Large Language Models (LLMs) have achieved remarkable performance across various artificial intelligence applications. However, current LLMs cannot be deployed directly on edge nodes due to their large number of parameters. Fortunately, model compression technology has been proposed to reduce the computational workload and memory usage of LLMs, enabling further edge-based LLM services. However, existing research typically concentrates on isolated compression algorithms and lacks a comprehensive perspective on how to leverage these techniques for practical, end-to-end LLM deployment in edge environments. In this survey, we review edge-oriented LLM compression techniques and software–hardware co-design strategies to enable efficient LLM deployment on resource-constrained edge systems and guide future research in this area. First, we analyze techniques for LLM compression from the perspective of cloud–edge collaborative intelligence, including model quantization, parameter pruning, and knowledge distillation. Second, we present several hybrid model frameworks tailored to dynamic, heterogeneous edge environments, based on model architecture, application scenarios, and combination selection. Third, we further refine a four-layer software–hardware codesign and an overhead-aware LLM deployment optimization. Finally, we discuss the challenges of current model compression approaches and offer insights into future research directions, with a focus on edge-based LLM services.

Person detection in videos is vital for area admission and public safety. Existing studies have made significant progress in improving the accuracy of this task on the cloud. Meanwhile, with people's increasing awareness of privacy protection, there is a surging demand for privacy not being transmitted and processed by the cloud. Thus, providing services on edges becomes a promising solution. The dilemma is that edges are typically resource-constrained and cannot support the deployment of large models. However, tiny models that fit resource-constrained edges generally have unsatisfactory performance in accuracy and efficiency. To this end, we propose a Logical Correction Enabled Collaborative Person Detection Inference (LC-CPDI) framework for resource-constrained edges. First, we formulate the problem studied with a delay minimization objective. Second, we design a logical correction scheme to perceive abnormal predictions and perform corrections to improve accuracy. Third, a hybrid position prediction algorithm is proposed to replace time-consuming inference for simple scenarios. Finally, we design a collaborative inference scheme that enables frame outsourcing to idle edges to reduce the inference delay. We implemented LC-CPDI on a testbed designed with commercial edges. The experiments on real-world datasets show the effectiveness of LC-CPDI with up to 41.8% delay reduction on average and near 2% recall improvement.

To support delay-critical applications, migrating services from the cloud to edge servers (ESs) can effectively reduce service delay. However, in such a resource-constrained scenario, providing heterogeneous services to meet diverse user needs poses significant challenges. Specifically, ESs have limited computational resources and are often energy-constrained, complicating service placement and provisioning. Existing studies propose collaboration schemes to improve resource utilization and reduce service delay. Nevertheless, these works heavily depend on full-service coverage by the cloud or rely on coarse-grained (e.g., time-cycle level) strategies that inevitably waste energy in idle time slots, which are unsuitable for energy-constrained settings and dynamic edge environments. To address these challenges, we propose a novel edge-edge collaboration method tailored for heterogeneous edge service provisioning in energy-constrained networks. First, we formulate the heterogeneous service provisioning problem with the objective of delay minimization under energy constraints and prove its NP-hardness. Our framework leverages latest task statistics to decide the service lifetime in a fine-grained time slot level, so that enables edge collaboration to maximize resource utilization and adaptability in resource-limited conditions. Specifically, we decompose the problem into three subproblems: service placement, service lifetime decision, and task scheduling, and we design targeted lightweight solutions for each, ensuring low delay and efficient energy usage. Finally, we validate our method through comprehensive simulations on real-world datasets and implement it on a testbed with three ESs. Results demonstrate that our approach reduces service delays by an average of 69.6% across various energy-constrained scenarios.

