关键词： Mobile edge computing  

摘要： Mobile Edge Computing (MEC) has become a critical paradigm. It extends traditional cloud com- puting capabilities by moving computation and storage closer to the network edge. A significant challenge in MEC is the inefficiency and high energy consumption in data offloading for mobile edge computing (MEC) systems due to traditional offloading techniques. The problems with these techniques are mainly limited cov- erage, high energy consumption and privacy concerns. The study explores the integration of unmanned aerial vehicles (UAVs) with artificial intelligence (AI) techniques to enhance data offloading in MEC environments. The primary objective is to utilize AI algorithms that enable UAVs to make intelligent decisions regarding data transmission. These AI-powered UAVs can dynamically adapt to changing network conditions and optimize data transmission processes by considering factors such as network conditions, device capabilities, and energy constraints. The study examines AI-driven strategies for UAV-based data offloading while focusing on how these techniques can improve network efficiency, reduce latency and enhance scalability in MEC systems. This demonstrates the potential of AI-powered unmanned aerial vehicles (UAV) as a viable solution for efficient data offloading in MEC. This research shows that AI-powered UAVs can enhance data offloading in MEC sys- tems by optimizing flight paths, boosting connectivity and improving overall network performance. While AI integration reduces latency and increases scalability, these UAVs offer high connectivity in demanding areas. However, further research is needed to tackle those challenges. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： OFDM   Optimization   Antennas   Channel estimation   Energy consumption   Central Processing Unit   Time-domain analysis   Sun   Servers   Mobile edge computing   reconfigurable intelligent surface   resource allocation   resource allocation  

摘要： This letter proposes a novel resource allocation algorithm to enable a reconfigurable intelligent surface (RIS) to support delay-bounded mobile edge computing (MEC) in orthogonal frequency division multiplexing (OFDM) settings, adhering to per-subcarrier power constraint. The energy requirement of the smart devices (SDs) is minimized through the joint optimization of task offloading parameters, RIS configuration, and the receive beamforming of the base station (BS). A key contribution is that we find the semi-closed-form receive beamformers as a function of the RIS phases. Hence, the joint optimization is decoupled between convex offloading scheduling and non-convex RIS configuration by alternating optimization (AO). Another contribution is that we convexify the per-subcarrier power constraint by exploiting successive convex approximation (SCA), and acquiring a Karush-Kuhn-Tucker (KKT) point solution for the RIS configuration. Numerical results show the superiority of the new approach compared with baseline schemes in energy saving, and reveal that the optimal RIS location hinges on the channel quality differences among the RIS, BS, and SDs.

关键词： Mobile edge computing   UAV communication   Secrecy rate maximization   Transmit beamforming   Trajectory design  

摘要： This paper is concerned with the optimization problem for dual unmanned aerial vehicle (UAV) mobile edge computing systems. One UAV acts as a computation server and the other is a jammer in the system, while the terminal device (TD) on the ground uploads its computation task to the UAV server with the presence of an eavesdropper. The design objective is to maximize the average secrecy rate of TD. Furthermore, the trajectories of UAVs, transmit beamformers, and computation bits are jointly optimized. As the formulated problem is non-convex, it is decomposed into two sub-problems. Then, we employ the successive convex approximation and alternative optimization method to solve the problem iteratively. Moreover, the zero-forcing (ZF) beamforming scheme is utilized for further simplification of calculation. Simulation results demonstrate that our algorithm is effective, and the proposed ZF scheme achieves close-to-optimal performance.

