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