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Environmental perception is pivotal for intelligent systems, enabling them to adeptly capture, interpret, and act upon contextual cues. Grasping the intricacies of the environment—its objects, occupants, floor plan, and dynamics—is fundamental for the effective deployment of technologies, including robotics, the Internet of Things (IoT), and augmented reality. Traditional perception mechanisms, such as video surveillance and sensor-based monitoring, are often hampered by privacy concerns, substantial infrastructural costs, energy inefficiencies, and limited coverage. In contrast, WiFi sensing stands out for its non-intrusive, cost-effective, and pervasive attributes. Capitalizing on ubiquitous WiFi signals that permeate both indoor and outdoor spaces, WiFi sensing delivers unparalleled advantages over its traditional counterparts, sidestepping the need for extra hardware yet offering profound environmental insights. Its capability to penetrate walls and other obstructions further broadens its range, covering areas beyond the reach of conventional sensors. These unique edges of WiFi sensing elevate its value across diverse applications, spanning smart homes, health monitoring, location-based services, and security systems. Amplifying environmental perception via WiFi sensing is more than just an innovation in ubiquitous computing; it's a leap towards forging safer, more efficient, and smarter environments. This dissertation explores monitoring and mapping environments leveraging motion analytics based on commodity WiFi.

In the first part of this dissertation, we introduce an efficient and cost-effective system for precise floor plan construction by integrating RF and inertial sensing techniques. The proposed system harnesses detailed insights from RF tracking and broad context from inertial metrics, such as magnetic field strength, to produce an accurate map. The system employs a robot for trajectory collection and requires only a single Access Point to be arbitrarily installed in space, both of which are widely available nowadays. Impressively, the system can produce detailed maps even with minimal data, making it adaptable for diverse structures such as shopping centers, offices, and residences without significant expenses. We validated the efficacy of the proposed system using a Dji RoboMaster S1 robot equipped with standard WiFi across three distinct buildings, demonstrating its capability to produce reliable maps for the intended regions. Given the widespread presence of WiFi setups and the increasing prevalence of domestic robots, the proposed approach paves the way for universal intelligent systems offering indoor mapping services.

In the second and third parts, we present two innovative strategies leveraging WiFi to identify the motion of human and various non-human subjects. Initially, we detail a novel passive, non-intrusive methodology tailored for edge devices. By extracting and analyzing motion's physically and statistically plausible features, our system recognizes human and diverse non-human subjects through walls using a singular WiFi link. Experimental results from four distinct buildings with various moving subjects validate its efficiency on edge devices. Advancing to more intricate cases, we put forth a deep learning-based WiFi sensing paradigm. This delves into the efficacy of diverse deep learning models on human and non-human object recognition and probes the feasibility of transferring image-trained models to fulfill the WiFi sensing task. Designed with a robust statistic invariant to the environment and position, this system efficiently adapts to new surroundings. Comprehensive experimental evaluations affirm our framework's precision in pinpointing intricate human and non-human subjects, and readiness for integration into prevalent intelligent systems, thereby boosting their perceptual capacities.

In the final part of this dissertation, we propose a pioneering through-wall indoor intrusion detection system that adeptly filters out interference from non-human subjects using ubiquitous WiFi signals. A novel deep learning architecture is proposed for single-link WiFi signal analysis. It employs a ResNet-18-based module to extract features of indoor moving subjects and an LSTM-based module to incorporate temporal information for efficient intrusion detection. Notably, the system is invariant to environmental changes, angles, and positions, enabling swift deployment in new environments without additional training. Evaluation in five indoor environments with various interference yielded high intrusion detection accuracy and a low false alarm rate, even without model tuning for unseen settings. The results underscore the system's exceptional adaptability, positioning it as a top contender for widespread intelligent indoor security applications.