In expectation of a strain on the ability of fifth generation (5G) networks to keep track of a rapidly growing number of mobile devices, Tufts University researchers have developed an improved algorithm for localizing and tracking these products. The scalable solution distributes the task among the devices themselves and could meet the demands of a projected 50 billion connected products in the internet of things by 2020.
Positioning of wireless devices is currently centralized and dependent on “anchors” with known locations such as cell towers or GPS satellites to communicate directly with each device. An increase in the number of devices will necessitate installation of anchors at higher density, suggesting that centralized positioning can become unwieldy as the number of items to track grows.
The new method of distributed localization in a 5G network has the devices locate themselves without all needing direct access to anchors. Sensing and calculations are done locally on the device, so there is no need for a central coordinator to collect and process the data.
The self-localization algorithm developed by Khan and his colleagues makes use of device-to-device communication, and so can take place indoors (e.g., in offices and manufacturing facilities), underground, underwater or under thick cloud cover. This is an advantage over GPS systems, which not only can go dark under those conditions, but also adds to the cost and power requirements of the device.
The mobility of the devices makes self-localization challenging. The key is to obtain positions rapidly to track them in real-time, which means the calculations must be simplified without sacrificing accuracy. The researchers achieved this by substituting the non-linear position calculations, which are computationally demanding and can miss their mark if the initial guess at position is in the wrong place, with a linear model that quickly and reliably converges on the accurate position of the device. The move to a computationally simpler linear calculation emerges as a result of the devices measuring their location relative to each other or a point representing the “center of mass” of neighboring devices, rather than having all of them reference a set of stationary anchors. Convergence to accurate positions is extremely fast, making real-time tracking of a large number of devices feasible.
The research is published in Proceedings of the IEEE.