High performance communication networks are omnipresent and readily available in our everyday life, yet their deployment in Cyber-Physical-Systems for Industry 4.0 faces significant challenges. Although modern Information and Communication Technologies are versatile and support a wide variety of setups, extreme application-specific requirements called for by Cyber-Physical-Systems may still be out of scope. As such, the Institute of Communication Networks are designing novel and optimizing existing communication protocols while focusing on the performance requirements of emerging Networked Control Systems. Our research enables safe and reliable operation of emerging, interconnected control applications.

Examples for Cyber-Physical-Systems in which the communication network is an integral part are plentiful.
For example, in distributed air traffic control for UAVs [(VEREDUS Project 2021-2024, funded by BMWi)](https://www.tuhh.de/et6/research/projects/veredus.html), path-planing and collision avoidance services are based on positional information of UAVs in close vicinity. Since these UAVs are moving, position estimates based on previously received position updates lose accuracy over time and new updates have to be received frequently as well as with low delay.
Similarly, in [high-way platooning scenarios](https://www.tuhh.de/et6/research/projects/platooning.html), autonomously driving trucks may form a platoon to increase their fuel efficiency. Since the fuel savings are correlated to the spacing between trucks, the cruise-control of a platoon member needs up-to-date information about velocity and acceleration of its preceding platoon member.
Finally, in future smart grids [(OUREL Project 2020-2023, funded by DFG)](https://www.tuhh.de/et6/research/projects/ourel.html), new technologies such as photovoltaic plants, heat pumps or electric vehicle chargers need to be tightly coordinated to ensure safe and stable grid operation.
Since the factors influencing the control of these components are time-varying (e.g., solar irradiance, ambient temperature, battery level, ...) optimal power flow can only be achieved when information at the grid controllers is as representative as possible.

In all of these scenarios, the task of the communication network is to provide periodic and recent information on time-varying information to a large quantity of monitor nodes. Such information-freshness problems are captured by the novel Age of Information performance metric for communication networks. The Age of Information is defined as the elapsed time since the creation of the most recently received status update and combines the well established update rate and delay metrics.

Age of Information as a function of time. After the sample generation, inflight information ages linearly in the network until reception. Between subsequent receive events, information ages in the buffer of the monitor node. The time between subsequent transmissions is denoted as update interval.

Depending on the application scenario, the physical processing occurring at source nodes may differ significantly and the requirements on adequate communication network performance will differ as well.
However, general analysis can still be conducted by considering representative signals.
The following figure shows the tracking of a Wiener process occurring at a source node by a single monitor node.
Since the signal state is only truly known at the time of sampling, the signal reconstruction at the monitor exhibits uncertainty about the signal state.
To improve performance and accuracy of the signal estimation, more frequent updates which experience low delays is key.