Animal‐borne wireless network: Remote imaging of community ecology

Abstract

This article describes the design, construction, and field-testing of a standalone networked animal-borne monitoring system conceived to study community ecology remotely. The system consists of an assemblage of identical battery-powered sensing devices with wireless communication capabilities that are each collar-mounted on a study animal and together form a mobile ad hoc network. The sensing modalities of each device include high-definition video, inertial accelerometry, and location resolved via a global positioning system module. Our system is conceived to use information exchange across the network to enable the devices to jointly decide without supervision when and how to use each sensing modality. The ultimate goal is to extend battery life while making sure that important events are appropriately documented. This requires judicious use of highly informative but power-hungry sensing modalities, such as video, because battery capacity is constrained by stringent weight and dimension restrictions. We have proposed algorithms to regulate sensing rates, data transmission among devices, and triggering for video recording based on location and animal group movements and configuration. We have also developed the hardware and firmware of our devices to reliably execute these algorithms in the exacting conditions of real-life deployments. We describe validation of the performance and reliability of our system using deployment results for a mission in Gorongosa National Park (Mozambique) to monitor two species in their natural habitat: the waterbuck and the African buffalo. We present movement data and snapshots of animal point-of-view videos collected by 14 fully operational devices collared on 10 waterbucks and 4 buffaloes. K E Y W O R D S environmental monitoring, sensor networks 1 | INTRODUCTION The study of social behavior of animal groups-as in predation, evasion, foraging, and migration-involves establishing hypotheses that explain how and why individuals in animal groups interact, and how the interactions lead to observed group phenomena (Ballerini et al., 2008; Couzin & Krause, 2003; Sumpter, 2006; Vicsek & Zafeiris, 2012). To validate hypotheses, extensive data on animal group behavior need to be collected and analyzed.Logging animal behavioral data by human observers is the most direct method, which requires ample time and effort, and is often hindered by spatiotemporal restrictions. In addition, human presence can influence the studied behavior (Altmann, 1974). Numerous efforts to develop and deploy animal-borne systems that collect behavioral data, over a range of dimensions (Dyo et al.& Martonosi, 2004) sought to overcome these limitations.Notably, there is growing interest in exploiting animal-borne imaging units to obtain animal point-of-view video recordings. In conjunction with geolocation data, they provide valuable information on an animal's interactions with the surrounding environment, other members of its species, and other species. However, because of battery capacity limitations, animal-born...

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