关键词： Internet of Things   Autonomous aerial vehicles   Urban areas   Optimization   Collaboration   Delays   Computer architecture   Quality of service   Computational modeling   Topology   Mobile edge computing   uncrewed aerial vehicles   task offloading   user satisfaction   service provider profit  

摘要： Mobile edge computing (MEC) is a promising technology to meet the increasing demands and computing limitations of complex Internet of Things (IoT) devices. However, implementing MEC in urban environments can be challenging due to factors like high device density, complex infrastructure, and limited network coverage. Network congestion and connectivity issues can adversely affect user satisfaction. Hence, in this article, we use uncrewed aerial vehicle (UAV)-assisted collaborative MEC architecture to facilitate task offloading of IoT devices in urban environments. We utilize the combined capabilities of UAVs and ground edge servers (ESs) to maximize user satisfaction and thereby also maximize the service provider's (SP) profit. We design IoT task-offloading as joint IoT-UAV-ES association and UAV-network topology optimization problem. Due to NP-hard nature, we break the problem into two subproblems: offload strategy optimization and UAV topology optimization. We develop a Three-sided Matching with Size and Cyclic preference (TMSC) based task offloading algorithm to find stable association between IoTs, UAVs, and ESs to achieve system objective. We also propose a K-means based iterative algorithm to decide the minimum number of UAVs and their positions to provide offloading services to maximum IoTs in the system. Finally, we demonstrate the efficacy of the proposed task offloading scheme over benchmark schemes through simulation-based evaluation. The proposed scheme outperforms by 19%, 12%, and 25% on average in terms of percentage of served IoTs, average user satisfaction, and SP profit, respectively, with 25% lesser UAVs, making it an effective solution to support IoT task requirements in urban environments using UAV-assisted MEC architecture.

关键词： NOMA   Interference   Security   Energy consumption   Eavesdropping   Optimization   Throughput   Mobile edge computing   non-orthogonal multiple access   physical layer security   marine Internet of Things  

摘要： In this paper, we investigate a marine Internet of Things (M-IoT) network with high altitude platform (HAP) secure computation offloading at risk of eavesdropping. To ensure the security of HAP's information transmission, we utilize a group of unmanned surface vehicles (USVs) with an HAP to form a non-orthogonal multiple access (NOMA) transmission group to provide co-channel interference. Our goal is to minimize the total energy consumption by jointly optimizing HAP's computation offloading workload, HAP's transmission power, data transmission time, and USV's transmission power while meeting the security and delay requirements. Although this problem is strictly non-convex optimization, we use problem transformation and vertical decomposition methods to decompose the problem into an underlying problem and a top-level problem. The underlying problem is to determine the HAP's transmission power and HAP's computation offloading workload, and the top-level problem is to determine the data transmission time, which is solved by using proximal policy optimization (PPO). The two subproblems are solved iteratively over each other to obtain the minimum total energy consumption. Simulation results show that the proposed algorithm converges faster and reduces the energy consumption of 30.34% compared to asynchronous advantage actor-critic (A3C).

关键词： Mobile edge computing  

摘要： The rapid advancement of immersive technologies has propelled the development of the Metaverse, where the convergence of virtual and physical realities necessitates the generation of high-quality, photorealistic images to enhance user experience. However, generating these images, especially through Generative Diffusion Models (GDMs), in mobile edge computing environments presents significant challenges due to the limited computing resources of edge devices and the dynamic nature of wireless networks. This paper proposes a novel framework that integrates contract-inspired contest theory, Deep Reinforcement Learning (DRL), and GDMs to optimize image generation in these resource-constrained environments. The framework addresses the critical challenges of resource allocation and semantic data transmission quality by incentivizing edge devices to efficiently transmit high-quality semantic data, which is essential for creating realistic and immersive images. The use of contest and contract theory ensures that edge devices are motivated to allocate resources effectively, while DRL dynamically adjusts to network conditions, optimizing the overall image generation process. Experimental results demonstrate that the proposed approach not only improves the quality of generated images but also achieves superior convergence speed and stability compared to traditional methods. This makes the framework particularly effective for optimizing complex resource allocation tasks in mobile edge Metaverse applications, offering enhanced performance and efficiency in creating immersive virtual environments. © 2025, CC BY.

