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.

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%.

In the era of Big Data, artificial intelligence and information science are the key technologies to extract the value of data and enhance the competitiveness of enterprises. The characteristics of distributed, small-scale, and sparse lead to the isolated data island problem. To solve these problems, Federated Learning is proposed. However, a large number of terminal models need to be uploaded to the server in Federated Learning, especially for the actual scenario of Internet of Things. Therefore, huge communication costs are required which dramatically increases the pressure on the backbone network. Furthermore, the low quality of the local model will lead to decreased accuracy and convergence rates of the model. To overcome the above limitations, we propose heterogeneous device collaboration based federated learning (HDCFL), which constructs a three-layer structure for Federated Learning by leveraging edge computing and designs a heterogeneous device collaboration method that groups the terminals based on their computing power, communication time, and data volume to train the model. Then, we conduct a theoretical analysis of the proposed algorithm which verifies its advantage. At last, the experimental result demonstrates that the proposed algorithm consistently achieves superior performance in terms of both convergence speed and accuracy compared with state-of-the-art baselines.

In smart cities, Multi-Camera Multi-Target pedestrian tracking and Re-identification (MCMT-ReID) is essential for effective surveillance, particularly in real-time scenarios, as it demands significant computational resources. Current edgecloud collaboration methods encounter issues such as high latency and potential data leakage due to the physical distance between cloud servers and cameras. To address these issues, we propose a novel Edge-to-Edge Collaboration (E2EC) system that fully utilizes collaboration between heterogeneous edge devices. E2EC partitions the MCMT-ReID task into two modular applications: Tracking and Re-identification (ReID), and employs a customized Kafka communication protocol to optimize data exchange efficiency. Moreover, E2EC dynamically orchestrates intermediate inference flows and transmits features instead of pedestrian detection frames to avoid data leakage. To enhance ReID accuracy, we introduce a real-time ReID Loop Confirmation (ReLC) algorithm, which continuously validates identities to boost reliability and accuracy. E2EC has been deployed and tested in a real-world campus environment to validate its effectiveness. Experimental results demonstrate that E2EC enhances the Rank-1 accuracy and mAP of pedestrian ReID by 36.88% and 46.00%, respectively. Furthermore, it achieves an increase of about 6.35%-12.66% in throughput and reduces latency by 35.01%-57.83% compared to baselines, ensuring realtime performance under dynamic workloads.

The proliferation of contaminated data on Internet of Things (IoT) devices has the potential to undermine the accuracy of data-driven decision-making by altering the distribution of original data. Existing data cleaning methods primarily depend on cloud center or cloud-edge cooperation, leading to prolonged data transmission delays and reduced cleaning accuracy. In this study, we identify edge server placement as a crucial step aligned with data cleaning and view the collaborative edge server placement with distributed data cleaning (SPDC) as a holistic problem. We comprehensively quantify the complexity of our issue through the analysis of numerous scenarios. To address this problem, we introduce a novel distributed collaborative edge framework comprising two key stages: server placement and data cleaning. We propose an optimized clustering algorithm for the former, considering the data distribution on the IoT layer and the constraints of the edge layer. For the latter, we introduce a gossip-based data cleaning algorithm that fully utilizes edge collaboration to enhance data cleaning accuracy. The algorithm exhibits an approximate performance complexity of O($\ln m$), where$m$represents the number of users’ tasks. Both theoretical analysis and experimental results reveal that our algorithm an average improvement in data cleaning accuracy of 9.02% and a reduction in delay of 36.61%, surpassing the performance of state-of-the-art works in various scenarios.

Cloud computing typically involves transferring users' requests to centralized cloud servers, a process that is inherently fraught with substantial delays due to the unpredictable nature of network transmissions. This inherent latency issue presents considerable challenges to applications that are highly sensitive to delay. We propose leveraging edge collaboration to minimize latency by enabling efficient user request scheduling within geographical proximities. However, cross-regional edge collaboration faces challenges due to the lack of real-time resource knowledge across regions, a problem we have identified as NP-hard. To address this, we introduce a model to connect edge nodes globally, thereby accurately reflecting their resource status. By employing an enhanced Dijkstra algorithm, we optimize the request routing process, achieving a notable reduction in delays compared to baseline methods, thus enhancing performance across various test scenarios.

