wifi ninjas

WN Podcast 031 – Surveys, Design & Teaching Tips with Ferney Munoz

Welcome to our new WiFi Ninjas Podcast episode!

Today we talk about WiFi design and teaching tips and some crazy, unconventional surveys using GPS & Segways in quite niche verticals like slums, city outdoors and mining. Welcome our special guest and a great teacher – Ferney Munoz!

Enjoy!

Tons of love x,

WiFi Ninjas

WN Podcast 030 – WiFi 6E – Our Wireless Salvation

Welcome to our new WiFi Ninjas Podcast episode!

Today we discuss new extension to the happy WiFi family – the WiFi 6E. Enjoy!

  • WiFi 6E
    • Stands for WiFi 6 “Extended”
    • Announced by WiFI Alliance on the 3rd Jan 2020
    • Not yet cleared by the FCC
  • Drivers
    • More need for capacity
      • CCI being major performance killer today
      • Vendors push for more bonding – practical to do so in 6GHz?
    • 8k, VR, AR, Enterprise Conferencing
    • WiFi boom in the industrial vertical (can’t blame them)
    • Ultra-fast broadband (300Mbps+) and fibre to the home are quickly becoming a norm, reaching 53% of properties in the UK in 2019. Both Ninjas have 50 down and 10 up. We’re embarassed sitting in the bottom feeding half of the population 🙂
  • Ofcom proposition for the UK
    • Ofcom is responsible for authorising use of the radio spectrum in the UK
    • Make the lower 6GHz band (5925-6425 MHz) available for WiFi
      • Including Very Low Power (VLP) outdoor use
      • Remove DFS from WiFi channels in the 5.8GHz band (5725-5850MHz, which is UNII-3, also referred to as Band-C)
        • “We made the 5725-5850 MHz band available for Wi-Fi use in 2017 and said we would keep the regulations under review. Our current analysis indicates that the band is very lightly used by Wi-Fi routers in the UK, which is in part due to the UK-specific requirement to implement DFS in this band, and that the interference risk to radars from indoor Wi-Fi use is very low. We are therefore proposing to remove DFS requirements for indoor use (up to 200mW) only from the 5725-5850 MHz band to increase use of the band and reduce congestion in other channels”
      • Ofcom consultation will be open until 20 March 2020
      • Ofcom believes that WiFi bands should be as globally harmonised as possible and intend to drive international discussions intending to promote the benefits of a simple regulatory regime
  • Broadcom launches world’s first WiFi 6E 6GHz chips
    • Intended for enterprise APs and residential networks
    • 4×4 dual band 160MHz support
    • 2×2 tri-band
    • 2×2 dual-band with ARM CPU
  • Intel apparently not far away from having their own 6GHz WiFi chipsets
    • Already demonstrated it at last MWC in Barcelona
  • No words from Qualcomm yet
    • Qualcomm still dominates WiFi 6 enterprise AP market
  • Regulators
    • FCC has not made its final decision as of Jan 2020
  • Numbers
    • 125 million WiFi6 smartphones have been shipped until now
  • New frequency
    • 1.2GHz wide new spectrum in the US
    • 500MHz wide new spectrum in the EU
    • Today in the UK we have 25x 5GHz (585MHz to be exact) and 4x 2.4GHz 20MHz (exactly 83MHz) wide non-overlapping channels
  • Newly proposed 6GHz band is approx. 3 times bigger than sum of total spectrum used today for WiFi in the US and almost doubles available spectrum width in the UK
  • This translates to:
    • New US Channels (5925MHz – 7125MHz): 59x 20MHz, 29x 40MHz, 14x 80MHz, 7x 160MHz
    • New UK Channels (5925MHz – 6425MHz): 24x 20MHz, 12x 40MHz, 6x 80MHz, 3x 160MHz
  • And this is how the new spectrum would look like:
    • From Aruba Chuck (thanks man):
  • Closer view at the new frequency in the UK:
  • Legacy support
    • IEEE decided that only WiFi6 will be operating in the 6GHz band
    • Totally legacy-free with ‘but’
