Lazy Estimation in Networked Systems

由电池驱动、分布广泛的设备提供的传感器数据量正在稳步增加。由于传感器数据通常被送入信息处理单元，因此值得考虑如何利用信息处理本身来减少通信和能源需求。为此目的，本项目侧重于信息处理技术，这些技术可以纳入由传输机制传达的隐含信息。尽管传感器节点决定不发送其数据，但接收器仍然可以利用数据的缺失来更新其状态估计。例如，可以将传感器读数与阈值进行比较，以决定传输。接收方可以将该判定规则转换为关于数据的信息，尽管没有发生传输。发送方和接收方可以协商这样的决策规则，以便最小化发送端的通信成本，并最大化接收端的可检索信息。由于基于阈值的策略对于观察到的时变系统来说过于严格，因此将研究基于模型和数据驱动的策略。该项目主要研究触发传输的随机决策规则。与确定性触发机制相比，随机机制可以保持隐含信息的高斯性，从而简化了接收方的估计器设计。例如，当没有传输事件被触发时，卡尔曼滤波器只需要较小的调整来合并隐含信息。本项目的目标是将随机触发原理向前推进，以建立一个全面的懒惰估计框架。首先，研究了懒惰状态估计的一般性质和智能触发判决的设计，以提高懒惰状态估计的有效性和稳健性。其中包括基于模型和数据驱动的触发机制、非周期和异步传输和处理时间，以及对不可靠通信链路的研究。这些结果为多传感器系统和高维状态表示的大规模延迟估计提供了基础。例如，多个系统协作监控一个动态系统，并融合交换的传感器数据和估计。这样的分布式数据融合问题导致了需要自适应触发机制的相关触发决策。特别是，该项目考虑了在目标跟踪中的应用，以评估派生的概念。延迟估计在处理神经形态传感器数据和实现状态保密方法方面显示出巨大的潜力。这两个方向都被作为懒惰估计的应用领域进行了研究。

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.