Edge-Computing Meshed Wireless Acoustic Sensor Network for Indoor Sound Monitoring

Abstract

This work presents the design of a wireless acoustic sensor network (WASN) that monitors indoor spaces. The proposed network would enable the acquisition of valuable information on the behavior of the inhabitants of the space. This WASN has been conceived to work in any type of indoor environment, including houses, hospitals, universities or even libraries, where the tracking of people can give relevant insight, with a focus on ambient assisted living environments. The proposed WASN has several priorities and differences compared to the literature: (i) presenting a low-cost flexible sensor able to monitor wide indoor areas; (ii) balance between acoustic quality and microphone cost; and (iii) good communication between nodes to increase the connectivity coverage. A potential application of the proposed network could be the generation of a sound map of a certain location (house, university, offices, etc.) or, in the future, the acoustic detection of events, giving information about the behavior of the inhabitants of the place under study. Each node of the network comprises an omnidirectional microphone and a computation unit, which processes acoustic information locally following the edge-computing paradigm to avoid sending raw data to a cloud server, mainly for privacy and connectivity purposes. Moreover, this work explores the placement of acoustic sensors in a real scenario, following acoustic coverage criteria. The proposed network aims to encourage the use of real-time non-invasive devices to obtain behavioral and environmental information, in order to take decisions in real-time with the minimum intrusiveness in the location under study.

Keywords

• wireless acoustic sensor network
• low-cost sensor
• meshed network
• acoustic quality
• real-time signal processing
• Raspberry Pi