Lazy Estimation in Networked Systems

• Applicant: Professor Dr.-Ing. Benjamin Noack
• Subject Area: Automation, Mechatronics, Control Systems, Intelligent Technical Systems, Robotics
• Term: since 2023
• Project identifier: Deutsche Forschungsgemeinschaft (DFG) - Project number 515674308

The amount of sensor data provided by battery-driven, widely distributed devices is steadily increasing. Since sensor data are typically fed into information processing units, it is worth considering how information processing itself can be exploited to reduce communication and energy demands. For this purpose, this project focuses on information-processing techniques that can incorporate implicit information conveyed by the transmission mechanism. Although a sensor node decides not to send its data, the receiver can still leverage the absence of data to update its state estimates. For instance, sensor readings can be compared against a threshold to decide for a transmission. The receiver can translate this decision rule into information about the data although no transmission took place. Sender and receiver can negotiate such decision rules in order to minimize communication costs, on the transmitting end, and to maximize the retrievable information, on the receiving end. Since threshold-based strategies are far too restrictive for time-varying systems being observed, model-based and data-driven policies will be investigated. This project primarily investigates stochastic decision rules to trigger transmissions. In contrast to deterministic triggers, stochastic mechanisms can preserve the Gaussianity of the implicit information simplifying the estimator design at the receiver. For instance, a Kalman filter only requires minor adaptions to incorporate implicit information when no transmission event is triggered. The goal of this project is to push the principles of stochastic triggering forward to establish a comprehensive framework of lazy estimation. First, the investigations are concerned with general properties and the design of intelligent trigger decisions to improve the effectiveness and robustness of lazy state estimation. These include model-based and data-driven trigger mechanisms, aperiodic and asynchronous transmission and processing times, as well as the study of unreliable communication links. The results provide the foundations for large-scale lazy estimation with respect to both multisensor systems and high-dimensional state representations. For instance, multiple systems collaboratively monitor a dynamic system and fuse exchanged sensor data and estimates. Such distributed data fusion problems lead to dependent trigger decisions that require self-adapting trigger mechanisms. In particular, the project considers applications in object tracking to evaluate the derived concepts. Lazy estimation shows great potential in the processing of neuromorphic sensor data and in implementing state secrecy methods. Both directions are studied as prospective fields of application of lazy estimation.

• DFG Programme: Research Grants