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Indoor Location Service for NG911 Using Bluetooth LE
Student Team
Alberto Gonzalez
Neil Okhandiar
Nthenya Matheka
Professor / Project Director
Carol Davids
NG911 Mentor and Lab Manager
Joe Cusimano
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Overview
NG911BT is a Bluetooth indoor location service project that uses Bluetooth beacons to provide the address, floor and closest room of a person making a 911 call.
This project provides a Proof of Concept implementation of a system that uses the following components to reach an emergency call taker:
Bluetooth information
Mobile phone application
Location database
IP-based NG911-network
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Problem Statement
In Emergency situations:
Information and Response Time are critical Elements
The first responders need the location information in order to reduce the amount of time spent locating the person who made the 911 call.
Improving location will save at least 10,000 lives a year.
In this context, 911 Emergency Services today:
Only receive the horizontal outdoor (physical address) location from mobile phone calls
Don’t use IP networks to carry location and other additional data
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FCC Requirements
Federal Communications Commission (FCC) mandated that cellular service providers meet the following requirements:
Horizontal location (x and y axis) and Vertical location (z axis) to the public-safety answering point (PSAP): Room Level Identification.
Use of information from WiFi Access Points or Bluetooth beacons to calculate the location.
30-second maximum time period after call is sent.
In 5 years they proposed to achieve an 80% reliability and accuracy below 50 meters for 911 emergency calls
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5. NG911BT Solution: Technologies
Bluetooth provides information on
The ID of the beacons that are seen by the mobile phone
The Received Signal Strength Indicator (RSSI) of each beacon
Session Initiation Protocol (SIP)
Delivers voice and video over IP Networks
Delivers location information
Is the protocol required by NENA
For developing our system we decide to use the following technologies…
So low Battery consumption that the device can last for more than one year with a single charge. Cost: less than $3!!
Bluetooth doesn’t have a use case for this kind of solution
Allow us deliver multimedia over IP (in our case location data but we could send images and even video)
SIP for building a Solution compatible with NG911 Emergencies. Standard defined by NENA: NATIONAL EMERGENCY NUMBER ASSOCIATION
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6. Modules of the PILoT Architecture
4 modules: iBeacon Module (1 or more), Android cellphone Module which will contain the NG911BL App, the Location Server and ESInet module (NG911 Network Module) Network to route the emergency call to the PSAP. This is the NG9-1-1 Test Bed in the IIT Real-Time Communications Lab.
ESINET=Emergency Services IP Network
ESRP=Emergency Services Routing Proxy
PSAP=Public Services Answering Point
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Module: IBeacon
Bluetooth devices were designed by our team member Bharat who will explain you more in the next Thursday presentation: PILoT.
IBeacons work in advertising mode only. They do not connect to the phone.
Smartphone will scan and collect iBeacon Bluetooth identification.
Particle Photon (WiFi)
Contains two main elements:
Photon Particle: IoT Microcontroller with Wifi chip integrated
Bluetooth 4.0 LE Module
Wifi updating a web application for managing the status of the iBeacons and Bluetooth advertising his identifier
Bluetooth Module HM-10
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8. Module: Smartphone Application Functionality
From the application standpoint what happens when a user dials 911 or clicks on “call 911” using this system?
Recognizes the 911 or click button (911 method for calling adding the XML) and checks Bluetooth status
Scans and collects Bluetooth data from the environment
Sent to the location server and wait response 10 seconds
If there is a response will add specific indoor location information embedded into the SIP Invite
Changes the request uri to target our ESINET (Emergency Services IP Network)
InviteNG911() will create a new Invite ( sent to set-up the VoIP call) but with the additional information on it.
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9. Plan For Grading The Algorithms
1. Place iBeacon devices at specific locations
2. Make test-calls at predetermined locations
3. Determine "successful" test -- within some meter distance of the caller's location
4. Change iBeacon locations, maintain unchanging test-call locations
5. Grade based on Algorithm's rate of success vs failure
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iBeacon Deployment
Deployed in Illinois Institute’s Stuart Building
81x46x10 meter building, 3 floors
20 devices placed on each floor
For the first experiment, used “intuitive” and “abundant” placement of BT devices.
One device at every room entrance, stairwell, and hallway
For the second experiment we halved the number of devices
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Testing
Experiment 1:
Call-testing was done at room entrances, between rooms (in the hallways), inside of rooms and in stairwells
Collected 10 seconds of Bluetooth data, and passes it to the Location server
Location server applies various algorithms to determine the BT device that was closest to the caller
from Neil to Everyone:
Notably:
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Valid Bluetooth devices for a given test were any devices within x meters of the location.
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Algorithms
Tried 3 simple algorithms to pick the likeliest BT device closest to the caller
Max - Simply look through all the RSSI values, and pick the BT that output the highest signal in the whole set.
Avg - For each BT device, take an average of the RSSI (in dBm) and pick the highest.
Log_avg - for each BT device, take the log of the geometric mean of the RSSI, and pick the highest.
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Results
Log_avg
Experiment 1:
80 % success at 10+ meters, for all three algorithms
100 % success at 17+ meters, for all three algorithms
Experiment 2:
100 % success at 19+ meters, for Avg and Log_Avg algorithm
100 % success at 21+ meters, for Max signal algorithm
Success Rates
1. Though somewhat few, we now have floor-misses on our maximum density tests
2. Max_Signal is significantly worse off with fewer devices. ## Possibly affected by the zombie devices? We checked for effects on packet density, not RSSI
meters = 80%+ accuracy on maximum density, for all three algorithms
meters = 100% accuracy on maximum density, for all three algorithms
meters = 80%+ accuracy on half-density, for all three algorithms
meters = 100% accuracy on half-density, for avg & log_avg. 21+ for max_signal.
7. Possibly significant: we're more accurate at 10 meters with half-density than with max-density. Might imply negative returns.
Maximum Radius
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Future
Change the deployment patterns to see how these variations impact our success rate – in particular, lower the density of the beacons deployed
Collect additional information about RF spectrum in the buildings to observe their impact on the success rate
Increase the scope of the deployment to more buildings
Keep improving our PILoT platform through:
-Additional indoor location Algorithm research
-Hardening the system
-Develop an API
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DEMO
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Questions?
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Thank You
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