Hand gesture recognition (HGR) is essential for human-machine interaction. Although the existing solutions achieve good performance in specific tasks, they still face challenges when users navigate through different application contexts, i.e., demanding multi-task ability to support newly arrived HGR tasks. In this paper, we propose the first IMU-vision based system hosted on wrist-worn devices to support multi-task HGR, denoted as MultiHGR. The system introduces a novel two-stage training strategy, i.e., task-agnostic stage to align cross-modal features from unlabeled arbitrary gesture through contrastive learning, and task-related stage to learn modality contributions with limited labeled data in specific tasks through self-attention mechanism. Since only the second task-related stage should be executed for each new task, MultiHGR could accommodate multiple tasks with significant reduced training cost and storage requirement. The evaluation results on three HGR tasks demonstrates that MultiHGR reduces 64.92% training time, and 24.04% storage as compared with traditional multimodal single-task models, and MultiHGR outperforms unimodal single-task models with 14.37%, 19.28%, and 31% improvements in these three tasks, respectively. As compared with state-of-the-art multimodal single-task model, MultiHGR achieves average 6.35% accuracy improvement, along with 65.74% training time reduction.

Every day, one person loses his life due to drowning in swimming pools, even with professional lifeguards present. Contrary to what the public might assume, drowning swimmers can hardly splash or yell for help. This life-threatening situation calls for a robust SOS channel between the swimmers and the lifeguards. This paper proposes Neusos, a neural-enhanced underwater SOS communication system based on commercial wearable devices and low-cost hydrophones deployed in the swimming pool. Specifically, we repurpose popular wearable devices (eg, smartwatches) as SOS transmitters, allowing swimmers to activate a distress signal by simply pressing one smartwatch button. In response, several underwater hydrophones in the swimming pool can detect SOS signals and alert lifeguards on duty immediately, enabling them to provide timely assistance. The main technical challenge lies in reliably detecting weak SOS signals in non-stationary underwater scenarios. To achieve so, we thoroughly characterize the property of underwater channels and examine the limitation of the traditional correlation-based signal detection method in underwater communication scenarios. Based on our empirical findings, we developed a robust SOS detection method enhanced with deep learning. By fully embedding visual hints into networks, Neusos outperforms state-of-the-art signal processing-based underwater SOS detection methods.

Gesture recognition possesses a vast potential for its application in the realms of human-computer interaction and virtual reality. The prevalent use of gesture recognition in domestic environments via Wi-Fi and acoustic sensing offers clear advantages for implementation. However, current techniques present significant challenges: acoustic sensing is vulnerable to environmental disturbances, whereas Wi-Fi necessitates prior knowledge of user's location for extracting features independent of the environment. To overcome these constraints, multimodal fusion appears as an effective solution, capitalizing on the complementary nature of these limitations.

Despite the promising performance shown by learning-based methods in facilitating multimodal fusion, they suffer from a lack of theoretical explanation for the integration of multimodal features. To address this gap, we introduce the concept of the "hybrid zone" in this paper. This theoretical model illuminates the process of merging acoustic and Wi-Fi sensing techniques. The "hybrid zone" model elucidates both the global perspective, which entails the fusion of acoustic and Wi-Fi sensing regions, and the local perspective, which involves the synthesis of acoustic and Wi-Fi fine-grained velocities.

In order to overcome the limitations of existing blood pressure (BP) measurement methods, we study the technology based on heart sounds, and find that the time interval between the first and second heart sounds (TIFS) is closely related to BP. Motivated by this, we propose HearBP, a novel BP monitoring system that utilizes in-ear microphones to collect bone-conducted heart sounds in the binaural canal. We first design a noise removing method based on U-net autoencoder-decoder to separate clean heart sounds from background noises. Then, we design a feature extraction method based on shannon energy and energy-entropy ratio to further mine the time domain and frequency domain features of heart sounds. In addition, combined with the principal component analysis algorithm, we achieve feature dimension reduction to extract the main features related to BP. Finally, we propose a network model based on dendritic neural regression to construct a mapping between the extracted features and BP. Extensive experiments with 41 participants show the average estimation error of 0.97mmHg and 1.61mmHg and the standard deviation error of 3.13mmHg and 3.56mmHg for diastolic pressure and systolic pressure, respectively. These errors are within the acceptable range specified by the FDA's AAMI protocol.

