Hey! Welcome to our latest WiFi Ninjas Blog 🙂 We’ve been busy lately testing some available RTLS solutions from a few different major wireless players and fell absolutely in love with the topic to the point, where we shake from overexcitement when thinking or talking about it ????

This summarises how we feel:

Steve Jobs once said, “people don’t know what they want until you show it to them”. He was right.

20 years ago, WiFi was “a nice to have”. 18 years ago, paper maps were at peak of their popularity, until GPS receivers got small enough to be put in the handheld devices. Is anyone not using Google Maps on their mobile? 15 years ago, not many people felt they needed a wireless headset, until Bluetooth was introduced. 7 years ago, NFC was a niche. Today, most of us use contactless payments using cards, phones or watches.

Indoor wireless RTLS is at the early adoption stage. Technology is available, we just need to make it more interesting and rewarding to use.

Satellite-based location positioning services are not always practical indoors. We could offer wireless RTLS functionality based on WiFi, BLE or soon, possibly, Ultra Wide Band (UWB). Modern smartphones have both WiFi and BLE radios built in. Additionally, new iPhones support UWB. Wireless vendors leverage cell of origin, trilateration, triangulation, angle of arrival, proximity and more with WiFi, BLE, UWB, GPS, NFC, mobile networks or any combination of those methods and technologies, offering different levels of accuracy and functionality. RTLS market is growing fast – according to Bluetooth.com, 1.7 billion devices will use indoor BLE RTLS by 2023, translating to even 500% increase in BLE RTLS implementations in some verticals!

So, what do we need to see the RTLS market explosion? We’ll be speculating, but our hearts tell us that indoor RTLS needs solve real challenges like indoor wayfinding or call for help and simply be more interesting not only for the business’ IT and marketing, but for everyone. We also need more skilled wireless professionals willing to embrace broader wireless technologies and emerging use-cases, like BLE and RTLS, on top of WiFi design, security and analysis skills, while not forgetting about programmability elements.

But enough of the babbling, let’s cut straight to the juicy content! Here is what we will cover in this blog:

Blog Structure:

• Theory
  • RTLS Technologies
    • WiFi
    • BLE
  • RTLS Tracking Methods
    • Cell of Origin
    • WiFi Trilateration
    • WiFi Angle of Arrival
    • Mist vBLE Arrays and Probability Surfaces
  • RTLS Functionality
    • Presence & Analytics
    • Location
    • Engagement & Actions
  • RTLS RF Design
    • Design Tips
    • Examples
• Demo Time!
  • Test Environment
  • WiFi Trilateration
  • WiFi Hyperlocation
  • Mist BLE Arrays
• Location API
  • Challenge 1 (Meraki Location API): Do You Need RTLS RF Design for WiFi Location & Presence Analytics?
  • Challenge 2 (Cisco CMX Location API): Can You Leverage Enterprise Messaging Solutions to Benefit from Indoor RTLS?
• Gotchas

Theory

RTLS Technologies

WiFi

• Most popular tracking technology, as it also provides access to the network.
• Can offer different levels of location accuracy, depending on the tracking method used (cell of origin, Trilateration, AoA, etc.).
• Multiple element antenna arrays can substantially improve location accuracy – Cisco Hyperlocation is a great example.
• Can be used to track associated and unassociated probing clients.
• Associated is proffered, since stations are chattier (with their screen on at least).
• Normally, WiFi based RTLS is based on RSSI/Location information from probe requests only, as probing client normally sends requests on multiple channels, and is therefore seen by multiple APs. This, however, results in very infrequent location calculation ranging from 10 seconds to 5 minutes (according to Cisco) but our own tests shown that modern unassociated stations don’t probe at all. Note, that happy (high RSSI/SNR) associated client probably won’t probe at all, and therefore its location could not be calculated with better accuracy than ‘cell of origin’ (see below) until its re-associating (roaming). Cisco has addressed this challenge with a feature called ‘FastLocate’. It uses a dedicated built in antenna array (4800) or a halo module (3600 / 3700) to scan multiple channels and get RSSI values from clients’ data packets across multiple channels without sacrificing performance of clients serving radio by going off-channel to do just that.
• To track unassociated station, two conditions must be met: station must not use MAC randomisation (they normally do) and it must be probing. Not all unassociated stations are probing.
• Client apps are optional and can be used to increase tracking accuracy, location calculation frequency and add engage or actions element. Note, that using mobile SDK in WiFi-based RTLS is challenging, as the application normally has no way of knowing the MAC address of the device it sits on.
• On top of location analytics, WiFi is normally used to provide presence analytics (more info below).

