An Efficient Localization Algorithm Focusing on Stop-and-Go Behavior of Mobile Nodes IEEE PerCom 2011 Takamasa Higuchi, Sae Fujii, Hirozumi Yamaguchi and Teruo Higashino Graduate School of Information Science and Technology, Osaka University 1-5 Yamadaoka, Suita, Osaka Japan Speaker: Wun-Cheng Li

Outline Introduction Network Model State Decision Process Localization Interval Protocol Design Simulation Conclusion 2

Introduction Location-aware services on cell phones have spread rapidly. ▫ Car navigation systems ▫ Pedestrian navigation applications 3

Introduction However, to provide real-time position information to people indoor is still a big challenge. ▫ Exhibition patrons ▫ Museum visitors ▫ Customers at shopping malls 4

Introduction Rely on large amounts of fixed infrastructure for positioning also requires a lot of installation and maintenance costs 5

Motivation Not all applications require accurate location information. ▫ Allow a certain range of localization error 6

Problem To accomplish acceptable accuracy of mobile nodes, frequency of position updates should be sufficiently high. How a certain error range enables mobile nodes to locate and reduce excessive localization frequency reduction. 7

Goals Propose an efficient localization algorithm of mobile nodes to ▫ decrease the localization overhead ▫ satisfy the constraint of tolerable position error of each sensor 8

Network Model 9 Anchor Nodes Unknown state Nodes Moving state Nodes Static state Nodes

Network Model Each mobile node is assumed to have both an ultrasound ranging device and a wireless device Applies a Time Difference of Arrival (TDoA) technique to measure the distance 10 RF signals ultrasound signals 10s 20s 5m, (x 1, y 1 ) A 1 (x 1, y 1 ) A0A0

Network Model Each node A i holds position (x i, y  ) and speed v  A 1 (x 1, y 1 ) v 1 = 0.0 m/s A 0 (x 0, y 0 ) v 0 = 1.1 m/s A 5 (x 5, y 5 ) v 5 = 0.0 m/s A 4 (x 4, y 4 ) v 4 = 1.0 m/s A 2 (x 2, y 2 ) v 2 = 0.0 m/s A 3 (x 3, y 3 ) v 3 = 0.0 m/s d1d1 movement d3d3 d5d5 d2d2 A0A0 measured distance 11

State Decision Process 12 A j (x j, y j ) djdj AiAi

State Decision Process 13

State Decision Process 14 A 3 (x’ 3, y’ 3 ) movement d3d3 d1d1 d4d4 d2d2 d3d3 Likelihood 2 Likelihood 1 Likelihood 3 A 1 (x 1, y 1 ) A 4 (x 4, y 4 ) A 2 (x 2, y 2 ) A0A0 A 3 (x 3, y 3 )

Localization Interval 15

Localization Interval The failure of movement detection by a single neighbor can be soon recovered by other neighbors. 16 A 0 (x 0, y 0 ) v 0 = 0.0 m/s d’ 1 A1A1 d1d1 A0A0 A 0 (x 0, y 0 ) v 0 = 0.0 m/s A1A1 movement A0A0 A2A2 d’ 1 d1d1 d2d2 d’ 2 movement

Protocol Design 17

Simulation QualNet 18 PARAMETER SETTINGS Environmental scenarios15m x15m Anchors4 Nodes30 Speed4.0~8.0 km/h RTM messages maximum range12m TDoA measurement signals maximum range6m max. speed (V max )10.0 km/h coefficient localization interval ()0.80 max. int. of moving nodes ()3.0 sec. localization int. of static nodes ()5.0 sec. coefficient of backoff time ()0.76 tolerable position error ()1.0m

Simulation Localization Error 19

Simulation Tracking Error 20

Simulation Localization Intervals 21

Simulation Impact of Ranging Error 22 Ranging Error[m]

Conclusions This paper proposed a distributed cooperative algorithm to localize mobile nodes with a small number of anchor nodes. Automatically adjusts localization frequency according to the estimated speed of nodes to reduce unnecessary localization attempts. 23

Thank you! 24