Energy-efficient computing for wildlife tracking

Juang, Philo; Oki, Hidekazu; Wang, Yong; Martonosi, Margaret; Peh, Li Shiuan; Rubenstein, Daniel I.

doi:10.1145/605406.605408

Cited by 45 publications

(3 citation statements)

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“…The algorithm operates over ad hoc communication networks, and naturally realizes a feedback mechanism in estimation over networks. In this sense, it is fundamentally different from broadcasting‐based communication protocols presented, for instance, in Juang et al ().…”

Section: Model‐based Algorithm Designmentioning

confidence: 88%

“…Implementation of such technologies using GPS has allowed researchers to remotely track animal locations and to understand how instrumented animals interact with group members and with surrounding environment (Cagnacci, Boitani, Powell, & Boyce, ). For instance, the ZebraNet project focused on designing a tracking system for wild zebras (Juang et al, ; Liu, Sadler, Zhang, & Martonosi, ; Zhang et al, ) in which the system adopts radiotelemetry to remotely collect GPS data at a stationary base camp. The value of data collected by GPS telemetry can be enhanced by incorporating satellite imagery, which provides useful information for studying animal–landscape interactions (Handcock et al, ).…”

Section: Related Workmentioning

confidence: 99%

“…In addition, human presence can influence the studied behavior (Altmann, ). Numerous efforts to develop and deploy animal‐borne systems that collect behavioral data, over a range of dimensions (Dyo et al, ; Juang et al, ; Krause et al, ; Marshall, ; Zhang, Sadler, Lyon, & Martonosi, ) sought to overcome these limitations.…”

Section: Introductionmentioning

confidence: 99%

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Animal‐borne wireless network: Remote imaging of community ecology

Park

Aschenbach

Ahmed

et al. 2019

Journal of Field Robotics

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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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Section: Model‐based Algorithm Designmentioning

confidence: 88%