SensIT CSP Workshop Day 2 J anuar y 16, 2001 P alo Alto, CA
Schedule 8:30 – 9:00 Br eakf ast 9:00 – 9:30 I ntr o Commons 9:30 – 10:30 Tr acker Design Aquar ium: # 5 (2-way tr af f ic) Commons: # 7 (per imeter violation) 10:30-10:45 BREAK 10:45-11:15 Summar ies: Tr acker Design 11:15-11:45 SenseI T Demo Context & Br ainstor m 11:45-1:30 WORKI NG LUNCH – Detailed Demo Discussions 3 Br eakout gr oups (Commons & Aquar ium) 1:30-2:00 Summar ies: Demo Discussions
Tracker Discussions The Task: Develop a t op-level, end-t o-end solut ion f or t wo benchmar k pr oblems. I dent if y all t he pieces. Why: • SenseI T t eams come f r om mult iple f ields, t ar get ing dif f er ent aspect s of t he pr oblem: • This is an oppor t unit y t o –Get all of t he common t echniques on t he t able and discuss inf or mally –I dent if y any t echnology gaps –See how t r acker design, signal pr ocessing and classif icat ion appr oaches complement each ot her –Syner gies bet ween t echniques her e may make good demo candidat es Not e: t hese ar e j ust st ar t ing point s, f eel f r ee t o modif y scenar ios in int er est ing ways or t o f ix bugs.
Issues / Subtasks • Quiescent St at e • Tar get det ect ion / wakeup • Clust er For mat ion • Signal P r ocessing – Dat a Associat ion – Sensor Fusion – Classif icat ion – Localizat ion – Compr ession • Tr acking – Tr ack gener at ion / dest r uct ion – Tr ack coalescence / split t ing – Dynamics – Reasoning about per sist ent ident it ies – Reasoning about Global P r oper t ies • Exf ilt r at ion • Quer y P r ocessing
Scenario # 5: Two-way traffic Task •Track target positions •Estimate target crossing time Challenges •Vehicles in close proximity, Nom. Crossing need to use dynamics to keep Point identities separate Task-Specific Benchmarks •Accuracy of crossing time estimate
Scenario # 5 Assumptions • Desir ed out put updat e r at e: 0.5 Hz • Desir ed posit ion r esolut ion: 25’ • Net wor k behavior – Lat ency: mean 0.1 secs per hop – P acket s may ar r ive out -of -or der – 10% packet loss – 19.2 kbps per clust er , each node can j oin up t o 2 • I nit ial Velocit ies: 25 f t / sec (each) • Dynamics – Max acceler at ion: +/ -50 f t / sec^2 (symmet r ic) – Not e t hat t anks can pivot 360 degr ees in place while at st andst ill • Sensor spacing: 100’ bet ween all sensor s • Vehicles: I dent ical, signat ur e aspect dependent • I sot r opic pr opagat ion of sound, vibr at ions, no wind, no inver sion layer – 1/ r ^2 at t enuat ion • Tar get class known a pr ior i, no gear shif t s
Scenario # 7: Perimeter Violation Sensing arrival at t=8:00:00 Task •Alert on violation of perimeter •Ignore activity outside of perimeter (distractors) •Identify violator type and track location 25’ Challenges •Filter out distractor •Respond quickly while 1000’ minimizing quiescent activity Task-Specific Benchmarks •Detection delay •Power usage during periods of no violation •Frequency of false positives all vs. distractor/violator arrival at t=0 source amplitude ratio
Scenario # 7 Assumptions • Desir ed det ect ion t ime: < 2 sec • Dist r act or ar r ives, or bit s f or 8 hour s pr ior t o int r uder • Dist r act or / I nt r uder sour ce amplit ude r at io: 40 dB • Const ant velocit ies: – I nt r uder 20 f t / sec – Dist r act or 40 f t / sec – I nt r uder can come f r om any dir ect ion • Net wor k behavior : – lat ency: mean 0.05 sec/ hop – P acket s may ar r ive out -of -or der – P acket Loss: 2% – 128 kbps/ clust er , each node can j oin up t o 4 • Sensor spacing: 100’ bet ween all sensor s • I sot r opic pr opagat ion of sound, vibr at ions, no wind, no inver sion layer – 1/ r ^2 at t enuat ion • I nt r uder & Dist r act or classes unknown a pr ior i, no gear shif t s, const ant speed
2001 Demo Discussions The Task • Develop candidate demos f or Mar ch/ Apr il and November . I dentif y inter ests of var ious teams. Constr aints • Mar ch: Field Exper iment using cur r ent nodes • Apr il: Lab Demo, Simulation and/ or Data exper iment using 29P alms or Mar ch 2001 dat a • November : Field Exper iment – WI NS NG nodes: – Wir eless networ k – May include RP V (i.e. f ixed/ mobile) complementing gr ound nodes
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