      • No legacy devices, sure
      • Legacy support mechanisms are still there
      • It’s still ‘just’ WiFi6 – preamble uses most robust data rate, so 6Mbps, etc.
      • Should it have been WiFi7 instead?
      • No new logo / notification required
  • Challenges
    • Differences in available spectrum in US vs rest of the world
    • WiFi 6E will overlap with widely used UWB channel 5
      • Some UWB systems using channel 5 will most likely move to channel 1 in 3GHz band
      • Poor UWB – CBRS will overlap UWB channel 1
  • WiFi6E Overlap with other tech, like mobile broadband backhauls, broadcasting, local authorities, etc.
  • Conflict with Facebook that was planning to use 6GHz band for AR/VR (app is called Spark)
  • Adoption
    • Once cleared, it should be quick and mind bending
    • WiFi will contribute to 1 trillion dollars in economic value in the US
    • Major chipset vendors already have 6GHz ready
  • Some comments about 6e from experts and vendors
    • “Wi-Fi has become the most important wireless technology for American consumers and businesses, and is projected to contribute almost $1 trillion in economic value to the United States by 2023. As the application and overall demand for Wi-Fi continue to surge, access to the 6 GHz unlicensed spectrum will enable Wi-Fi to continue delivering the vast innovations and socioeconomic benefits it is bringing to the market today while helping to ensure Wi-Fi can meet the new promises of the 5G era and beyond.” – Chuck Lukaszewski, vice president of Wireless Standards and Strategy for Aruba, a Hewlett Packard Enterprise company
    • “Wi-Fi has changed the world, and we are excited to work with Wi-Fi Alliance to ensure Wi-Fi will continue changing the world. Wi-Fi 6’s growth into the 6 GHz spectrum is a game changer for two reasons – the availability of the additional channels and the ability to finally use 160Mhz for high bandwidth applications like AR and VR; this provides enormous opportunities to build new applications and experiences for both consumers and businesses. By standardizing on Wi-Fi 6E, Cisco Meraki and others in the industry can begin delivering next-generation wireless experiences to customers.” – Jayanthi Srinivasan, Director of Product Management, Cisco Meraki
    • “With every increase in available bandwidth, new devices and applications come along that leverage that space to provide experiences we never before imagined, yet quickly become part of the fabric of our everyday lives. Brand new Wi-Fi spectrum in the 6 GHz range will more than double available Wi-Fi frequencies and have a profound effect on Wi-Fi enabled communications. This additional bandwidth not only enables higher Wi-Fi 6 performance with less congestion, but also delivers sufficient spectrum to effectively deploy 80 MHz or 160 MHz-wide channels, severely restricted at 5 GHz. 6 GHz finally and legitimately provides the higher data rates required to drive virtual and augmented reality forward, giving users and organizations the ability to develop a whole new world of use cases.” – Perry Correll, Director of Product Management, Extreme Networks
    • “I’m not sure AR should be considered the main factor behind the WiFi6E new spectrum availability, but leveraging 160MHz channels in this new-generation, market shifting, brain smashing, WiFi ‘plus’, ‘pro’ or ‘premium’ band would certainly allow me to stream 16k 480Hz turbo-HDR videos from next-generation Netflix. Lol. Now, seriously – it’s a chance for a fresh start, so I really hope that lack of support for legacy PHY will be accompanied by alterations to the WiFi6E, so we can move on from using legacy mechanisms requiring us to use robust PHY rates for some WiFi transmissions. Fingers crossed.” – Mac, WiFi Ninjas

