Search for: All records

The increasing prevalence of smart devices spurs the development of emerging indoor localization technologies for supporting diverse personalized applications at home. Given marked drawbacks of popular chirp signal-based approaches, we aim at developing a novel device-free localization system via the continuous wave of the inaudible frequency. To achieve this goal, solutions are developed for fine-grained analyses, able to precisely locate moving human traces in the room-scale environment. In particular, a smart speaker is controlled to emit continuous waves at inaudible20kHz, with a co-located microphone array to record their Doppler reflections for localization. We first develop solutions to remove potential noises and then propose a novel idea by slicing signals into a set of narrowband signals, each of which is likely to include at most one body segment’s reflection. Different from previous studies, which take original signals themselves as the baseband, our solutions employ the Doppler frequency of a narrowband signal to estimate the velocity first and apply it to get the accurate baseband frequency, which permits a precise phase measurement after I-Q (i.e., in-phase and quadrature) decomposition. A signal model is then developed, able to formulate the phase with body segment’s velocity, range, and angle. We next develop novel solutions to estimate the motion state in each narrowband signal, cluster the motion states for different body segments corresponding to the same person, and locate the moving traces while mitigating multi-path effects. Our system is implemented with commodity devices in room environments for performance evaluation. The experimental results exhibit that our system can conduct effective localization for up to three persons in a room, with the average errors of 7.49cmfor a single person, with 24.06cmfor two persons, with 51.15cmfor three persons. 

The proliferation of IoT devices, with various capabilities in sensing, monitoring, and controlling, has prompted diverse emerging applications, highly relying on effective delivery of sensitive information gathered at edge devices to remote controllers for timely responses. To effectively deliver such information/status updates, this paper undertakes a holistic study of AoI in multi-hop networks by considering the relevant and realistic factors, aiming for optimizing information freshness by rapidly shipping sensitive updates captured at a source to its destination. In particular, we consider the multi-channel with OFDM (orthogonal frequency-division multiplexing) spectrum access in multi-hop networks and develop a rigorous mathematical model to optimize AoI at destination nodes. Real-world factors, including orthogonal channel access, wireless interference, and queuing model, are taken into account for the very first time to explore their impacts on the AoI. To this end, we propose two effective algorithms where the first one approximates the optimal solution as closely as we desire while the second one has polynomial time complexity, with a guaranteed performance gap to the optimal solution. The developed model and algorithms enable in-depth studies on AoI optimization problems in OFDM-based multi-hop wireless networks. Numerical results demonstrate that our solutions enjoy better AoI performance and that AoI is affected markedly by those realistic factors taken into our consideration. 

Indoor localization has played a significant role in facilitating a collection of emerging applications in the past decade. This paper presents a novel indoor localization solution via inaudible acoustic sensing, called EchoSpot, which relies on only one speaker and one microphone that are readily available on audio devices at households. We program the speaker to periodically send FMCW chirps at 18kHz-23kHz and leverage the co-located microphone to capture the reflected signals from the body and the wall for analysis. By applying the normalized cross-correlation on the transmitted and received signals, we can estimate and profile their time-of-flights (ToFs). We then eliminate the interference from device imperfection and environmental static objects, able to identify the ToFs corresponding to the direct reflection from human body. In addition, a new solution to estimate the ToF from wall reflection is designed, assisting us in spotting a human location in the two-dimensional space. We implement EchoSpot on three different types of speakers, e.g., Amazon Echo, Edifier R1280DB, and Logitech z200, and deploy them in real home environments for evaluation. Experimental results exhibit that EchoSpot achieves the mean localization errors of 4.1cm, 9.2cm, 13.1cm, 17.9cm, 22.2cm, respectively, at 1m, 2m, 3m, 4m, and 5m, comparable to results from the state-of-the-arts while maintaining favorable advantages.