New platform creates shortcut for field data analysis

Oak Ridge researchers developed the framework for data analysis

Researchers at the Oak Ridge National Laboratory have developed a framework for gathering and analyzing data from mobile devices such as smart phones in the field without depending on central repositories or back-end systems. That means that users can send information directly to colleagues in control centers or bases without the slowdown that comes with more familiar procedures.

The Oak Ridge team addressed two challenges in managing large volumes of dynamic, distributed data needed in military and emergency response operations: How to avoid bottlenecks in delivering data being collected in the field on mobile devices, and how to avoid data overload when vast amounts of stored information is being analyzed.

“The problem now is not getting data; everyone has too much,” said Brian Klump, research associate and computer scientist at ORNL and co-developer of the Knowledge Acquisition Ubiquitous Agent Infrastructure. The problem is accessing and analyzing it in a timely way, he said.

KAUAI uses the increased power and functionality of mobile devices such as laptop PCs, smart phones and other handheld, wireless-enabled devices that not only are capable of gathering and transmitting data, but also of doing computational tasks.

“Why not take advantage of that power and move some of the analysis out to the front?” Klump said.

The result is a Java-based mobile agent framework that makes each mobile device a part of a distributed database that can be queried centrally or by other mobile devices. One of the primary applications envisioned by the developers of the system is gathering and analyzing data in military theaters of operations.

“You don’t have to bother the soldier and say 'send me this or that,' ” Klump said. Instead, the person or device seeking the data queries the soldier's device directly using a software agent. The device analyzes the data using its own computational power and returns the requested information, if it has it..

"The concept is very simple," Klump said. The realities of dealing with a multitude of device types, each with bandwidth and battery power constraints, was a challenge, however.

KAUAI is an infrastructure rather than an application, and it can be used for multiple applications. It was ported from an infrastructure developed several years ago as an agent system for desktop PCs, and optimized for running on mobile devices.

“The basic infrastructure is very light,” Klump said. “It doesn’t take many resources. It depends on what the task is” that is being requested by the agent sent to the device. KAUAI runs on both the standard J2SE Java Virtual Machine and a mobile JVM for Windows Mobile called CrE-ME. “We’re fairly agnostic about the device.”

Tasks could be requests for data from reports or communications on the device, or information from photos or other graphics. The request from the agent could be as simple as “find me white vans,” or more complex, such as “find me a white van with a certain license number, moving in a certain direction at a certain time.” The agent request could come from a central command for from other devices in the field.

The typical smart phone can handle simple text searches easily, but higher end devices could perform a fair amount of onboard analysis before replying. The test platform set up by the researchers had a response time on requests that ranged from six to 16 seconds, with the deployment time for the agent taking four seconds of that time.

“Distributed solutions outperform the centralized solution in terms of speed for each query, and the speed of the distributed search depends on the amount of query-related data in the system,” the researchers wrote.

KAUAI has been demonstrated for Defense Department and other potential users. “Everyone seems interested when you show it to them,” Klump said, but so far nobody is buying. The project, funded by the ORNL Directed Research and Development program, is on hiatus until a user is found to provide funding for production development.

“There would probably be six months to a year of development left to get something you could put into the field,” Klump said. “There is definitely some engineering yet to be done to harden it, and lots of room for algorithm development.”

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