机构:
NYU, Mus & Audio Res Lab, 550 1St Ave, New York, NY 10012 USA
NYU, Ctr Urban Sci & Progress, Brooklyn, NY 11201 USANYU, Mus & Audio Res Lab, 550 1St Ave, New York, NY 10012 USA
Mydlarz, Charlie
[1
,2
]
Sharma, Mohit
论文数: 0引用数: 0
h-index: 0
机构:
NYU, Ctr Urban Sci & Progress, Brooklyn, NY 11201 USANYU, Mus & Audio Res Lab, 550 1St Ave, New York, NY 10012 USA
Sharma, Mohit
[2
]
Lockerman, Yitzchak
论文数: 0引用数: 0
h-index: 0
机构:
NYU, Tandon Sch Engn, Brooklyn, NY 11201 USANYU, Mus & Audio Res Lab, 550 1St Ave, New York, NY 10012 USA
Lockerman, Yitzchak
[3
]
Steers, Ben
论文数: 0引用数: 0
h-index: 0
机构:
NYU, Ctr Urban Sci & Progress, Brooklyn, NY 11201 USANYU, Mus & Audio Res Lab, 550 1St Ave, New York, NY 10012 USA
Steers, Ben
[2
]
Silva, Claudio
论文数: 0引用数: 0
h-index: 0
机构:
NYU, Tandon Sch Engn, Brooklyn, NY 11201 USANYU, Mus & Audio Res Lab, 550 1St Ave, New York, NY 10012 USA
Silva, Claudio
[3
]
Bello, Juan Pablo
论文数: 0引用数: 0
h-index: 0
机构:
NYU, Mus & Audio Res Lab, 550 1St Ave, New York, NY 10012 USA
NYU, Ctr Urban Sci & Progress, Brooklyn, NY 11201 USANYU, Mus & Audio Res Lab, 550 1St Ave, New York, NY 10012 USA
Bello, Juan Pablo
[1
,2
]
机构:
[1] NYU, Mus & Audio Res Lab, 550 1St Ave, New York, NY 10012 USA
[2] NYU, Ctr Urban Sci & Progress, Brooklyn, NY 11201 USA
sensor;
network;
noise;
environmental;
monitoring;
smart cities;
IoT;
internet of things;
D O I:
10.3390/s19061415
中图分类号:
O65 [分析化学];
学科分类号:
070302 ;
081704 ;
摘要:
Noise pollution is one of the topmost quality of life issues for urban residents in the United States. Continued exposure to high levels of noise has proven effects on health, including acute effects such as sleep disruption, and long-term effects such as hypertension, heart disease, and hearing loss. To investigate and ultimately aid in the mitigation of urban noise, a network of 55 sensor nodes has been deployed across New York City for over two years, collecting sound pressure level (SPL) and audio data. This network has cumulatively amassed over 75 years of calibrated, high-resolution SPL measurements and 35 years of audio data. In addition, high frequency telemetry data have been collected that provides an indication of a sensors' health. These telemetry data were analyzed over an 18-month period across 31 of the sensors. It has been used to develop a prototype model for pre-failure detection which has the ability to identify sensors in a prefail state 69.1% of the time. The entire network infrastructure is outlined, including the operation of the sensors, followed by an analysis of its data yield and the development of the fault detection approach and the future system integration plans for this.