The rapid growth in data generated by the Artificial Internet of Things (AIoT) necessitates an increase in computational power and presents challenges to cloud infrastructure, including traffic congestion and latency issues in AIoT systems. This trend has fostered a shift toward edge-layer computation, with cooperative edge computing emerging as a potent solution to these challenges. However, the diversity and heterogeneity of AIoT systems present significant challenges with regard to service placement, cross-regional request scheduling, and efficient resource caching. My research aims to enhance cooperative edge computing by designing an optimized clustering algorithm for efficient service placement and data processing, developing a cooperative edge request scheduling method using digital twin technology to minimize system transmission delays, and devising a resource caching method employing deep reinforcement learning in cooperative game scenarios to optimize resource allocation. This research endeavors to enhance service placement and request scheduling efficiency, thereby offering substantial computational support to AIoT systems.

In cloud computing, user requests sent to centralized servers often encounter delay due to network unpredictability, impacting the Quality of Service (QoS) for time-sensitive applications. We propose edge collaboration, utilizing the coordination of edge nodes within regions to handle requests more efficiently and reduce latency. However, edge nodes across different regions struggle with lack of immediate data on resources cached elsewhere, complicating inter-regional request scheduling. To address it, we introduce a federated digital twin model that creates a network linking edge nodes to reflect and update resource statuses in real time. Additionally, we refine the Dijkstra algorithm to optimize routing to the nearest edge nodes based on current network conditions, thereby minimizing delay. Our analyses show that our method significantly lowers delay, enhancing effectiveness over baseline methods.

The growing popularity of contact-free smart sensing has contributed to the development of the Artificial Intelligence of Things (AIoT). The contact-free sensory data has great potential to mine and analyze the hidden information for AIoT-enabled applications. However, due to the limited storage resource of contact-free smart sensing devices, data is naturally stored in the cloud, which is at risk of privacy leakage. Cloud storage is generally considered insecure. On one hand, the openness of the cloud environment makes the data easy to be attacked, and the complex AIoT environment also makes the data transmission process vulnerable to the third party. On the other hand, the Cloud Service Provider (CSP) is untrusted. In this article, to ensure the security of data from contact-free smart sensing devices, a Cloud-Edge-End cooperative storage scheme is proposed, which takes full advantage of the differences in the cloud, edge, and end. Firstly, the processed sensory data is stored separately in the three layers by utilizing well-designed data partitioning strategy. This scheme can increase the difficulty of privacy leakage in the transmission process and avoid internal and external attacks. Besides, the contact-free sensory data is highly time-dependent. Therefore, combined with the Cloud-Edge-End cooperation model, this article proposes a delta-based data update method and extends it into a hybrid update mode to improve the synchronization efficiency. Theoretical analysis and experimental results show that the proposed cooperative storage method can resist various security threats in bad situations and outperform other update methods in synchronization efficiency, significantly reducing the synchronization overhead in AIoT.

The proliferation of dirty data on Internet of Things (IoT) devices can undermine the accuracy of data-driven decision-making by affecting the distribution of original data. The Quality of Service (QoS) of data cleaning on these devices is heavily impacted by processing delay and accuracy. In this paper, we find that edge service placement is a key step aligned with data cleaning and consider the collaborative edge service placement with distributed data cleaning (SPDC) problem. To address this issue, we propose a novel distributed collaborative edge-based architecture that effectively balances the demands of storage, communication, computation, and load constraints. Experimental results show that the proposed approach significantly improves the accuracy of data cleaning by 0.31%-86.07% and reduces delay by 2.73%-58.71% compared to state-of-the-art baselines.

Modern societies are becoming increasingly reliance on inter-connected digital systems. Despite numerous benefits, it is important to overcome existing security problems in a highly inter-connected system, like Sensor-Cloud systems. Sensor-Cloud is the product of the integration of wireless sensor networks and cloud computing. However, when a physical sensor node receives multiple service commands simultaneously, there will be some service collisions, namely, coupling security problem. This coupling security problem may lead to the failure of sustainable services and the system security threat. In order to solve the problem, sustainable resource management and maximum resource utilization are important. In this paper, we extend the Kuhn-Munkres algorithm based on fog computing to achieve sustainability. To begin with, we design a buffer queue in fog computing layer which will return the result to the cloud layer directly to increase the resource utilization. Then, we extend the Kuhn-Munkres algorithm to get the initial assignments of resources. The last step is to determine whether the initial assigned resources can be further scheduled, which means that we further improve the resource utilization to realize sustainable resource management. The results demonstrate that our method outperforms the traditional scheduling methods, which decreases both of the rounds and computational costs of scheduling by 24.04-57.78 percent and 9.88-31.51 percent, respectively. The experimental evaluations proved that the performance of the proposed fog-based scheme can effectively solve coupling security problem for sustainable Sensor-Cloud systems.