## 1 Introduction  
Avian ecologists study the behavior of birds during the nesting season to answer biological questions that relate to the laying, incubation and hatching of eggs and the fledging and survival of nestlings. Studies on the breeding biology of species of birds that normally build their nests in natural holes in trees are part of the research effort that has been taking place for several years at the James San Jacinto Mountains Reserve [1]. For such species, researchers often place wooden boxes, called nestboxes, on trees that birds can use for nesting, because they allow the researcher to view the contents of the nest more readily compared to a cavity within a tree. Nestboxes have been placed around the reserve and have been occupied by breeding birds. However, observing the day-to-day changes of breeding behavior in these boxes is extremely labor intensive. To answer certain kinds of scientific questions, the interior of each box must be checked daily by a biologist in the field or it must be wired with a permanent camera for remote observation. Thirteen of the boxes at the Reserve have indeed been wired to give high quality and high resolution images, but they have limitations. The number of wired boxes is low, as they are restricted to locations near sources of power and Ethernet. Furthermore, in biological studies using nestboxes, it can be hard to predict which nestboxes will be occupied by birds in each year prior to the initiation of the bird nesting season; therefore, a system that relies on wired connections to nestboxes has the disadvantage of being more difficult to relocate when an individual nestbox that has a buried wired connection is found to be unoccupied in a given year. Similar challenges exist for studying the biology of lizards and amphibians using pitfall trap arrays at James Reserve. To address these issues, we needed an imaging system that can be deployed easily and moved flexibly over a large area at places where wired infrastructures are less available. The system should support a large number of cameras and should operate for long periods of time without excessive maintenance. Furthermore, it needs to be easy to run and re-configure. In this article, we devised and implemented a low-power, scalable, wireless imaging system using off-the-shelf hardware and Tenet, a readily-available open-source software package for programming tiered wireless sensor networks [2]. We also discuss two real-world deployments of image-based environmental monitoring sensor application using this wireless sensor network system; one for pitfall trap monitoring and another for bird nest monitoring, both deployed at James Reserve. Low-power sensor networks are not a new theme, and there already exist various systems that can take environmental measurements and return them in near real time. The work most close to ours is [3], which reports a similar deployment for the bird nests at James Reserve, but does not provide a fully end-to-end system, nor does it provide multihop capability, reliability, congestion control and many other features explained in the following sections. [PERSON] _et al._[4] reported a complete end-to-end wireless sensor network system for swift bird farm monitoring using a similar more platform to ours, but their sensors were mainly temperature and humidity and did not include a camera for image-basedmonitoring. [PERSON] _et al._[5] presented a WLAN-based camera sensor network for vineyard monitoring, and [PERSON] _et al._[6] studied an agricultural environment monitoring server system using a wireless sensor network. Furthermore, [PERSON] _et al._[7] analyzed a large-scale habitat monitoring application using wireless sensor platforms similar to ours, and [PERSON] _et al._[8] discussed a communication and data aggregation architecture for WSN-based habitat monitoring; however, neither used camera sensors. However, there are few such systems for low-power high data rate retrieval of images, which are also configurable and usable by those with little knowledge of embedded programming.