A Visual Sensor Network (VSN) is a network of spatially distributed cameras. The primary difference between VSN and
other type of sensor networks is the nature and volume of information. A VSN generally consists of cameras,
communication, storage and central computer, where image data from multiple cameras is processed and fused. In this
paper, we use optimization techniques to reduce the cost as derived by a model of a VSN to track large birds, such as
Golden Eagle, in the sky. The core idea is to divide a given monitoring range of altitudes into a number of sub-ranges of
altitudes. The sub-ranges of altitudes are monitored by individual VSNs, VSN1 monitors lower range, VSN2 monitors
next higher and so on, such that a minimum cost is used to monitor a given area. The VSNs may use similar or different
types of cameras but different optical components, thus, forming a heterogeneous network. We have calculated the cost
required to cover a given area by considering an altitudes range as single element and also by dividing it into sub-ranges.
To cover a given area with given altitudes range, with a single VSN requires 694 camera nodes in comparison to
dividing this range into sub-ranges of altitudes, which requires only 88 nodes, which is 87% reduction in the cost.