关键词： Mobile edge computing   Differential privacy   Auction   Resource allocation   Task offloading  

摘要： The proliferation of mobile devices (MDs) has accelerated the rapid development of various Internet of Things (IoT) applications, which are seeking efficient task execution paradigms. Following this trend, mobile edge computing (MEC) has recently emerged as a promising paradigm that provides offloading services to MDs at the edge of radio access networks. Since the computation and communication resources of edge service providers (ESPs) are limited, effective resource allocation mechanisms are crucial for realizing the MEC paradigm, enabling ESPs to collaborate and provide resources across JointCloud. However, existing works overlook the dependencies among tasks in IoT applications and their associated quality of service (QoS) requirements, while also failing to account for the potential privacy leakage that MDs may suffer from inference attacks. To overcome the above issues, we propose a privacy-preserving and truthful auction-based resource allocation mechanism for task offloading (PRATO) in an MEC system. Specifically, we first design a dependency-aware task offloading strategy determination algorithm, aiming to acquire the resource profiles and the corresponding task offloading solutions of MDs under their QoS requirements. Building on this algorithm, we further design the differentially private winner determination and pricing algorithm to determine the unit payment prices of combinatorial resources as well as the set of winning MDs. Strict theoretical analysis proves that PRATO achieves differential privacy, individual rationality, computational efficiency, truthfulness, and approximate revenue maximization. Extensive simulation results also validate the effectiveness of PRATO.

关键词： Mobile edge computing  

摘要： Next-generation touristic services will rely on the advanced mobile networks’ high bandwidth and low latency and the Multi-access Edge Computing (MEC) paradigm to provide fully immersive mobile experiences. As an integral part of travel planning systems, recommendation algorithms devise personalized tour itineraries for individual users considering the popularity of a city’s Points of Interest (POIs) as well as the tourist preferences and constraints. However, in the context of next-generation touristic services, recommendation algorithms should also consider the applications (e.g., social network, mobile video streaming, mobile augmented reality) the tourist will consume in the POIs and the quality in which the MEC infrastructure will deliver such applications. In this paper, we address the joint problem of recommending personalized tour itineraries for tourists and efficiently allocating MEC resources for advanced touristic applications. We formulate an optimization problem that maximizes the itinerary of individual tourists while optimizing the resource allocation at the network edge. We then propose an exact algorithm that quickly solves the problem optimally, considering instances of realistic size. Using a real-world location-based photo-sharing database, we conduct and present an exploratory analysis to understand preferences and users’ visiting patterns. Using this understanding, we propose a methodology to identify user interest in applications. Finally, we evaluate our algorithm using this dataset. Results show that our algorithm outperforms a modified version of a state-of-the-art solution for personalized tour itinerary recommendation, demonstrating gains up to 11% for resource allocation efficiency and 40% for user experience. In addition, our algorithm performs similarly to the modified state-of-the-art solution regarding traditional itinerary recommendation metrics. © 2025, CC BY-NC-ND.

关键词： Mobile edge computing  

摘要： Edge computing has emerged as a paradigm to bring low-latency and bandwidth-intensive applications close to end-users. However, edge computing platforms still face challenges related to resource constraints, connectivity, and security. We present GENIO, a novel platform that integrates edge computing within existing Passive Optical Network (PON) infrastructures. GENIO enhances central offices with computational and storage resources, enabling telecom operators to leverage their existing PON networks as a distributed edge computing infrastructure. Through simulations, we show the feasibility of GENIO in supporting real-world edge scenarios, and its better performance compared to a traditional edge computing architecture. Copyright © 2025, The Authors. All rights reserved.

