Computer science collaboration leads to improvements in data transmission in disasters Posted By: Ryan Owens on Monday, June 13th, 2016 Prasad Calyam, Brittany Morago, Rengarajan Pelapur and Kannappan Palaniappan were part of a team that demonstrated a novel cloud resource management architecture for processing the wealth of needed data in disaster situations. Photo by Hannah Sturtecky. The ability to process a massive amount of visual data quickly and efficiently is critical in a disaster scenario, where a few minutes can be the difference between life and death. A group of researchers from the MU Computer Science Department teamed up to develop a visual cloud computing architecture to streamline the process. Previous Next
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Computer science collaboration leads to improvementsin data transmission in disasters
Posted By: Ryan Owens on Monday, June 13th, 2016
Prasad Calyam, Brittany Morago, Rengarajan Pelapur and Kannappan Palaniappan were part of a teamthat demonstrated a novel cloud resource management architecture for processing the wealth of neededdata in disaster situations. Photo by Hannah Sturtecky.
The ability to process a massive amount of visual data quickly and efficiently is critical ina disaster scenario, where a few minutes can be the difference between life and death.A group of researchers from the MU Computer Science Department teamed up todevelop a visual cloud computing architecture to streamline the process.
“Incidentsupporting visual cloud computing utilizing softwaredefined networking”recently appeared in the journal IEEE Transactions on Circuits and Systems for VideoTechnology in a special issue on cloud computing for mobile devices. AssistantProfessor Prasad Calyam, Associate Professor Kannappan Palaniappan, AssociateProfessor Ye Duan and their respective labs collaborated on the study, whichdemonstrated a novel cloud resource management architecture for processing thewealth of needed data in disaster situations. Guna Seetharaman of the U.S. NavalResearch Laboratory also was a coauthor on the study.
The premise of the study is that in disaster scenarios, the abundance of security andcivilian cameras, personal mobile devices with cameras and sensors, as well as aerialvideo from planes or unmanned aerial vehicles (UAVs) provide useful data for firstresponders and law enforcement. That data can be critical in terms of knowing where tosend emergency personnel and resources, contain hazardous materials, as well astracking suspects in manmade disasters. Rescue crews may also require 3Dreconstructions of the area, creating even more data.
This abundance of visual data, especially highresolution video streams, is difficult toprocess even under normal circumstances. But in a disaster situation, the computingand networking resources needed to process it may not be available at the desiredcapacity in the general vicinity of the disaster. So the question then becomes what is themost efficient way to process the most necessary data and how to quickly present themost relevant visual situational awareness for first responders and law enforcement?
The research team proposed a collection, computation and consumption architecture,linking devices at the networkedge of the cloud processing system, or “fog” withscalable computation and big data in the core of the cloud. Visual information flows fromthe collection fog — the disaster site — to the cloud and finally to the consumption fog—the devices being used by first responders’, emergency personnel and lawenforcement. The system works similar to the way mobile cloud services are providedby Apple, Amazon, Google, Facebook and others.
“It works just like we do now with Siri,” Palaniappan said. “You just say, ‘Find me a pizzaplace.’ What happens is the voice signal goes to Apple’s cloud, processes theinformation and sends it back to you. Currently we can’t do the same with rich visualdata because the communication bandwidth requirements may be too high or thenetwork infrastructure may be down [in a disaster situation].”
The workflow of visual data processing is only one part of the equation, however. Indisaster scenarios, the amount of data generated could create a bottleneck in thenetwork.
“The problem really is networking,” Calyam said. “How do you connect back into thecloud and make decisions because the infrastructure as we know it will not be thesame. No street signs, no network, and with cell phones, everybody’s calling to saythey’re OK on the same channel. There are challenging network management problemsto pertinently import visual data from the incident scene and deliver visual situationalawareness.”
The answer to that problem is algorithms designed to determine what information needsto be processed by the cloud and what can be processed on local devices, such aslaptops and mobile devices, spreading out the processing over multiple devices. Theteam also developed an algorithm to aggregate similar information to limit redundancy.
“Let’s say you’re taking pictures of crowds say from surveillance cameras because it’s alawenforcement type of event,” Palaniappan said. “There could be thousands of suchphotos and videos being taken. Should you transmit terabytes of data?
“What you’re seeing is often from overlapping cameras. I don’t need to send twoseparate pictures; I send the distinctive parts. That mosaic stitching happens in theperiphery or edge of the network to limit the amount of data that needs to be sent. Thiswould be a natural way of compressing visual data without losing information. Toaccomplish this needs clever algorithms to determine what types of visual processing toperform in the edge or fog network, and what data and computation should be done inthe core cloud using resources from multiple service providers in a seamless way.”
The study benefitted from the work of a variety of doctoral students with differentcomputer science backgrounds, including networking, computer vision, target tracking,network management, image processing and machine learning. Recent Ph.D. recipientsBrittany Morago and Rengarajan Pelapur and current doctoral students DmitriiChemodanov, Rasha Gargees, and Zakariya Oraibi brought that variety of skillstogether on this project.
“They had all kind of worked together before this project happened. They had takenclasses together, worked on projects together, and a few of them were taking Prasad’sclass on cloud computing,” Palaniappan said. “So part of it evolved as a class project.Two or three of them worked on it as a class project. We encouraged them to worktogether over the summer, then we built on top of that.”
Funding for the project came from a combination of ongoing grants from the NationalScience Foundation, Air Force Research Laboratory and the U.S. National AcademiesJefferson Science Fellowship.
The hope for future research is to expand the algorithm development and field testing ofthe visual cloud computing system on specific types of disasters and work closely with
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public safety groups who can benefit from this research and related technologyoutcomes.
“(Secretary of State) John Kerry particularly spoke at a commencement speech inNortheastern University about how the administration is looking at more investment inextreme events,” Calyam said.
“If you could actually invest in technology and preparedness earlier [before extremeevents], you could minimize the postdisaster impact.”
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