"Leformance adaptation and ortspecific rules of behaviour for a nsustainable IoT sensor technology in the Gilding equipment for the modular Vnetting of Eing units"

HA project no. 802/19-122

Participants

• Prof. Dr Jens-Peter Akelbein, overall project management
• Mario Hoss, Bettina Kurz, Patrick Naili

Project partners

• Rhine-Main University of Applied Sciences Prof. Marcus Thoss
• Thermokon Sensortechnik GmbH
• Merck KgaA
• Entega AG
• Vonovia SE

Duration 09/2019 - 12/2021

Abstract : The project motivation is the trend towards a drastic increase in product service and operating costs resulting from the ever-increasing use of wireless Internet of Things (IoT) sensor technology. Compared to cable solutions, installations in locations with unstable operation or periodic battery changes are seen as causes of higher costs and unsustainable resource consumption. With LONG MOVE, sensor products should behave adaptively depending on their respective conditions at the installation site. In particular, sensors should be able to secure their energy supply autonomously over long periods of time, such as 20 years, through energy harvesting (e.g. using solar cells). Modelling and simulations during product development should make it possible to predict the permanent behaviour over such long periods of time. A database for this is created by measuring the energy input at different locations. Furthermore, energy cost functions in the heterogeneous sensor network are to achieve the possibility of an energy-optimised distribution of tasks.

Project description

The project "LONG MOVE - Performance adaptationand location-basedbehavioural rules for sustainableIoT sensor technology in building equipmentfor modular networkingof units" is intended to consider possibilities for modelling and simulating the behaviour of sensor devices and their energetic behaviour both in terms of energy input and energy consumption, as well as approaches for distributed optimisation of the energy cost factor in the network.

One research approach focuses on the behaviour of the individual sensor device, which is often referred to as a (sensor) node. Methods for modelling the behaviour are used. To analyse the energy input, light sensors are developed for the project that collect data at a wide variety of locations during the project period, which represent the measurement location over time in the form of an energy input curve. By looking at real hardware and the software running on it, a gradually refined model is created that simulates the behaviour of an individual node. In order to be able to change the behaviour, profiles are derived from the simulation that can be validated on the real hardware. By selecting a profile, a node can adapt autonomously to its environment. This results in an adaptation behaviour of a sensor node to the locally available ambient energy, which can also be used on hardware with very limited resources.

The second research approach considers a number of different nodes that are connected to form a network. Here, functions can be assigned to different nodes depending on the availability of energy required for execution. The type of energy source can be evaluated differently or be included as a different cost factor in the decision to localise the function. For this purpose, the local energy model is extended in the form of a distributed energy model for all network participants. In particular, nodes that are permanently supplied with energy, e.g. through a connection to the power grid, can also be used specifically to perform energy-intensive tasks in the network.

By combining both approaches, the LONG MOVE project aims to ensure that the approaches and methods developed can be integrated into a development process for future sensor products. This should lead to a sustainable methodology to develop IoT products not only specifically according to the functions to be fulfilled. Instead, the design and development process should focus on energy-related behaviour as a central product requirement. Transferable approaches are to be developed as best practices in embedded development.

The LONG MOVE project aims to ensure that autonomous sensors for recording measured values can be wirelessly networked at almost any location. This is an essential prerequisite for further digitalisation in various application areas. In particular, the project is looking at innovative solutions for assisting elderly people in their own rented flat, automation in production technology as Industrial IoT (IIoT) and applications for a regional utility company.

The project consortium consists of the Darmstadt and RheinMain universities and Thermokon Sensortechnik GmbH. The associated corporate partners Entega AG, Merck KGaA and Vonovia SE are supporting the project. Possible applications are particularly promoted by the Darmstadt IoT network as a project of the Digital City Darmstadt and the networking of different fields of application in a joint project consortium.

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