile edge computing   computation offloading   multi-objective optimization   deep reinforcement learning  

摘要： With the increasing scale of industrial equipments, delay and energy consumption have emerged as critical concerns within the Industrial Internet of Things (Industrial IoT). Mobile edge computing (MEC) offloads tasks to nearby edge servers to meet the demands of delay-sensitive applications. However, the limitations of edge computing resources can lead to significant processing delays or even task failures when offloading numerous tasks. Furthermore, it is difficult for existing centralized algorithms to acquire global information within large-scale industrial environment. To tackle these challenges, the computation offloading problem is transformed into a decentralized partially observable Markov decision process (Dec-POMDP) with rewards for delay and energy consumption, and a decentralized multi-objective computation offloading method is proposed to achieve the long-term reward maximization. Specifically, two deep neural networks, namely the delay and energy network, are designed to estimate the expected rewards for each offloading decision in terms of delay and energy consumption. Meanwhile, to achieve dynamic offloading, gated recurrent unit (GRU) is introduced to predict the occupancy of edge computing resources, and an adaptive weight network is devised to dynamically adjust the weights of optimization objectives based on historical information. Comprehensive experiments demonstrate that the proposed method effectively meets the requirements of delay-sensitive tasks, as well as minimizing long-term delay and energy consumption.

关键词： Evolutionary multitasking   Multifactorial evolutionary algorithm   Multi-form evolutionary optimization   Service migration optimization   Mobile edge computing  

摘要： In high-speed mobile networks, mobile edge computing is tasked with service migration optimization, i.e., assigning mobile users to the right servers to minimize the response time. Service migration optimization is a complex problem posing significant challenges to conventional optimization methods. To tackle this problem, we develop a multifactorial memetic algorithm with adaptive auxiliary tasks or MFMA-AAT for short. MFMA-AAT solves the target service migration optimization problem and an adaptively selected auxiliary task simultaneously, where the auxiliary task is a simplified version of the target problem to guide the search towards promising regions faster via knowledge transfer. Multiple auxiliary tasks are pre-constructed based on the distribution of the mobile users and the one with the best improvement at each generation is selected for knowledge transfer. A community detection-based memetic operator is also introduced to accelerate the local convergence of the proposed algorithm. Experimental results on test problems demonstrate that MFMA-AAT is more efficient than traditional service migration approaches and other state-of-the-art multifactorial evolutionary algorithms.

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： Internet of vehicles   Mobile edge computing   Deep reinforcement learning   Task offloading   Resource allocation  

摘要： In vehicular networks, the increasing demand for computational resources often exceeds the capabilities of in-vehicle devices. To address these challenges, we propose a cloud-edge-device collaborative framework integrated with a Multi-Agent Deep Reinforcement Learning (MADRL) algorithm for dynamic optimization of task offloading and resource allocation. Experimental evaluations demonstrate the proposed algorithm's superiority over traditional methods, achieving an 11% reduction in energy consumption and a 23% increase in task completion rate compared to local processing-only strategies, while reducing average task delay by 50% relative to static offloading approaches. The MADRL-based framework not only ensures efficient task distribution but also adapts to fluctuating network conditions, achieving a resource utilization rate of 85%. These findings underscore its potential to enhance performance in intelligent transportation systems by balancing computational efficiency, energy consumption, and task latency.

关键词： UAV   mobile edge computing   dynamically processes   computing resources   communication technology  

摘要： With the advancement of communication technology, mobile edge computing (MEC) is considered a key technology for handling computation-intensive and latency-sensitive tasks. However, in scenarios such as disaster response and emergency rescue, edge servers cannot be quickly deployed to provide task processing services. The impressive mobility and simplicity of deployment of unmanned aerial vehicles (UAVs) have garnered a lot of attention for their potential to help MEC. However, allocating resources is made more difficult by the fact that UAVs have limited energy and computational capabilities. A suggested resource-efficient technique for multi-UAV collaboration (LUAVs-Cor) aims to tackle this issue. Tasks are dynamically processed by multi-UAV collaboration in this technique. Using a search for the ideal task combination, it finds task transmission mechanisms to make the most of UAVs' idle processing capabilities. Additionally, by estimating UAV processing capabilities, task quantity, and processing status, it simplifies the process of deploying UAVs to lower power usage. We observed that the LUAVs-Cor method boosts service capacity by around 6.8% and decreases total UAV energy consumption by 10.3% through comprehensive simulated trials. More customers may be served at reduced energy costs with the suggested LUAVs-Cor method, which involves minimum cooperation expenses for UAVs.

