location-sensing using the ieee 802.11 infrastructure and the peer-to-peer paradigm for mobile...

Location-sensing using the IEEE 802.11 Infrastructure and the Peer-to-peer Paradigm for mobile computing applications

Anastasia KatranidouSupervisor: Maria Papadopouli

Master Thesis, University of Crete – ICS-FORTH Hellas 20 February 2006

2Master Thesis, University of Crete – ICS-FORTH, Hellas

Overview Location-sensing Motivation Proposed system (CLS) Evaluation of CLS Comparison with related work Conclusions - Future Work

3Master Thesis, University of Crete – ICS-FORTH, Hellas

Pervasive computing century Pervasive computing

enhances computer use by making many computers available throughout the physical environment but effectively invisible to the user

4Master Thesis, University of Crete – ICS-FORTH, Hellas

Why is location-sensing important ? Mapping systems Locating people & objects Wireless routing Smart spaces Supporting location-based applications

transportation industry medical community security entertainment industry emergency situations

5Master Thesis, University of Crete – ICS-FORTH, Hellas

Location-sensing properties Metric (signal strength, direction, distance) Techniques (triangulation, proximity, scene analysis) Multiple modalities (RF, ultrasonic, infrared) Limitations & dependencies (e.g., infrastructure vs. ad

hoc) Localized or remote computation Physical vs. symbolic location Absolute vs. relative location Scale Cost Hardware availability Privacy

6Master Thesis, University of Crete – ICS-FORTH, Hellas

Related work

GPS satellite localization, absolute, outside buildings only

Active Badge infrared, symbol, absolute, extensive hardware

APS with AoA RF, ultrasound, physical, relative, extensive hardware

RADAR IEEE 802.11 infrastructure, physical absolute, triangulation

Ladd et al. IEEE 802.11 infrastructure, physical, relative

Cricket ultrasound, RF from IEEE 802.11

Savarese et al. ad hoc networks

7Master Thesis, University of Crete – ICS-FORTH, Hellas

Motivation Build a location-sensing system for mobile computing

applications that can provide position estimates: within a few meters accuracy without the need of specialized hardware and extensive

training using the available communication infrastructure operating on indoors and outdoors environments using the peer-to-peer paradigm, knowledge of the

environment and mobility

8Master Thesis, University of Crete – ICS-FORTH, Hellas

Design goals Robust to tolerate network failures, disconnections,

delays due to host mobility Extensible to incorporate application-dependent

semantics or external information (floorplan, signal strength maps)

Computationally inexpensive Scalable Use of cooperation of the devices and information

sharing No need for extensive training and specialized

hardware Suitable for indoor and outdoor environments

9Master Thesis, University of Crete – ICS-FORTH, Hellas

Thesis contributions Implementation of the Cooperative Location

System (CLS) protocol on a different simulation platform (ns-2)

Extensive performance analysis Extension of CLS

signal strength map information about the environment (e.g., floorplan)

Study the impact of mobility Extension of CLS algorithm under mobility Study the range error in ICS-FORTH

10Master Thesis, University of Crete – ICS-FORTH, Hellas

Cooperative Location System (CLS) Communication Protocol

Each host estimates its distance from neighboring peers refines its estimations iteratively as it receives new

positioning information from peers Voting algorithm

accumulates and evaluates the received positioning information

Grid-representation of the terrain

11Master Thesis, University of Crete – ICS-FORTH, Hellas

CLS beacon neighbor discovery protocol with single-hop broadcast beacons respond to beacons with positioning information (positioning entry & SS)

CLS entry set of information (positioning entry & distance estimation) that a host

maintains for a neighboring host CLS update messages

dissemination of CLS entries CLS table

all the received CLS entries

Peer id

Position

Time Range Weight

Distance Vote

A (xA,yA) tn RA wA (du,A- e , du,A+ e)

Positive

C (xC,yC) tk RC wC (RC, ) Negative

CLS table of host u with entries for peers A and C

Positioning entry Distance estimation

CLS entries

Communication protocol

12Master Thesis, University of Crete – ICS-FORTH, Hellas

Voting algorithm Grid for host u (unknown

position) Corresponds to the terrain Peer A has positioned itself Positive votes from peer A

A cell is a possible position The value of a cell = sum of the

accumulated votes The higher the value of a cell, the more

hosts agree that this cell is likely position of the host

Peer B has positioned itself Positive votes from peer B Negative vote from peer C

13Master Thesis, University of Crete – ICS-FORTH, Hellas

Voting algorithm termination Set of cells with maximal values defines possible

position If there are enough votes (ST) and the precision is

acceptable (LECT) Report the centroid of the set as the host position

14Master Thesis, University of Crete – ICS-FORTH, Hellas

Evaluation of CLS Impact of several parameters on the accuracy:

