One who walks in another's tracks leaves no footprints.
I am a Ph.D. candidate in the Department of Computer Science at Columbia University, advised by Prof. Henning Schulzrinne and affiliated with the Internet Real-Time Laboratory. My research interests include internet services and protocols, cyber-physical systems (internet of things), distributed computing, operating systems, and mission-critical communications systems and protocols. I received a Master's degree in Computer Science from the Czech Technical University in Prague and worked at the Fraunhofer Institute for Open Communication Systems (FOKUS) in Berlin, Germany. Previously, I was a co-founder of Iptel.org GmbH, a FOKUS spin-off developing software for internet real-time multimedia services (acquired by Tekelec, now part of Oracle).
Outside work, I enjoy reading, hiking, running, and cross-country skiing. I thru-hiked the John Muir Trail in 2019.
My main research area is network services and protocols for cyber-physical systems, the Internet of Things (IoT), and multi-media network services. My research aims to enable self-managing IoT systems that automatically adapt to a changing environment and make automated decisions based on high-level programs and policies. I am also interested in developing programming abstractions for IoT applications that follow data, i.e., applications deployable to the network edge.
At Columbia, I supervise student projects at the undergraduate and graduate levels. I also occasionally mentor high-school students interested in computer science. Our paper on the design of a wireless networking lab won the Best Educational Paper Award at the Second GENI Educational and Research Workshop in 2013. I helped to design the homework assignments for the Advanced Programming.
With over 20 million units sold since 2015, Amazon Echo, the Alexa-enabled smart speaker developed by Amazon, is probably one of the most widely deployed Internet of Things consumer devices. Despite the very large installed base, surprisingly little is known about the device's network behavior. We modify a first generation Echo device, decrypt its communication with Amazon cloud, and analyze the device pairing, Alexa Voice Service, and drop-in calling protocols. We also describe our methodology and the experimental setup. We find a minor shortcoming in the device pairing protocol and learn that drop-in calls are end-to-end encrypted and based on modern open standards. Overall, we find the Echo to be a well-designed device from the network communication perspective.
When the electrical grid in a region suffers a major outage, e.g., after a catastrophic cyber attack, a "black start" may be required, where the grid is slowly restarted, carefully and incrementally adding generating capacity and demand. To ensure safe and effective black start, the grid control center has to be able to communicate with field personnel and with supervisory control and data acquisition (SCADA) systems. Voice and text communication are particularly critical. As part of the Defense Advanced Research Projects Agency (DARPA) Rapid Attack Detection, Isolation, and Characterization Systems (RADICS) program, we designed, tested and evaluated a self-configuring mesh network prototype called the Phoenix Secure Emergency Network (PhoenixSEN). PhoenixSEN provides a secure drop-in replacement for grid's primary communication networks during black start recovery. The network combines existing and new technologies, can work with a variety of link-layer protocols, emphasizes manageability and auto-configuration, and provides services and applications for coordination of people and devices including voice, text, and SCADA communication. We discuss the architecture of PhoenixSEN and evaluate a prototype on realistic grid infrastructure through a series of DARPA-led exercises.
The COVID-19 pandemic and related restrictions forced many to work, learn, and socialize from home over the internet. There appears to be consensus that internet infrastructure in the developed world handled the resulting traffic surge well. In this paper, we study network measurement data collected by the Federal Communications Commission's Measuring Broadband America program before and during the pandemic in the United States (US). We analyze the data to understand the impact of lockdown orders on the performance of fixed broadband internet infrastructure across the US, and also attempt to correlate internet usage patterns with the changing behavior of users during lockdown. We found the key metrics such as change in data usage to be generally consistent with the literature. Through additional analysis, we found differences between metro and rural areas, changes in weekday, weekend, and hourly internet usage patterns, and indications of network congestion for some users.
The serverless and functions as a service (FaaS) paradigms are currently trending among cloud providers and are now increasingly being applied to the network edge, and to the Internet of Things (IoT) devices. The benefits include reduced latency for communication, less network traffic and increased privacy for data processing. However, there are challenges as IoT devices have limited resources for running multiple simultaneous containerized functions, and also FaaS does not typically support long-running functions. Our implementation utilizes Docker and CRIU for checkpointing and suspending long-running blocking functions. The results show that checkpointing is slightly slower than regular Docker pause, but it saves memory and allows for more long-running functions to be run on an IoT device. Furthermore, the resulting checkpoint files are small, hence they are suitable for live migration and backing up stateful functions, therefore improving availability and reliability of the system.