Noise pollution in urban environments is becoming increasingly common and it has potential to negatively impact people’s health and decrease overall productivity. In order to alleviate these effects, it is important to better quantify noise patterns and levels through data collection and analysis. Wireless sensor networks offer a method for achieving this with a higher level of granularity than traditional handheld devices. In this study, a wireless sensing unit (WSU) was developed that possesses the same functionality as a handheld sound level meter. The WSU is comprised of a microcontroller unit that enables on-board computations, a wireless transceiver that uses Zigbee protocol for data transmission, and an external peripheral board that houses the microphone transducer. The WSU utilizes on-board data processing techniques to monitor noise by computing equivalent continuous sound levels, LeqT, which effectively minimizes data transmission and increases the overall longevity of the node. Strategies are also employed to ensure real-time functionality is maintained on the sensing unit, with a focus on preventing bottlenecks between data acquisition, data processing, and wireless transmission. Four units were deployed in two weeks field validation test and were shown to be capable of monitoring noise for extended periods of time.

Structural monitoring for civil infrastructure is a rapidly developing field that has made significant advancements over the last decade. However, a number of performance bottlenecks remain including challenges with cost-effectively scaling monitoring systems up to large nodal counts. Due to the many parallels between biological sensory systems and engineered sensing systems, the biological nervous system can offer potential solutions to the current deficiencies of structural monitoring systems. The nervous system is capable of real-time processing and data transmission of external stimuli through an extremely condensed format with very basic processing units. This study explores the mammalian auditory system for inspiration because it achieves efficient data acquisition processes that outperform existing engineered sensing systems. Specifically, the auditory system realizes this through three steps: (1) real-time decomposition of a convoluted time-based signal into frequency components, (2) information compression for each component, and (3) efficient high-speed data transmission to the auditory cortex. In this paper, these three main mechanisms are explored and a bio-inspired structural monitoring system is proposed. The functionality of the proposed system is compared to traditional data compression techniques (wavelet transforms and compressed sensing) on various vibratory signals. While the wavelet transform is able to outperform the proposed sensor by minimizing signal reconstruction errors, the proposed bio-inspired sensor achieves similar compression rates but, unlike the others, does so using real-time processing.

While wireless sensor networks have been successfully deployed on a variety of civil infrastructure systems for structural monitoring, past studies have shown that there is room for improvement in terms of network robustness and overall resource consumption efficiency. The mechanisms employed by biological nervous systems (e.g. signal modulation, communication, and integration) can be used as inspiration for overcoming the performance bottlenecks seen in existing wireless sensor nodes and networks. The mammalian auditory system is of particular interest due to its unique signal decomposition techniques (performed by the cochlea) that enable real-time processing of complex sound signals. In this article, a novel wireless sensor architecture based on the operational principles of cochlea is described. The performance of the proposed sensor is validated on a single-degree-of-freedom structure that is excited by seismic ground motion signals, thus demonstrating its real-time monitoring capabilities while maintaining high data compression rates.

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A dichotomy exists in structural monitoring installations: those with sensors tethered to the data repository with wires, and those which use wireless communication to create a distributed network of sensors. This chapter discusses the impact of the paradigm shift from the wired monitoring systems traditionally used, to the wireless systems recently developed. This new technology is capable of achieving effective measurements on par with its predecessor, and introduces new possibilities of large-scale networks of hundreds of sensors made possible by low installation costs and scalable in-network data processing. Design and selection considerations covered in this chapter include network architecture, wireless node hardware, and distributed embedded software. Although rapidly maturing and reaching commercialization, research opportunities still exist in the fi elds of data and power management, which are discussed as the chapter culminates.