## 2 Related Work  
While PIV is one of the most important measurement methods in fluid dynamics [10; 11], it has also been used to measure and characterize insect flight patterns and topologies, because flying insects leave behind air motions that can be captured in regular or digital photos. PIV analysis of flight patterns improves our understanding of the insects' behavior and ecology. Several research groups have used PIV to investigate insect flight. [PERSON] et al. [12] applied PIV to measure fluid velocities of a fruit fly and to determine the contribution of the leading-edge vortex to overall force production. [PERSON] et al. [13] performed a detailed PIV analysis of the flow field around the wings of the tobacco hawkmoth (_Manduca sexta_) in a wind tunnel. Flow separation was associated with a saddle between the wing bases on the surface of the hawkmoth's thorax. [PERSON] [14] used PIV to examine and analyze sound and air flows generated by dancing honeybees. PIV was used to map the air flows around wagging bees. It was discovered that the movement of the bee's body is followed by a thin (1-2 mm) boundary layer and other air flows lag behind the body motion and are insufficient to fill the volume left by the body or remove the air from the space occupied by the bee body. The PIV analysis showed that flows collide and lead to the formation of short-lived eddies. Several video-based electronic beehive monitoring (EBM) projects are related to our research. [PERSON] et al. [15] proposed a system that uses trajectory tracking to distinguish entering and leaving bees, which works reliably under smaller bee traffic levels. The dataset used by the researchers to evaluate their system consists of 100 fully annotated frames, where each frame contains 6-14 honeybees. [PERSON] et al. [16] proposed a system for pollen bearing honeybee detection in videos obtained at the entrance of a hive. The system's hardware includes a specially designed wooden box with a naspberry pi camera module inside. The box is mounted on the front side of a standard hive above the hive entrance. A glass plate is placed on the bottom side of the box, 2 cm above the flight board, which forces the bees entering or leaving the hive to crawl a distance of \(\approx\)11 cm. Consequently, the bees in the field of view of the camera cannot fly. The training dataset contained 50 images of pollen bearing honeybees and 50 images of honeybees without pollen. The test dataset consisted of 354 honeybee images. The research reported in this article has been influenced and inspired by our ongoing research and development work on the mobile instructional particle image velocimetry (mI-PIV) system [17; 18]. The system uses a smartphone's internal camera for image acquisition and performs PIV analysis either on a web server or directly on the smartphone running Android using both open-source and our own PIV algorithms. We have been investigating the effects of different PIV parameters on the accuracy of the mI-PIV system. This system is an innovative stepping stone in making PIV less expensive and more widely available, particularly for application within educational settings. Our main design objective is to achieve convenience and safety without sacrificing too much accuracy so that the system is available not only to physics, engineering, and computer science university majors and professionals but also to high school students who have never had any exposure to PIV due to the expensive nature of commercial PIV systems. We have designed and completed various experiments to benchmark the mI-PIV system as part of our iterative design-based research process.