00 Informatik, Wissen, Systeme
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Online Learning algorithms and Indoor Positioning Systems are complex applications in the environment of cyber-physical systems. These distributed systems are created by networking intelligent machines and autonomous robots on the Internet of Things using embedded systems that enable the exchange of information at any time. This information is processed by Machine Learning algorithms to make decisions about current developments in production or to influence logistics processes for optimization purposes. In this article, we present and categorize the further development of the prototype of a novel Indoor Positioning System, which constantly adapts its knowledge to the conditions of its environment with the help of Online Learning. Here, we apply Online Learning algorithms in the field of sound-based indoor localization with low-cost hardware and demonstrate the improvement of the system over its predecessor and its adaptability for different applications in an experimental case study.
Containerization is one of the most important topics for modern data centers and web developers. Since the number of containers on one- and multi-node systems is growing, knowledge about the energy consumption behavior of single web-service containers is essential in order to save energy and, of course, money. In this article, we are going to show how the energy consumption behavior of single containerized web services/web apps changes while creating replicas of the service in order to scale and balance the web service.