Smart system design involves multiple uncertainty sources that significantly impact performance. Traditional deterministic design approaches cannot adequately address these uncertainties. This work is innovative because it provides systematic methodology using hierarchical Bayesian networks to propagate and analyze multiple uncertainty sources.
This paper develops a model-based framework for uncertainty quantification in smart systems using dynamic Bayesian networks. The work addresses sensor uncertainty, hardware resource constraints, and system-level effects on performance metrics. The approach enables design exploration under uncertainty through probabilistic inference and Monte Carlo analysis.
The paper demonstrates uncertainty quantification for smart indoor heating systems, analyzing how sensor uncertainty and communication delays affect comfort and energy performance. Results show the methodology enables exploration of design alternatives and identification of robust configurations.
@inproceedings{Nannapaneni2017,
author = {Nannapaneni, Saideep and Dubey, Abhishek and Mahadevan, Sankaran},
booktitle = {2017 {IEEE} SmartWorld},
title = {Performance evaluation of smart systems under uncertainty},
year = {2017},
acceptance = {28},
pages = {1--8},
abstract = {This paper develops a model-based framework for the quantification and propagation of multiple uncertainty sources affecting the performance of a smart system. A smart system, in general, performs sensing, control and actuation for proper functioning of a physical subsystem (also referred to as a plant). With strong feedback coupling between several subsystems, the uncertainty in the quantities of interest (QoI) amplifies over time. The coupling in a generic smart system occurs at two levels: (1) coupling between individual subsystems (plant, cyber, actuation, sensors), and (2) coupling between nodes in a distributed computational subsystem. In this paper, a coupled smart system is decoupled and considered as a feed-forward system over time and modeled using a two-level Dynamic Bayesian Network (DBN), one at each level of coupling (between subsystems and between nodes). A DBN can aggregate uncertainty from multiple sources within a time step and across time steps. The DBN associated with a smart system can be learned using available system models, physics models and data. The proposed methodology is demonstrated for the design of a smart indoor heating system (identification of sensors and a wireless network) within cost constraints that enables room-by-room temperature control. We observe that sensor uncertainty has a higher impact on the performance of the heating system compared to the uncertainty in the wireless network.},
bibsource = {dblp computer science bibliography, https://dblp.org},
biburl = {https://dblp.org/rec/bib/conf/uic/NannapaneniDM17},
category = {selectiveconference},
contribution = {colab},
doi = {10.1109/UIC-ATC.2017.8397430},
file = {:Nannapaneni2017-Performance_evaluation_of_smart_systems_under_uncertainty.pdf:PDF},
keywords = {uncertainty quantification, Bayesian networks, smart systems, performance evaluation, probabilistic inference},
project = {cps-reliability},
tag = {platform},
timestamp = {Wed, 16 Oct 2019 14:14:50 +0200},
url = {https://doi.org/10.1109/UIC-ATC.2017.8397430}
}