关键词： UAV   Mobile edge computing   Task offloading   Multi-cache placement   Deep reinforcement learning  

摘要： Unmanned aerial vehicles (UAVs) are being developed as a promising technology to assist mobile edge computing (MEC) systems due to their reliable wireless communication, flexible computing service capabilities, and flexible deployment. However, in the face of huge information and demanding task delay, it is a challenging problem to reduce the system cost. This paper studies task offloading and cache space placement for ground users, proposes a multi-UAV assisted computing framework, which is a four-layer transmission system composed ground users (UE), UAVs, edge data centers (EDC) and remote clouds. By jointly optimizing UAV cache flight path, offloading decision, channel ratio, and battery power, we formulate the problem to minimize the term average weighted cost of the system under the constraint of cache space and computing resources. Since problem is a mixed integer variable problem, we design a task offloading and cache placement algorithm on deep reinforcement learning, namely the Cooperative Long-term Average Cost Minimization Optimization Algorithm (CLACMO). Firstly, we transform the mixed action variable space by using embedded tables conditional variational autoencoders (VAE) combined with latent space, and map the mixed action variable latent action space. This approach effectively unifies discrete and continuous actions, addressing the challenge of mixed action spaces that traditional deep reinforcement learning algorithms struggle with. Secondly, on the deep reinforcement learning (DRL), we achieve the long-term system average weighted cost minimization more efficiently under the constraints of task offloading and cache placement. The results show that compared with the PER-UOS-RL, MASAC, and MADDPG algorithms, the average reward has increased by 54.5%, 66.7%, 69.7% respectively, and the average task completion rate has increased by 12.9%, 38.1%, and 9.11% respectively, demonstrating the effectiveness of our novel method.

关键词： Task offloading   Resource allocation   UAV-enabled systems   Deep reinforcement learning   Mobile edge computing  

摘要： The rapid and continuous growth of the Internet of Things has led to a rising demand for processing and storage solutions that can overcome the limitations of user devices. While cloud computing provides scalability, it often introduces considerable delay. To address this challenge, a computing paradigm that brings resources closer to user devices is preferable. Unmanned Aerial Vehicles (UAVs) have emerged as an effective solution to enhance communication quality and coverage in wireless systems, particularly in specific conditions. This paper presents a UAV-enabled Mobile Edge Computing (MEC) system, where UAVs equipped with computational capabilities provide task offloading services to users. In this system, users process part of their computing tasks locally, while offloading the remaining tasks to UAVs for processing. The primary goal of the proposed algorithm is to minimize processing delays, taking into account the environmental and energy constraints of the UAVs, such as movement boundaries, link blockages, transmission delays, and battery consumption for computation and flight. To achieve this, we propose a Deep Reinforcement Learning algorithm based on the Rainbow Deep Q-Network. This algorithm explores the dynamic and stochastic environment of MEC to deploy an appropriate computation offloading policy. The extensive experiments conducted in this study demonstrate the superior performance of the proposed method. The results show fast convergence to an optimal value and an average improvement of 12.63 percent in delay compared to state-of-the-art methods.

