In the field of automation, reliability is a key aspect to enable resilient systems. Especially, in areas with extreme conditions a reliable monitoring is necessary such as factory, volcano, or laboratory monitoring. These are environments where devices could be stressed uncommonly high and thus more devices could fail in a shorter time period in the worst case. Centralized monitoring systems, which work in real-time for security reasons, contain a single point of failure in the form of a central control instance. Additionally, if the central instance fails no data is available any more as the central instance usually works as the only data sink in the system. Furthermore, with an increasing number of devices this system does not scale well.
As the number of devices and their performance will prospectively increase, a new approach is necessary to handle these large-scale systems. Therefore, in this paper a Peer-to-Peer-based data storage and retrieval system for scenarios with real-time requirements called PSP-Auto is presented, which bases on the Peer-to-Peer network Kad. A prototype has been developed to derive performance results for a large-scale simulation of the PSP-Auto system. The results show a high resilience whereby data recovery can still be ensured at 100 % even if each device fails up to eight times a day.