In the case of wireless networks where time-sensitive information is shared rapidly, it would not be adequate to transmit data quickly. It is also necessary that the data is fresh. Take for instance various sensors in a car. Although it takes less than a second for a majority of the sensors to transmit a data packet to a central processor, the age of that data may differ based on the frequency at which a sensor relays readings.
In an ideal network, these sensors should have the ability to constantly transmit updates, giving the most current and freshest status for each measurable feature, such as proximity of obstacles, tire pressure, and so on. However, the wireless channel can transmit only a specific amount of data without entirely overwhelming the network.
Then, how would it be possible for a constantly updating network — of data-sharing vehicles, drones, or sensors — reduce the age of the information received by it at any moment, while also preventing data congestion?
Engineers from MIT’s Laboratory for Information and Decision Systems have come up with an answer to this question by devising a way to provide the freshest possible data for a simple wireless network.
According to the engineers, their technique might be applied to simple networks, such as sensors in an industrial plant that relay status updates to a central monitor, or multiple drones transmitting position coordinates to a single control station. Ultimately, the researchers believe they can confront even highly complex systems, such as networks of vehicles wirelessly sharing traffic data.
If you are exchanging congestion information, you would want that information to be as fresh as possible. If it’s dated, you might make the wrong decision. That’s why the age of information is important.
Eytan Modiano, Professor of Aeronautics and Astronautics and a member of MIT’s Laboratory for Information and Decision Systems
Modiano and his team have presented their technique in a paper at IEEE’s International Conference on Computation Communications (Infocom), where it was awarded a Best Paper Award. The paper will be published online soon. The lead author of the paper is graduate student Igor Kadota; former graduate student Abhishek Sinha is also a co-author.
Keeping it Fresh
Conventional networks are designed such that they maximize the amount of data that they can transmit across channels, and reduce the time taken for that data to reach its destination. Researchers have taken the age of the information — how stale or fresh information is from the point of view of its recipient — into account only recently.
I first got excited about this problem, thinking in the context of UAVs—unmanned aerial vehicles that are moving around in an environment, and they need to exchange position information to avoid collisions with one another. If they don’t exchange this information often enough, they might collide. So we stepped back and started looking at the fundamental problem of how to minimize age of information in wireless networks.
In this innovative study, Modiano and his colleagues searched for means to provide the freshest possible data to a simple wireless network. They modeled a basic network including a single data receiver (for example, a central control station) and multiple nodes (for example, multiple data-transmitting drones).
The team presumed that only one node had the ability to transmit data over a wireless channel at any given point of time. The question that they sought to answer was which node has to transmit data at which instant of time, to guarantee that the network receives the freshest possible data, on average, from all nodes?
“We are limited in bandwidth, so we need to be selective about what and when nodes are transmitting,” stated Modiano. “We say, how do we minimize age in this simplest of settings? Can we solve this? And we did.”
An optimal age
The solution proposed by the researchers relies on a simple algorithm that typically calculates an “index” for every node at any given moment. The index of a node is dependent on various factors: the age, or freshness, of the data being transmitted; the reliability of the channel over which it communicates; and the overall priority of that node.
“For example, you may have a more expensive drone, or faster drone, and you’d like to have better or more accurate information about that drone. So, you can set that one with a high priority,” explained Kadota.
Higher-priority nodes with a more reliable channel and older data are assigned a higher index, rather than nodes that are comparatively low in priority, communicating over unreliable channels, with fresher data, labeled with a lower index.
The index of a node could vary every moment. At any known moment, the algorithm makes the node that has the highest index to transmit its data to the receiver. Through this prioritization, the researchers discovered that the network is ensured to receive the freshest possible data on average, from all nodes, without overwhelming its wireless channels.
The researchers calculated a lower bound, that is, an average age of information for the network that is fresher when compared to that which can be achieved by any algorithm. It was found that the researchers’ algorithm had the ability to perform very close to this bound, and that it is close to the best that any algorithm could perform with regard to providing the freshest possible data for a simple wireless network.
We came up with a fundamental bound that says, you cannot possibly have a lower age of information than this value—no algorithm could be better than this bound—and then we showed that our algorithm came close to that bound. So it’s close to optimal.
The researchers aim to test its index scheme on a simple network of radios, where one radio could serve as a base station and receive time-sensitive data from various other radios. Modiano’s team has also been working to develop algorithms for optimizing the age of information in highly complex networks.
“Our future papers will look beyond just one base station, to a network with multiple base stations, and how that interacts,” stated Modiano. “And that will hopefully solve a much bigger problem.”
The National Science Foundation (NSF) and the Army Research Office (ARO) partially funded this study.