Matt, chicken, didn’t want to comment 😉

See you in 2 weeks!

Tons of love,

WiFi Ninjas

xXx

WN Podcast 029 – RTLS with Bob Friday

Welcome to our new WiFi Ninjas Podcast episode!

Today we discuss indoor RTLS with an industry legend Bob Friday. Enjoy!

  • What friction / hurdles are stopping indoor location from becoming a must have
  • Mist implementation of indoor RTLS using BLE
  • RF design for Mist BLE
  • Mist BLE vs competition
  • Mobile stations with app vs without app
  • Assets tracking
  • Is there still a place for BLE beacons
  • Location API – integration examples
  • Mobile SDK – integration examples
  • Apple’s adoption of UWB in the latest iPhones and what that means for the industry
  • Who are the early adopters of RTLS
  • As major market disruptors, who are you targeting?

Tons of love x,

WiFi Ninjas

WN Podcast 028 – Channel vs Spectrum Utilisation & Ghost Frames with Ben Miller – Part 2

Welcome to our new WiFi Ninjas Podcast episode!

This is the continuation of our chat with Ben Miller, where we’re discussing channel utilisation vs spectrum utilisation vs duty cycle, ghost frames and potential impact of setting minimum data rates too high.

Channel Utilization

  • What is it
  • Does lower % always mean a better RF?
  • Good vs bad channel utilisation
  • Does channel bonding affect channel utilization?
  • Shall we rely on channel utilization for tshooting?

Internet’s favourite: channel utilization vs spectrum utilization vs duty cycle

  • Real Time FFT [dBm]
    • Current spectrum utilisation
  • Spectrum Utilisation [%] = FFT Duty Cycle [%]
    • Spectrum utilisation over a short time period
  • Waterfall = Swept Spectrogram
    • RF Power over time

Tools

  • Tools of choice for channel / spectrum utilisation
  • Protocol analysis

And a very helpful screenshot from Joel’s presentation!

Tons of love x,

WiFi Ninjas

WN Podcast 027 – Channel vs Spectrum Utilisation & Ghost Frames with Ben Miller – Part 1

Welcome to our new WiFi Ninjas Podcast episode! We are mega privileged to have Ben Miller on our show today, where we’re discussing channel utilisation vs spectrum utilisation vs duty cycle, ghost frames and potential impact of setting minimum data rates too high.

Podcast frames below.

Channel Utilization

  • What is it
  • Does lower % always mean a better RF?
  • Good vs bad channel utilisation
  • Does channel bonding affect channel utilization?
  • Shall we rely on channel utilization for tshooting?

Internet’s favourite: channel utilization vs spectrum utilization vs duty cycle

  • Real Time FFT [dBm]
    • Current spectrum utilisation
  • Spectrum Utilisation [%] = FFT Duty Cycle [%]
    • Spectrum utilisation over a short time period
  • Waterfall = Swept Spectrogram
    • RF Power over time

Tools

  • Tools of choice for channel / spectrum utilisation
  • Protocol analysis

And a very helpful screenshot from Joel’s presentation!

Tons of love x,

WiFi Ninjas

WN Blog 023 – Indoor RTLS with WiFi and BLE – Deep Dive

Hey! 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:

Excited Ninjas

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

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BEAMS 1 HEAR 
CLOUD
  • Unique to Mist.
  • Every Mist AP has 16 Directional Antennae Bluetooth Array – 8 reflectors and 8 directional antennas.
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  • 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:
A screenshot of a video game

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  • 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 3rd 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 1st 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 3rd 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

WN Podcast 026 – RTLS – Part 2 – Real World Testing

Welcome to our new WiFi Ninjas Podcast episode!

This is the continuation of the RTLS discussion, focusing on real-world tests and demos!

Three things have changed since we recorded the podcast:

  • We’ve confirmed that you can draw wayfinding paths in Prime, called ‘Rails’ in a Cisco world!
  • We’ve discussed Mist presence and analytics network-wide stats that you can include in a report; new functionality has just been added, where you can see all that stats live! Big thing from Mist, thank you guys 🙂
  • We are discussing WiFi Trilateration (lateration, so distance based, without any special AP requirements – internal omni is OK) RTLS and NOT WiFi Triangulation (that is angle based and requires multiple antenna arrays or mechanically agile antennas)

With tons of love x,

WiFi Ninjas

WN Blog 020 – Postman Variables

In our previous blog (please start there), we were typing in all the site IDs, WLAN IDs, API authentication tokens & Mist API HTTPS URL manually. It’s no fun, right?

Since we’re likely to interact with Mist API using one Mist account, we’ll probably use one API key globally.

Additionally, we’ll probably interact with specific organisations, sites or attributes (like WLANs) that are more ‘local’. It might be separate clients in a managed service environment or separate sites under one org. Whatever it is, it makes sense to think of it as a specific, local environment, something that is not global.

Surprise! We can define our variables on both “Environment” and “Global” level in Postman.

API token will be set as a “Global” variable, and all the rest attributes we used so far will be defined inside an “Environment” called “Mist WiFi Ninjas”.

Mission:

  • Use Global and Environment Variables to make our lives easier

Environment Variables

First, note that we are not using any environments right now.

Our request still has long, manually pasted streams representing site, WLAN and Mist API URL.