Delay-critical small object detection is crucial for real-time applications, such as autonomous driving and aerial surveillance. While cloud-based approaches incur substantial transmission latency, edge-based detection can avoid such delays; however, it faces its own challenges: limited computational capacity at the edge often leads to unsatisfactory inference delays and low accuracy when employing lightweight models. To tackle these issues, this paper presents an adaptive image batching and slicing framework for delay-sensitive small object detection in distributed edge environments. We first formulate the problem as a mixed-integer nonlinear programming model, which is NP-hard. The proposed scheme then aggregates tasks with tight deadlines into batches processed locally or cooperatively across the edge network, thus meeting stringent timing constraints. For tasks with relatively relaxed deadlines, we introduce an adaptive image slicing strategy that preserves fine-grained pixel information to boost detection accuracy without violating deadline requirements. The framework is implemented on NVIDIA Jetson edge devices and evaluated using real-world datasets. Experimental results show that our approach outperforms state-of-the-art baselines by an average accuracy gain of 0.5%, while improving the task success rate by 19%.

The Artificial Intelligence Generated Content (AIGC) technique has gained significant traction for producing diverse content. However, existing AIGC services typically operate within a centralized framework, resulting in high response times. To address this issue, we integrate collaborative Mobile Edge Computing (MEC) technology to reduce processing delays for AIGC services. Current collaborative MEC methods primarily support single-server offloading or facilitate interactions among fixed Edge Servers (ESs), limiting flexibility and resource utilization across all ESs to meet the varying computing and networking requirements of AIGC services. We propose AMCoEdge, an adaptive multi-server collaborative MEC approach to enhancing AIGC service efficiency. The AMCoEdge fully utilizes the computing and networking resources across all ESs through adaptive multi-ES selection and dynamic workload allocation, thereby minimizing the offloading make-span of AIGC services. Our design features an online distributed algorithm based on deep reinforcement learning, accompanied by theoretical analyses that confirm an approximate linear time complexity. Simulation results show that our method outperforms state-of-the-art baselines, achieving at least an$11.04\%$reduction in task offloading make-span and a$44.86\%$decrease in failure rate. Additionally, we develop a distributed prototype system to implement and evaluate our AMCoEdge method for real AIGC service execution, demonstrating service delays that are$9.23\% - 31.98\%$lower than the three representative methods.

The Artificial Intelligence-Generated Content (AIGC) technique has gained significant popularity in creating diverse content. However, the current deployment of AIGC services in a centralized framework leads to high response times. To address this issue, we propose the integration of collaborative Mobile Edge Computing (MEC) technology to decrease the processing delay of AIGC services. Nevertheless, existing collaborative MEC methods only facilitate collaborative processing among fixed Edge Servers (ESs), limiting flexibility and resource utilization across heterogeneous ESs for different computing and networking requirements associated with AIGC tasks. This poses challenges for efficient resource allocation. We present an adaptive multi-server collaborative MEC approach tailored for heterogeneous edge environments to achieve efficient AIGC by dynamically allocating task workload across multiple ESs. We formulate our problem as an online linear programming problem aiming to minimize task offloading make-span. This problem is proved to be NP-hard and we propose an online adaptive multi-server selection and allocation algorithm based on deep reinforcement learning that effectively addresses this problem. Additionally, we provide theoretical performance analysis, demonstrating that our algorithm achieves near-optimal solutions within approximate linear time complexity bounds. Finally, experimental results validate the effectiveness of our method by showcasing at least 11.04% reduction in task offloading make-span and a 44.86 % decrease in failure rate compared to state-of-the-art methods.

Collaborative edge computing enables low service delay for many delay-sensitive Internet of Things applications through edge-edge and edge-cloud collaborations. Due to the limited edge resources and varying task demands, optimizing Joint Service Placement and Task Offloading (JSPTO) becomes crucial in minimizing overall processing delays. However, existing JSPTO methods overlook the impact of service startup delay, which may undermine total latency reduction, especially in scenarios with large startup delays. This paper introduces an online JSPTO method that integrates the consideration of service startup delay to enhance task offloading efficiency. However, a significant challenge is ensuring timely service response with large startup delays. We formulate this problem as an integer linear programming problem, aiming to minimize the total service startup and task processing delay. We propose a novel algorithm called SD-JSPTO, which performs online JSPTO in the presence of large startup delays. Theoretical performance analyses reveal that SD-JSPTO attains a near-optimal solution within polynomial time, demonstrating a competitive ratio of $1 + \frac{{{A_2}}}{{V{T^{{\text{opt}}}}}}$. Experimental evaluations demonstrate that our method significantly reduces the total delay by no less than 18.72% compared to state-of-the-art baseline methods while preserving system stability.

