Multimodal species identification in wireless sensor networks

This paper deals with a multimodal approach to identifying species in a Versatile Service-Oriented Wireless Mesh Sensor Network. This type of network is distinguished by the presence of heterogeneous networks, which may posses low storage capabilities. Hence, an optimal multimodal classifier is introduced, which employs audio and image features to enhance its performance in noisy environments. The classifier is a neural network which is evolved with an evolutionary algorithm. Results demonstrate that the classifier can achieve high performance, which is not degraded as it scales to classifying more classes.