Postman 
File Edit View 
Import 
Runner 
Q Filter 
History 
% MyWorkspace • 
GET httpswapi.mist.corn"api/...• 
Invite 
GET httpswapi.mist.com//api.. 
Collections 
GET httpswapi.mist.conwapi/.... 
APIs 
Untitled Request 
Clear all 
Save Responses 
Today 
https://api mist.com.'/api/vl 'sites,'0fc98886-fb c48ef-b905-819200d57d54/wIans,'a88d6cdd-6aa5-4eb20... 
Upgrade 
Save 
Headers (9) 
Test Results 
Visualize 
Body 
pre•request Script 
VALUE 
Value 
Tests 
Settings 
DESCRIPTION 
Description 
status: 2000K Time: 1 71 ms 
Cookies 
Code 
GET 
GET 
PUT 
https://api.mist.com//api/vl 'sites/Ofc 
Params 
98886-fb 1 c-48ef-b905-819200d57d5 
4/wlans/a88d6cdd-6aa54eb2-8cd9-fb 
Authorization 
Query Params 
https://api.mist.com//api/vl 'sites/Ofc 
98886-fb 1 c-48ef-b905-819200d57d5 
4,'wlans/a88d6cdd-6aa5-4eb2-8cd9-fb 
Key 
Body Cookies Headers (16) 
Size: 451 KB 
Bulk Edit 
Save Response 
{(wIan_5ghz)} 
((wlan_5ghz)) 
{(m nsJ 
((wlan_5ghz)) 
https:,'/api mist.com//api/vl 'sites/Ofc 
98886-fb 1 c48ef-b905-819200d57d5 
https//api mist.com//api/vl 'sites/Ofc 
98886-fb 1 c48ef-b905-819200d57d5 
https„'/api mist.com//api/vl "'sites/Ofc 
98886-fb 1 c-48ef-b905-819200d57d5 
Pretty Raw 
Preview 
"class": "best effort" 
"overwrite": false 
id": "d2b94bf9-22bø-4af1-aød1-68e162fbe199" 
"dtim": 2, 
"hide ssid": false, 
"acct servers": 
"created time": 
"disable_uapsd" : 
[1, 
1557492085, 
false, 
"allow mdns": false,

Let’s create our new Environment and name it “Mist WiFi Ninjas”

GET https:,',/agi.rr 
Untitled Request 
Headers (8) 
MANAGE ENVIRONMENTS 
GET https:'%vj•_mi 
)/vvlans/K• 
GET 
An environment is a set of variables that allow you to switch the context of your requests Environments can be shared 
Mist WiFi Ninjas 
Send 
ok 
Save Response 
bet%'een multiple workspaces. Learn more about environments 
Mist WiFi Ninjas 
Globals 
Share 
Import

Define our variables on the Environment level

Postman 
File View 
History 
Today 
Help 
GET 
Untitled Request 
GET 
% My Workspace 
GET https:napinis con-Ina; 
Invite 
GET https:.'/api_mist.com//api„, 
Mist WiFi Ninjas 
https://api.mist.com//api/vl/sitesJOfc98886fb 1 c 4e 52 •8, , , 
Send 
https:.'/api.mist.com//api/vl'sites/Of 
Params 
9200d57di 
GET 
"wIans/aggd6cdd-6aaS-4eb2-8cd( 
I 'sites," 
98886-fbl 9200d5 
4/wIans/aggd6cdd-6aaS-4eb2-gc 
((y.'Ien_Sghzp 
gg8g6-fh 1 c-4gef-hg05-g1 9200dS 
4/wIans/e88d6cdd-6ae54eb2-8c 
https://api.rmst.com//api/vl'site 
MANAGE ENVIRONMENTS 
Environment Name 
Mist WiFi Ninjas 
VARIABLE 
'ave Response 
INITIAL VALUE O 
a88d6cdd-6ass-4eb2-... 
0 08886- 
https://api.mist.com//... 
CURRENT VALUE O 
Persist All 
Reset All 
wlan 
site 
uc ton 
a88d6cdd-6ea5-4eb2-8cdg-fb8731ab18e2 
0 08886- lc-48e- 905-819200 57 54 
https://api.mist.com//api/vl 
Add a new variable

