Olga Saukh

Assistant Professor at TU Graz, Institute for Technical Informatics
Scientist at Complexity Science Hub Vienna

Research Activities

I am thirsty for knowledge and I always feel incredibly excited when a good solution to a problem comes to my mind. This is the reason I did my Ph.D. and continued with postdoctoral training at ETH Zurich. This page describes several research topics I have been working on and contributed to.

Modeling and Data Mining

Environmental Datasets. I have always been curious about the data and the dependencies, evidences and anomalies they are hiding. In the OpenSense project, our work focused on pushing the limits of today's air quality maps by extending the conventional network of static measurement stations with ten mobile stations. Yearly, we obtain tens of millions of pollution measurements from all over the city. The main challenge is to show that despite using low-cost (less precise, low-resolution, less stable, noisy) gas sensors, we are able to construct high-resolution air quality maps of Zurich. We explored various dependencies between measured pollutant concentrations and many freely-available land-use datasets for Zurich such as traffic distribution, elevation, population density, etc.. A land-use regression model is now applied to construct high-resolution air quality maps for a desired temporal resolution. Spatial resolution is as good as the resolution of available land-used data. This work received much attention in the research community (including engineers, environmental scientists, and health specialists) and local media.

  • Our PerCom'14 paper on pushing the limits of air-quality models got the best paper award and an extended version was published in the PMC journal'15.
  • Seasonal maps for 2014 are available on the OpenSense Zurich deployment page.
  • An iPhone app, developed by David Hasenfratz, computes a healthier route between any two locations using the constructed pollution maps.

Political Datasets. Even though I am not actively following and participating in politics I have developed a very strong interest in the way elections are held and how they can be influenced. Main driver behind this are recent developments in my home country Ukraine which made me wonder how election data can be used to predict the fairness and accuracy of elections according to democratic principles. Around two years ago, I started learning and implementing forensic indicators of election fraud and testing them on publicly available Ukrainian, Russian, Swiss and German datasets. This work has been done in my private time and is not in relation to my activities at ETH Zurich, hence no official publications are available. Amongst many sources of information I can recommend the following book for interested readers:

Calibrating Mobile Sensors

Today, calibrating a network of static sensors is well understood. Each sensor has to be calibrated separately. Parallel calibration of sensors is only possible if a value of the signal of interest is the same at both sensor locations. Calibrating a network of mobile sensors presents new calibration opportunities: when two sensors rendezvous, they share the same location in time and in space, and are thus exposed to the same signal value. We use sensor measurements at rendezvous to improve sensor calibration and to detect transient and permanent sensor faults.

Detecting Network Boundaries

Consider a network of communicating devices deployed somewhere in an unknown or unobserved environment. Given solely a network's communication graph, how can one determine holes in the deployment and the network's outer boundary? It turns out that even suggesting a meaningful mathematically sound definition of network boundaries is challenging. Our contribution to boundary recognition literature is an accurate boundary definition and an algorithm which determines provably inner nodes of the network and proclaims all other nodes to be boundary nodes. Compared to state-of-the-art, the developed approach is scalable, range-free, and does not require high network density to compute a reasonable network boundary.

Data Routing

The first research topic I started working on as a Ph.D. student was energy-efficient routing in sensor networks. The proposed optimization leveraged the observation that a routing path is symmetric from the data yield prospective, but asymmetric from the energy consumption prospective. The latter implies that if a packet has to be lost, it is better to loose it as early as possible. This statement is somewhat counter-intuitive when thinking about getting the packets through the network, but makes more sense when optimizing energy-efficiency defined as the price for data delivery.

As a postdoc at ETH Zurich, I had the pleasure of working together with Federico Ferrari and Marco Zimmerling on a communication primitive called Glossy, which proposes a radically different approach to routing in wireless networks. Glossy is fast, energy-efficient and achieves remarkable data yields. It laid down the ground for a communication protocol stack LWB and further concepts on top of it that provide low-level guarantees. This work is a huge step forward for numerous control applications that wish to leverage wireless communication.

  • My first paper on energy-efficient routing metric presented at EWSN'06.
  • An IPSN'11 paper on efficient network flooding and time synchronization with Glossy.

Research Community Involvement

2018: IMWUT (Associate editor), DCOSS'18 (TPC member)

2017: IMWUT (Associate editor), ACM TOSN (External reviewer), FailSafe'17 (TPC member), SenseApp'17 (TPC member), DCOSS'17 (TPC member)

2016: DCOSS'16 (TPC member), EWSN'16 (TPC member, session chair)

2015: SenseApp'15 (TPC member), DCOSS'15 (TPC member), ICDCS'15 (TPC member)

2014: DCOSS'14 (TPC member)

2013: REALWSN'13 (TPC member), SENSORCOMM'13 (TPC member), IEEE/ACM IPSN'13 (TPC member IP track, Ph.D. Forum PC member)

2012: CPSCOM'12 (TPC member), SENSORCOMM'12 (TPC member), EUC'12 (TPC member), PerSeNS'12 (session chair)