Recent technological advances have reduced the complexity and cost of developing sensor networks for remote environmental monitoring. However, the challenges of acquiring, transmitting, storing, and processing remote environmental data remain significant. The transmission of large volumes of sensor data to a centralized location (i.e., the cloud) burdens network resources, introduces latency and jitter, and can ultimately impact user experience. Edge computing has emerged as a paradigm in which substantial storage and computing resources are located at the “edge” of the network. In this paper, we present an edge storage and computing framework leveraging commercially available components organized in a tiered architecture and arranged in a hub-and-spoke topology. The framework includes a popular distributed database to support the acquisition, transmission, storage, and processing of Internet-of-Things-based sensor network data in a field setting. We present details regarding the architecture, distributed database, embedded systems, and topology used to implement an edge-based solution. Lastly, a real-world case study (i.e., seismic) is presented that leverages the edge storage and computing framework to acquire, transmit, store, and process millions of samples of data per hour.

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Many creatures, including the myopic rhinoceros, depend upon hearing and smell to determine their environment. Nature is dominated by meaningful biophonic and geophonic information quickly absorbed by soil and vegetation, while anthrophonic urban soundscapes exhibit vastly different physical and semantic characteristics, sound repeatedly reflecting off hard geometric surfaces, distorting and reverberating, and becoming noise. Noise damages humans physiologically, including reproductively, and likely damages other mammals. Rhinos vocalize sonically and infrasonically, but audiograms are unavailable. They generally breed poorly in urban zoos, where infrasonic noise can be chronic. Biological and social factors are studied, but little attention if any is paid to soundscape. We present a methodology to analyze the soundscapes of captive animals according to their hearing range. Sound metrics determined from recordings at various institutions can then be compared and correlations with the health and wellbeing of their animals can be sought. To develop this methodology we studied the sonic, infrasonic, and seismic soundscape experienced by the white rhinos at Fossil Rim Wildlife Center, one of the few U.S. facilities to successfully breed this species in recent years. Future analysis can seek particular parameters known to be injurious to human mammals, plus parameters known to invoke response in animals.

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Measuring the sonic, infrasonic and seismic soundscape of the Southern White Rhinoceros (Ceratotherium simum simum) at a wildlife park conservation center