BLE/vBLE

• BLE relies on physical beacons, often battery operated; vBLE moves beacon role from BLE beacons to APs .
• BLE is often used to offer proximity-triggered actions and vBLE can be used for location analytics on top or instead of WiFi
• Client app is required for fast (sub-second with Mist) and accurate (1-3 metres) BLE/vBLE. Station listens to the BLE transmissions coming from the BLE AP (BLE APs are transmitting). Mobiles listens to all (directional with Mist) beams from all BLE APs in the area and sends RSSI values with beams ID to the server/cloud, where location engine will calculate station XY coordinates and send them to the station. Note, that mobile station can send that information using mobile network; device doesn’t have to be associated using WiFi for BLE RTLS to work.
• Clients with no apps will be treated / located as BLE tags / assets. In this scenario, client is sending BLE transmissions to BLE APs (BLE APs are receiving). Mist uses its directional arrays to pinpoint asset tag transmission location with mind-blowing accuracy, all other vendors will typically rely on tag proximity to BLE radio built into the AP / anchor.
• More BLE beams heard = better accuracy.
• More element antenna arrays can substantially improve location accuracy – Mist BLE Arrays are a great example.
• BLE seems to be very well suited for RTLS, as it doesn’t travel as far as WiFi, therefore offering theoretically better accuracy than WiFi. Note that both BLE (2.4GHz) and WiFi (2.4 or 5GHz) are using the same frequency (actually WiFi can even sit on a higher frequency with 5GHz, and therefore theoretically not travel as far as 2.4GHz) but BLE (Bluetooth LOW Energy) uses significantly lower power levels to operate.
• XY coordinates are calculated for vBLE and are typically not calculated for BLE (proximity used instead, but there are exceptions to that rule!).

RTLS Tracking Methods

Cell of Origin

• Simplest location technique.
• Typically leveraging location of AP that clients are connected to but might be the AP that sees the probing or associated client with strongest RSSI.
• Great for simple zone-wide accuracy.
• Requires at least 1 AP per zone and careful RF design.
• XY coordinates are not calculated.

WiFi Trilateration

• Distance (lateration) based location technique using RSSI in 802.11, measured by either STA or AP/Sensor.
• Adds more accuracy within a zone (tested with Meraki and Cisco) – 2-3m labbed (in perfect environment), 5-7m marketed, 7-10m real.
• Real accuracy is lower than labbed and marketed, as it’s difficult to have LOS between 3x AP and STA everywhere. Also, cross floor leakage, atriums, walls and signal deterioration – all affect accuracy.
• Note, that we normally use Trilateration in WiFi RTLS and it’s often confused with Triangulation.
• Trilateration: requires 3 APs with known distance between them; uses RSSI (distance, lateration) to calculate intersection (client XY coordinates) between three circles (as shown above).
• Triangulation: requires at least 2 APs with known distance between them; uses this baseline and multiple-element antenna arrays (that we typically don’t have in WiFi APs, with Hyperlocation being exception here) to calculate arriving signal angles to find XY coordinates of the client (as shown below).
• XY coordinates are calculated.

