Handling Communication Dropouts in an Artificial Hormone and DNA System

Embedded systems are growing very complex because of the increasing chip integration density and larger number of chips in distributed applications and demanding application fields. Bio-inspired techniques like self-organization are a key feature to handle this increasing complexity. The artificial hormone system (AHS) and the artificial DNA (ADNA) are exploiting such principles to enable self-organizing, self-building and self-healing distributed embedded real-time systems. The ADNA represents the building plan for the system stored in each node of the distributed structure. The AHS manages the decentralized allocation of the components (tasks) of the building plan to the nodes. Overall, a flexible and robust system is created. However, the AHS/ADNA system is vulnerable to communication dropouts between the nodes. If such dropouts last long enough, the system structure is disturbed by spurious multiple assignment of tasks. This causes conflicts in the data output of these tasks. This paper presents an approach to recover from such dropouts. We propose two recovery measures to detect such situations and to quickly remove the spurious tasks and to prevent output conflicts. The evaluation shows the effectiveness of these measures at the example of a self-balancing robot vehicle.

Automated summary

Embedded systems are becoming more complex due to increasing chip integration density and the use of bio-inspired techniques like self-organization to handle this complexity. The artificial hormone system (AHS) and artificial DNA (ADNA) are used to enable self-organizing, self-building, and self-healing distributed real-time systems. However, the system is vulnerable to communication dropouts between nodes, causing conflicts in task assignments. Recovery measures are proposed to quickly detect and address these issues, with evaluation on a self-balancing robot vehicle demonstrating their effectiveness.