edge computing

Service Enhancement and Reliability Assurance in 6G Vehicular Networks via a Stackelberg Game-Theoretic Approach

With the rapid development of 6G and Internet of Vehicles (IoV) technologies, the volume of computation-intensive tasks generated by intelligent vehicles is growing exponentially. Given limited onboard processing capabilities, vehicles increasingly rely on edge servers deployed by service providers (SPs) at roadside units to offload tasks. Vehicle clients can offload the tasks to SPs to mitigate their onboard computation load, while SPs derive economic benefits through the provision of computation resources. However, this interaction introduces a conflict of interest, as vehicles aim to minimize their offloading costs, while SPs seek to maximize revenue. To address this problem, we propose SPOR, a Stackelberg game-based service priority-aware computation offloading and resource pricing scheme in IoV. SPOR is a hierarchical game-theoretic framework in which SPs act as leaders setting prices, while vehicles act as followers determining their offloading strategies. A novel service prioritization function is introduced, incorporating booking price, system load, and reputation to ensure fair and balanced resource allocation. We provide a theoretical proof of the existence and uniqueness of a Nash equilibrium. Extensive experiments on a real-world vehicle edge computing dataset show that SPOR outperforms baseline methods in delay, energy consumption, average load, and task completion rate. Notably, SPOR maintains task completion rates above 97% even under heavy workloads, demonstrating its effectiveness in enhancing system reliability and overall performance.

MERA: A Green Edge Resource Control System With Privacy-Preservation via Mean-Field Reinforcement Learning

The global rollout of 5G networks has spurred the rapid deployments of edge servers for hosting latency-sensitive web applications, which improves quality of experience (QoE). However, current efforts fall short in the substantial energy costs associated with the 24/7 operation of edge servers and overlook user privacy by requiring accurate user information for service provision, eroding the sustainability of multi-access edge computing (MEC). To enhance the QoE and service performance while ensuring privacy in MEC, we systematically formulate the interaction among edge servers as a privacy-preserving experience-aware edge resource control (PEERC) problem. To address this, we conduct a global resource control and propose a collaborative resource allocation system named MERA. MERA leverages <inline-formula><tex-math notation="LaTeX">$k$</tex-math><alternatives><mml:math><mml:mi>k</mml:mi></mml:math><inline-graphic xlink:href="xia-ieq1-3705464.gif"/></alternatives></inline-formula>-anonymity data obfuscation to protect user location and resource demand privacy while enhancing service performance and energy efficiency with mean-field multi-agent reinforcement learning. Extensive experiments based on a synthetic real-world dataset demonstrate that MERA significantly surpasses benchmarks in terms of QoE, user coverage, privacy, and energy efficiency by <inline-formula><tex-math notation="LaTeX">$1.18\times$</tex-math><alternatives><mml:math><mml:mrow><mml:mn>1</mml:mn><mml:mo>.</mml:mo><mml:mn>18</mml:mn><mml:mo>×</mml:mo></mml:mrow></mml:math><inline-graphic xlink:href="xia-ieq2-3705464.gif"/></alternatives></inline-formula>, <inline-formula><tex-math notation="LaTeX">$1.24\times$</tex-math><alternatives><mml:math><mml:mrow><mml:mn>1</mml:mn><mml:mo>.</mml:mo><mml:mn>24</mml:mn><mml:mo>×</mml:mo></mml:mrow></mml:math><inline-graphic xlink:href="xia-ieq3-3705464.gif"/></alternatives></inline-formula>, <inline-formula><tex-math notation="LaTeX">$1.63\times$</tex-math><alternatives><mml:math><mml:mrow><mml:mn>1</mml:mn><mml:mo>.</mml:mo><mml:mn>63</mml:mn><mml:mo>×</mml:mo></mml:mrow></mml:math><inline-graphic xlink:href="xia-ieq4-3705464.gif"/></alternatives></inline-formula>, and <inline-formula><tex-math notation="LaTeX">$1.27\times$</tex-math><alternatives><mml:math><mml:mrow><mml:mn>1</mml:mn><mml:mo>.</mml:mo><mml:mn>27</mml:mn><mml:mo>×</mml:mo></mml:mrow></mml:math><inline-graphic xlink:href="xia-ieq5-3705464.gif"/></alternatives></inline-formula> on average.

