Connected devices can now share position information, even in noisy, GPS-denied areas.
A system designed by researchers at MIT and elsewhere enables interconnected smart devices to cooperatively pinpoint their positions in noisy environments where GPS usually fails, which is useful for emerging "localization-of-things" applications.
Image: Christine Daniloff, MIT
A new system developed by researchers at MIT and elsewhere helps networks of smart devices cooperate to find their positions in environments where GPS usually fails.
Today, the "internet of things" concept is fairly well-known: Billions of interconnected sensors around the world - embedded in everyday objects, equipment, and vehicles, or worn by humans or animals - collect and share data for a range of applications.
An emerging concept, the "localization of things," enables those devices to sense and communicate their position. This capability could be helpful in supply chain monitoring, autonomous navigation, highly connected smart cities, and even forming a real-time "living map" of the world. Experts project that the localization-of-things market will grow to $128 billion by 2027.
The concept hinges on precise localization techniques. Traditional methods leverage GPS satellites or wireless signals shared between devices to establish their relative distances and positions from each other. But there's a snag: Accuracy suffers greatly in places with reflective surfaces, obstructions, or other interfering signals, such as inside buildings, in underground tunnels, or in "urban canyons" where tall buildings flank both sides of a street.
Researchers from MIT, the University of Ferrara, the Basque Center of Applied Mathematics (BCAM), and the University of Southern California have developed a system that captures location information even in these noisy, GPS-denied areas. A paper describing the system appears in the Proceedings of the IEEE.
When devices in a network, called "nodes," communicate wirelessly in a signal-obstructing, or "harsh," environment, the system fuses various types of positional information from dodgy wireless signals exchanged between the nodes, as well as digital maps and inertial data. In doing so, each node considers information associated with all possible locations - called "soft information" - in relation to those of all other nodes. The system leverages machine-learning techniques and techniques that reduce the dimensions of processed data to determine possible positions from measurements and contextual data. Using that information, it then pinpoints the node's position.
In simulations of harsh scenarios, the system operates significantly better than traditional methods. Notably, it consistently performed near the theoretical limit for localization accuracy. Moreover, as the wireless environment got increasingly worse, traditional systems' accuracy dipped dramatically while the new soft information-based system held steady.
