The convergence of edge intelligence and networked medical infrastructures in the Artificial Intelligence of Medical Things (AIoMT) is transforming healthcare toward personalization and predictive intervention. In this paradigm, high-resolution physiological data continuously flow between wearable or implantable devices, edge nodes, and cloud analytics platforms. Such connectivity enables advanced diagnostic modeling and real-time decision support. However, it also enlarges the attack surface. AIoMT components are exposed to impersonation, replay, and man-in-the-middle attacks. Therefore, secure data exchange becomes essential. Authentication and key exchange (AKE) schemes address this requirement by enabling mutual authentication and session key establishment over insecure channels. Nevertheless, many existing centralized designs suffer from single points of failure and insider threats. Several blockchain-assisted approaches still retain centralized identity traceability. In addition, most AKE schemes either neglect physical security, lack tolerance to intrinsic physical unclonable function (PUF) noise, or store sensitive PUF challenge–response pairs, which increases the risk of modeling attacks. To address these issues, we propose a fully decentralized authentication and key exchange scheme (FDAKES) for AIoMT. FDAKES adopts a$(t,n)$threshold-based root of trust across multiple registration centers (MRCs) to remove unilateral control in registration and tracing. Its server-independent AKE process combines threshold-protected identities, dynamic nonces, timestamps, and PUF and biometric enhanced credentials to achieve perfect forward secrecy. Decentralized conditional traceability preserves user anonymity while allowing identity recovery only with unanimous MRCs consent. By integrating PUF with a fuzzy extractor, FDAKES enables stable secret regeneration without storing raw challenge–response pairs, thereby mitigating modeling threats. We formally prove protocol correctness for login authentication and mutual key agreement. We further establish semantic security of the session key under the real-or-random model, showing that the adversary advantage is negligible in the random oracle model. An extensive informal analysis demonstrates resistance to impersonation, replay, guessing, modeling, physical, man-in-the-middle, and key compromise attacks. Experimental evaluation demonstrates that FDAKES reduces total computational overhead by up to 40.95% and at least 17.25% percent compared with recent state-of-the-art AKE schemes, while communication cost is reduced by up to 76.73% and at least 5% across representative baselines. This work establishes a robust and fully decentralized trust foundation for next-generation smart healthcare systems.

Graph neural networks (GNNs) have shown great success in IoT applications, but many IoT scenarios further involve evolving graph data over time. Streaming graph learning (SGL) tackles the issue by updating GNN models continuously, incorporating significant historical node data for tasks like node classification. However, existing research on SGL often neglects memory limitations during historical node selection, and efficient distributed training is challenging due to the coupling of node selection, placement, and parallelization. This article proposes an efficient distributed SGL system that optimizes node selection and storage in multi-GPU environments, while reducing communication overhead to accelerate training. Extensive evaluations demonstrate the effectiveness of the approach.

Graph neural networks (GNNs) have attracted significant research attention because of their impressive capability in dealing with graph-structure data, such as energy networks, that are crucial for sustainable computing. We find that the communication of data loading from main memory to GPUs is the main bottleneck of GNN inference because of redundant data loading. In this paper, we propose RAIN, an efficient GNN inference system for graph learning. There are two key designs. First, we explore the opportunity of conducting similar inference batches sequentially and reusing repeated nodes among adjacent batches to reduce redundant data loading. This method requires reordering the batches based on their similarity. However, comparing the similarity across a large number of inference batches is a difficult task with a high computational cost. Thus, we propose a local sensitive hash (LSH)-based clustering scheme to group similar batches together quickly without pair-wise comparison. Second, RAIN contains an efficient adaptive sampling strategy, allowing users to sample target nodes’ neighbors according to their degree. The number of sampled neighbors is proportional to the size of the node's degree. We conduct extensive experiments with various baselines. RAIN can achieve up to 6.8X acceleration, and the accuracy decrease is smaller than 0.1%.

The abundance of data provided by mobile edge devices enables a wide range of mobile edge computing (MEC) applications. Numerous studies have investigated efficient offloading methods for bandwidth savings in MEC. However, they focus on trading the device's computational cost for a reduction in communication, while edge devices can be rather resource-limited and must handle several jobs simultaneously. In this paper, the computation overhead on the device is pushed to its absolute minimum (almost no overhead), and consideration is given to enhancing the accuracy of the image recognition task within the constraints of the transmission volume limitation. We propose a mask-reconstruct system called MOT to mask images on the device side and recover images with the Masked Autoencoders (MAE)-based model on the server side. We further design a feedback-driven scheme to achieve content-aware transmission. Extensive experiments have been conducted to verify the effectiveness of the MOT.

Federated learning has been proposed as a promising distributed machine learning paradigm with strong privacy protection on training data. Existing work mainly focuses on training convolutional neural network (CNN) models good at learning on image/voice data. However, many applications generate graph data and graph learning cannot be efficiently supported by existing federated learning techniques. In this paper, we study federated graph learning (FGL) under the cross-silo setting where several servers are connected by a wide-area network, with the objective of improving the Quality-of-Service (QoS) of graph learning tasks. We find that communication becomes the main system bottleneck because of frequent information exchanges among federated severs and limited network bandwidth. To conquer this challenge, we design Glint, a decentralized federated graph learning system with two novel designs: network traffic throttling and priority-based flows scheduling. To evaluate the effectiveness of Glint, we conduct both experiments on a testbed and trace-driven simulations. The results show that Glint can significantly outperform existing federated learning solutions.

Human behavior recognition which is the indispensable technology for Artificial Intelligence(AI) application like smart home and other practical applications, is very challenging as the optimal recognition generally is required to be non-invasive and easy to deploy. An increasing interest has been paid on the human behavior recognition with off-the-shelf Wi-Fi devices. However, most of existing works just limit their focus on the small-scale scene while human behavior recognition will be quite different in large areas for a larger number of antennas and correspondingly a more complex antenna layout. For example, if we want to build a complete behavior awareness system using the distributed Wi-Fi equipment of the entire building, though collecting and using all antennas’ data is feasible maybe, the overhead concerns of computing and bandwidth resources, and the operation complexity will be hard to lessen in practice. In this paper, we first present analyses of the signal performances between different antenna pairs. Then closely following these analyses, we propose a novel scheme for the large-area human behavior recognition. Finally, we conduct extensive confirmatory experiments to verify the validity of our proposed scheme.

Emotion is a unique feature of human beings. Recent research in emotion sensing has already revealed its potentials in enhancing our living experiences through applications like emotion companion and autism treatment. However, existing solutions exploring audiovisual clues or psychological sensors have several critical concerns such as the availability (specialized hardware), reliability (illumination and line-of-sight constraints) and privacy issues (being watched). To this end, we present EmoSense, a first-of-its-kind WiFi-based emotion sensing system leveraging the temporal and frequency fingerprints on the wireless channel data induced by the physical expression of emotion. EmoSense has been prototyped with off- the-shelf WiFi devices and evaluated by comparing with the main-stream sensor-based approach in real environments. Experimental results demonstrate its effectiveness and robustness. Considering that EmoSense is compatible with existing WiFi infrastructures, it constitutes a low-cost yet promising solution for emotion sensing.