ST and LECT thresholds Range error Density of peers and landmarks

15Master Thesis, University of Crete – ICS-FORTH, Hellas

Impact of range error Simulation setting (ns-

2) 10 landmarks + 90

stationary nodes avg connectivity degree =

10 transmission range (R) =

20m

avg connectivity degree = 12

16Master Thesis, University of Crete – ICS-FORTH, Hellas

Impact of connectivity degree & percentage of landmarks For low connectivity

degree or few landmarks the location error is bad

For 10% or more landmarks and connectivity degree of at least 7 the location error is

reduced considerably

5% range error

17Master Thesis, University of Crete – ICS-FORTH, Hellas

Extension of CLS Incorporation of:

signal strength maps information about the environment (e.g., floorplan) confidence intervals topological information pedestrian speed

18Master Thesis, University of Crete – ICS-FORTH, Hellas

Signal Strength map training phase:

each cell & every AP 60 measured SS values

(one SS value per sec)

estimation phase: SS measurements in 45

different cells

95% - confidence intervals If LBi[c] ≤ ŝi ≤ UBi[c]: the cell

c accumulates a vote from APi

final position: centroid of all the cells with maximal values

19Master Thesis, University of Crete – ICS-FORTH, Hellas

CLS with signal strength map

95% - confidence intervals no CLS: 80% hosts ≤ 2 m extended CLS: 80% hosts

≤ 1 m

20Master Thesis, University of Crete – ICS-FORTH, Hellas

Impact of mobility Movement of mobile nodes Speed of the mobile nodes Frequency of CLS runs

21Master Thesis, University of Crete – ICS-FORTH, Hellas

Impact of movement of mobile nodes Simulation

setting 10 different

scenarios 10 landmarks, 10

mobile, 80 stationary nodes

max speed = 2m/s time= 100 sec

22Master Thesis, University of Crete – ICS-FORTH, Hellas

Impact of the speed of the mobile nodes Simulation setting

6 times the same scenario

fixed initial and destination position of each node at each run.

10 landmarks, 10 mobile, 80 stationary nodes

time = 100 sec

23Master Thesis, University of Crete – ICS-FORTH, Hellas

Impact of the frequency of CLS runs Simulation setting

6 times the same scenario (every 120, 60, 40, 30, 20 sec)

CLS run = 1, 2, 3, 4, 6 times speed = 2m/s. 10 landmarks, 10 mobile, 80

stationary nodes time = 120 sec

Tradeoff accuracy vs. overhead message exchanges computations

24Master Thesis, University of Crete – ICS-FORTH, Hellas

Evaluation of the extended CLS under mobility Incorporation of:

topological information signal strength maps pedestrian speed

Simulation setting 5 landmarks, 30 mobile, 15 stationary nodes Speed = 1m/s range error = 10% R R = 20 m time = 120 sec CLS every 10 sec

25Master Thesis, University of Crete – ICS-FORTH, Hellas

Use of topological information

mobile node cannot walk through walls and cannot enter in some forbidden areas (negative weights)

a mobile node follows some paths (positive weight)

'mobile CLS': 80% of the nodes have 90% location error (%R)

'extended mobile CLS with walls': 80% of the nodes have 60% location error (%R)

26Master Thesis, University of Crete – ICS-FORTH, Hellas

Use of signal strength maps

'extended mobile CLS with walls & SS': 80% of the nodes have

30% location error (%R)

27Master Thesis, University of Crete – ICS-FORTH, Hellas

Use of the pedestrian speed

pedestrian speed: 1 m/s time instance t1: at point

X after t sec: at any point of

a disc centered at X with radius equal to t meters

'extended mobile CLS with walls & SS, pedestrian': 80% of the nodes have

13% location error (%R)

28Master Thesis, University of Crete – ICS-FORTH, Hellas

Estimation of Range Error in ICS-FORTH 50x50 cells, 5 APs

For each cell we took 60 SS values 95% confidence intervals (CI) for

each cell c and the respective APs I

Range errori[c] = max{|d(i,c) - d(i,c’)|}, c' such that: CIi[c]∩CIi[c’] ≠ Ø

90% cells ≤ 4 meters range error (10% R)

Maximum range error due to the topology ≤ 9.4 meters

29Master Thesis, University of Crete – ICS-FORTH, Hellas

Conclusions Evaluation and extension of the CLS algorithm Evaluation of the system under mobility Good accuracy with mobility without additional

hardware, training and infrastructure

30Master Thesis, University of Crete – ICS-FORTH, Hellas

Future work Incorporate heterogeneous devices (e.g, RF tags,

sensors) to enhance the accuracy Provide guidelines for tuning the weight votes of

landmarks and hosts Incorporate mobility history Employ theoretical framework (e.g., particle filters) to

support the grid-based voting algorithm