关键词： Encryption   Blockchains   Access control   Data privacy   Keyword search   Data integrity   Internet of Vehicles   Servers   Multi-access edge computing   Vehicle dynamics   Blockchain   attribute-based encryption   ranked multi-keyword search   mobile edge computing  

摘要： The Internet of Vehicles (IoV) faces critical challenges in balancing real-time data processing, privacy preservation, and secure data sharing amid growing intelligent transportation demands. While mobile edge computing (MEC) reduces latency by offloading tasks to MEC servers, efficient encrypted search and dynamic access control remain unresolved. Attribute-based keyword search (ABKS) enables privacy-preserving queries on encrypted data but exhibits critical limitations such as lack of revocable access for dynamic user privileges, exposed access policy that risk sensitive attribute leakage, and data integrity verification. Moreover, existing ABKS schemes further suffer from centralized key management in attribute-based encryption (ABE), introducing single points of failure and key escrow issues. To address these issues, we propose BC-RMABSE, a blockchain-enabled ABKS scheme. Our scheme leverages the vector space model to enable ranked multi-keyword searches, returning top-k relevant results for improved efficiency. Policy-hiding mechanisms and attribute revocation ensure flexible fine-grained access control while safeguarding sensitive attributes. A decentralized key distribution strategy using Pedersen's (k, n) secret sharing protocol eliminates reliance on central authority, mitigating security risks. Blockchain technology enforces data integrity through tamper-proof consensus and resolves the "service-payment" imbalance via smart contracts, ensuring transactional fairness between users and untrusted service providers. Experimental analysis indicates that our scheme performs well in terms of both security and search efficiency.

关键词： Blockchain   Hybrid intrusion   Intrusion detection   Machine learning   Mobile edge computing  

摘要： The growing use of mobile edge computing (MEC) has had a positive impact on user experience and reduced latency. However, this closeness also makes MEC environments vulnerable to a number of security risks. This research article presents an edge-based hybrid intrusion detection system for MEC and the Internet of Things (IoT). The system uses techniques like behavioral analysis, anomaly detection, and signature-based detection, ensuring real-time response and reduced bandwidth usage. The system also addresses challenges in data acquisition and cleaning due to potential threats from malicious users and noise. The model uses smoothing filters, unsupervised learning, and deep learning techniques to detect anomalies and threats, reducing bandwidth. According to the findings, securing MEC environments against changing cyber threats can be accomplished using an edge-based hybrid intrusion detection system. © Little Lion Scientific.

关键词： Mobile edge computing   Task offloading   Meta-reinforcement learning   Unmanned aerial vehicle  

摘要： Existing task offloading models struggle to rapidly adapt to dynamic environmental changes in emergency scenarios, such as natural disasters or unforeseen events. To address this challenge, a meta-reinforcement learning-based task offloading method (MRLTO) for unmanned aerial vehicle (UAV)-enabled mobile edge computing (MEC) is proposed. First, an objective function for optimizing task offloading is formulated based on communication, local computing, and UAV computing models. Subsequently, the task offloading problem is reformulated as a Markov decision process (MDP). By incorporating a meta-learning framework, our approach consolidates the learning experiences of various mobile devices across diverse environments, allowing the offloading model to rapidly adapt to dynamic environmental changes. Furthermore, a dueling deep Q-network (D3QN) is employed to decouple state values and action advantage values, thereby improving task offloading decision mapping and dynamically generating more efficient offloading decisions. Finally, an experimental scenario is set up to compare MRLTO with RNOF, AGGO, and DTTO. The results show that MRLTO reduces the average latency by 8.14%, lowers the average energy consumption by 6.89%, and decreases the task drop rate by 12.89%.

关键词： mathematical methods   artificial intelligence   mobile edge computing  

摘要： The integration of mathematical methods with artificial intelligence (AI) and mobile edge computing (MEC) has emerged as a promising research direction to address the growing complexity of intelligent distributed systems. To chart the landscape of this interdisciplinary field, we first examine recent surveys that primarily focus on architectural designs, learning paradigms, and system-level deployments in edge AI. However, these studies largely overlook the theoretical foundations essential for ensuring reliability, interpretability, and efficiency. This paper fills this gap by conducting a comprehensive survey of mathematical methods and analyzing their applications in AI-enabled MEC systems. We focus on addressing three key challenges: heterogeneous data integration, real-time optimization, and computational scalability. We summarize state-of-the-art schemes to address these challenges and identify several open issues and promising future research directions.

