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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.