Edge-assisted visual SLAM stands as a pivotal enabler for emerging mobile applications, such as search-and-rescue and industrial inspection. Limited by the computing capability of lightweight mobile devices, current innovations balance system accuracy and efficiency by allocating lightweight and time-sensitive tracking tasks to mobile devices, while offloading the more resource-intensive yet delay-tolerant map optimization tasks to the edge. However, our pilot study reveals several limitations of such a tracking-optimization decoupled paradigm, arising due to the disruption of inter-dependencies between the two tasks concerning data, resources, and threads.

In this paper, we design and implement AirSLAM, an innovative system that reshapes the edge-assisted visual SLAM by tightly integrating tracking and partial-yet-crucial optimization on mobile. AirSLAM harnesses the hierarchical and heterogeneous computing units offered by the latest commercial systems-on-chip (SoCs) to enhance the computational capacity of mobile devices, which in turn, allows AirSLAM to design a suit of novel algorithms for map sync, optimization, and tracking that accommodate such architectural upgrade. By fully embracing the on-chip intelligence, AirSLAM simultaneously enhances system accuracy and efficiency through software-hardware co-design. We deploy AirSLAM on a drone for industrial inspection. Comprehensive experiments in one of the world's largest oil fields over three months demonstrate its superior performance.

Container technology has revolutionized service deployment, offering streamlined processes and enabling container orchestration platforms to manage a growing number of container clusters. However, the deployment of containers in distributed edge clusters presents challenges due to their unique characteristics, such as bandwidth limitations and resource constraints. Existing approaches designed for cloud environments often fall short in addressing the specific requirements of edge computing. Additionally, very few edge-oriented solutions explore fundamental changes to the container design, resulting in difficulties achieving backward compatibility. In this paper, we reevaluate the fundamental layer-based structure of containers. We identify that the proliferation of redundant files and operations within image layers hinders efficient container deployment. Drawing upon the crucial insight of enhancing layer reuse and extracting benefits from it, we introduce BREAK, a holistic approach centered on layer structure throughout the entire container deployment pipeline, ensuring backward compatibility. BREAK refactors image layers and proposes an edge-oriented cache solution to enable ubiquitous and shared layers. Moreover, it addresses the complete deployment pipeline by introducing a customized scheduler and a tailored storage driver. Our results demonstrate that BREAK accelerates the deployment process by up to 2.1× and reduces redundant image size by up to 3.11× compared to state-of-the-art approaches.

Collaborative Edge Computing (CEC) is a new edge computing paradigm that enables neighboring edge servers to share computational resources with each other. Although CEC can enhance the utilization of computational resources, it still suffers from resource waste. The primary reason is that end-users from the same area are likely to offload similar tasks to edge servers, thereby leading to duplicate computations. To improve system efficiency, the computation results of previously executed tasks can be cached and then reused by subsequent tasks. However, most existing computation reuse algorithms only consider one edge server, which significantly limits the effectiveness of computation reuse. To address this issue, this paper applies computation reuse in CEC networks to exploit the collaboration among edge servers. We formulate an optimization problem that aims to minimize the overall task response time and decompose it into a caching subproblem and a scheduling subproblem. By analyzing the properties of optimal solutions, we show that the optimal caching decisions can be efficiently searched using the bisection method. For the scheduling subproblem, we utilize projected gradient descent and backtracking to find a local minimum. Numerical results show that our algorithm significantly reduces the response time under various situations.

Edge computing is emerging as a complementary architecture to cloud computing to address some of its associated issues. One of the major advantages of edge computing is that edge data centers are usually much closer to users compared to traditional cloud data centers. Therefore, it is commonly believed that for developers of latency-sensitive applications, they can effectively reduce the overall end-to-end latency by simply transitioning from a cloud deployment to an edge deployment. However, as recent work has shown, the performance of an edge deployment is vulnerable to a couple of factors which under many practical scenarios can lead to edge servers providing worse end-to-end response time than cloud servers. This phenomenon is referred to as edge performance inversion. In this paper, we propose resource allocation algorithms and workload scheduling algorithms that actively prevent edge performance inversion. Our algorithms are based on queueing theory results and optimization techniques. Evaluation results show that INVAR can find a near-optimal solution that outperforms the performance of a cloud deployment by an adjustable margin. Simulation results based on production workloads from Akamai data centers show that INVAR can outperform common heuristic-based edge deployment by 11% to 24% in real-world scenarios.