WiFi Angle of Arrival

• AoA (angulation) location technique using angle of incidence at which STA signals arrive at the receiving sensors.
• Requires at least 2 APs / sensors / modules (at least 3 recommended for better accuracy).
• Note, that AoA with three APs is in fact a tri-angulation.
• Requires multiple element antenna arrays or antenna mechanical agility.
• Cisco Hyperlocation uses a mix of WiFi Trilateration (RSSI), WiFi AoA and BLE
• Cisco 3600/3700 Hyperlocation module has 32-element antenna array
• Cisco 4800 built-in Hyperlocation has 16-element antenna array
• According to Cisco, Hyperlocation module used with older APs and 4800 built-in array offers same levels of accuracy
• AoA is more accurate than Trilateration (tested with Cisco Hyperlocation) – 1-3m labbed, 1-3m marketed, 1-5m real
• Real accuracy is lower than labbed and marketed for similar reasons as discussed for Trilateration. Additionally, AoA requires extremely careful mounting and calibrating APs positions in maps services (height, azimuth).
• XY coordinates are calculated.

Mist vBLE Array and Probability Surfaces

• Unique to Mist.
• Every Mist AP has 16 Directional Antennae Bluetooth Array – 8 reflectors and 8 directional antennas.

• Uses BLE concept, where Mist SDK on the mobile device is listening the beacons from the beams and sends the RSSI and device sensor information back to the Mist cloud through either WiFi or cellular. Mist also supports assets tracking.
• Mist is not using standard Trilateration, but ‘Probability Surfaces’, where combination of listening to directed AP Beams and machine learning constantly evaluating Path Loss Formula (PLF) per device to calculate station location.
• Extremely good accuracy: 1-2m labbed, 1-3m marketed, 1-3m real (mind blasting).
• XY coordinates are calculated.

RTLS Functionality

Presence & Analytics

• Typically relies on WiFi
• Provides network level stats, such as:
  • Current visitors: devices count, dwell time, gender and age split, device types present
  • Avg. visit distribution: time of day, day of week, new vs repeat, duration, gender, age
  • Bounce rate (stayed vs bounced; enter and stay for longer than 3 min)
  • Conversion rate (converted vs passed; clients that passed the venue but didn’t enter)
  • Visitors engagement ratio (stayed connected over specified time)
• We can leverage social media, mobile apps or splashpage forms to onboard WiFi clients and grab users’ details (be careful with GDPR!)

Location

• Typically relies on WiFi, BLE/vBLE, GPS, Mobile, UWB, Ultrasounds
• Provides zone/location level insight, such as:
  • Zone analytics & zone paths
  • Location on the map (dashboard or client app – blue dot)
• Some location features can be leveraged directly through specific web user interfaces (CMX, Purple, Mist or Meraki Dashboards) but it’s generally more powerful and useful to get, manipulate, filter and visualise location data using API – output can be crafted to any business needs. Typically, the following attributes can be grabbed (or subscribed to if you’re using Mist Webhooks) via location API:
  • Client XY coordinates
  • Zone entry and exit events
  • Virtual beacon (Mist), beacon proximity (everyone else)
  • Raw tracked client’s data: RSSI, time, MAC, etc.
• Zone analytics importance
  • Zone analytics is extremely valuable for businesses, especially retail, to understand impact of their actions (new displays, stands, brands, promotions, etc.) on paths users are taking and stats for each zone (dwell time, count, etc.)
• CMX GUI – Zone Paths example:

• Purple GUI Zone – Paths example:

Engagement & Actions

• Uses Presence, Analytics and Location insight to create personalised user experience
• Example 1:
  • Returning user that has opted in and has previously bought a pizza (tagged with ‘pizza lover’ tag) entering 1st Floor dining area after 3.15pm will get a 50% off offer delivered via App Push Notification (This would also require some CRM integration)
• Example 2:
  • When user is in a proximity to a BLE beacon or a vBLE area, open wifininjas.net website in a loyalty app integrated browser
• Example 3:
  • Zone A (sports cars) will use different Captive Portal than Zone B (donuts)
• Example 4:
  • User uses app for indoor wayfinding – where am I and how can I find client X reception and how to get there?
• Example 5:
  • User requires assistance in the shop (looking for a certain size of shoes to try); user can use the mobile app to ‘call for help’ and grab attention of staff; staff knows where to find a client calling for assistance as they also use location-aware mobile app
    

