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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. I. INTRODUCTIONT HE NUMBER of solutions that provide Artificial Intelligence (AI) and Machine Learning (ML) based systems has been growing recently. These solutions facilitate the creation of new smart products and services in many different fields. In addition, sensor networks are undergoing great expansion and development and the integration of AI and sensor networks benefits many areas such as Industry 4.0, healthcare, mobility, logistics, and many other Internetof-Things (IoT) applications. However, this has also put new challenges in front of researchers and practitioners. New realtime AI and ML algorithms are needed along with different strategies to embed these algorithms in sensor boards and network nodes such as fog/edge nodes. For example, edgebased AI requires robust and adaptive models that take into account the temporal component of a data flow and allow for vertical and horizontal scaling of the decision-making process. These models must employ efficient learning algorithms that are capable of dealing with information varying over time and coping with large scale missing and inaccurate values. In addition, the decision-making models should be composable so that they can be distributed on the edge devices in order to This work is part of the Sony RAP 2020 Project "Distributed and Adaptive Edge-based AI Models for Sensor Networks"1 Daisen is a volcanic mountain located in Tottori Prefecture, Sanin Region of Japan. ensure a trade-off between the decision accuracy, latency, and consumed energy per decision.The IoT is an emerging key technology for future industries and the everyday lives of people, e.g., it has been playing an increasingly important role in healthcare, agriculture, home services, industrial processes, and transportation. Wireless Sensor Network (WSN) is an enabling technology for IoT [1], and, by definition, is the bridge between the physical world and the intelligence residing on the Internet. The integration of AI and sensor networks (by means of the IoT) are now realities that are changing our lives. Sensor networks are widely used to collect environmental parameters, e.g., in homes, buildings, and vehicles, where they a...

In smart buildings, many different systems work in coordination to accomplish their tasks. In this process, the sensors associated with these systems collect large amounts of data generated in a streaming fashion, which is prone to concept drift. Such data are heterogeneous due to the wide range of sensors collecting information about different characteristics of the monitored systems. All these make the monitoring task very challenging. Traditional clustering algorithms are not well equipped to address the mentioned challenges. In this work, we study the use of MV Multi-Instance Clustering algorithm for multi-view analysis and mining of smart building systems’ sensor data. It is demonstrated how this algorithm can be used to perform contextual as well as integrated analysis of the systems. Various scenarios in which the algorithm can be used to analyze the data generated by the systems of a smart building are examined and discussed in this study. In addition, it is also shown how the extracted knowledge can be visualized to detect trends in the systems’ behavior and how it can aid domain experts in the systems’ maintenance. In the experiments conducted, the proposed approach was able to successfully detect the deviating behaviors known to have previously occurred and was also able to identify some new deviations during the monitored period. Based on the results obtained from the experiments, it can be concluded that the proposed algorithm has the ability to be used for monitoring, analysis, and detecting deviating behaviors of the systems in a smart building domain.