Indoor Location Accuracy for Wireless 9-1-1 NARUC 2015 Winter Committee Meeting 02/18/2015

Indoor Location Accuracy (ILA) for 9-1-1 Establishing a Baseline for Wireless 9-1-1 Indoor Location Challenge Evidence of a Problem Statistics Live 9-1-1 Call Data Tarrant County, Texas, 2013 Baltimore City, Maryland, 2014 2 2

Wireless E9-1-1 Call/Data Flow: A Baseline 1: Person dials 9-1-1 4: E9-1-1 Center gets enhanced location 2: MSC requests routing instructions 5: PSAP queries for enhanced location 3: MSC routes call to nearest PSAP 6: PSAP dispatches assistance Wireless Carrier (202) 555-1212 3 9-1-1 a - Tower 911 Communicator Mobile Switching Center ALI 1 b - Enhanced 5 LEC Dispatch to Enhanced Location PSAP 2 6 4 Voice Data MPC ESRK ESRK PDE CRDB ALI CRDB – Call Routing Data Base PDE - Position Determination Entity MPC – Mobile Positioning Center ESRK = Emergency Services Routing Key TCS E9-1-1 Center

Wireless E9-1-1: Indoor Location Problem 1: Smartphones with GPS 4: Failing to get an enhanced location! 2: Home “cuts the cord” 3: 9-1-1 Calls from Indoor Locations Wireless Carrier (202) 555-1212 9-1-1 a - Tower 911 Communicator 1 Mobile Switching Center ALI 3 b – NO Enhanced LEC Dispatch to Tower location/verbal info PSAP 2 Voice Data 4 MPC ESRK PDE ESRK CRDB ALI CRDB – Call Routing Data Base PDE - Position Determination Entity MPC – Mobile Positioning Center ESRK = Emergency Services Routing Key TCS E9-1-1 Center

Problem of Indoor Location for Wireless 9-1-1 We “should” have a problem 5 5

More calls should be coming from indoors 40% of US population has “cut the cord” 2013 CDC study (37% of adults; 45% of children) http://www.cdc.gov/nchs/data/nhis/earlyrelease/wireless201306.pdf 70% of 9-1-1 calls come from wireless 2012 King County, WA statistic 6 6

Problem of Indoor Location for Wireless 9-1-1 We “should” have a problem 40% of US population has “cut the cord” 70% of 9-1-1 calls come from wireless We “see” a problem 7 7

Real-World Data Can Guide Our Testing Actual 911 calls Tarrant County All carriers August, 2013 Random? Uniformly distributed? Which are Indoors? Which are Outdoors? Color-code X/Y locations based upon Horizontal Uncertainty: Brown = Phase I only Green = meets stricter requirement. Red = misses looser requirement. Yellow = between strict/loose

More “bad” location fixes – due to indoors? /2007 2013 7.5% exceeded Phase II upper bound (red) 2007 3.3% exceeded Phase II upper bound (red) 3.3%  7.5% (more calls from indoor locations?)

“Bad” location fixes – probably indoors… Tarrant County, TX 9-1-1 Calls – August, 2013

Data Trends Reveal Indoor Challenges Problem area seen in 2011 2007 2011 Nonexistent in 2007 Major problem area in 2011 2012 2013 Greatly improved in 2013 Improved in 2012 Goodrich Warehouse Built in 2007

Problem of Indoor Location for Wireless 9-1-1 We “should” have a problem 40% of US population has “cut the cord” 70% of 9-1-1 calls come from wireless We “see” a problem Tarrant County, TX (Aug, 2013) “Bad” HUNC values increased: 2007=3.3%; 2013=7.5% Baltimore 9-1-1 calls (Nov, 2014) “Bad” Horizontal Uncertainty (HUNC) carol HUNC is a distance/range calculated by the Location Engine Determines the range of location “error” based on Confidence value Confidence (90% here) expresses likelihood to find device within range 12 12

Problem of Indoor Location for Wireless 9-1-1 We “should” have a problem 40% of US population has “cut the cord” 70% of 9-1-1 calls come from wireless We “see” a problem Tarrant County, TX (Aug, 2013) “Bad” HUNC values increased: 2007=3.3%; 2013=7.5% Baltimore 9-1-1 calls (Nov, 2014) “Bad” HUNC values were significant So how do we solve it? carol 13 13

Thank you! Timothy James Lorello Senior Vice President, TCS 410-280-1275 TLorello@telecomsys.com www.telecomsys.com 275 West Street Annapolis, MD 21401 14