RTLS RF Design

Design Tips

• APs should be located around zone perimeters to create a convex hull
• Each client should be within convex hull of at least 3 APs with solid RSSI (-65dBm is OK)
  • It’s ideal for all tracking methods, even though AoA and Mist BLE require just 2 APs to work; still, more APs give better accuracy
• Ideally, use dedicated radio or module for RTLS to maximise location scanning performance and minimise performance impact to client-serving radios
• Ensure LOS is maintained between APs and clients (AP behind ceiling tiles is a no-no)
• Don’t mount APs too high! Not more than 4.5m is OK
• Validate secondary and tertiary signal strength in your favourite survey tool

• Cisco specific:
  • Enter thick walls into Prime and ‘Enable OW (Outer Walls) Location’ in CMX to use it for calculation
  • Always use FastLocate functionality (leverage client data packets and probes to calculate location)

Examples

Note, that number of APs has tripled! Generally speaking, if the design is good for RTLS, it’s also good for any other use case (data, voice, most high-density scenarios, etc.). Sometimes some WiFi radios need to be put into non-client-serving mode as too much WiFi can be damaging to the performance of the network (topic for a different discussion).

Demo Time!

Test Environment

• All APs were positioned quite nicely, LOS with a client maintained everywhere within a convex hull making it slightly unrealistically great (normally possible in lab environment only).
• Laser tool was used to measure height of the APs.

WiFi Trilateration

Components

• Tested with traditional Cisco and Cisco Meraki
• We have deemed Meraki Dashboard unusable for RTLS (shows only point of association and overlays all clients from all floors on every floor map) so we’ve also used Purple as an RTLS Dashboard overlay with Meraki testing
• Prime (or DNAC) is needed to create maps, place APs, set their height, azimuth, specify zones, inclusion & exclusion areas, walls, rails (wayfinding paths)
• Theoretically CMX is not required if Hyperlocation is not in use and we could connect AireOS or C9800 WLC directly to the DNAS. Unfortunately, we couldn’t do it as:
  • Direct connection requires DigiCert CA Root Certificate that we don’t have on the WLC
  • C9800 code version 16.12.2 is required, but 16.12.1 was newest publicly available at time of testing

Accuracy

• Theoretical expected accuracy of 5-7m
• Better than expected accuracy when inside the convex hull (2.55m average; 2.69m 90% error distance)
• Still good accuracy when on the edge of the convex hull (2.57m average; 3.38m 90% error distance)
• Location Computation Frequency of 11-15 seconds (with screen on, associated and active device) is not enough to offer blue dot experience but it’s still good for basic RTLS functionality like simple, static wayfinding or zone analytics with zones sized accordingly to solution accuracy

Cisco Hyperlocation

Components

• Prime or DNAC still needed for maps
• Overall complicated setup, prone to user errors and bugs
• CMX on-prem required to do all data crunching – it’s quite a lot of data to go through!
• DNAS can be used to access presence, analytics and location data in the cloud if required
• Note, that at the time of testing DNAS was lagging behind CMX and was generally slower and less accurate than CMX!
• DNAS can also be used for many more things, like integration with 3^rd party tools or Open Roaming integration (amazing idea that we feel will change the way we use public WiFi – let’s leave it for a future blog ????)