关键词： Premium services emerge   Hyper-service network   Network evolution   Preferred connection   Mobile edge computing  

摘要： Recently, massive services from multiple domains are available on the network with the rapid development of Mobile Edge Computing (MEC) technology. These various services construct a service network through services' collaboration, which can provide users with rich resources at anytime and anywhere. However, in MEC, due to the proliferation of services, as well as the dynamics of Quality of Service (QoS), it becomes more and more difficult for users to discover the premium services effectively, which seriously impacts users' satisfaction and the utilization of resources. Therefore, how to proactively perceive the emergence of premium services in the service networks has become an challenge. To tackle this issue, we propose a method for perception of the premium services based on the evolution of hyper-service network (named as PPSHSN). First, based on the hypergraph theory, we construct a hyper-service network with the optimal connection method. Then, we develop the evolution mechanism of the hyper-service network and optimize the hyper-service network with the hyperdegree premium value evolution algorithm, thus to conduct the evolution of the service networks. Next, we devise the evaluation indexes based on the topological characteristics of hypergraphs, which are updated according to the evolution of the hyper-service network. Finally, we complete perception of premium services based on the evolution results of the hyper-service network. The effectiveness and feasibility of our proposed method are verified through extensive simulation experiments.

关键词： Autonomous aerial vehicles   Collaboration   Artificial neural networks   Internet of Things   Computational modeling   Servers   Heuristic algorithms   Cloud computing   Real-time systems   Energy consumption   Deep neural network   generative artificial intelligence (AI)   mobile edge computing   multiagent reinforcement learning (MARL)   path planning   task assignment   uncrewed aerial vehicle (UAV) network  

摘要： uncrewed aerial vehicles (UAVs) offer high mobility and flexible deployment capabilities, making them ideal for Internet of Things (IoT) applications. However, the substantial amount of data generated by various applications within the existing low-altitude network requires processing through deep neural networks (DNN) on UAVs, which is challenging due to their limited computational resources. To address this issue, we propose a two-stage optimization method for flight path planning and task allocation based on a mother-child UAV swarm system. In the first stage, we employ a greedy algorithm to solve the path planning problem by considering the task size of the target area to be inspected and the shortest flight path as constraints. The goal is to minimize both the flight path of the UAV and the overall cost of the system. In the second stage, we introduce a novel DNN task assignment algorithm that combines multiagent deep deterministic policy gradient (MADDPG) and generative diffusion models (GDMs), named GDM-MADDPG. This algorithm takes advantage of the reverse denoising process of GDM to replace the actor network in MADDPG. It enables UAVs to generate specific DNN task assignment actions based on agents' observations in a dynamic environment, thereby improving the efficiency of task assignment and overall system performance. The simulation results demonstrate that our algorithm outperforms the benchmarks in terms of path planning, Age of Information (AoI), task completion rate, and system utility, demonstrating its effectiveness.

关键词： User allocation   Multi-hop   Mobile edge computing   Game theory  

摘要： PurposeThe purpose of this study is to solve user allocation problem in a multi-hop user allocation (MUA) environment and balance the service on ***/methodology/approachThe authors choose to describe the MUA problem as a MUA game. Then, this paper proves the existence of Nash equilibrium in the game. Next, the paper proposes a game-based multi-hop user allocation (GMUA) algorithm to get the *** convergence analysis and comparison experiments are carried out to evaluate the performance of the GMUA algorithm. The results show that the GMUA algorithm is better than other methods. And the iteration time has limitation which is related to user's number and resouce ***/valueThis study discusses user allocation problem in multi-hop environment. The authors prove that this problem is NP-hard. They introduce it to a potential game and propose a GMUA algorithm to solve it. The authors analyze this algorithm theoretically and experimentally.