Meta-Reinforcement Learning for Computation Offloading and Resource Allocation in MEC-Enabled Immersive Metaverse

D3NN: Adaptive Partitioning and Cross-Tier Resource Orchestration for Cloud–Edge Collaborative Inference

Deep Neural Networks (DNNs) have become foundational to intelligent systems, yet deploying them efficiently under strict latency, resource, and privacy constraints remains challenging. While cloud-only inference suffers from transmission latency and privacy risks, and edge-only execution is limited by hardware capacity, cloud–edge collaborative inference offers a practical middle ground by combining the cloud’s compute strength with the edge’s proximity to data sources for low-latency, scalable, and privacy-aware inference. However, realizing this potential requires adaptive DNN partitioning that responds to dynamic workloads and network conditions, as well as fine-grained cross-tier resource orchestration to avoid bottlenecks and ensure system stability. To this end, we propose DDPG-DRPA-driven Deep Neural Network(D3NN), a novel and efficient framework for partitioned DNN deployment across cloud and edge resources. We formulate the pipeline partitioning of DNNs as a Markov Decision Process (MDP). A value function is trained using the Deep Deterministic Policy Gradient (DDPG) algorithm, and a Dynamic Resource Partitioning Agent (DRPA) allocates suitable cloud or edge resources to each DNN layer according to specific task types. As a result, D3NN adapts dynamically to both environmental conditions and task requirements. Under maximum task arrival rate scenarios, our approach reduces inference latency by 13.7% compared to pure cloud-based inference and by 33.5% compared to pure edge-based inference, demonstrating its practical effectiveness in resource-constrained cloud–edge systems.

Toward 6G Edge Intelligence: Lightweight LLMs for Intent-Driven Network Automation

Future 6G networks are envisaged to tightly integrate communication, sensing, and computing, demanding real-time, intent-driven intelligence at the edge. While large language models (LLMs) excel in intent recognition and semantic reasoning, their application to real-time network lifecycle management at the edge is limited by heterogeneous application intents (APPIs), dynamic network conditions, and severe resource constraints. This paper proposes a novel lightweight LLM architecture, KGLlama-KD, that synergizes knowledge graphs (KGs) with knowledge distillation (KD) to enable intent-driven networking and enhance 6G edge intelligence. Specifically, a KG is constructed to formally describe the relationships among application scenarios, functional primitives, performance requirements within APPIs, and the correspondences between APPIs and network service requests (NSRs), thereby producing a structured intent training dataset. Building upon the Llama 3 foundation model, a two-phase optimization framework is designed to support lightweight edge deployment while preserving translation fidelity. The LLM is first fine-tuned with KG guidance and compressed via KD in the cloud, and then deployed on resource-constrained edge nodes to perform real-time, accurate, and efficient APPIs interpretation. Experiments validate that KGLlama-KD achieves 95% accuracy for APPI understanding, surpassing DeepSeek and Qwen by an average of 8%. The distilled model reduces inference latency by 60% compared to full-scale LLMs, fulfilling the sub-100 ms requirement for 6G latency-sensitive services.

Salmonella risk prediction in poultry farms via deep learning image classification Models, Cross-Region Validation, and edge computing

DDPG-Attention-Based Resource Allocation and Trajectory Optimization in Hierarchical MEC

Multi-access Edge Computing (MEC) can effectively process Internet of Things (IoT) data by transferring computing intensive tasks to edge servers, and has become an effective mechanism to meet the growing demand for computing. The flexible Uncrewed Aerial Vehicle (UAV) and High-Altitude Platform (HAP) with powerful resources working together can significantly improve the efficiency of edge computing system. This paper investigates the resource allocation and trajectory optimization problems in HAP-UAV-MEC system with a Non-Orthogonal Multiple Access (NOMA) communication scenario. By utilizing Wireless Power Transfer (WPT) technology to provide energy support for UAV, we jointly optimize UAV trajectories, resource allocation, and offloading decisions to minimize the energy cost of IoT devices and the energy cost of UAV. This problem is described as a multi-stage Mixed Integer Nonlinear Programming (MINLP) problem. A Deep Deterministic Policy Gradient (DDPG)-Attention-based Resource Allocation and Trajectory Optimization (DART) algorithm combining Deep Reinforcement Learning (DRL) and Lyapunov optimization techniques is proposed to address this issue. DART algorithm utilizes the Lyapunov technique to transform the multi-stage MINLP problem into a deterministic optimization problem, and decomposes the original problem into four parallel subproblems. Through DDPG-attention algorithm based on reinforcement learning and deep learning attention mechanisms, we solve the problems of trajectory optimization and offloading decision. Meanwhile, for remaining subproblems related to resource allocation, convex optimization is used to solve them. The experimental results verify that the DART algorithm can significantly reduce the total cost while ensuring system stability and performance.