Global Variables

Go to Globals

File Edit View, 
History 
Today 
• My Workspace • 
GET https:\' 
Untitled Request 
Invite 
GET "mist}}. 
Mist WiFi Ninjas 
https://ap...X 
Clear all 
GET 
1 1 9200d57d54,'wlans/ s-4eb2-E 
Send 
PUT 
PUT 
PUT 
PUT 
PUT 
PUT 
vrwIan_5ghzp 
jJwIan_SEh7P 
https://api.mist.comnapi/vl/site 
a88d6cdd-6aa5-4eb2-8c 
https://epi.mist.cum,qapi/vl/site 
98885fb1c48eft905819200d5 
4,'wIans/a88d6cdd-6aa5-4eb2-8c 
' 'nlan_5ghz} 
unlan_5ghzp 
UwIan_5ghzp, 
https://api.mist.com//api/vl/site 
https://api.mist.com//api'vl/sire 
• 'wIans,'a88d6cdd-6aa54eb2-8c 
https://api.mist.comnapi/vl/site 
https://api.mist.com//api/vl/site 
ggg86-fh1c-4gef-hgos-g1g200ds 
• 'wIans/a88d6cdd-6ea5-4eb2-8c 
https://api.mist.comnapi/vl/site 
qggg6-fh1 q200dS 
https://api.mist.com//api/vl/site 
9ggg6-fb1 c-48ef-b90S-819200dS 
https://api.mist.com//api/vl/site 
woods 
d/"' lans/a88d6cdd-6aa5-4eb2-8c 
https://api.mist.com//api/vl/site 
https://api.mist.comnapi/vl/site 
d/wIans/a88d6cdd-6aa5-4eb2-8c 
https://api.mist.com//api/vl/site 
• 'wIans/a88d6cdd-6aa5-4eb2-8c 
https://api.mist.comnapi/vl/site 
98886 f'01c48ef4-,905819200d5 
MwIans/a88d6cdd-6aa5-4eb2-8c 
https://api.mist.comnapi/vl/site 
• iwIans/a88d6cdd-6aa5-4eb2-8c 
https://epi.mist.com,qapi/vl/site 
98885fb1c48eft905819200d5 
MANAGE ENVIRONMENTS 
An environment is a set of variables that allow you to switch the context of your requests. Environments can be shared 
between multiple Learn more about environments 
Mist WiFi Ninjas 
Globals 
Share 
Import 
Yesterday

Define your global token.

Remember to include “Token ” prefix before pasting actual token value.

MANAGE ENVIRONMENTS 
Global variables for a workspace are a set of variables that are always available with 
can be viewed and edited by anyone in that workspace, Learn more about globals 
Globals 
VARIABLE 
to en 
Add a new variable 
INITIAL VALUE O 
CURRENT VALUE O

Replace our manual values with variables

%stman 
File Edit View 
Q Filter 
History 
Import 
Collections 
Runner 
AP IS BETA 
Clear all 
GET https:/,'api.... 
Untitled Request 
GET 
httpswapi.... 
My Workspace • Invite 
GET httpswap... 
GET {Cmistmsi.... 
Mist WiFi Ninjas 
GET {{mist»/si... 
Save Responses 
Today 
Pa rams 
0 
Code 
Headers (9) 
Test Results 
Visualize 
Body 
Cookies 
Presets 
GET 
GET 
GET 
PUT 
PUT 
PUT 
GET 
GET 
{Cm ({site_ ns} 
{(wlan_5ghz)) 
{(wIan_5ghz}) 
{(wlan_5ghz)) 
https://api.mist.com//api/vl/sites/ofc 
98886.fb1 c48ef.b905-819200dS7dS 
4/wIans/a88d6cdd-6aa 54eb2-8cdg-fb 
httpsWapi mist.com/,'api/vl'sites/Ofc 
98886-fbl c48ef-b905-819200dS7dS 
4/wIans/a88d6cdd-6aa 54eb2-8cdg-fb 
{Km 
{(wlan_5ghz)) 
{Cm nsJ 
{(wlan_5ghz)) 
{(wlan_5ghz)) 
https:/fapi 
98886-fb1c48ef-b905-819200d57d5 
https:/fapi mist.com/,'api/vl'sites/Ofc 
98886-fb1c48ef-b905-819200d57d5 
https://api 
98886-fbl c48ef-b905-819200d57d5 
https://api.mist.com//api/vl'sites/Ofc 
98886-fbl c48ef-b905-819200d57d5 
https://api.mist.com//api/vl'sites/Ofc 
98886-fbl c48ef-b905-819200d57d5 
https://api.mist.com//api/vl"sites/Ofc 
98886-fbl c48ef-b905-819200d57d5 
4/wIans 
Authorization 
Headers (2) 
Authorization 
Content-Type 
Key 
Temporary Headers (7) O 
Body Cookies Headers (16) 
Pre-request Script 
VALUE 
"token}) 
application/json 
Value 
DESCRIPTION 
Description 
Time: 687ms 
Bulk Edit 
Status: 2000K 
Size: 452 KB 
Save Response 
Pretty 
Raw 
Preview 
"class": 
"best effort" 
"overwrite": false 
"org_id • 
"d2b94bf9-22bØ-4af1-aØd1-68e162fbe199", 
"dtim": 2, 
"hide ssid": false, 
"acct servers": 
"created time": 1557492085, 
"disable_uapsd": false, 
"allow mdns": false, 
"apply_to": "site", 
"app_limit": { 
"apps" • 
"enabled": false, 
"wxtag_ids . 
. "a88d6cdd-6aa5-4eb2-8cd9-fb8731ab18e2" , 
"vian id": 
"wxtag_ids " : 
null, 
"mxtunnel id": null, 
"ssid": 
"vlan_pooling": false, 
"wlan limit down": 20ØØØ, 
"wxtunnel id": null,

