How accurate is accurate enough when it comes to location data?
Location has been a huge enabler for a wealth of applications impacting consumers and businesses, from mobile marketing to asset tracking. GPS is an early example of how combining a smartphone with positioning would change the way both consumer and commercial vehicles navigate the roadways. Drivers kept their expectations low and assumed that during a trip of any length, or even surrounded by the concrete jungle of a parking garage, the GPS would recalculate a few times as the signal from the positioning satellites deteriorated and the smartphone or other terminal, like their dash-mounted Garmin or TomTom device, lost track. GPS was accurate, and for the most part was “accurate enough” for general civilian location use.
Through the years, new technologies have entered the market as location-based services, machine-to-machine communications and IoT began to require location capabilities, primarily starting from outdoor and then extending to indoor asset-tracking use cases in a wide range of industries. Wi-Fi, active RFID, Bluetooth beacons and other technologies emerged with rudimentary capabilities to meet this need, in essence analyzing the power of the received signal strength (RSSI). The problem? These technologies weren’t built specifically for positioning, never mind the real-time nature that emerging applications required, limiting their effectiveness and their accuracy. Still, for the most part, they were “accurate enough,” with a “tolerable latency” for the requirements of the applications for which they were being used.
Over the last few years, however, the growth of IoT and its emergence in nonindustrial B2B markets has changed the mindset about what is required in terms of location accuracy. Spurred by the efficiencies they began to see across their businesses, organizations began to envision a wide range of applications for which they could use IoT, such as tracking smaller items and even people via embedded sensors on ID badges, with the aim of interacting with the environment. Effectively for every type of environment, several use cases started to emerge, even having different stakeholders within the same area. At the same time, huge technological advances across a wealth of technologies have emerged in the form of real-time location systems (RTLS) to deliver sub-meter location capabilities. And even more recently, the industry is abuzz over centimeter-level — and in some cases even smaller — positioning for emerging cutting-edge applications.
But is centimeter-level positioning necessary for IoT and other applications? First, it makes sense to take a look at what location accuracy really means for applications.
Accuracy in the RTLS sense can be defined as a combination of precision and delay, or latency. High accuracy refers to the ability for an RTLS system to achieve from sub-meter (less than 1 meter) to centimeter-level precision, while still performing in real time with latency down to a fraction of a second in tracking moving targets. However, achieving accuracy with low latency comes at a cost — regardless of technology. In general, high-accuracy real-time tracking is solved by covering the area of interest with equipment and creating data redundancy, which results in increasing the initial system’s cost and, in some cases, the total cost of ownership as well.
Delay is another factor in RTLS accuracy. Not every application requires real-time location capabilities. For example, slow-moving heavy equipment may require location data with interval requirements of minutes — a 10-ton object does not move without a crane — whereas when tracking sports athletes, a delay of longer than 300 milliseconds is inadequate for augmented reality applications.
In most IoT applications today, neither centimeter-level precision nor real-time tracking is a key requirement. For example:
- Locating a forklift in a warehouse: Accuracy within a few meters is acceptable, as is receiving the location within a few seconds instead of real-time.
- Locating a container in a shipyard: Accuracy within a few meters is acceptable, as is receiving the location within a minute.
- Moving large equipment around an oil field: This application may require location data with intervals of minutes, and understanding location within a few meters is generally acceptable.
However, there are emerging applications where high-accuracy tracking is a requirement. These may or may not include a requirement for real-time capabilities. Some examples of applications where a high level of accuracy is required include:
- Deriving game analytics: Tracking the movements of athletes or objects, like pucks as they zip around an arena. This requires real-time tracking down to a few centimeters as players and equipment are always in motion and the relative position to each other is essential for characterizing the game dynamics and isolating specific events.
- Smart buildings: This could be related to optimizing the workflow in hospitals while digitizing the ambient environment with a rules engine mimicking the real-world logic; interacting with domotics for home automation; or deriving metrics computing contextual information. Examples include turning on the light when someone enters a meeting room or analyzing the path journey of a shopper in a supermarket for deriving dwell-time metrics and product interactions.
- Employee safety in an industrial environment: In an application used in warehouses, where workers and autonomous equipment move rapidly from place to place, determining location may require higher accuracy tracking in real time; for example, in the case of collision avoidance between forklifts and workers.
- Security and monitoring: This applies to any mission-critical scenario where high-reliability data and consistency are required; i.e., for surveillance and access control.
Finally, the percentile of standard deviation — also characterized by the cumulative distribution function — is another key aspect of location accuracy. To say that a location is highly accurate in real time means that it needs to meet those criteria of high-accuracy positioning with low latency consistently — for example, less than 1 meter for 90% of the time.
The bottom line
The accuracy needed to locate a person or object depends on the specific requirements of the application itself and the business needs it supports. Looking at the examples above, it’s clear that in some cases, certain applications will require more accurate, lower-latency location capabilities than others. Organizations will determine what their requirements are for real-time location based on the specific applications they are developing, and advancements in precision will continue to open the door to a wealth of new applications.
It’s important to note that while organizations are determining their needs today, they also need to consider future applications, what level of accuracy these applications will require as they emerge and at what scale. This is a critical aspect for minimizing costs, improving profitability and ensuring a healthy long-term investment. Utilizing an RTLS that can easily scale and incorporate these new requirements as business needs dictate is paramount. That calls for the implementation of very flexible RTLS technologies where the system can be configured to operate across borders and from low to high accuracy. This makes it suitable for a wide range of applications, including security, safety and reliable workflow management, as well as pushing toward augmented reality or virtual reality applications as those needs emerge.
Determining the precise location of a person or object, consistently and in real time, is complex. It is often more difficult to track static objects than moving ones. There is no silver bullet that optimally solves all use cases. Organizations must weigh their specific requirements against system costs — considering both initial investments and total cost of ownership — required to achieve the location capabilities they target to deliver a return on investment that satisfies their business goals.
All IoT Agenda network contributors are responsible for the content and accuracy of their posts. Opinions are of the writers and do not necessarily convey the thoughts of IoT Agenda.