关键词： Mobile networks   5G communications   Vehicle-to-Everything   Mobile edge computing   Mobile Netzwerke   5G-Kommunikation   Vehicle-to-Everything-Kommunikation   Mobile Edge Computing  

摘要： In the context of the growing demand for ultra-reliable and low-latency communications (URLLC) in Vehicle-to-Everything (V2X) applications and edge computing offloading, there is a growing imperative to understand the end-to-end Quality of Service (QoS) performance in real-world cellular networks. This study presents an empirical evaluation of the performance of the 4G LTE and 5G Non-Standalone (NSA) network performance, based on extensive drive tests conducted in Bratislava, Slovakia. The analysis of the empirical cumulative distribution function (ECDF) of key Quality of Service (QoS) metrics, specifically latency and throughput, has highlighted the variability of end-to-end performance in different mobility and coverage scenarios. The results obtained have provided insights into the suitability of current cellular infrastructures to support latency-sensitive applications, particularly in vehicular environments where seamless connectivity and real-time data processing are essential. The need for field measurements to complement theoretical and simulation-based studies is emphasized in the findings, which offer valuable input for network optimization and future 5G Standalone (SA) deployments. This work contributes to ongoing efforts to improve the reliability of cellular networks for mission-critical applications, ensuring robust connectivity for next generation intelligent transportation systems. Angesichts der wachsenden Nachfrage nach ultra-zuverl & auml;ssiger und latenzarmer Kommunikation (URLLC) in Vehicle-to-Everything-Kommunikation (V2X)-Anwendungen sowie bei der Auslagerung von Rechenprozessen an das Edge Computing, wird ein vertieftes Verst & auml;ndnis der End-to-End Quality of Service (QoS) in realen Mobilfunknetzen zunehmend unerl & auml;sslich. Diese Studie pr & auml;sentiert eine empirische Bewertung der Leistungsf & auml;higkeit von 4G LTE- und 5G Non-Standalone (NSA)-Netzen, basierend auf umfangreichen Fahrtests in Bratislava, Slowakei. Die Analyse

关键词： Servers   Mobile handsets   Costs   Computational modeling   Pricing   Training   Performance evaluation   Optimization   Collaboration   Adaptation models   Federated edge learning (FEL)   incentive mechanism   bargaining model   mobile edge computing  

摘要： Devices and servers in Federated Edge Learning (FEL) are self-interested and resource-constrained, making it critical to design incentives to improve model performance. However, dynamic network conditions raise energy consumption, while data heterogeneity undermines device cooperation. Current research overlooks the interplay between system efficiency and device clustering, resulting in suboptimal updates. To address these challenges, we develop BENCH, a bilateral pricing mechanism consisting of three core rules aimed at incentivizing participation from both devices and servers. Specifically, we first design a reward allocation rule, based on the Rubinstein bargaining model, which dynamically allocates rewards. Theoretically, we derive a closed-form solution for this rule, demonstrating BENCH achieves Nash equilibrium. Secondly, we design a device partitioning rule that leverages modularity to group similar devices, facilitating personalized edge aggregation to accelerate local data adaptation. Thirdly, we design an edge matching rule that employs the Kuhn-Munkres algorithm to balance the load at edge servers, thus minimizing the congestion. Together, these three rules enable hierarchical optimization of pricing and associations, effectively mitigating the impact of dynamic costs and device heterogeneity. Extensive experiments demonstrate BENCH's effectiveness in increasing device participation by 28.81% and improving model performance by 2.66% compared to state-of-the-art baselines.

关键词： Dynamic voltage and frequency scaling   mobile edge computing   partial offloading   resource allocation   stackelberg game   Dynamic voltage and frequency scaling   mobile edge computing   partial offloading   resource allocation   stackelberg game  

摘要： Due to the limited computing resources of both mobile devices (MDs) and the mobile edge computing (MEC) server, devising reasonable strategies for MD task offloading, MEC server resource pricing, and resource allocation is crucial. In this paper, a scenario is considered, comprising multiple MDs and a single MEC server. Each MD has a divisible task in each time slot, allowing for partial offloading and the option to discard parts of the task. The MEC server contains multiple computing units with the same computing power, and its computing resources can be dynamically adjusted through dynamic voltage and frequency scaling (DVFS) according to the size of tasks offloaded by MDs. At any given time slice, a Stackelberg game is formulated based on the strategies of the MDs and the strategy of the MEC server. An iterative evolution algorithm is employed to explore the optimal strategies for MDs and the MEC server. Simulation results demonstrate that both parties can reach an equilibrium state through the game, and these experiments confirm that the algorithm effectively enhances system efficiency.