Accuracy

• Theoretical expected accuracy of 1-3m
• Totally brain smashing accuracy inside the convex hull (0.73m average; 0.73m 90% error distance) for a non-moving client
• Good accuracy at the edge of the convex hull (1.78m average; 2.03m 90% error distance) for a non-moving client
• Note, that accuracy of mobile clients will depend on their speed of movement and since location computation frequency is 2.3-2.4 seconds at best, the distance travelled within that timeframe will be typically added to the calculated location error distance

Demo

Let’s look at the video showing CMX app, CMX Dashboard and Matt walking around my house ????

• Accuracy is generally really good!
• Blue dot location update (location calculation frequency) of 2-3 seconds is not bad and should be enough for most indoor RTLS use cases.
• We didn’t draw rails (wayfinding paths) in Prime so what we’ve seen here is raw XY calculations displayed on the map. Rails around the path Matt’s taken would snap him into it, making blue dot appear more accurate.
• CMX has great zone path visualisations built in and it’s generally a rarity (Purple also has it built in and it’s as good; note that not everyone will leverage GUI-based zone paths analytics since we can use API and visualise it ourselves and integrate it with existing tools but it requires clearly defined requirements and solid skills and effort).
  

Mist: vBLE Array

Components

• Yup, that’s it ???? Class leading simplicity. We’ve had a lab running by lunchtime and never had a chance to play with Mist before!
• All maps management, wayfinding paths, beacons and zones done easily in the Dash.

Accuracy

• Less than 1m inside the convex hull.
• Reliable sub second Location Computation Frequency when used with an app, making it difficult for other major WiFi vendors to compete with.

Demo

The video screams million words! Let’s take a look ????

• Absolutely mind-bending accuracy with quickest location computation frequency we’ve seen to date in indoor RTLS space!
• It is expected to still use WiFi to get network-wide presence and analytics stats, despite using BLE for location.
• Mobile app is required for the proper BLE solution to work (might not be a bad thing, since mobile app is required anyway to get the most of any indoor RTLS solution, despite the technology used).
• Mist has no zone path visualisations built in (we only have basic zone analytics) – it’s expected to leverage API to access / visualise this data.

Accuracy Summary

Below is a quick summary showing results of our tests for different indoor RTLS solutions!

Location API integration with existing infrastructure – two examples!

Meraki API – Cell of Origin Example

Background

We have recently worked on project for a beautiful, listed retail store in London, where following a very successful design and implementation of Meraki data WiFi network in the store, retail RTLS topic has emerged. With RF design crafted for data, we have faced a very real challenge – would the existing placement, type and number of APs be useful for indoor Real Time Location Services with a zone-level accuracy?What would be the challenges, limitations and reasonable expectations in terms of RTLS usage and accuracy? Could Meraki API help achieve goals set by the business?

Challenges

RTLS was not in the original scope and, combined with a store physical environment, we have faced some challenges:

• RF Design: the network was designed with data in mind. We didn’t have APs placed on the outside coverage zones’ perimeters, had just 1 or 2 APs per zone in most areas and proved with a detailed WiFi survey/assessment that zone accuracy leveraging WiFi trilateration should not be expected.
• Physical Environment: the building was listed, APs/antennas mounting options limited, walls and ceiling mostly wooden, with very low attenuation. All three sections (east, west, central) had big atriums contributing to cross-floor RF leakage strongly. Building had 6 levels.
• Vendor Choice: we had Cisco Meraki and Purple already in place. Purple leverages Meraki API and uses Meraki pre-calculated XY coordinates (this can’t be changed) to display users’ location on the map. With data design, we didn’t have enough APs per zone to rely on the Meraki XY coordinates. Trilateration requires min. 3 APs in a zone to work reliably. Normally, we would have just one best AP (strongest RSSI) in every zone, with additional two APs needed for trilateration being located in adjacent zones or even a floor or two away! This has contributed to wildly inaccurate location readings in Purple (approx. 22m 90% error distance, often on a wrong floor) and could not be relied on. Note, that network-wide WiFi-based Presence & Analytics stats were perfectly reliable.

