Device-free Wi-Fi localization is a promising technology to localize users who do not carry smart devices. The basic idea is to separate and analyze the signals reflected off human body from the multi-path signals. However, previous works could only localize moving users, because they can not distinguish the signals reflected off static users or objects like walls and furniture. This paper presents the Respiration Localization system,RespLoc, which for the first time enables device-free Wi-Fi localization system for static users. To recognize static users, the key insight is that people can breathe but objects cannot. However, it is non-trivial to extract user’s location from the respiration signal, because the respiration signal is significantly weaker than the regular motion signal. To this end, we propose the equivalent analysis method. Instead of using traditional signal separation, which suffers from severe noise due to residual signal components, we propose to construct an equivalent signal with the following properties: First, the equivalent signal follows the same variation law as the respiration signal; Second, the equivalent signal is not affected by irrelevant static signals. Based on this equivalent signal, we are able to resolve location features from the multi-path signals directly without separating them. We implementRespLocon commodity Wi-Fi devices, and extensive experimental results demonstrate thatRespLoccan localize static users with a median error of 0.89 meters.

In an era marked by technological innovation, visual applications have become ubiquitous in everyday life. Harnessing the power of computer vision, these applications process and interpret video data from edge cameras, facilitating tasks such as object detection and vehicle counting. Yet, implementing complex deep learning models on cameras with limited computational capacity poses significant challenges. Furthermore, the bandwidth constraints and fluctuating nature of wide-area networks present substantial difficulties for video analysis systems dependent on cloud computing. This paper first characterizes the relationship between different parameter combinations (such as frame rate and resolution) and video analysis accuracy through offline analysis. It proposes a video stream analysis configuration selection scheme, SPStream, for slowly changing scenes, and a configuration file switching strategy, SPStream+, for rapidly changing scenes. These strategies use idle resources at the camera edge end to select the optimal configuration in real-time, adjust video encoding quality, and dynamically switch configuration files based on the changing states of object motion. Finally, a real-time video stream analysis system for vehicle counting and pedestrian detection suitable for both scenarios is designed, which saves bandwidth to the greatest extent while meeting the accuracy requirements of users and achieving high accuracy of video analysis.

Voice command systems have been widely deployed on many smart devices for remote control. To further enrich the intelligence of these smart devices, the location of sound plays an important role in context-aware acoustic services. Despite initial steps made toward reliable voice localization, the state of the arts rely on prior knowledge of device location, device orientation and an indoor electronic map. To mitigate this additional cost, this paper presents VoiceMap, an autonomous mapping system of acoustic devices for voice localization. The insight behind VoiceMap is to explore the cooperation of sweeping robots and voice devices. Specifically, the sweeping robot is responsible for exploring the electronic map of the environment, while the microphone array is responsible for localizing the sweeping robot, so that we can establish the positional relationship between them. The core challenges are how to accurately locate the continuously moving robot, and how to synchronize the coordinate systems of the sweeping robot and the voice devices. To this end, we first design an inertial-based super-resolution method to estimate the angle of arrival (AoA) with respect to the robot. Then, we develop an effective coordinate synchronization mechanism, so that VoiceMap can automatically locate the voice devices on the electronic map generated by the robot. Finally, we implement a prototype system using commercial devices, and conduct comprehensive experiments to verify the proposed system. The experimental results show that we can realize a median error of 0.12m in terms of device localization.