Vital sign (breathing and heartbeat) monitoring is essential for patient care and sleep disease prevention. Most current solutions are based on wearable sensors or cameras; however, the former could affect sleep quality, while the latter often present privacy concerns. To address these shortcomings, we propose Wital, a contactless vital sign monitoring system based on low-cost and widespread commercial off-the-shelf (COTS) Wi-Fi devices. There are two challenges that need to be overcome. First, the torso deformations caused by breathing/heartbeats are weak. How can such deformations be effectively captured? Second, movements such as turning over affect the accuracy of vital sign monitoring. How can such detrimental effects be avoided? For the former, we propose a non-line-of-sight (NLOS) sensing model for modeling the relationship between the energy ratio of line-of-sight (LOS) to NLOS signals and the vital sign monitoring capability using Ricean K theory and use this model to guide the system construction to better capture the deformations caused by breathing/heartbeats. For the latter, we propose a motion segmentation method based on motion regularity detection that accurately distinguishes respiration from other motions, and we remove periods that include movements such as turning over to eliminate detrimental effects. We have implemented and validated Wital on low-cost COTS devices. The experimental results demonstrate the effectiveness of Wital in monitoring vital signs.

Federated learning (FL) has gained increasing attention due to its ability to collaboratively train while protecting client data privacy. However, vanilla FL cannot adapt to client heterogeneity, leading to a degradation in training efficiency due to stragglers, and is still vulnerable to privacy leakage. To address these issues, this paper proposes RingSFL, a novel distributed learning scheme that integrates FL with a model split mechanism to adapt to client heterogeneity while maintaining data privacy. In RingSFL, all clients form a ring topology. For each client, instead of training the model locally, the model is split and trained among all clients along the ring through a pre-defined direction. By properly setting the propagation lengths of heterogeneous clients, the straggler effect is mitigated, and the training efficiency of the system is significantly enhanced. Additionally, since the local models is blended, it is less likely for an eavesdropper to obtain the complete model and recover the raw data, thus improving data privacy. The experimental results on both simulation and prototype systems show that RingSFL can achieve better convergence performance than benchmark methods on independently identically distributed (IID) and non-IID datasets, while effectively preventing eavesdroppers from recovering training data.

Unmanned aerial vehicle (UAV) systems are of increasing interest to academia and industry due to their mobility, flexibility and maneuverability, and are an effective alternative to various uses such as surveillance and mobile edge computing (MEC). However, due to their limited computational and communications resources, it is difficult to serve all computation tasks simultaneously. This article tackles this problem by first proposing a scalable aerial computing solution, which is applicable for computation tasks of multiple quality levels, corresponding to different computation workloads and computation results of distinct performances. It opens up the possibility to maximally improve the overall computing performance with limited computational and communications resources. To meet the demands for timely video analysis that exceed the computing power of a UAV, we propose an aerial video streaming enabled cooperative computing solution namely, UAVideo, which streams videos from a UAV to ground servers. As a complement to scalable aerial computing, UAVideo minimizes the video streaming time under the constraints on UAV trajectory, video features, and communications resources. Simulation results reveal the substantial advantages of the proposed solutions. Besides, we highlight relevant directions for future research.

Gestures constitute an important form of nonverbal communication where bodily actions are used for delivering messages alone or in parallel with spoken words. Recently, there exists an emerging trend of WiFi sensing enabled gesture recognition due to its inherent merits like device-free, non-line-of-sight covering, and privacy-friendly. However, current WiFi-based approaches mainly reply on domain-specific training since they don’t know “\emph{where to look}’‘ and “\emph{when to look}”. To this end, we propose WiGRUNT, a WiFi-enabled gesture recognition system using dual-attention network, to mimic how a keen human being intercepting a gesture regardless of the environment variations. The key insight is to train the network to dynamically focus on the domain-independent features of a gesture on the WiFi Channel State Information (CSI) via a spatial-temporal dual-attention mechanism. WiGRUNT roots in a Deep Residual Network (ResNet) backbone to evaluate the importance of spatial-temporal clues and exploit their inbuilt sequential correlations for fine-grained gesture recognition. We evaluate WiGRUNT on the open Widar3 dataset and show that it significantly outperforms its state-of-the-art rivals by achieving the best-ever performance in-domain or cross-domain.

Volumetric video (or hologram video), the medium for representing natural content in VR/AR/MR, is presumably the next generation of video technology and a typical use case for 5G and beyond wireless communications. To realize volumetric video applications, efficient volumetric video streaming is in critical demand. This article responds to the challenges of and propose solutions to wireless transmission systems of point cloud video, which is the most popular and favored way to represent volumetric media and significantly differs from the other types of videos. In particular, we first introduce point cloud video technology and its applications, and then discuss the challenges of and solutions to point cloud video streaming, including encoding, tiling, viewing angle prediction, decoding, quality assessment and transmission optimization. Furthermore, we explain a prototype of MPEG DASH-based point cloud video streaming system as a preliminary study, along with more simulation results to verify its performance. Finally, we identify future research directions for providing high-quality point cloud video streaming.

Point cloud video provides 6 degrees of freedom (6DoF) viewing experiences to allow users to freely select the viewing angles of 3D scenes and is expected to be the next-generation video. This paper studies the point cloud video streaming and proposes a fuzzy logic-based point cloud video streaming scheme to solve the inherent technical issues. In particular, a point cloud video is first partitioned into smaller tiles, along with a low-quality base layer covering the entire video. Each tile is encoded into different quality levels, and both the compressed and uncompressed (i.e., decoded) versions of each tile are prepared for selection. Then, based on the user’s viewing angle and predicted future network bandwidth condition, fuzzy logic empowered quality level selection, with properly defined novel fuzzification, fuzzy rules, and defuzzification, is conducted to maximize the received point cloud video quality under the communication resource, computational resource and quality requirements constraints. Extensive simulations based on real point cloud video sequences and network traces are conducted, and the results reveal the superiority of the proposed scheme over the baseline scheme. To the best of our knowledge, this is the first work studying point cloud video streaming using fuzzy logic.

A document by Pengyuan Zhou . Click on the document to view its contents.

Some vehicular Internet-of-Things (IoT) applications have a strict requirement on the end-to-end delay where edge computing can be used to provide a short delay for end-users by conducing efficient caching and computing at the edge nodes. However, a fast and efficient communication route creation in multi-access vehicular environment is an underexplored research problem. In this paper, we propose a collaborative learning-based routing scheme for multi-access vehicular edge computing environment. The proposed scheme employs a reinforcement learning algorithm based on end-edge-cloud collaboration to find routes in a proactive manner with a low communication overhead. The routes are also preemptively changed based on the learned information. By integrating the “proactive’‘ and “preemptive” approach, the proposed scheme can achieve a better forwarding of packets as compared with existing alternatives. We conduct extensive and realistic computer simulations to show the performance advantage of the proposed scheme over existing baselines.