关键词： application deployment   genetic algorithm   IoT   microservices   mobile edge computing  

摘要： With the increasing popularity of cloud native and DevOps, container technology has become widely used in combination with microservices. However, deploying container-based microservices in distributed edge-cloud infrastructure requires complex selection strategies to ensure high-quality service for users. Existing container orchestration tools lack flexibility in selecting the best deployment location based on user cost budgets and are insufficient in providing personalized deployment solutions. This paper proposes a genetic algorithm-based Internet of Things (IoT) application deployment and selection strategy for personalized cost budgets. The application deployment problem is defined as an optimization problem that minimizes user service latency under cost constraints, which is an NP-hard problem. The genetic algorithm is introduced to solve this problem effectively and improve deployment efficiency. Comparative results show that the proposed algorithm outperforms four baseline algorithms, including time-greedy, cost-greedy, random, and PSO, using real datasets and some synthetic datasets. The proposed algorithm provides personalized deployment solutions for edge-cloud infrastructure. It supports flexibly selecting the best microservice deployment location in hybrid cloud-edge infrastructure according to the user's cost budget, with personalized deployment requirements. The application deployment problem is defined as an optimization problem that minimizes user service latency under cost constraints and could be solved by the genetic algorithm. image

关键词： Mobile edge computing   Deep reinforcement learning   Density clustering   Offloading decision   Ensemble learning  

摘要： Mobile edge computing offloads compute-intensive tasks generated on mobile wireless devices (WD) to edge servers (ES), which provides mobile users with low-latency computing services. Opportunistic computing offloading is effective to enhance computing performance in dynamic edge network environments;however, careless offloading of tasks to ESs can lead to WDs preempting network computing resources with limited bandwidth, thereby resulting in inefficient allocation of computing resources. To address these challenges, this paper proposes the density clustering and ensemble learning training-based deep reinforcement learning (DCEDRL) method for task offloading decision-making in mobile edge computing (MEC). Firstly, DCEDRL utilizes multiple deep neural networks to explore the environment. It trains multiple models using ensemble learning methods to obtain a combination of prediction results. Secondly, DCEDRL utilizes an optimized density clustering method to identify and classify computing tasks with similar characteristics to improve subsequent task scheduling and resource allocation efficiency. Finally, according to the stored priority information, DCEDRL utilizes the priority weight to resample the samples, adjust the sampling strategy in real time, and improve the adaptability and robustness of the system. Simulation results demonstrate that the proposed DCEDRL method can reduce the backlog of tasks by greater than over 21% compared to the baseline algorithms.

关键词： Mobile edge computing   Vehicular Ad-hoc network   Sticky bacteria   Link stability   Routing  

摘要： With the rapid development of Telematics, the role of edge computing (Mobile Edge Computing, MEC) is becoming more and more significant. Mobile users can obtain massive computing and storage resources in a local way, thus effectively solving the congestion problem of the core network. However, in general urban edge network scenarios of VANET (Vehicular Ad-hoc Network), due to low node density and poor connectivity, there are few chances to encounter suitable forwarding nodes. In order to avoid the above situation and adapt to sparse scenarios, we propose new link-stabilized routing method & protocol based on sticky bacteria algorithm in this paper. The new idea and the significant findings of this proposed algorithm in enhancing the routing protocol is as follows: five factors affecting routing decision are identified: evaluation distance, deflection angle, number of neighboring nodes, rate difference, and the traffic of the road section at which it is located, and then these five factors are comprehensively evaluated by using the our designed Analytic Hierarchy Process (AHP) strategy, so as to determine the score of each node and the candidate routing paths;And the best routing path between two communicating nodes is found through the stronger global exploration capability of the sticky bacteria algorithm. Our experiments (that are based on our developed C++ programs) have proved that the method proposed in this paper has stronger link stability and the highest packet delivery rate. And the experiments and their results has enhanced our new method credibility in the field of ad hoc networks.

关键词： Autonomous aerial vehicles   Quality of experience   Delays   Optimization   Servers   Resource management   Multi-access edge computing   Content caching   service placement   task offloading   UAV networks   mobile edge computing  

摘要： In this paper, we consider an unmanned aerial vehicle (UAV)-enabled mobile edge computing (MEC) network, where multiple UAVs with caching and computation functionalities are deployed to satisfy the heterogeneous content and service requests from the user equipments (UEs). In order to comprehensively characterize the capability of our considered network in satisfying the UEs' requests, we define the weighted sum of the content cache hit ratio and the service delay shrinkage ratio as the average quality-of-experience (QoE) of our network and adopt it as the performance metric. Through analysis, we show how the average QoE of our network is dependent on the content cache and service placement decisions at the UAVs, as well as the computation task offloading decisions at the UEs, thus enabling us to formulate an average QoE maximization problem, subject to practical constraints on the UAVs' caching and computation capabilities. To solve this NP-hard problem, we decompose it into two sub-problems, namely, the content cache and service placement optimization sub-problem and the task offloading optimization sub-problem. Gibbs sampling-based and matching game-based algorithms are proposed to efficiently solve these sub-problems iteratively. Via numerical results, we validate the effectiveness of our proposed algorithms. Compared to various benchmarks, we demonstrate that our proposed algorithms can significantly improve the average QoE of our considered network, especially when the caching and computation resources of the UAVs are limited.

