Energy-aware portable video communication system design for wildlife activity monitoring

He, Zhihai; Eggert, Jay; Cheng, Wenye; Zhao, Xiwen; Millspaugh, J. J.; Moll, Remington J.; Beringer, Jeff; Sartwell, Joel

doi:10.1109/mcas.2008.923007

Cited by 19 publications

(12 citation statements)

References 27 publications

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“…We collected one fresh (,10-min-old) fecal sample, which consisted of all pellets from a given defecation event, from each deer each day during 3 treatment periods: 1) pretreatment, from 29 January to 14 February 2007, during which no animals wore dummy AVEDs, 2) treatment, from 15 February to 28 February 2007, during which treatment individuals wore dummy AVEDs, and 3) posttreatment, from 1 March to 14 March 2007, during which no animals wore dummy AVEDs. Thus, treatment deer wore AVEDs for 2 weeks, a typical time of deployment for terrestrial, store onboard AVEDs (He et al 2008). Within each sex we randomly assigned individuals to the control or treatment group to obtain 9 control (5 F, 4 M) and 8 treatment subjects (4 F, 4 M).…”

Section: Methodsmentioning

confidence: 99%

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Physiological Stress Response of Captive White‐Tailed Deer to Video Collars

et al. 2009

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Animal‐borne video and environmental data collection systems (AVEDs) are an advanced form of biotelemetry that combines video with other sensors. As a proxy for physiological stress, we assessed fecal glucocorticoid metabolite (FGM) excretion in 7 white‐tailed deer (Odocoileus virginianus) fitted with AVED dummy collars; 9 additional deer served as controls. We collected fecal samples over 3 2‐week periods: pretreatment, treatment, and posttreatment periods. There were no differences in FGMs across time periods (F2,218 = 1.94, P = 0.147) and no difference between FGMs of control and treatment individuals (F1,14 = 0.72, P = 0.411). Fecal glucocorticoid metabolite excretion in AVED‐collared deer was indistinguishable from uncollared animals and within the normal, baseline range for this species. Absence of an adrenal response to collaring suggested that AVED collaring does not induce physiological stress in deer.

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Section: Methodsmentioning

confidence: 99%

“…Animal-borne video and environmental data collection systems (AVEDs; Moll et al 2007) are an advanced form of biotelemetry that can combine video with a variety of sensors (e.g., Global Positioning System [GPS], audio, accelerometer; He et al 2008). In addition to location information, these systems offer video from the animal's perspective.…”

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confidence: 99%

Physiological Stress Response of Captive White‐Tailed Deer to Video Collars

et al. 2009

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“…The work shows good potential if transmission delay is the main objective, but it is not proved to scale in more sophisticated multi-objective optimization scenarios. A very relevant example of application-driven design is proposed in [9], where a multi-objective optimization involving all the system components is provided in the field of wildlife monitoring. The work shows how a deep knowledge of the final application can lead to an optimized node lifetime, while guaranteeing the quality of service (high data rate and low distortion).…”

Section: Related Workmentioning

confidence: 99%

Model-based design for wireless body sensor network nodes

Beretta

Rincon

Khaled

et al. 2012

2012 13th Latin American Test Workshop (LATW)

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Abstract-Wireless body sensor networks (WBSNs) are a rising technology that allows constant and unobtrusive monitoring of the vital signals of a patient. The configuration of a WBSN node proves to be critical in order to maximize its lifetime, while meeting the predefined performance during signal sensing, preprocessing, and wireless transmission to the base station. In this work, we propose a model-based optimization framework for WBSN nodes, which is centered on a detailed analytical characterization of the most energy-demanding components of this application domain. We also propose a multi-objective exploration algorithm to evaluate the node configurations and the corresponding performance tradeoffs. A case study is discussed to validate the proposed framework, proving that our model captures the behavior of real WBSNs and efficiently leads to the determination of the Pareto-optimal configurations.

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“…However, automatic DSE is still immature in WSN design, where the complexity of the network and the peculiarity of each application domain often push the developers towards manual and ad hoc optimizations (e.g., the work in [10] for wildlife monitoring).…”

Section: State Of the Artmentioning

confidence: 99%

Knowledge-based design space exploration of wireless sensor networks

Grassi

Beretta

Rana

et al. 2012

Proceedings of the Eighth IEEE/ACM/IFIP International Conference on Hardware/Software Codesign and System Synthesis

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The complexity of Wireless Sensor Networks (WSNs) has been constantly increasing over the last decade, and the necessity of efficient CAD tools has been growing accordingly. In fact, the size of the design space of a WSN has become large, and an exploration conducted by using semi-random algorithms (such as the popular genetic or simulated annealing algorithms) requires an unacceptable amount of time to converge due to the high number of parameters involved. To address this issue, in this paper we introduce a knowledgebased design space exploration algorithm for the WSN domain, which is based on a discrete-space Markov decision process (MDP). In order to enhance the performance of the proposed algorithm and to increase its scalability, we tailor the classical MDP approach to the specific aspects that characterize the WSN domain. We exploit domain-specific knowledge to choose the best node-level configuration in WSNs using slotted star topology in order to reduce the exploration time. The proposed approach has been tested on IEEE 802.15.4 star networks with various configurations of the number of nodes and their packet rates. Experimental results show that the proposed algorithm reduces the number of simulations required to converge, with respect to state-of-the-art algorithms (e.g., NSGA-II, PMA and MOSA), from 60 to 87%.

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Energy-aware portable video communication system design for wildlife activity monitoring

Cited by 19 publications

References 27 publications

Physiological Stress Response of Captive White‐Tailed Deer to Video Collars

Physiological Stress Response of Captive White‐Tailed Deer to Video Collars

Model-based design for wireless body sensor network nodes

Knowledge-based design space exploration of wireless sensor networks

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