Enjoy Postman environment that is easier on the eyes! Easy peasy lemon squeezy!

With love,

WiFi Ninjas x

WN Blog 019 – Getting Started with Mist API

Let’s face the truth. We can’t avoid dev stuff in WiFi forever! REST API, Python, JSON, Ansible and whatever else is needed – here we come!

It’s time. This is day 1 of our studies!

The mission for today? Actually we have a few 🙂

  • Prepare the environment to work with Mist REST API using Postman
  • List all WLANs created in WiFi Ninjas site using Mist API using GET
  • Narrow the list down to a specific WLAN – “WiFiNinjas(.)(.)”
  • Rename “WiFiNinjas(.)(.)” WLAN to “WiFiNinjasTitties” using PUT

Extreeeemely complicated, we know 😉

Let’s assume everyone knows what the API is. In case it’s not the case, here is a quick read:

https://www.howtogeek.com/343877/what-is-an-api/

Mist (and most other Enterprise solutions) use REST:

https://restfulapi.net/

Lastly, from Mist Documentation:

https://www.mist.com/documentation/category/api/

Steps to complete our little task below!

Install Postman

You can get it here:

https://www.getpostman.com/downloads/

Generate Token

Token is needed so the Mist Dash can authorize us to access API.

Log into Mist Dash, then go to and hit ‘POST’:

https://api.mist.com/api/v1/self/apitokens

All 
api.mist.com/api/vl/self/apitokens 
Lab Natilik WN 
CWNP Gml 
GM T LN Is 
Django REST framework 
Self ! Getall Create Apitoken 
Getall Create Apitoken 
POST /api/vl/self/apitokens 
HTTP 2øØ 0K 
Allow: POST, OPTIONS, GET 
Content—Type: i cation/ j son 
Vary: Accept 
"created time": 1574783690, 
"last used • • : 
null, 
" pc cml , 
OPTIONS 
GET 
Media type: 
Content: 
application/json

If you hit ‘POST’ again, new token will be generated!

Take note of the “key” – it’s our token.

Let’s assume we want to list created WLANs in the test ‘WiFi Ninjas’ site.

Check API URL to list Site WLANs

Mist API is nicely documented. You can go to Mist API documentation page to check the URL for everything that’s possible using RESTful API:

https://api.mist.com/api/v1/docs/Home

api.mist.com/api/v1/docs/Home 
Natilik 
CWNP Gml 
GM 
TOC 
Overview 
Overview 
permiesion 
Query 
Rate Limit 
Mist GithUb Repo 
Authentication 
Login 
Privileges 
Register 
AudiU.ogs 
Integration 
Site 
WIan 
Map
C api.mist.corn/api/vl /docs/Site#wlan 
Lab Natilik WN 
CWNP Gml 
WIan 
Wireless LAN definition 
WIans are the wireless networks created under a site. 
Base URI 
Example 
"ssid"• "corporate" 
"auth": { 
"type": "psk 
// type 
is psk 
"enable 
mac auth": false, 
GM

We now know the syntax to get our WLANs.

