Positioning of wireless devices is centralized, depending on “anchors” with known locations such as cell towers or GPS satellites to communicate directly with each device. As the number of devices increases, anchors must be installed at higher density. Centralized positioning can become unwieldy as the number of items to track grows significantly.
Anticipating a critical strain on the ability of fifth generation (5G) networks to keep track of a rapidly growing number of mobile devices, an improved algorithm enables the localizing and tracking of these products by distributing the task among the devices themselves. The scalable solution could meet the demands of a projected 50 billion connected products in the Internet-of-Things by 2020, and would enable a widening range of location-based services.
The devices locate themselves without all of them 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 makes use of device-to-device communication, and 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 add 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. This was accomplished 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.