Preprint Article Version 1 This version is not peer-reviewed

Accurate Indoor Sound Level Measurement on a Low-Power and Low-Cost Wireless Sensor Node

Version 1 : Received: 20 June 2018 / Approved: 20 June 2018 / Online: 20 June 2018 (15:59:02 CEST)

A peer-reviewed article of this Preprint also exists.

Risojević, V.; Rozman, R.; Pilipović, R.; Češnovar, R.; Bulić, P. Accurate Indoor Sound Level Measurement on a Low-Power and Low-Cost Wireless Sensor Node. Sensors 2018, 18, 2351. Risojević, V.; Rozman, R.; Pilipović, R.; Češnovar, R.; Bulić, P. Accurate Indoor Sound Level Measurement on a Low-Power and Low-Cost Wireless Sensor Node. Sensors 2018, 18, 2351.

Journal reference: Sensors 2018, 18, 2351
DOI: 10.3390/s18072351

Abstract

Wireless sensor networks can provide a cheap and flexible infrastructure to support the measurement of noise pollution. However, the processing of the gathered data is challenging to implement on resource-constrained nodes, because each node has its own limited power supply, low-performance and low-power micro-controller unit and other limited processing resources, as well as limited amount of memory. We propose a sensor node for monitoring of indoor ambient noise. The sensor node is based on a hardware platform with limited computational resources and utilizes a number of simplifications to approximate more complex and costly signal processing stage. Furthermore, to reduce the communication between the sensor node and a sink node, as well as the power consumed by the IEEE 802.15.4 (ZigBee) transceiver, we perform digital A-weighting filtering and non-calibrated calculation of the sound pressure level on the node. According to experimental results, the proposed sound level meter can accurately measure the noise levels of up to 100~dB, with the mean difference of less than 2~dB compared to Class 1 sound level meter. The proposed device can continuously monitor indoor noise for several days. Despite the limitations of the used hardware platform, the presented node is a promising low-cost and low-power solution for indoor ambient noise monitoring.

Subject Areas

environmental noise monitoring; noise sensing; A-weighting; hardware platform; wireless sensor network

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