IoT technology is a key driver for building smart city infrastructure. The potentials for urban management problems requiring process control and allocation mechanisms has long been acknowledged. However, up to now the potentials of equipping urban objects with sensors, information processing capability, and actuators to enable adaptation to pedestrians' individual needs have not yet been exploited. The objective of our research is to design smart urban objects that enhance usability and safety of the urban space for pedestrians. We report on our conceptual design for an IoT platform that connects the novel smart urban object adaptive park bench with an adaptive light system to actively support pedestrians in the urban environment, in particular senior citizens with handicaps.

In this article we present an approach to an adaptive lighting system as an intelligent object supporting urban space, especially for the elderly. This intelligent lighting system is used as an instrument to improve the feeling of safety in everyday life by overcoming barriers such as dark areas at night. The intelligence of this system is based on a personalized and position-dependent adaptation of light, whereby intensity and color can be varied. This article focuses on the technical implementation of a corresponding lighting system. In this context, the main point of emphasis is the overall architecture, especially from the point of view of an application system.

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The irreversible process of demographic change, especially in Germany, leads to numerous challenges. According to this, research has to face the task to integrate the constantly ageing population into the urban and public space in such a way that there are as few barriers as possible. With the support of digitalization, so-called smart urban objects are being designed in order to do make integration, so that people and the available technology can be used most efficiently. A special ontology has been developed to meet this demand.

Energy efficiency in mobile health applications is a relevant problem for long-term monitoring and user acceptance. Various parameters influence the runtime of the system to some degree. One of the parameters is the sampling rate of the individual distributed sensors. Increasing the sampling rate can lead to an increase in energy consumption within the system. By contrast, a reduction can lead to a loss of the data quality, which reduces the informative value of the results of algorithms that use this data. Using optimization methods from reinforcement learning and deep learning to adaptive adjust the sampling rates during runtime, energy efficiency could be improved in only 40 training runs without losing data quality during sampling.