关键词： Mobile edge computing  

摘要： The widespread adoption of machine learning (ML) on edge devices, such as mobile phones, laptops, IoT devices, etc., has enabled real-time AI applications in resource-constrained environments. Existing solutions for managing computational resources often focus narrowly on accuracy or energy efficiency, failing to adapt dynamically to varying workloads. Furthermore, the existing system lack robust mechanisms to adaptively balance CPU utilization, leading to inefficiencies in resource-constrained scenarios such as real-time traffic monitoring. To address these limitations, we propose a Self-Adaptive approach that optimizes CPU utilization and resource management on edge devices. Our approach EdgeMLBalancer balances between models through dynamic switching, guided by real-time CPU usage monitoring across processor cores. Tested on real-time traffic data, the approach adapts object detection models based on CPU usage, ensuring efficient resource utilization. The approach leverages epsilon-greedy strategy which promotes fairness and prevents resource starvation, maintaining system robustness. The results of our evaluation demonstrate significant improvements by balancing computational efficiency and accuracy, highlighting the approach’s ability to adapt seamlessly to varying workloads. This work lays the groundwork for further advancements in self-adaptation for resource-constrained environments. © 2025, CC BY.

关键词： Confidence threshold   Cooperative inference   Mobile edge computing   Surveillance  

摘要： This study proposes a novel collaborative inference method between edge device and edge server for intelligent surveillance services. In the proposed method, the device uses a small neural network (NN) and filters ambiguous input images by setting two confidence thresholds. These filtered images are then forwarded to the edge server for re-evaluation with a larger NN. Simulation results show that the proposed collaborative inference method, using optimal confidence thresholds, significantly reduces latency while meeting the required accuracy. © 2025, Korean Institute of Communications and Information Sciences. All rights reserved.

关键词： Edge computing devices   Mobile edge computing   Multi-objective optimization   Multi-objective particle   Road side unit   swarm-optimization  

摘要： The rise of intelligent and connected vehicles has led to new vehicular applications, but vehicle computing capabilities remain limited. Mobile edge computing (MEC) can mitigate this by offloading computation tasks to the network's edge. However, limited computational capacities in vehicles lead to increased latency and energy consumption. To address this, roadside units (RSUs) with cloud servers, known as edge computing devices (ECDs), can be expanded to provide energy-efficient scheduling for task computation. A new energy-efficient scheduling method called multi-objective optimization energy computation (MOEC) is proposed, based on multi-objective particle swarm optimization (MOPSO) to reduce ECDs' energy usage and execution time. Simulation results using MATLAB show that MOEC can balance the trade-off between energy usage and execution time, leading to more efficient offloading. © 2025 Institute of Advanced Engineering and Science. All rights reserved.

关键词： urban air mobility   mobile edge computing   resource allocation   federated learning   reinforcement learning   emergency scenario  

摘要： With the rapid expansion of urban populations and the accelerated pace of urbanization, the concept of urban air mobility (UAM) has emerged. During flights, UAM aircraft need to transmit real-time sensing information to base stations for further processing and analysis. Large-scale real-time data require leveraging the computing capabilities of edge servers at the network edge to reduce transmission delay and energy consumption of UAM aircraft. In cases where edge servers are unable to process information, an unmanned aerial vehicle (UAV) equipped with computing capabilities and operating in low-altitude airspace can serve as a relay to assist in communication and computation. Due to the limited payloads and flight times of UAVs and UAM aircraft, delay and energy consumption within the system pose significant challenges. To tackle these challenges, two fundamental objectives have been proposed: minimizing delay and minimizing energy consumption. Furthermore, an optimization problem has been proposed to minimize the weighted sum of delay and energy consumption. Then, a UAM federated twin delayed deep deterministic policy gradient (UF-TD3) algorithm has been proposed to solve the original problems characterized by complex, non-convex, and inseparable variables. Simulation results show that the proposed UF-TD3 algorithm converges quickly and significantly outperforms four other baseline algorithms in optimizing delay and energy consumption performance. Moreover, compared to the conventional delay minimization strategy and energy minimization strategy, the proposed strategy of minimizing the weighted sum of delay and energy consumption can reduce the delay by 63.8% and reduce energy by 73.96%.

