Verizon Hyper-Precise Location: Is This a Game-Changer for IoT?

K. Weldon
K. Weldon

Summary Bullets:

  • The current generation of location services based on GPS and GNSS can only guarantee precision from three to nine meters; according to Verizon, this is not accurate enough to serve applications that depend on very precise real-time location information such as autonomous vehicles, drones, and AI-enhanced construction.
  • Verizon’s SaaS-based hyper-precise location (HPL) offering, now integrated into its ThingSpace IoT platform, may be a game-changer, unless other operators have capabilities in the works that can match self-correcting accuracy that provides precision to the centimeter level.

Verizon has been working on integration of hyper-precise location technology into its ThingSpace IoT platform for several years. Now that its 5G and edge technologies are also starting to be more widely available, the operator has launched a service that takes advantage of a capability called real-time kinematics (RTK), which has been available for a decade but has had limited coverage and couldn’t scale to large fleets or IoT deployments.  Verizon is using its cloud backend to make the technology available as a service in 100 markets, with the aim of ‘hypercharging’ new solutions for 4G/5G.  It is currently designed for use cases that rely on very precise location.  The service uses reference stations with known locations and then collects signals and observes the error between what the signal suggests as the location and where the reference station actually is located.  It uses this to reverse-calculate the error and provide error correction to all nearby devices very rapidly (i.e. data is collected/corrected every second).  Verizon has deployed multiple reference stations in each of its 100 markets in order to provide ‘correction as a service.’  The autonomous driving segment (including location information for C-V2X protocols) is the most obvious market to require this kind of precision (not only for truly autonomous vehicles, but also for interim capabilities such as lane keeping, as RTK knows where in the lane the vehicle is located).  However, it seems clear that other IoT use cases such as robotics ‘last-mile’ functionality, AI-enhanced construction, and pedestrian safety will be emerging applications.

Verizon notes that 3GPP Release 15/16 standards for 5G will provide support for advanced positioning technologies such as A-GNSS, RTK,and OTDOA, and it acknowledges that RTK technology is already a global standard.  This means that, theoretically, competitors or even automotive companies or solutions providers can also launch HPL solutions.  However, other operators may be waiting for their 5G and edge networks to be more ubiquitous before investing in solutions for HPL.  In any case, it appears that Verizon will have a time-to-market advantage, as it is already in the process of an aggressive deployment plan, with power, space, and backhaul at its reference stations and a plan to expand the service nationwide towards the end of 2021 and into 2022.  The operator also notes that it can already support millions of simultaneous users and is applying for patents for its service elements.  The service is available via restful APIs through its ThingSpace platform.  Verizon also provides carrier-class redundancy, Web 2.0 authentication, and the ability to ensure privacy for data by ensuring it is not shared or stored.  HPL will be priced per device, per month, with a small upsell charge beyond regular IoT connectivity service fees.  For now, it is an outdoor service, but it could potentially be part of a private network indoor deployment in the future.

While others can piece together solutions using the same technologies as Verizon, the integration work, the ThingSpace APIs, the correction-as-a-service concept, and the work to set up reference stations in 100 markets should give it a leg up against other operators or solution providers. It seems clear that autonomous vehicles in particular will require the kind of location precision provided by HPL.

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