Floor diagrams below show the difference in RF Design for Data (what we have) vs RF Design for RTLS (what we would need for WiFi Trilateration to work):

Expectations

Based on the additional very detailed survey (more than 100 test locations across multiple floors with 3 different device types: flagship Windows 10 laptop, Android phone and iPhone), where we temporarily installed several new APs on tripods, we concluded that achieving satisfactory and reliable zone-location accuracy  (10m 90% error distance) using WiFi trilateration would require to approximately double the number of APs. The business has decided not to install additional APs and knowing that a mixture of Meraki and Purple could not offer reliable location insights and that WiFi trilateration would not be practical to use at all (because of the design, regardless of a vendor), we looked for alternative solutions and new success criteria were defined:

• Location analytics with zone accuracy was the new goal; access to zone-based stats & clients’ paths deemed critical.
• Zone sizes were increased; sometimes a zone would be as big as a 30x20m open space, sometimes as small as 8x8m room.
• Still use Purple for presence & analytics, guest splash page & social media integration.

Solution

We have gathered some facts: data & voice performance over WiFi, capacity, coverage and roaming were all rock solid across all WLANs (MAB/CWA for guests and EAP-TLS machine auth for corp) and for all test devices (company issued laptops, major OSes, newer and older phones). High density areas, like ground floor entrance hall, could get very busy during holiday periods (200+ associated devices in a small 15x20m area) and were covered by three high-gain dual-band sector antennas. 5GHz was almost free of channel contention with careful APs placement around atriums and use of properly tweaked RF Profiles: 20MHz channels width, limited max and min Tx power, disabled data rates of 11Mbps and lower. 2.4GHz was tweaked even more, with several APs having their radios off and max Tx power set to the level, where RSSI was generally lower on 2.4GHz than on 5GHz throughout the store and only OFDM rates were allowed. 2.4GHz was still considered best effort. Presence & analytics with Purple was spotless.

Switching focus to location zone analytics, we have confirmed with a detailed post-deployment survey, that our test clients were reliably associating with APs installed in the zones they were in. WiFi scans from unassociated devices taken in multiple spots in each zone revealed that we could use strongest RSSI reading from one best AP to accurately pinpoint the probing or associated device to the zone it was in. We concluded that we could potentially leverage two attributes to correlate WiFi devices with their current zones:

• Associated devices only: use MAC address of the AP the device is associated with.
• Associated OR unassociated probing devices: use MAC address of the AP that reports probing or associated device with strongest RSSI.

Two things to note:

• WiFi MAC randomisation makes it impossible to track unassociated devices that use it.
• New mobile platforms battery saving modes make those devices very quiet – they often won’t probe at all.

To further simplify our zone analytics calculations, we have decided to use just one attribute moving forward – MAC address of the AP that reports the probing or associated devices with strongest RSSI.

We have discovered that it is possible to get the RSSI attribute for every client seen by all near APs with Meraki API location data. Readings are provided every minute and contain RSSI values covering last 60 seconds. While it’s not very fast and could not be used for a blue dot experience, it is enough for our use case – historical view of zone paths and analytics.

Using strongest RSSI proved to be 95% accurate across entire building and 100+ test spots when correlating user (reporting AP) location with a zone.

Raw Data from Meraki API – note ap_mac, seen_time, client_mac and RSSI

As we know exactly which zone all the APs are installed on, we could easily generate reports showing clients paths with zone accuracy, time spent in each zone, number of clients per zone, etc. without relying on wrongly pre-calculated Meraki XY coordinates.

To automate the zone paths visualisation and zone analytics, client’s software development team has created a tool to do just that. At this point, imagination was the limit.

