Knowing where someone is can be important because you are then in a better position to do something for or with them. This is the basic concept behind location-based Wi-Fi services, so-called LBS.
By knowing where clients are, companies are able to help them get wherever they need to go, make the network experience better for them, use data from their location to optimize their experience, or offer and tell them stuff along the way.
Think of Wi-Fi location as indoor GPS. Wi-Fi-based positioning systems are used where GPS is inadequate due (typically) to signal blockage. Though the Wi-Fi protocol fundamentals haven’t changed much in the past few years as it concerns location technology, the ecology of Wi-Fi location services have completely flipped.
Now that almost every human on the planet has multiple Wi-Fi-enabled devices—in pocket, on hip, in hand, on desk—businesses from retail and hospitality to healthcare and education are looking to capitalize. With that shift, new techniques to improve accuracy are emerging, user behavior and expectations are changing, and new location service models are being built.
Wi-Fi supports a number of different location approaches today, but the two most common are localization based on signal strength (using multiple received signal measurements to calculate the source’s location) and RF fingerprinting (collecting on-site RF data to map signal measurements to locations).
But to really make sense of the location evolution within Wi-Fi, we have to put it in context of the historical goals and techniques. Asset tagging is the historical solution using Real-time location service (RTLS) tags.
So called asset tags were designed to track and monitor things, like shipping containers, medical assets, or even tag-toting people. The tags periodically collect AP signal data and report to a network-side server that does the calculating and tracking using RSSI (received signal strength indicator)-based localization and/or a previous RF fingerprint (a walkabout calibration). The server displays tag location on a map to help the end-user find something/someone. Or, geo-fencing concepts are used to trigger alerts when specific conditions are met (e.g. a tagged asset exits the building). Despite being relatively easy to overlay on existing Wi-Fi infrastructures, asset-tracking solutions require network-side servers, and have not seen any major overhaul in the past few years.
For the consumer world, mobile devices are displacing tags as the “thing” to locate. And like everything else in the mobile ecosystem of connected things, the breadth of appeal for phone-based apps is very wide, touching every industry and almost every user in some way.
Given Apple’s critical role in the smartphone market, a potent drawback to mobile location apps has been Apple’s notoriously limited Wi-Fi API access, which prevents developer access to RSSI metrics on the device itself. For this reason, client-side location processing is a major challenge, and network-side sensors and engines are necessary for RSSI calculations. Client-side data engines also have a consequence for battery life. Without iOS support, any mobile app is constrained to a limited user group or device set, and no one wants to build a customer, guest, or user-focused app that excludes Apple. Riots follow.
But, while some companies are retooling the client-side approach, mobile-focused companies are also rethinking location algorithms altogether using machine learning techniques to track indoor location. Some companies think of device location as a complex “DNA chain,” whereby using RSSI fingerprints, RSSI trilateration and/or Time difference of arrival (TDoA – discussed below) can provide initial location context (i.e. the server tells you where you are); then, by pairing successive RF fingerprints (where is the user walking?) with inertial phone sensors (gyroscope, accelerometer, compass), location can be tracked with very high accuracy, down to 2-3 meter mark.
If that isn’t good enough, other mechanisms are added to improve reliability; for example, map processing can also be used to improve accuracy by ruling out impossible paths on the map—also known as error cancellation. But again, one of the limitations to app-based approaches is that not all mobile devices have the same capabilities, so it is more challenging to build an all-inclusive app-based service for all device types (iOS, Android, Windows, etc.).
So the future of Wi-Fi location is clearly focused on mobile devices. But, the trend today is still focused on infrastructure-side location engines. With that in mind, there are a few techniques to discuss.
* Wi-Fi Signal-Based Localization and RF Fingerprinting. RSSI-based localization and RF fingerprinting provide reasonable accuracy, hovering on the disappointing side of room- or aisle-level precision. Without aid from other technologies (exciters, chokepoints, external systems like video systems, advanced antenna systems), 3-10 meter accuracy is about as good as it gets.
