This article discusses the experiences from the development and deployment of two image-based environmental monitoring sensor applications using an embedded wireless sensor network. Our system uses low-power image sensors and the Tenet general purpose sensing system for tiered embedded wireless sensor networks. It leverages Tenet's built-in support for reliable delivery of high rate sensing data, scalability and its flexible scripting language, which enables mote-side image compression and the ease of deployment. Our first deployment of a pitfall trap monitoring application at the James San Jacinto Mountain Reserve provided us with insights and lessons learned into the deployment of and compression schemes for these embedded wireless imaging systems. Our three month-long deployment of a bird nest monitoring application resulted in over 100,000 images collected from a 19-camera node network deployed over an area of 0.05 square miles, despite highly variable environmental conditions. Our biologists found the on-line, near-real-time access to images to be useful for obtaining data on answering their biological questions.
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机译：本文讨论了使用嵌入式无线传感器网络开发和部署两个基于图像的环境监控传感器应用程序的经验。我们的系统将低功率图像传感器和Tenet通用感测系统用于分层嵌入式无线传感器网络。它利用Tenet的内置支持来可靠地交付高速率传感数据，可伸缩性及其灵活的脚本语言，从而实现了端侧图像压缩和易于部署。我们在James San Jacinto Mountain Reserve首次部署了陷阱陷阱监视应用程序，这为我们提供了有关这些嵌入式无线成像系统的部署和压缩方案的见解和经验教训。尽管环境条件变化很大，但我们为期三个月的鸟巢监控应用程序的部署仍从19个摄像机节点网络（覆盖0.05平方英里）中收集了100,000多张图像。我们的生物学家发现，对图像的在线，近实时访问对于获取有关回答其生物学问题的数据非常有用。
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