Get Org ID and Site ID

Next we need to know the “WiFi Ninjas” Site ID to check against.

Go to Mist Dash. You can get Org ID and Site ID from the browser URL:

All Lab Natilik WN 
Inbox - mac.deryng@gmaiLcorn X 
Mist *stems Administration 
Access Points 
• WiFi Ninjas 
Mist University I Al for IT Mist AP 
SITE ID 
IP Address 
x 
C •a0d1 
CWNP Gml @ G A GM T LN 
ORG ID 
site Buckton Fields 
MAC Address 
pp 
MiSt 
Monitor 
Marvisw 
O) Clients 
Access Points 
Switches 
Location 
Analytics 
CJ Network 
organization 
WIFI NINJAS 
o 
Filter 
Site I API I Mist 
NO. Clients 
X Getall Create Apito 
uptime 
There are no APS found in this site 
Claim your Access Points by providing claim codes 
Claim APS

We now have everything we need to list our WLANs in ‘WiFi Ninjas’ site!

Use obtained API token, WLAN GET URL and Site ID to list the WLANs

  • Create new GET Request by hitting ‘+
  • Type https://api.mist.com//api/v1/sites/<siteID>/wlans into request URL
    • Ensure to include https:// prefix!
  • Go to ‘Headers‘ tab and specify (type in) Authorization (use Token obtained in previous steps in VALUE fields; ensure to include key word ‘Token ‘ before pasting in the actual token!) and Content-Type (type ‘application/json’ in VALUE field)
  • Hit ‘SEND
O Postman 
File Edit View 
Q Filter 
History 
Help 
Import 
Collections 
Runner 
APIs BETA 
Clear all 
My Workspace • 
Invite 
NO Environment 
GET https•.//api.mist.com//api/vl/sit.., • 
Untitled Request 
(D Save Responses 
Today 
https://api.mist.com//ag 
O GET 98886fb1c48ef.b905-8 + 
4/"' lans 
May 8 
Params 
Authorization 
upgrade 
Save 
Cookies Code 
presets 
Headers (2) 
Authorization 
Content-Type 
Key 
Temporary Headers (7) O 
Body Cookies Headers ( 1 6) 
Headers (9) 
Test Results 
Visualize 
Body Pre-request Script 
Tests 
VALUE 
Token UTEmqoKcpxImnGodssHzozcn8Sk7PCcm. 
application/json 
Value 
status: 2000K 
DESCRIPTION 
Description 
Time: 673ms 
Bulk Edit 
Size: 8.45 KB 
Save Response 
Pretty Raw 
Preview 
"class": "best effort", 
"over"Arite": false 
"org_id": "d2b94bf9-22be-4af1-aød1-68e162fbe199", 
"dtim": 2, 
"hide 55id": false, 
"acct_servers": t], 
"created time": 1557492085, 
"disable_uapsd": false, 
"allow mdns": false, 
"apply_to"• "site", 
"app_limit": { 
"enabled": false, 
"apps" • 
"wxtag_id5": O 
: "a88d6cdd-6aaS-4eb2-8cd9-fb8731ab18e2" , 
"vlan id": 20, 
"wxtag_ids": null, 
"SSI t" 
v an_poo Ing : 
"wlan limit dcmn": 2øøøe, 
"wxtunnel id": null, 
"auth serwers nas id": " 
false, 
"site id • 
". "efc98886-fb1c-48ef-b9ø5-81920edS7dS4", 
"disable Hmm": false, 
"airwatch": { 
"username" . 
"api_key"• " , 
"console url": " 
"password" • " 
"enabled": false 
"schedule": {

