Mixture-of-Experts (MoE) is a sparse neural architecture that significantly increases model capacity while maintaining low computational complexity. However, deploying MoE-based large language models (LLMs) on memory-constrained edge devices remains challenging due to their substantial memory requirements. To address this issue, we propose FIRM-MoE, a fine-grained expert offloading framework designed to enable flexible and efficient MoE inference. The core insight of our approach is to reduce the risk of inaccurate expert loading by decomposing each expert into fine-grained sub-experts and then dynamically allocating them through a fine-grained scheduling strategy. To further reduce the error in expert loading, we introduce a multi-layer expert prediction mechanism and a resource-adaptive expert pre-loading algorithm to enable more robust expert allocation. This design allows our model to achieve more efficient expert utilization and improved resilience to prediction errors. We conduct extensive experiments to demonstrate the superiority of FIRM-MoE across diverse memory constraints. The results show that FIRM-MoE achieves up to 1.5× speedup and 2.8× memory savings in decoding, compared to state-of-the-art MoE offloading strategies.

3D reconstruction plays a critical role in applications such as augmented reality (AR) and robotic systems. However, implicit neural representations (INRs), widely used in modern 3D reconstruction systems, demand substantial communication and computational resources, making the reconstruction process excessively slow and costly. In this paper, we introduce EAR-Mapping, a novel edge-assisted online 3D reconstruction framework designed for latency-sensitive mobile applications. EAR-Mapping incorporates an innovative sampling mechanism that seamlessly integrates explicit and implicit methods, enabling selective processing of camera data to maximize reconstruction performance. In addition, we utilize a value-based representation module to maximize computation resource efficiency. Finally, we design a framework that minimizes communication overhead through ROI-based data transmission. Our prototype implementation on a mobile-edge testbed demonstrates that EAR-Mapping achieves up to a 1.2x reduction in reconstruction latency and a 3.5x reduction in bandwidth usage, offering a significant advancement in the efficiency of 3D reconstruction for mobile-edge systems.

Cross-source query serves as a proxy for scene understanding to support many web applications such as rec-ommendation systems, e-commerce, and e-learning applications. In this paper, we propose SVQA that semantically combines the knowledge from available images and graphs to answer the complex question. To this end, we design a graph-based method to unify various data sources into one representation. We then develop a complex question parse method that utilizes the structure of languages to transform the query into a query graph. A graph query engine that performs the query graph over the unified data source while optimizing the query process. To evaluate the proposed system, we build a vanilla dataset called MVQA and show that the state-of-the-art (SOTA) VQA models fail to perform our task. The comprehensive evaluations show that the proposed SVQA is able to reason implicit relationships over multiple images and external knowledge to correctly answer a complex query. We hope that our first attempt provides researchers with a fresh taste of multimodal data analysis.

Edge computing has gained momentum in recent years, and can provide more immediate analysis of streaming video data. However, the edge devices often lack the computing capabilities (processing power, memory) to guarantee reasonable performance (e.g., accuracy, latency, throughput) for complex video analytics tasks. To alleviate this critical problem, the prevalent trend is to offload some video analytics tasks from the edge devices to the cloud. However, existing offloading approaches fail to consider the dynamic nature of the video analytical tasks (e.g., varying encoding format for different video content) and are unable to adapt system dynamics (e.g., varying workload between the edge and the cloud). To overcome the limitation of existing approaches, we develop an edge-cloud offloading performance model based on the concept of hierarchical queues. The resource constraints (e.g., computing capacity and network bandwidth) of each edge nodes and dynamic edge-cloud network conditions are used to parameterize the performance model. Since finding optimal solutions for the performance model is NP-hard, we develop a two-stage gradient-based algorithm and compare it with some state-of-the-art (SOTA) solutions (e.g., FastVA, DeepDecision, Hill Climbing). Experiments have shown our performance model's advantages and the stability of the proposed offloading approach given different systems (edge-cloud) and video analytics application dynamics.

This article focuses on a simple, yet fundamental question of distributed edge computing: “how to handle IoT traffic with different levels of sensitivity and criticality by satisfying the application-specific latency constraints?” This question arises in the practical deployment of edge computing, where user data can arrive at a much faster rate than that they can be processed by an edge node. Addressing this question is critical for meeting the latency requirement for latency-sensitive applications, but existing approaches are inadequate to the problem. We presentJanus, a multi-level traffic scheduling system for managing multiple data streams with various degrees of latency constraints. At the edge node level,Janususes multi-level queues to manage data streams with different latency constraints. It then allocates the output bandwidth of the edge node according to the requirements of applications in different priority queues, aiming to reduce the queuing and processing delay of latency-sensitive streams while maximizing the edge-node throughput. At the network level,Janusactively redirects incoming data streams to the less-loaded ones to achieve better network-wide load balance and improve the overall throughput. Experiments show thatJanusreduces the latency to only 16.6% of a non-priority based solution and improves the throughput by 1.7x of a state-of-the-art priority-aware data stream scheduling approach.

Wireless capsule endoscopy is a modern non-invasive Internet of Medical Imaging Things that has been increasingly used in gastrointestinal tract examination. With about one gigabyte image data generated for a patient in each examination, automatic lesion detection is highly desirable to improve the efficiency of the diagnosis process and mitigate human errors. Despite many approaches for lesion detection have been proposed, they mainly focus on large lesions and are not directly applicable to tiny lesions due to the limitations of feature representation. As bleeding lesions are a common symptom in most serious gastrointestinal diseases, detecting tiny bleeding lesions is extremely important for early diagnosis of those diseases, which is highly relevant to the survival, treatment, and expenses of patients. In this article, a method is proposed to extract and fuse multi-scale deep features for detecting and locating both large and tiny lesions. A feature extracting network is first used as our backbone network to extract the basic features from wireless capsule endoscopy images, and then at each layer multiple regions could be identified as potential lesions. As a result, the features maps of those potential lesions are obtained at each level and fused in a top-down manner to the fully connected layer for producing final detection results. Our proposed method has been evaluated on a clinical dataset that contains 20,000 wireless capsule endoscopy images with clinical annotation. Experimental results demonstrate that our method can achieve 98.9% prediction accuracy and 93.5% score, which has a significant performance improvement of up to 31.69% and 22.12% in terms of recall rate and score, respectively, when compared to the state-of-the-art approaches for both large and tiny bleeding lesions. Moreover, our model also has the highest AP and the best medical diagnosis performance compared to state-of-the-art multi-scale models.