Heterogeneous graph-enhanced multi-objective dependency-aware task offloading in vehicular edge computing

FlexiTensor: Adaptive Multi-Task Deployment of LLMs on Resource-Constrained Heterogeneous Edge Devices

The drive for privacy-preserving and low-latency artificial intelligence necessitates executing Large Language Models (LLMs) directly on heterogeneous, resource-constrained edge devices. This paradigm presents a challenge: efficiently running large models across multiple end/edge devices under a strict energy budget. Especially, the problem becomes more complicated when it comes to orchestrating multiple complex tasks using large models at the same time. To address this, we introduce FlexiTensor, an offline planning and deployment system for LLM inference, scaling from a single task to multiple concurrent tasks. FlexiTensor first considers LLM inference for a single task under a strict energy budget. We design a heuristic optimization algorithm to minimize the latency under a strict energy budget. Specifically, we select an optimized subset of devices and tensor allocations. Based on the selection, latency can be reduced by using non-uniform tensor parallelism with quantization. FlexiTensor can be extended to multi-task cases. We model the execution structure of multi-agent workflows as pre-defined Directed Acyclic Graphs (DAGs) and reformulate the problem with energy consumption constraints. FlexiTensor introduces a novel hybrid evolutionary algorithm to address joint task scheduling and resource allocation in multi-task cases. This approach navigates the vast search space of task placement, tensor splitting, and thread assignment to minimize the overall workflow makespan. All optimization algorithms run offline before deployment, using pre-profiled device characteristics to compute a static execution plan. Extensive experiments on a physical testbed of heterogeneous edge devices demonstrate that FlexiTensor significantly outperforms baselines. We accelerate single-task inference by up to 50%. For multi-task workflows, we consistently find superior scheduling solutions, achieving speed improvements by an average of 50% against competitive baselines and by up to 100% in certain scenarios, showcasing its effectiveness and adaptability for real-world edge LLM-based applications.

Collaborative task offloading for electric vehicle supply equipment in smart charging networks: A distributed edge computing approach from a generalized Nash equilibrium perspective

AoI Minimization in Heterogeneous MEC Networks: A Federated Learning-Assisted Hybrid DRL and Convex Approach

This paper investigates a dynamic heterogeneous mobile edge computing network (HMECN), where mobile devices (MDs) could offload their full tasks to a small base station (SBS) directly or the macro base station (MBS) in direct or relay mode. As age of information (AoI) is a comprehensive and accurate metric to capture the freshness of computation results, we formulate a long-term weighted sum AoI (LWSA) minimization problem in the HMECN by jointly optimizing the offloading decisions of MDs as well as the bandwidth and computation resource allocation of all base stations, subject to energy, delay and peak AoI constraints. To address the formulated non-convex mixed integer nonlinear programming problem, we decompose it into the offloading decision optimization (ODO) top-problem and the resource allocation optimization (RAO) sub-problem. Based on the decomposition, we propose a federated learning (FL)-assisted hybrid DRL and convex approach that is comprised of a safe multi-agent DRL algorithm, convex optimization and FL. The ODO top-problem is solved by the safe multi-agent DRL algorithm, which strictly ensures that the actions of each agent do not exceed its energy constraint and then paves the way for using convex optimization to solve the RAO sub-problem. FL is used to alleviate the training instability problem aggravated by multi-agent settings via breaking the limitation of partial knowledge for each individual agent. Simulation results demonstrate the superiority of the proposed approach in terms of the LWSA, convergence, scalability and robustness in dynamic environments.

Chance constraint-based dependent task offloading for multi-access edge computing under uncertainty