关键词： 5G   AI   Mobile edge computing   Offloading   Reinforcement learning   V2X  

摘要： Traffic offloading is crucial for reducing computing latency in distributed edge systems such as vehicle-to- everything (V2X) networks, which use roadside units (RSUs) and access network mobile edge computing (ANMEC) with ML agents. Traffic offloading is part of the control plane problem, which requires fast decision-making in complex V2X systems. This study presents a novel ratio-based offloading strategy using the twin delayed deep deterministic policy gradient (TD3) algorithm to optimize offloading ratios in a two-tier V2X system, enabling computation at both RSUs and the edge. The offloading optimization covers both vertical and horizontal offloading, introducing a continuous search space that needs fast decision-making to accommodate fluctuating traffic in complex V2X systems. We developed a V2X environment to evaluate the performance of the offloading agent, incorporating latency models, state and action definitions, and reward structures. A comparative analysis with metaheuristic simulated annealing (SA) is conducted, and the impact of single versus multiple offloading agents with deployment options at a centralized central office (CO) is examined. Evaluation results indicate that TD3's decision time is five orders of magnitude faster than SA. For 10 and 50 sites, SA takes 602 and 20,421 seconds, respectively, while single-agent TD3 requires 4 to 24 milliseconds and multi-agent TD3 takes 1 to 3 milliseconds. The average latency for SA ranges from 0.18 to 0.32 milliseconds, single-agent TD3 from 0.26 to 0.5 milliseconds, and multi-agent TD3 from 0.22 to 0.45 milliseconds, demonstrating that TD3 approximates SA performance with initial training.

关键词： Mobile edge computing   STAR-RIS   NOMA   User mobility   DDPG   Dueling DQN  

摘要： Reconfigurable intelligent surface (RIS) is expected to be able to significantly reduce task processing delay and energy consumptions of mobile users (MUs) in mobile edge computing (MEC) by intelligently adjusting its reflecting elements' phase-shifts and amplitudes. Nevertheless, both the passive and active RISs have the disadvantage of only reflecting the received signals, which means that the transmitters and receivers must be located on the same side of the RIS. This maybe unrealistic due to the movement of MUs. Simultaneously transmitting and reflecting (STAR) RIS, which can simultaneously transmit and reflect incident signals to achieve full-area coverage, has been recognized as a revolutionary technique to solve the above-mentioned problem. For the STAR-RIS-aided non-orthogonal multiple access (NOMA) communication MEC, we first formulate an optimization problem to minimize the sum of weighted delay and energy consumptions of all MUs which can move randomly at low speeds. Then, under the practical coupled phase-shift model of STAR-RIS, we propose hybrid deep reinforcement learning (DRL) scheme, in which we determine the amplitudes and phase-shifts of STAR-RIS, task offloading decisions of MUs, and computation resource allocations of MEC servers by using the deep deterministic policy gradient (DDPG) and Dueling deep Q learning (DQN). Finally, we validate and evaluate the performances of our proposed scheme through extensive simulations, which show that our proposed scheme outperforms the existing baseline schemes and its performance can indeed be improved due to the use of STAR-RIS.

关键词： Wireless communication   NOMA   Internet of Things   Wireless sensor networks   Recycling   Array signal processing   Resource management   Optimization   Time division multiple access   Computational modeling   Reconfigurable intelligent surface   mobile edge computing   wireless power transfer   multiple access   resource allocation  

摘要： This paper investigates a reconfigurable intelligent surface (RIS)-assisted wireless powered mobile edge computing (MEC) system, where an access point (AP) integrated with an MEC server first transmits energy signals to charge multiple energy-constrained devices in the downlink (DL), and then the devices utilize the harvested energy to perform MEC offloading in the uplink (UL) assisted by an RIS. Specifically, three multiple access protocols for MEC offloading, namely pure non-orthogonal multiple access (NOMA), pure orthogonal multiple access, and hybrid NOMA, are proposed to exploit dynamic RIS beamforming and energy recycling among devices to enhance the efficiencies of energy harvesting and MEC offloading. For each of the three protocols, a joint resource allocation framework of the AP/devices transmit power, the RIS phase shifts, and the DL/UL time allocation is formulated to minimize the energy consumption at the AP. Because of those highly coupled optimization variables, the formulated optimization problems are first shown to be non-convex, and then the intrinsic structure of the problems is exploited to devise computationally-efficient algorithms and solve them iteratively. Numverical results are provided to demonstrate the performance improvement of our proposed designs compared to various benchmark schemes, and reveal the practical significance of the considered multiple access protocols with dynamic RIS beamforming and energy recycling for spectral and energy efficient MEC offloading.