Sept. 24 — For office and household users, the intermittent unreliability of computer networks is a frustrating inconvenience, but for scientific researchers it can disrupt data pipeline necessary to advance knowledge. To address this problem, Computer Science and Engineering Assistant Professor Engin Arslan is developing network monitoring and management infrastructure for distributed science applications.
He recently received two grants to support his research in the use of network infrastructure for science projects. The National Science Foundation Campus Cyberinfrastructure Office awarded Arslan a 2-year, $998,568 grant to research sensor networks. He also received an additional 3-year grant of $499,982 with collaborators at University at Buffalo to study high performance networks.
Arslan’s first grant is titled “Robust and Predictable Network Infrastructure for Wide-Area Distributed Sensor Networks.”
“This project aims to develop robust network management and monitoring framework for distributed sensor networks,” Arslan said. “The project has potential to accelerate the adoption of Internet of Things (IoT) devices in many science and engineering areas by addressing their networking needs in an automated fashion.”
Arslan said his team’s goal is to work closely with three University-led projects on city transportation, wildfire monitoring and climate observations to develop and demonstrate automated network technologies. The challenges facing wide-area networks include technical issues like efficient data routing as well as early anomaly detection and troubleshooting. Arslan’s research will develop a set of tools to address these issues quickly to keep essential systems online.
The research project will integrate Software-Defined Networking with network monitoring systems which will enhance quality of service for distributed sensor devices. Coupled with efficient streaming of sensor data across wide-area networks is the cybersecurity issue of anomaly detection.
Anomaly detection is the identification of items or information different from the majority information in the network. Distributed network analysis devices will inspect network traffic in real-time to identify anomalous information that could indicate security breaches. Essentially if data coming from sensor devices is out of ordinary, anomaly detection will detect it and take necessary steps to ensure the authenticity and integrity of data.
This research will ultimately increase reliability of partner networks for scientific and engineering purposes such as the ALERTWildfire network led by Dr. Graham Kent in the Nevada Seismological Laboratory, a system of cameras that provide early fire detection across Nevada and neighboring states. Because of the remote locations of the cameras, ALERTWildfire relies on complex wide-area networks to deliver data and observation control to regional wildfire response teams. Given the increased prevalence of wildfires across the West, this project will help systems in wildfire detection and monitoring.
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Source: Sarah Strang, University of Nevada, Reno
