This position paper describes the aims and preliminary results of the Distributed and Adaptive Edge-based AI Models for Sensor Networks (DAISeN) 1 project.The project ambition is to address today's edge AI challenges by developing advanced AI techniques that model knowledge from the sensor network and the environment to support the deployment of sustainable AI applications.We present one of the use cases being considered in DAISeN and review the state-of-the-art in three research domains related to the use case presented and directly falling into the project scope.We additionally outline the main challenges identified in each domain.The developed Global Navigation Satellite Systems (GNSS) activation model addressing the use case challenges is also briefly introduced.The future research studies planned for the remaining period of the project are finally outlined.

Automatic classification of environmental sounds, such as dog barking and glass breaking, is becoming increasingly interesting, especially for mobile devices. Most mobile devices contain both cameras and microphones, and companies that develop mobile devices would like to provide functionality for classifying both videos/images and sounds. In order to reduce the development costs one would like to use the same technology for both of these classification tasks. One way of achieving this is to represent environmental sounds as images, and use an image classification neural network when classifying images as well as sounds. In this paper we consider the classification accuracy for different image representations (Spectrogram, MFCC, and CRP) of environmental sounds. We evaluate the accuracy for environmental sounds in three publicly available datasets, using two well-known convolutional deep neural networks for image recognition (AlexNet and GoogLeNet). Our experiments show that we obtain good classification accuracy for the three datasets.