Edge-cloud collaborative computing (ECCC) has emerged as a pivotal paradigm for addressing the computational demands of modern intelligent applications, integrating cloud resources with edge devices to enable efficient, low-latency processing across distributed communication networks. Recent advancements in AI, particularly deep learning and large language models (LLMs), have dramatically enhanced the capabilities of these networked systems, yet introduce significant challenges in model deployment, network resource management, and cross-layer optimization. In this survey, we comprehensively examine the intersection of distributed intelligence and model optimization within edge-cloud environments, providing a structured tutorial on fundamental architectures, communication protocols, and network-aware computing frameworks. Additionally, we systematically analyze model optimization approaches, including compression, adaptation, and neural architecture search, alongside AI-driven resource management strategies that balance performance, energy efficiency, and communication overhead across heterogeneous networks. We further explore critical aspects of privacy protection and security enhancement within ECCC systems and examine practical deployments through diverse networked applications, spanning autonomous driving, healthcare, and industrial automation. Performance analysis and benchmarking techniques are also thoroughly explored to establish evaluation standards for these complex distributed systems. Furthermore, the review identifies critical research directions including LLMs deployment, 6G integration, neuromorphic computing, and quantum computing, offering a roadmap for addressing persistent challenges in heterogeneity management, real-time processing, and scalability. By bridging theoretical advancements in communications with practical deployments, this survey offers researchers and practitioners a holistic perspective on leveraging AI to optimize distributed computing environments over next-generation communication networks, fostering innovation in intelligent networked systems.

This paper focuses on the crack detection and digital restoration of ancient mural cultural heritage, proposing a comprehensive method that combines the Unet network structure and diffusion model. Firstly, the Unet network structure is used for efficient crack detection in murals by constructing an ancient mural image dataset for training and validation, achieving accurate identification of mural cracks. Next, an edge-guided optimized masking strategy is adopted for mural restoration, effectively preserving the information of the murals and reducing the damage to the original murals during the restoration process. Lastly, a diffusion model is employed for digital restoration of murals, improving the restoration performance by adjusting parameters to achieve natural repair of mural cracks. Experimental results show that comprehensive method based on the Unet network and diffusion model has significant advantages in the tasks of crack detection and digital restoration of murals, providing a novel and effective approach for the protection and restoration of ancient murals. In addition, this research has significant implications for the technological development in the field of mural restoration and cultural heritage preservation, contributing to the advancement and technological innovation in related fields.

Image Inpainting is an age-old image processing problem, with people from different eras attempting to solve it using various methods. Traditional image inpainting algorithms have the ability to repair minor damage such as scratches and wear. However, with the rapid development of deep learning in the field of computer vision in recent years, coupled with abundant computing resources, methods based on deep learning have increasingly highlighted their advantages in semantic feature extraction, image transformation, and image generation. As such, image inpainting algorithms based on deep learning have become the mainstream in this domain.In this article, we first provide a comprehensive review of some classic deep-learning-based methods in the image inpainting field. Then, we categorize these methods based on component optimization, network structure design optimization, and training method optimization, discussing the advantages and disadvantages of each approach. A comparison is also made based on public datasets and evaluation metrics in image inpainting. Furthermore, the article delves into the applications of current image inpainting technologies, categorizing them into three major scenarios: object removal, general image repair, and facial inpainting. Finally, current challenges and prospective developments in the field of image inpainting are discussed.

Blockchain, a distributed ledger technology (DLT), refers to a list of records with consecutive time stamps. This decentralization technology has become a powerful model to establish trust among trustless entities, in a verifiable manner. Motivated by the recent advancement of multi-access edge computing (MEC) and artificial intelligence (AI), blockchain-enabled edge intelligence has become an emerging technology for the Internet of Things (IoT). We review how blockchain-enabled edge intelligence works in the IoT domain, identify the emerging trends, and suggest open issues for further research. To be specific: (1) we first offer some basic knowledge of DLT, MEC, and AI; (2) a comprehensive review of current peer-reviewed literature is given to identify emerging trends in this research area; and (3) we discuss some open issues and research gaps for future investigations. We expect that blockchain-enabled edge intelligence will become an important enabler of future IoT, providing trust and intelligence to satisfy the sophisticated needs of industries and society.

Video surveillance as an important function of Internet of Video Things (IoVT) system has been widely used in coal mine monitoring for coal mine safety with excellent results, however, there are still many shortcomings: 1) existing coal mine IoVT systems have limited detection accuracy for small-sized objects; 2) coal mine video surveillance systems generally adopt centralized cloud computing, transmission of massive data causes high latency, which seriously affects the response speed of object detection function; and 3) the concept drift caused by the data stream seriously affect the detection effect of the offline algorithm. To address the above issues, we propose a small object detection method-based federated intelligence (FI) to assist coal mine IoVT for object detection. First, we design a lightweight neural network Rep-ShuffleNet to improve YOLOv8, the state-of-the-art YOLO algorithm, to maintain high detection accuracy while dramatically increasing the inference speed, and with the advantage of lightweight, it can be deployed to embedded devices for low-latency edge computing; Moreover, we design a federated learning (FL)-based multilayer collaborative FL algorithm for local algorithms’ automatic and efficient optimization by asynchronous communication and data interaction reduction strategy. The experimental results show that with the assistance of FI model optimization strategies, the lightweight YOLOv8 has excellent detection performance (mean average precision: 94.6%, APsmall: 86.7%, and frame per second: 21.6), thus to assist coal mine IoVT to realize accurate and real-time underground small object detection.

Leveraging various mobile devices to train the shared model collaboratively, federated learning (FL) can improve the privacy and security of 6G communication. To economically encourage the participation of heterogeneous mobile devices, an incentive mechanism and a fair trading platform are needed. In this paper, we implement a blockchain-based FL system and propose an incentive mechanism to establish a decentralized and transparent trading platform. Moreover, to better understand the mobile devices’ behaviors, we provide economic analysis for this market. Specifically, we propose two strategy models for mobile devices, namely the discrete strategy model (DSM) and the continuous strategy model (CSM). Also, we formulate the interactions among the non-cooperative mobile devices as a dynamic game, where they adjust their strategies iteratively to maximize the individual payoff based on others’ previous strategies. We further prove the existence in Nash equilibrium (NE) of two different models and propose algorithms to achieve them. Simulation results demonstrate the convergence of the proposed algorithms and show that the CSM can effectively increase the mobile devices’ payoffs to 128.1 percent at most compared with DSM.