Collaborative perception, fusing information from multiple agents, can extend perception range so as to improve perception performance. However, temporal asynchrony in real-world environments, caused by communication delays, clock misalignment, or sampling configuration differences, can lead to information mismatches. If this is not well handled, then the collaborative performance is patchy, and what's worse safety accidents may occur. To tackle this challenge, we propose CoDynTrust, an uncertainty-encoded asynchronous fusion perception framework that is robust to the information mismatches caused by temporal asynchrony. CoDynTrust generates dynamic feature trust modulus (DFTM) for each region of interest by modeling aleatoric and epistemic uncertainty as well as selectively suppressing or retaining single-vehicle features, thereby mitigating information mismatches. We then design a multi-scale fusion module to handle multi-scale feature maps processed by DFTM. Compared to existing works that also consider asynchronous collaborative perception, CoDynTrust combats various low-quality information in temporally asynchronous scenarios and allows uncertainty to be propagated to downstream tasks such as planning and control. Experimental results demonstrate that CoDynTrust significantly reduces performance degradation caused by temporal asynchrony across multiple datasets, achieving state-of-the-art detection performance even with temporal asynchrony. The code is available at https://github.com/CrazyShout/CoDynTrust.

Advancements in Earth observation sensors on low Earth orbit (LEO) satellites have significantly increased the volume of remote sensing images. This growth has led to challenges such as higher storage demands, downlink bandwidth stress, and transmission delays, particularly for real-time remote sensing image scene classification (RSISC). To address this, we propose a novel Satellite-Terrestrial Collaborative Scene Classification (STCSC) framework that integrates transmission and computation. The framework employs an attention-aware policy on the satellite, which adaptively determines the sequence of images and selection of image blocks for transmission, as well as these blocks' sampling rates. This policy is based on image complexity and the real-time data transmission rate, prioritizing blocks crucial for downstream tasks. On the ground, a classification model processes the received image blocks, balancing classification accuracy and transmission delay. Moreover, we have developed a comprehensive simulation system to validate the performance of our framework, including simulations of the satellite, transmission, and ground modules. Simulation results demonstrate that our STCSC framework can reduce transmission delay by 76.6% while enhancing classification accuracy on the ground by 0.6%. Additionally, our attention-aware policy is compatible with any ground classification model.

The rapid growth of deep learning-based services and applications underscores the need for efficient neural network model deployment. Traditional cloud-centric solutions, despite their computational power, face significant challenges such as high energy consumption, network transmission delays, and user privacy concerns. Conversely, performing high-performance inference on resource-constrained mobile devices, especially for tasks like advertising click prediction, presents its own set of difficulties. To address these challenges, we propose a device-cloud collaboration system utilizing a difficult-case discriminator. This system classifies input samples based on semantic information into difficult and simple cases. Difficult cases are processed in the cloud using a large model, while simple cases are handled on the device by a smaller model. This approach maximizes system resources and protects user privacy. Evaluations on public datasets show that our system significantly outperforms other advertisement methods in click prediction accuracy and uploading efficiency. Compared to the device-only approach, our system improves the area under the curve (AUC) by 8.9%, and compared to the cloud-centric approach, it reduces the upload ratio by 34%. Moreover, deploying our system on a specific smartphone demonstrates substantial improvements in private real datasets.

Recently, edge computing (EC) has attracted wide attention as a novel and promising computing mode with high real-time and low-latency characteristics. However, users' privacy and the limited resources have become major concerns in the implementation of EC because edge devices are usually heterogeneous and untrustworthy. Although many related works have protected the user's privacy, they did not take the storage resource limitation of heterogeneous edge devices into consideration and their schemes may cause high communication load. In this paper, we propose PCHEC, a Private Coded computation scheme for Heterogeneous Edge Computing, to protect the user's privacy and minimize the communication load. Specifically, PCHEC first gives a storage allocation scheme to minimize the communication load in EC where the heterogeneous edge devices have different storage limits. Secondly, PCHEC utilizes linear coding to mix the target data with other information for the protection of the user's privacy. To evaluate the efficiency of PCHEC, we make theoretically analysis and conduct extensive simulations. The experiments show PCHEC effectively reduces the communication load by up to 70% compared with other schemes.

Nowadays, the combination of edge computing and artificial intelligence has become a mainstream trend. Based on edge computing and image classification technologies, we design and implement a secure distributed image classification reasoning system for heterogeneous edge computing. The functions of the system consists of two parts: model distributed deployment and image classification reasoning. Firstly, we have designed three distributed deployment schemes for the model deployment on edge devices: random, static and dynamic deployment schemes. Secondly, we have designed three secure distributed image classification reasoning schemes: uncoded, 2-replication and MDS coding reasoning schemes. These reasoning schemes can protect the security of image data in the process of image reasoning and meet the weak security standard. Our system uses edge devices as computing devices, so it has the advantages of low computing cost and saving bandwidth. The experimental results show that our system can protect the security of image data, also has favorable stability and efficiency under the environment of heterogeneous edge computing.

This paper designs an Intelligent Ashcan Management System (IAMS) which is one of the most important part in a smart city. Traditional ashcan management is inefficient and has many disadvantages, because managers cannot obtain the realtime state of ashcans efficiently. As a result, it often happens that ashcans are full but not collected in time. Moreover, in special cases that ashcans fall, catch fire, etc., managers should find these ashcans and handle these emergencies as soon as possible. To manage ashcans efficiently, economically and intelligently, in this paper, we propose an edge computing based IAMS. Specifically, in IAMS, each ashcan has an intelligent user equipment (UE) equipped with sensors to measure distance, temperature, smog and tilt. For data transmission, IAMS uses Narrow Band Internet of Things (NB-IoT), which has advantages of large transmission range and low cost. Combined with edge computing, the collected data can be processed rapidly and managers can obtain the real-time state of each ashcan. Moreover, managers can view global information through web browser and mobile devices. According to the real-time state of ashcans, we also design an IAMS algorithm to get an efficient garbage collection path based on genetic algorithm. Finally, we deployed the proposed IAMS in Soochow University and experimental results show its efficiency and stability.