Smart City Gnosys
Smart city article details
| Title | Infrastructure-Free, Deep Learned Urban Noise Monitoring At 100Mw |
|---|---|
| ID_Doc | 31565 |
| Authors | Yun J.; Srivastava S.; Roy D.; Stohs N.; Mydlarz C.; Salman M.; Steers B.; Bello J.P.; Arora A. |
| Year | 2022 |
| Published | Proceedings - 13th ACM/IEEE International Conference on Cyber-Physical Systems, ICCPS 2022 |
| DOI | http://dx.doi.org/10.1109/ICCPS54341.2022.00012 |
| Abstract | The Sounds of New York City (SONYC) wireless sensor network (WSN) has been fielded in Manhattan and Brooklyn over the past five years, as part of a larger human-in-the-loop cyber-physical control system for monitoring, analyzing, and mitigating urban noise pollution. We describe the evolution of the 2-tier SONYC WSN from an acoustic data collection fabric into a 3-tier in situ noise complaint monitoring WSN, and its current evaluation. The added tier consists of long range (LoRa), multi-hop networks of a new low-power acoustic mote, MKII ('Mach 2'), that we have designed and fabricated. MKII motes are notable in three ways: First, they advance machine learning capability at mote-scale in this application domain by introducing a real-time Convolutional Neural Network (CNN) based embedding model that is competitive with alternatives while also requiring 10x lesser training data and 2 orders of magnitude fewer runtime resources. Second, they are conveniently deployed relatively far from higher-tier base station nodes without assuming power or network infrastructure support at operationally relevant sites (such as construction zones), yielding a relatively low-cost solution. And third, their networking is frequency agile, unlike conventional LoRa networks: it tolerates in a distributed, self-stabilizing way the variable external interfer-ence and link fading in the cluttered 902-928MHz ISM band urban environment by dynamically choosing good frequencies using an efficient new method that combines passive and active measure-ments. © 2022 IEEE. |
| Author Keywords | Audio representations; Convolutional Neural Networks; Infrastructure-free; LoRa external inter-ference; Low-power; Resource-efficient deep learning; Robustness; Smart cities |
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