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This paper presents ISLA, a system that enables low power IoT nodes to self-localize using ambient 5G signals without any coordination with the base stations. ISLA operates by simply overhearing transmitted 5G packets and leverages the large bandwidth used in 5G to compute high-resolution time of flight of the signals. Capturing large 5G bandwidth consumes a lot of power. To address this, ISLA leverages recent advances in MEMS acoustic resonators to design a RF filter that can stretch the effective localization bandwidth to 100 MHz while using 6.25 MHz receivers, improving ranging resolution by 16x. We implement and evaluate ISLA in three large outdoors testbeds and show high localization accuracy that is comparable with having the full 100 MHz bandwidth. 

Stress plays a critical role in our lives, impacting our productivity and our long-term physiological and psychological well-being. This has motivated the development of stress monitoring solutions to better understand stress, its impact on productivity and teamwork, and help users adapt their habits toward more sustainable stress levels. However, today's stress monitoring solutions remain obtrusive, requiring active user participation (e.g., self-reporting), interfering with people's daily activities, and often adding more burden to users looking to reduce their stress. In this paper, we introduce WiStress, the first system that can passively monitor a user's stress levels by relying on wireless signals. WiStress does not require users to actively provide input or to wear any devices on their bodies. It operates by transmitting ultra-low-power wireless signals and measuring their reflections off the user's body. WiStress introduces two key innovations. First, it presents the first machine learning network that can accurately and robustly extract heartbeat intervals (IBI's) from wireless reflections without constraints on a user's daily activities. Second, it introduces a stress classification framework that combines the extracted heartbeats with other wirelessly captured stress-related features in order to infer a subject's stress level. We built a prototype of WiStress and tested it on 22 different subjects across different environments in both stress-induced and free-living conditions. Our results demonstrate that WiStress has high accuracy (84%-95%) in inferring a person's stress level in a fully-automated way, paving the way for ubiquitous sensing systems that can monitor stress and provide feedback to improve productivity, health, and well-being.