Conclusion

Sometimes we are limited by a solution functionality when trying to meet client’s requirements. In our example, Purple could only use Meraki pre-calculated XY coordinates and it was not practical or accurate enough (even for a zone-wide accuracy) to use with a data / voice RF design. Let’s answer our initial question. Do You Need RTLS RF Design for WiFi Location, Presence & Analytics? Short answer is “no” for presence & analytics and “it depends” for location. Presence & analytics provide us with network-wide stats, without the location awareness and therefore will not require RTLS design. Location, however, requires very careful RF design to provide different levels of accuracy. With a solid data RF design, where association and roaming trends are reliably predictable, we could expect solid zone-wide accuracy. Anything more accurate (trilateration, Hyperlocation, vBLE) would require more APs, detailed survey, RTLS RF design, careful placement of APs/antennas and pedantic maps services configuration with spotless APs positions, height and azimuth set.

Cisco CMX API – Location Analytics integration with WebEx Teams Chatbot Example

Background

At Natilik, we’re currently in the process of upgrading our showcase with Cisco DNA. The plan is to have full SDA Fabric, C9800-based Fabric Wireless, proper RF design with Cisco 4800 APs, CMX and DNAS in one part of the office and a mixture of AP43 with BT11 in the other part. We’d love to not only showcase Cisco Hyperlocation and Mist BLE Arrays in action, but also leverage the tech to offer indoor wayfinding with turn by turn navigation and onboard guests using Hotspot 2.0 (called Open Roaming by Cisco) for our visitors WiFi.

As of today, we don’t have the new showcase fully running yet, so all we have to play with is ‘standard’ corporate WiFi design for Voice and Data.

The above shows one out of the two floors we occupy. With current design, all we can use is Cell of Origin and, around reception, WiFi Trilateration. And that’s it. Location Calculation Frequency of 11-15 seconds is not enough to offer blue dot experience, but we thought it could still be good enough to solve some wayfinding challenges!

Challenge

We have Cisco AireOS Wireless with CMX, Cisco WebEx Teams and everyone has a corporate phone enrolled with Meraki MDM.

We would love to use the kit we have today to locate a colleague, zone or a meeting room by asking WebEx Teams chatbot about the location of person/location we’re after without the need to install the mobile application, as we can’t have a turn-by-turn indoor navigation just yet anyway.

Let’s imagine that Mac wants to find Matt (yeah, we don’t hold our hands all the time and sometimes attend different meetings on different floors, lol). How do I do it? How would WebEx Teams chatbot know about my or Matt’s location? How could it show or tell me how to find him? Is it really that helpful with approximately 10-12m 90% error distance accuracy? Can we still draw an indoor map showing where should Mac go to find Matt?

Solution

First, we had to set realistic expectations. We shouldn’t expect the solution to pinpoint user to his or her desk knowing, that calculated user location has 10-12m accuracy. Instead, we have decided to use zones that are big enough to account for that location accuracy.

Here are the zones we’ve created, ensuring that each zone has at least one AP. We’ve also considered expected AP association ensuring it all makes sense:

Now, we need to figure out what happens when Mac is asking WebEx Teams chatbot about Matt’s location.

Wait, what is a chatbot? Good question! Most modern enterprise messaging solutions allow to create custom chatbots with custom functionality. We are fortunate enough to have a proper coding nerd in our ranks, Darren, that finds coding chatbots as easy as you probably find putting your socks on. Darren has created NatBot (I wonder where the name comes from!), that can translate natural language with certain key words to specific actions.

To make it all fly, we’ll leverage Cisco CMX Location API, Cisco Meraki MDM API and a cloud map service (i.e. Mapwize) to visualise the results.

Finally, let’s look at detailed steps that are happening in the background when Mac is looking for Matt ????

