Modeling and optimization of dynamic signal processing in resource-aware sensor networks

Bhattacharyya, Shuvra S.; Plishker, William; Sane, Nimish; Shen, Chung-Ching; Wu, Hsiang-Huang

doi:10.1109/avss.2011.6027374

Cited by 2 publications

(1 citation statement)

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“…Furthermore, dataflow representations can be applied to guarantee useful implementation properties, such as bounded memory requirements and deadlock-free operation [12]. For more details on the utility of dataflow methods in the context of resource-constrained sensor networks, we refer the reader to [13].…”

Section: Dataflow-based Design Approachmentioning

confidence: 99%

Adaptive tracking of people and vehicles using mobile platforms

Salem

Damarla

Sudusinghe

et al. 2016

EURASIP J. Adv. Signal Process.

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Tracking algorithms have important applications in detection of humans and vehicles for border security and other areas. For large-scale deployment of such algorithms, it is critical to provide methods for their cost-and energy-efficient realization. To this end, commodity mobile devices have significant potential for use as prototyping and testing platforms due to their low cost, widespread availability, and integration of advanced communications, sensing, and processing features. Prototypes developed on mobile platforms can be tested, fine-tuned, and demonstrated in the field and then provide reference implementations for application-specific disposable sensor node implementations that are targeted for deployment. In this paper, we develop a novel, adaptive tracking system that is optimized for energy-efficient, real-time operation on off-the-shelf mobile platforms. Our tracking system applies principles of dynamic data-driven application systems (DDDAS) to periodically monitor system operating characteristics and apply these measurements to dynamically adapt the specific classifier configurations that the system employs. Our resulting adaptive approach enables powerful optimization of trade-offs among energy consumption, real-time performance, and tracking accuracy based on time-varying changes in operational characteristics. Through experiments employing an Android-based tablet platform, we demonstrate the efficiency of our proposed tracking system design for multimode detection of human and vehicle targets.

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Section: Dataflow-based Design Approachmentioning

confidence: 99%