Knowing where someone is is important. If you know where someone is, you are 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).
Indoor location technologies have received a lot of attention in the mobile world recently, with Apple's acquisition of WiFiSLAM, Google's increasing support for indoor locations in Google Maps, and Microsoft’s expansion of indoor maps in Bing (source: Seeking Alpha)
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, offer them stuff, and tell them something along the way.
As Smart Wi-Fi solves the capacity, reliability, and performance problems on Wi-Fi infrastructures, enterprises and carriers have become keenly interested in offering LBS services to their customers and their' clients.
Different Approaches to Using Wi-Fi to Determine Location
Think of Wi-Fi location as indoor GPS. Wi-Fi-based positioning systems are used where GPS is inadequate due to various causes including multipath and signal blockage indoors. Though the Wi-Fi protocol fundamentals haven’t changed much in the past few years, 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 signal strength 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) have been the most common. Most of the focus on location whas been asset tracking or locating clients and rogue APs.
Real-time location service (RTLS) tags, commonly 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-based localization and/or a previous RF fingerprint (a walkabout calibration). The server displays tag location on a map or uses geo-fencing concepts to trigger alerts. 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.
As a second option, mobile device apps—focusing on indoor Wi-Fi, where GPS is inaccurate—are gaining traction. 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.
The potent drawback to mobile location apps has been Apple’s notoriously limited Wi-Fi API access, which prevents developer access to RSSI metrics. 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,” wherebyusing RSSI fingerprints, RSSI trilateration, and/or TDoA can provide initial location context; 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.).
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), 3-10 meter accuracy is about as good as it gets.
With RSSI trilateration, the key problem is that RF signal strength varies widely at a moment’s notice, causing unreliability in measurements. Minimally, three signal sources 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. 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 becomes a repetitious process.
Time Difference of Arrival (TDOA)
Time difference of arrival (TDoA) 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 have a unique opportunity to correlate antenna metrics to determine client location and further improve accuracy—collective techniques ultimately contribute to precision.
Crunching Location Data Improves Reliability
With mobile devices as the catalyst, a more user- and consumer-centric approach to location is taking form, where businesses seek to benefit indirectly by adding value to their customers, guests, or end-users. The breadth of appeal for mobile and the increasing use of Wi-Fi also enables businesses to justify the cost of application development (and the Wi-Fi network itself), because suddenly Wi-Fi is tied to revenue instead of expenses.
Borrowing a theme from the mobile ecosystem, location platforms are creating easy-to-use APIs and SDKs, simplifying the integration and customization issues. Instead of building a generic application tailored for some specific customers, location vendors build the location tools and then allow the customer to build their unique application.
Beyond the Infrastructure: Data is King
It's important to note that the biggest single benefit of LBS services is gathering data and analytics from users that can be used by organizations to improve the user experience and customer service. 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 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 trends, capture rates, return rates, and similar. But with more information, retail centers can optimize stores 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 service 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 customer service enhancements. And the wheels are spinning in other industries, like transportation, manufacturing, healthcare, stadiums, and other venues. Expect Wi-Fi to provide much more than Internet access; as the trend matures, users will begin looking for site/venue-specific apps on arrival.
Beyond the enterprise, carriers have an even stronger interest in offering location services and analytics – not only to better tune their network but to also help monetize them. Smarter Wi-Fi services that add granular location details of users leveraging basic network information allows carriers and their customer to deliver much a higher quality experience to end users.
If it’s not already, put this topic on your radar. Location may be the next place to be.