• Mac Deryng asks NatBot via WebEx Teams: “Where is Matt Starling”?
• NatBot notes who is asking and who is he/she looking for
  • Mac Deryng is asking
  • He’s looking for Matt Starling
• Find correlation between names and corporate iPhones MAC addresses
  • NatBot leverages Meraki MDM API to find MAC address of Mac’s phone (MDM returns MAC address)
  • NatBot leverages Meraki MDM API to find MAC address of Matt’s phone (MDM returns MAC address)
• Find location of MAC address on the map and correlate it with a zone overlaid on the map
  • NatBot leverages Cisco CMX Location API to find location / zone that Mac is in (CMX returns zone name)
  • NatBot leverages Cisco CMX Location API to find location / zone that Matt is in (CMX returns zone name)
• Show results for Mac
  • NatBot displays zone name that Matt is in and displays a map of the floor, highlighting that zone
  • Additionally, NatBot displays a ‘map’ button
• Mac knows which zone Matt is in, but he’s not sure how to get there; Mac clicks on the ‘map’ button in his WebEx Teams
  • NatBot builds a URL using specific Mapwaze syntax that contains start zone (Mac) and target zone (Matt) and opens that URL
  • Browser displays Mapwaze service, that uses pre-created zones, lifts, staircases and paths and shows exactly how to get from start zone to target zone

Sounds complicated? Watching this short video should clear things up!

Sky is the limit

This is just the tip of the iceberg and we have some more ideas about what to do next, once the new showcase is in! ???? Here is the list:

• Add integration with voice assistants – why type when can just ask a question?
  • Mac: “Alexa, where is Matt Starling?”
  • Alexa: “He’s in the toilet on the 1^st floor. Take a lift or stairs, go right, and right again after 10 metres. You can also see the map on your screen”
• Add integration with calendar
  • Mac: “Alexa, what’s my next meeting?”
  • Alexa: “Your next meeting is recording podcast with Matt. It’s in Fiennes meeting room on the 3^rd floor. You’re also on the third floor. Walk out of the back office and take first left and look for second door on the right. You can also see the map on your screen”
• Add full wayfinding functionality leveraging mobile SDK
  • Support both Cisco Hyperlocation and Mist BLE
  • Offer full turn-by-turn indoor wayfinding
• Leverage mobile sensors to enhance the blue dot experience
  • Use phone accelerometer and compass to make the experience smoother
• Offload indoor location with Hyperlocation to GPS and 5G
  • Why limit yourself to just inside?

Gotchas 

Lastly, here is some stuff that we think might save you some ball ache ????

• Location Computation Frequency for your mobile device will be affected when your screen is off or when your phone goes to sleep
  • Screen off (WiFi-based RTLS): typically doubles the time needed to calculate device location; normally your mobile will go to sleep after several seconds with screen off unless some apps running in the background require connectivity
  • Sleep (all methods): typically your device can’t be tracked when asleep; if apps are used, they would have to be excluded from battery saving modes for tracking to happen
• NTP
  • Always use NTP – AoA won’t work when AP, WLC or CMX have even slight time differences
• Components Compatibility
  • Always check compatibility matrix!
  • Newest software across the board (CMX, Prime, WLC) does not mean it will work (we’ve learned it the hard way)
• APs Mounting
  • Mount APs carefully, especially if multi-element arrays are in use; make sure they’re level!
• Maps Services Fine-Tuning
  • Set APs exact locations, height and azimuth in maps services! It MUST be spotless for the solution to work
• Don’t mix Hyperlocation with non-Hyperlocation APs
  • Device associated with a non-Hyperlocation AP on will always be shown as ‘RSSI’ (and not ‘AoA’) in CMX, even if there are Hyperlocation APs nearby
• Associate WiFi Clients
  • Modern devices won’t probe when not associated (with WiFi on), so tracking unassociated devices, in most cases, provide very little value (and even less with WiFi MAC randomisation)
• Use Mobile Apps
  • Optional for WiFi, but can increase accuracy and sampling frequency; mandatory for BLE
• Add C9800 to CMX as ‘Unified WLC’ using SSH, as opposed to ‘WLC’ using SNMP 

You’ve made it! This blog has almost 6000 words. Well done! ????

With tons of love,

WiFi Ninjas x