Summary Camera recording and video analysis have emerged as a successful non‐invasive method for collecting a wide range of biological data on many different taxa of animals. However, camera monitoring has rarely been applied to long‐term surveillance of cavity or box‐nesting species and ordinary off‐the‐shelf cameras are employed. We present methodology and data on the effectiveness of nest box monitoring using a camera system embedded in four ‘smart nest boxes’ (SNBoxes). We applied the SNBoxes to eight Tengmalm's owl (Aegolius funereus) nests in the Czech Republic during a 5‐month period in 2014. Each SNBox consisted of a pair of cameras with infrared lighting, an event detector, a radiofrequency identification reader, auxiliary sensors and a 60 Ah 12 V battery to power the whole system. All devices used were centrally managed by an embedded computer with specifically developed software. Using four SNBoxes, we observed owl nesting continually during the incubation, nestling and fledgling phases, in total 309 days, resulting in 3382 owl video events. Batteries were changed every 6·5 days. A memory of 4 GB was found sufficient to store monthly data. We identified 12 types of male and female parental activities and their timing, the diet composition and frequency of prey delivery, the manner of prey storage, the light intensity at the time of each parental activity, the temperature inside the clutch and outside the box and the duration of nestling period of each young. We also produced a video on owl nesting for the general public. The SNBox and related methodology show enormous potential as a non‐invasive tool for monitoring animals using boxes or natural cavities. The main advantage of the SNBox is the possibility to study both nocturnal and diurnal animal species and great flexibility in use of the software and hardware for different tasks. As a result, the SNBox provides an opportunity for novel insights into the breeding, roosting, hibernating, and food storage activities of a wide range of cavity‐living birds, mammals and reptiles.

While networked sensors are becoming a ubiquitous part of many human lives, their applications to the study of wild animals have been largely limited to off-the-shelf and stand-alone technologies such as web cameras. However, purpose-designed systems, applying features found in Internet-of-Things devices, enable more efficient gathering, managing, and disseminating of a diverse array of data needed to study the life histories of wild animals. We illustrate these claims based on our development of a system of networked nest boxes that we created to study nesting birds in urban environments. This system uses general-purpose processors within nest boxes to perform edge computing to control data acquisition, processing, and management from multiple sensors. A central data-management system permits easy access to all data, once downloaded, which has facilitated our uses to date of this system for formal university-and school-level education, and informal science education.

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