With RSSI localization (sometimes called triangulation or trilateration), the key problem is that RF signal strength varies widely at a moment’s notice, causing unreliability in measurements. Minimally, three signal sources (APs) are necessary for each measurement, but with varying levels of RF attenuation (due to walls, doors, windows, elevators, etc.) between client and AP, the RSSI-to-distance correlation is somewhat shaky, reducing accuracy.
RF fingerprinting suffers from the same RF variation problems. If you take five “fingerprints” from a single location, the fingerprint will look different each time. Laws of averages help the issue, but it’s never perfect. Additionally, RF environments change over weeks and months, so an RF fingerprint taken today may not be valid for that building down the road. Calibration or fingerprinting may become a repetitious process over time.
* Time Difference of Arrival. Time difference of arrival is another technique to determine client location that takes advantage of the constant travelling speed of radio waves, using round-trip time (RTT) of frame exchanges to measure distance. Very fast chip clocks are required to measure nanosecond time granularity; as clock speed increases in Wi-Fi chips in the future, accuracy of TDoA will increase with it.
Wi-Fi products with dynamic, directional antenna systems also have a unique opportunity to correlate antenna metrics to determine client location and further improve accuracy. At the end of the day, collective techniques ultimately contribute to precision. And once you know where the user is, the applications emerge.
Borrowing a theme from the broader mobile ecosystem, Wi-Fi location providers are making it easier for customers to build meaningful solutions by creating easy-to-use APIs and SDKs. Location companies could attempt to build a single killer app, but for Wi-Fi companies, the more scalable strategy is to provide the location engine and the tools that enable customers to build custom apps that make sense to the customer’s unique situation.
Beyond the Infrastructure: Data is King
Since mobile devices have become critical to consumers, businesses have realized the opportunity to benefit—directly and indirectly—by adding value to their customers, guests, and employees. The breadth of appeal for everything mobile and the increasing use of Wi-Fi also enables businesses to justify the cost of location-based application development (and the Wi-Fi network itself), because suddenly Wi-Fi is tied to marketing and revenue instead of IT expenses.
It's important to note that the biggest single benefit of LBS services is gathering data and analytics from users. This data can be used by organizations to improve the user experience and customer service. But, almost always, when you hear pundits talk about location services, they cite the usefulness of location to push people advertisements and coupons. This is interesting and potentially useful but users find it bothersome at best.
Naturally, a lot of focus has been on retail, where location and analytics are wed. As we’re already seeing, many solutions focus on higher-level analytics with rough RSSI data to evaluate customer traffic patterns, capture rates, return rates, and similar. But with more information, retail centers can optimize store layouts based on typical customer traffic paths, or venue owners can charge more for premium storefront or high-view ad spots.
But look at verticals such as hospitality. They have elements of retail (bar, restaurant, spa/massage services). Then they have navigation challenges (where is the conference room, bar, my child, pool, fitness area, etc.) where a site mapping/navigation app could be helpful.
Then there’s the huge premium on customer service, where location services could be tied to customer management systems—personalized greetings for loyalty members, quicker in-app check-in on arrival, and you can dream up any number of ways to pamper guests with location-specific enhancements and offerings.
And the wheels are spinning in other industries, like transportation, manufacturing, healthcare, conference centers, stadiums, and other venues. Beyond the enterprise, carriers also have a strong interest in offering location services and analytics – not only to better tune their network but to also help monetize them. Everyone has some use for location information.
In the very near future, expect Wi-Fi to provide much more than Internet access. As the trend matures and users see a value, they will begin looking for site/venue-specific apps on arrival. Initial efforts may be an exercise in trial-and-error (on the border of clumsy), but as network implementers find their stride, location solutions will benefit everyone. When it’s done right, both users and network operators gain value.
You want value from your Wi-Fi solution? Location services are a great place to find it. And if it’s not already, put this topic on your radar. Location may be the next place to be.
Contact the author at Marcus.firstname.lastname@example.org