Collaborative Mobile Edge Computing (MEC) has emerged as a promising approach to provide low service latency for computation-intensive Internet of Things applications, facilitated by the cooperation of edge-edge and edge-cloud resources. However, existing collaborative MEC methods typically restrict the collaborative processing between any two Edge Servers (ESs) or one ES and the cloud server for a task request, limiting the exploitation of available resources on other ESs. Moreover, these conventional methods rely on offline task partitioning, potentially leading to extended make-span, especially when ES computing capacities exhibit heterogeneity. In this paper, we propose an innovative method named SMCoEdge. This method performs dynamic parallel multi-ES selection and workload allocation in heterogeneous collaborative MEC environments, thus simultaneously enabling multiple ESs' idle resources to accelerate task processing. We formulate our problem into an online linear programming problem, with the objective of minimizing task computing and transmission make-spans. To enhance computational efficiency, we decompose the problem into two stages: multi-ES selection and workload allocation. Then, we propose an online Deep Reinforcement Learning based Simultaneous Multi-ES Offloading (DRL-SMO) algorithm along with a top-$k$deep Q-learning network model to effectively solve our problem, where an efficient algorithm is proposed to achieve the optimal solution for the workload allocation stage. Furthermore, we provide a theoretical performance analysis, demonstrating that the DRL-SMO algorithm achieves a near-optimal solution for our problem within an approximate linear time complexity. Finally, our extensive experimental results demonstrate the substantial advantages of our method. It consistently reduces the average make-span by 19.63% and keeps a lower offloading failure rate, when compared to state-of-the-art methods. These findings underline the efficacy of our method in enhancing collaborative MEC performance.

To meet the growing need of mobile traffic with Ultra-Reliable and Low Latency Communication (URLLC) requirement, 5G New Radio (NR) is extending from licensed band to unlicensed band on which Wi-Fi has already been operated, resulting in coexisting NR/Wi-Fi network. Existing works have made great efforts on throughput and latency of coexisting NR/Wi-Fi network. However, excessive NR requests offloaded from licensed band lead to unfair utilization of unlicensed band, which further causes unsatisfaction on URLLC and performance degradation of Wi-Fi. In this paper, we propose a novel Reinforcement Learning based Transmission Revoking Approach (RL-TRA) to address this problem aiming at fairer utilization of unlicensed band restrained by URLLC. Firstly, we formulate the coexistence problem of NR/Wi-Fi as integer non-linear programming and show its NP-hardness. Secondly, we decompose the problem into three sub-problems, namely redundancy determining, request scheduling, and transmission revoking. The former two sub-problems are solved with our proposed method to satisfy URLLC requirement. We further propose a novel transmission revoking mechanism when tackling transmission revoking sub-problem, aiming at maintaining fairness of coexisting NR/Wi-Fi network. Finally, simulation results verify the effectiveness of RL-TRA. By using our method, the fairness is improved by 16.5% averagely with only 1.77% loss on success rate of URLLC requests compared with baselines.

To deal with the problems of charging for inspection robots in substations such as frequent charging, complex mechanical interface, and insufficient battery capacity, a dynamic wireless charging system is proposed. The structure design of energy pickup device and the indispensable positioning strategy is presented in this paper. First, the dynamic wireless charging systems based on centralized energy pickup and decentralized energy pickup (DEP) are investigated and the unified circuit model is built for two kinds of systems simultaneously. Based on the comparison of working performances, the DEP structure is selected as the pickup device in this paper. Then, the positioning scheme is realized based on imitative relaying coil structure including the DEP device and the additional sensor coil. A switching control strategy of the segmented transmitting coils is proposed further to implement the dynamic wireless charging for inspection robots. The theoretical analyses are validated by the relevant experiments.