Dead reckoning is a promising yet often overlooked smartphone-based indoor localization technology that relies on phone-mounted sensors for counting steps and estimating walking directions, without the need for extensive sensor or landmark deployment. However, misalignment between the phone’s direction and the user’s actual movement direction can lead to unreliable direction estimates and inaccurate location tracking. To address this issue, this paper introduces SWiLoc (Smartphone and WiFi-based Localization), an enhanced direction correction system that integrates passive WiFi sensing with smartphone-based sensing to form Correction Zones. Our two-phase approach accurately measures the user’s walking directions when passing through a Correction Zone and further refines successive direction estimates outside the zones, enabling continuous and reliable tracking. In addition to direction correction, SWiLoc extends its capabilities by incorporating a localization technique that leverages corrected directions to achieve precise user localization. This extension significantly enhances the system’s applicability for high-accuracy localization tasks. Additionally, our innovative Fresnel zone-based approach, which utilizes unique hardware configurations and a fundamental geometric model, ensures accurate and robust direction estimation, even in scenarios with unreliable walking directions. We evaluate SWiLoc across two real-world environments, assessing its performance under varying conditions such as environmental changes, phone orientations, walking directions, and distances. Our comprehensive experiments demonstrate that SWiLoc achieves an average 75th percentile error of 8.89 degrees in walking direction estimation and an 80th percentile error of 1.12 m in location estimation. These figures represent reductions of 64% and 49%, respectively for direction and location estimation error, over existing state-of-the-art approaches.

Dead reckoning is a promising yet often overlooked smartphone-based indoor localization technology that relies on phone-mounted sensors for counting steps and estimating walking directions, without the need for extensive sensor or landmark deployment. However, misalignment between the phone’s direction and the user’s actual movement direction can lead to unreliable direction estimates and inaccurate location tracking. To address this issue, this paper introduces SWiLoc, an enhanced direction correction system that integrates passive WiFi sensing with smartphone-based sensing to form Correction Zones. Our two-phase approach accurately measures the user’s walking directions when passing through a Correction Zone and further refines successive direction estimates outside the zones, enabling continuous and reliable tracking. In addition to direction correction, SWiLoc extends its capabilities by incorporating a localization technique that leverages corrected directions to achieve precise user localization. This extension significantly enhances the system’s applicability for high-accuracy localization tasks. Additionally, our innovative Fresnel zone-based approach, which utilizes unique hardware configurations and a fundamental geometric model, ensures accurate and robust direction estimation, even in scenarios with unreliable walking directions. We evaluate SWiLoc across two real-world environments, assessing its performance under varying conditions such as environmental changes, phone orientations, walking directions, and distances. Our comprehensive experiments demonstrate that SWiLoc achieves an average 75th percentile error of 8.89 degrees in walking direction estimation and an 80th percentile error of 1.12 meters in location estimation. These figures represent reductions of 64% and 49%, respectively for direction and location estimation error, over existing state-of-the-art approaches.

Body Mass Index (BMI) is a critical metric for assessing public health and identifying populations at risk for obesity-related conditions. Traditional BMI monitoring methods often raise privacy concerns and require active cooperation from individuals, limiting their applicability in real-world scenarios. This paper introduces a novel approach to BMI monitoring that leverages proximal federated learning (PFL) using commodity WiFi devices. Our method addresses the challenges of data heterogeneity and intermittent connectivity in FL. By our approach, the Adaptive Elastic Stochastic Alternating Direction Method of Multipliers (AESADMM), an optimization algorithm designed to handle data heterogeneity and intermittent connectivity in FL scenarios, our system collects Channel State Information (CSI) from WiFi signals to passively classify BMI based on the impact of different body shapes on signal propagation. This approach ensures privacy preservation and eliminates the need for active participant involvement. Theoretical analysis and empirical results demonstrate the superior accuracy, reduced communication costs, and enhanced scalability of our proposed method compared to existing personalized FL frameworks, showcasing its potential as an effective tool for large-scale BMI monitoring in diverse environments.

ShakeAlert is an earthquake early warning (EEW) system that detects significant earthquakes so quickly that alerts can reach many people before shaking arrives. The current ShakeAlert system has been developed incrementally over the past decade, following an old-fashioned system design. The system's tight coupling between physical sensors and data processing modules has made it difficult to expand new sensors, adopt novel networking technology advances, or change the system's behavior at runtime, thus hindering the system's extensibility, scalability, and reliability. To address these limitations, this paper proposes to expand the existing system design by adding a "digital twin" middleware layer as virtual representations of physical sensor stations, which provides a standardized software interface for running distributed data processing applications and conceals the hardware/software differences in physical sensors. These virtual stations are placed at the edge of the network near physical stations, acting as a middleware layer between sensors and the ShakeAlert system. By incorporating digital twins, we can leverage cutting-edge networking technologies (such as edge/fog computing) to improve scalability, accept trustworthy sensor data from diverse sources to enhance extensibility, and move data processing functions closer to the sensor stations to reduce response time during large earthquakes when network throughput is impacted. Ultimately, this will enhance the reliability of the ShakeAlert system and help keep communities safe during earthquakes.