关键词： Mobile edge computing   Edge sever placement   Multi-objective ant colony optimization   Semi-greedy   Load balancing   Network traffic  

摘要： The importance of Mobile Edge Computing (MEC) systems has risen due to increasing data volumes from Internet of Things (IoT) devices and connected technologies. These systems place computation power and data storage near the network edge to improve response times and minimize bandwidth usage. One important thing in MEC is the edge server placement (ESP) problem for which placing servers strategically enhances system performance and user experience. Given the complexity of ESP, finding the optimal solution is challenging as it is an NP-hard problem. Reducing network traffic and achieving load balancing among edge servers are crucial objectives in ESP to alleviate bandwidth consumption and enhance the user experience. This paper introduces a mixed integer programming (MIP) model that addresses these issues and proposes a multi-objective ant colony optimization algorithm for solving this model. A semi-greedy approach is utilized to enhance the proposed algorithm's efficiency, accelerating convergence and reducing the search space when generating the initial population. To evaluate the performance of the proposed method, extensive simulation experiments were conducted using a real-world dataset from Shanghai Telecom's base stations. The results demonstrate the effectiveness of our method, showing a load balancing improvement of over 24% and a network traffic reduction exceeding 19% compared to other algorithms.

关键词： Synchronization   Autonomous aerial vehicles   Internet of Things   Costs   Heuristic algorithms   Minimization   Data collection   Digital twins   DT state staleness   mobile edge computing   DT synchronizations with physical objects   cost modeling   algorithm design and analysis   prediction mechanism  

摘要： With the advance of mobile edge computing (MEC) and the Internet of Things (IoT), digital twin (DT) has become an emerging technology for provisioning IoT services between the real world and the cyber world. In this paper, we consider the state updating of DTs in an MEC network through synchronizing DTs with their physical objects. We make use of an energy-constrained UAV for data collection in a sensor network, as an illustrative example for the DT state updating of each object (sensor), and then use the DT data of objects (sensors) later for fidelity-aware query services. To this end, we first formulate a novel DT state staleness minimization, under a given update budget per update round. We then propose an optimal algorithm for a special case of the problem where the budget per update round is exactly K objects synchronizing with their DTs. We then devise an algorithm for the DT state staleness minimization problem by reducing to the award collection maximization problem, assuming that the volume of the update data generated by each object per update round is given. Otherwise, we adopt a deep learning method to predict the volume of the update data. To demonstrate the importance of the DT state staleness in practical applications, we consider fidelity-aware query services in the MEC network, and we develop a cost-effective evaluation plan for each query. We finally evaluate the performance of the proposed algorithms through simulations. Simulation results demonstrate that the proposed algorithms are promising.

关键词： Autonomous aerial vehicles   Optimization   Wireless communication   Training   Computational modeling   Uncertainty   Energy consumption   Servers   Accuracy   Base stations   Mobile edge computing   federated learning   energy consumption   robust optimization  

摘要： Federated Learning (FL) and Mobile Edge Computing (MEC) technologies alleviate the burden of deploying artificial intelligence (AI) on wireless devices with low computational capabilities. However, they also introduce energy consumption challenges in FL model training and data processing. In this paper, we employ Unmanned Aerial Vehicles (UAVs) to collect data from wireless devices and carry edge servers to assist the central server located at the base station in training FL model. We also consider the deviation of UAVs' locations to address its impact on network performance. Specifically, we formulate a robust joint optimization problem to minimize the energy consumption of UAVs, considering the computational resources, transmit power, transmission time, and FL model accuracy. Moreover, Gaussian-distributed uncertainties caused by deviation in UAV locations result in probabilistic constraints on data offloading. We initially employ the Bernstein-type inequality (BTI) to transform probabilistic constraints into deterministic forms. Subsequently, we adopt the Block Coordinate Descent (BCD) to separate the problem into three subproblems. Simulation results demonstrate a significant reduction in energy consumption and superiority in robustness.