For a wireless sensor network consisting of numerous sensors, spread over a large
area with no direct power supply, energy efficiency is of paramount importance. As
most power is consumed by the communication module, we should pay special attention
to reduce communication needs as much as possible. The more data we send, the larger
the power requirement of the sensor module. Preprocessing can help in reducing the
amount of data to send. However, it also consumes energy. This paper focuses on this
tradeoff between preprocessing, pre-filtering and preselecting of sensor data on one
hand, and uploading of unprocessed and unfiltered raw data on the other hand, for
the special case of protecting vineyards from starlings. We propose a two-phase decision
mechanism based on machine learning: the less complex first phase is executed on the
microcontroller of the sensor module, while the more complex, more accurate second
phase is performed in the cloud. Individual noise sensors monitor the environment,
and try to detect starling songs, using a simple, SVM-based classification. These
sensors are grouped into clusters, through a mechanism similar to the well-known LEACH
protocol, and signal to the current cluster-head the likelihood of starling presence.
If several alerts are received to justify further investigation, the cluster-head
asks the node with highest starling detection likelihood to upload a 1 s sound sample
to the cloud. There, the more complex and more accurate second phase sound matching
is performed, and the actuators deployed in the field are remotely triggered, if needed.
