Network for
Anomaly Detection and Incident Management
in IoT Environments

There is a huge amount of different technologies in the IoT field. Often various technologies must be applied to solve a specific use case. A challenging task is to combine and integrate these IoT technologies in a solution. For instance, an suitable incident management system receives data from different IoT sources. The analyses of these IoT sources requires the data integration to have a common data set. Based on this data set, algorithm can find anomalies. For this purpose, the network addresses the area of system integration, particular approaches to integrate different IoT platforms. This field comprises methods, tools and platform in order to integrate various IoT technologies.

The detection of errors or incidents require the observation and analysis of various system parameters. IoT technologies ease the collection of data but the analysis of collected data is a challenging task. The objective of this network is identification of existing mathematical methods and the development of new innovative methods. Currently, there is an enormous need for research in the field of self-configuring analysis techniques using machine-learning techniques.

A detected data anomaly in an IoT environment can be an indicator of a possible incident. The handling of inciddenct can be a complex process including various steps, rules and stakeholders. Modern communication technologies offers new ways to handle and solve incidents. The network addresses this topic and research approaches to describe and handle incidents.

We want to make research for practical problems in order to support real world problems. For this purpose, we are looking for scenarios from industrial partners. We want to apply our solutions in various application domains such as: manufacturing, facilities, cities, agriculture, grids, logistics etc.