Note the “id” of “WiFiNinjas(.)(.)” WLAN

Copy WLAN “id” to our GET URL

*jstman 
File Edit View 
Import 
Runner 
APIs 
Clear all 
My Workspace • 
Invite 
No Environment 
History 
Collections 
GET httpswapi.mist.com"api/vl/sit... • 
Untitled Request 
GET httpswapi.mist.com"api/vl/sit... • 
Save Responses 
Today 
httpswapi.mist.com.'/api/vl 'sites,'0fc98886-fb 1 c-48ef-b905-819200d57d54/wIan . 
Send 
https://api.mist.com//api/vl 'sites/Ofc 
c-48ef-b905-819200d57d5 
4/wlans 
https://api.mist.com//api/vl 'sites/Ofc 
988864b 1 c-48ef-b905-819200d57d5 
4/wlans/a88d6cdd-6aa54eb2-8cd9-fb 
https://api.mist.com//api/vl 'sites/Ofc 
98886-fb 1 c-48ef-b905-819200d57d5 
4/wlans/a88d6cdd-6aa54eb2-8cd9-fb 
https://api.mist.com//api/vl 'sites/Ofc 
98886-fb 1 c-48ef-b905-819200d57d5 
4,'wlans/a88d6cdd-6aa54eb2-8cd9-fb 
https://api.mist.com//api/vl 'sites/Ofc 
98886-fb 1 c48ef-b905-819200d57d5 
4,'wlans/a88d6cdd-6aa5-4eb2-8cd9-fb 
https://api.mist.com//api/vl 'sites/Ofc 
1 9200d57d5 
4,'wlans/a88d6cdd-6aa5-4eb2-8cd9-fb 
https://api.mist.com//api/vl 'sites/Ofc 
1 c48ef-b905-819200d57d5 
4,'wlans/a88d6cdd-6aa5-4eb2-8cd9-fb 
https://api.mist.com//api/vl 'sites/Ofc 
1 c48ef-b905-819200d57d5 
4/wlans 
params 
Authorization 
Headers (9) 
Test Results 
Visualize 
Body 
pre-request Script 
VALUE 
Tests 
Code 
GET 
GET 
PUT 
Settings 
Save 
Cookies 
Presets 
Headers (2) 
Authorization 
Content-Type 
Key 
Temporary Headers (7) O 
Body Cookies Headers ( 1 6) 
Bulk Edit 
Token UTEmqoKcpxlmnGodssHZOZCn8Sk7PC 
application/json 
Value 
status: 200 0K 
Description 
Time: 176ms 
size: 8.5 KB 
Save Response 
P retty 
Raw Preview 
Yesterday 
May 8 
"template åd": null, 
leøøe 
"90s": { 
"class": "best effor•t" 
' false 
"org_åd 
"d2b94bf9-22be-aaf1-aød1-68e162fbe199", 
"dtim": 2, 
"hide ssid": false, 
"acct serwers : 
"created tine . 
"disable_uapsd" 
1557492085, 
. false, 
"allow mdns": false, 
"apply _ to": "site" , 
"app_limit": { 
• 'enabled": false, 
• 'apps": 
"wxtag_ids 
a88d6cdd-6aa5-1eb2-8cd9-fb8731ab18e2

We should now see the details of just “WiFiNinjas(.)(.)” WLAN.

PS. We love that (.)(.) suffix!

Change WLAN name

Let’s rename “WiFiNinjas(.)(.)” to “WiFiNinjasTitties”! 🙂

Since we’re changing WLAN config, we’ll use PUT this time.

Change request type to PUT, go to “Body” tab, ensure “JSON” type is selected and write down lines that you want changed between curly brackets.

Note, that there is no comma at the end of the line.

You can now refresh your Mist Dash or GET the WLAN info via API to check if the name was changed.

Worked like charm! 🙂

What’s Next?

Finally, we can use Postman to translate our actions into code (we’ll stick to Python), that can be used in scripts.

That’s it!

We have successfully listed all Mist WLANs, then narrowed it down to one WLAN and finally we’ve changed the name of that WLAN. All with REST API GET and PUT requests – how cool!

With love,

WiFi Ninjas x

WN Podcast 025 – RTLS – Part 1 – The Theory

Welcome to our new WiFi Ninjas Podcast episode!

Today we kick of the RTLS discussion, starting with a theory! We’ll cover how different technologies like WiFi or BLE can make RTLS work and what the RTLS really is.

We’ll discuss RTLS functional blocks like network wide presence stats, location aware functions like blue dot and zone location analysis and actions and engagement.

 It’s a vast topic, and we’ll release a blog about it shortly, so this show notes are rather short! Stay tuned 🙂

With tons of love x,

WiFi Ninjas