Copyright 2016 davids@iit.edu 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 Copyright 2016 davids@iit.edu 1

Copyright 2016 davids@iit.edu 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 Copyright 2016 davids@iit.edu 2

Copyright 2016 davids@iit.edu 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 Copyright 2016 davids@iit.edu 3

Copyright 2016 davids@iit.edu 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 Copyright 2016 davids@iit.edu 4

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 Copyright 2016 davids@iit.edu 5

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 Copyright 2016 davids@iit.edu 6

Copyright 2016 davids@iit.edu 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 Copyright 2016 davids@iit.edu 7

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. Copyright 2016 davids@iit.edu 8

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 Copyright 2016 davids@iit.edu 9

Copyright 2016 davids@iit.edu 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 Copyright 2016 davids@iit.edu 10

Copyright 2016 davids@iit.edu 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: Copyright 2016 davids@iit.edu 11

Copyright 2016 davids@iit.edu Valid Bluetooth devices for a given test were any devices within x meters of the location. Copyright 2016 davids@iit.edu 12

Copyright 2016 davids@iit.edu 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. Copyright 2016 davids@iit.edu 13

Copyright 2016 davids@iit.edu 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 3. 10+ meters = 80%+ accuracy on maximum density, for all three algorithms 4. 17+ meters = 100% accuracy on maximum density, for all three algorithms 5. 10+ meters = 80%+ accuracy on half-density, for all three algorithms 6. 19+ 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 Copyright 2016 davids@iit.edu 14

Copyright 2016 davids@iit.edu 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 Copyright 2016 davids@iit.edu 15

Copyright 2016 davids@iit.edu DEMO Copyright 2016 davids@iit.edu 16

Copyright 2016 davids@iit.edu Questions? Copyright 2016 davids@iit.edu 17

Copyright 2016 davids@iit.edu Thank You Copyright 2016 davids@iit.edu 18