A fresh perspective

Faulkner, Matthew; Liu, Annie H.; Krause, Andreas

doi:10.1145/2461381.2461387

Cited by 8 publications

(3 citation statements)

References 14 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Existing works have proposed methods to denoise sensor data or adapt the model to noisy measurements [13,27]. The majority of these strategies, however, is custom-tailored to specific domains of application, such as COTS WiFi-based motion sensing [65], image data processing under various weather conditions [7], speech recognition [29], and clinical data analysis [40].…”

Section: Denoising Methodsmentioning

confidence: 99%

Physics-Informed Data Denoising for Real-Life Sensing Systems

Zhang,

Fu,

Teng

et al. 2023

Proceedings of the 21st ACM Conference on Embedded Networked Sensor Systems

View full text Add to dashboard Cite

Sensors measuring real-life physical processes are ubiquitous in today's interconnected world. These sensors inherently bear noise that often adversely affects performance and reliability of the systems they support. Classic filtering-based approaches introduce strong assumptions on the time or frequency characteristics of sensory measurements, while learning-based denoising approaches typically rely on using ground truth clean data to train a denoising model, which is often challenging or prohibitive to obtain for many real-world applications. We observe that in many scenarios, the relationships between different sensor measurements (e.g., location and acceleration) are analytically described by laws of physics (e.g., second-order differential equation). By incorporating such physics constraints, we can guide the denoising process to improve even in the absence of ground truth data. In light of this, we design a physics-informed denoising model that leverages the inherent algebraic relationships between different measurements governed by the underlying physics. By obviating the need for ground truth clean data, our method offers a practical denoising solution for realworld applications. We conducted experiments in various domains, including inertial navigation, CO 2 monitoring, and HVAC control, and achieved state-of-the-art performance compared with existing denoising methods. Our method can denoise data in real time (4ms for a sequence of 1s) for low-cost noisy sensors and produces results that closely align with those from high-precision, high-cost alternatives, leading to an efficient, cost-effective approach for more accurate sensor-based systems.

show abstract

Section: Denoising Methodsmentioning

confidence: 99%

Physics-Informed Data Denoising for Real-Life Sensing Systems

Zhang,

Fu,

Teng

et al. 2023

Proceedings of the 21st ACM Conference on Embedded Networked Sensor Systems

View full text Add to dashboard Cite

show abstract

“…This has the advantage over solutions with a single classifier or global threshold (e.g. [FLK13,BMHH09]) that the system can capture local differences in the sensor readings, e.g. environmental conditions.…”

Section: Background Literaturementioning

confidence: 99%

“…Detection might be noisy i.e. flipped with a certain probability [FLK13]. In this work the network is assumed to be reliable, so detection labels are always correct.…”

Section: Background Literaturementioning

confidence: 99%

PriMaL: A Privacy-Preserving Machine Learning Method for Event Detection in Distributed Sensor Networks

Bennati¹,

Jonker²

2017

Preprint

View full text Add to dashboard Cite

This paper introduces PriMaL, a general PRIvacy-preserving MAchine-Learning method for reducing the privacy cost of information transmitted through a network. Distributed sensor networks are often used for automated classification and detection of abnormal events in high-stakes situations, e.g. fire in buildings, earthquakes, or crowd disasters. Such networks might transmit privacy-sensitive information, e.g. GPS location of smartphones, which might be disclosed if the network is compromised. Privacy concerns might slow down the adoption of the technology, in particular in the scenario of social sensing where participation is voluntary, thus solutions are needed which improve privacy without compromising on the event detection accuracy.PriMaL is implemented as a machine-learning layer that works on top of an existing event detection algorithm. Experiments are run in a general simulation framework, for several network topologies and parameter values. The privacy footprint of state-of-the-art event detection algorithms is compared within the proposed framework. Results show that PriMaL is able to reduce the privacy cost of a distributed event detection algorithm below that of the corresponding centralized algorithm, within the bounds of some assumptions about the protocol. Moreover the performance of the distributed algorithm is not statistically worse than that of the centralized algorithm.

show abstract

Cybersecurity – Kleiner Katalog der Cyberrisiken

Gaycken

2015

Handbuch Sicherheitsgefahren

View full text Add to dashboard Cite

A fresh perspective

Cited by 8 publications

References 14 publications

Physics-Informed Data Denoising for Real-Life Sensing Systems

Physics-Informed Data Denoising for Real-Life Sensing Systems

PriMaL: A Privacy-Preserving Machine Learning Method for Event Detection in Distributed Sensor Networks

Cybersecurity – Kleiner Katalog der Cyberrisiken

Contact Info

Product

Resources

About