Location-sensing using the IEEE Infrastructure and the Peer-to-peer Paradigm for mobile computing applications Anastasia Katranidou Supervisor: Maria Papadopouli Master Thesis, University of Crete – ICS-FORTH Hellas 20 February 2006

Master Thesis, University of Crete – ICS-FORTH, Hellas 2 Overview Location-sensing Motivation Proposed system (CLS) Evaluation of CLS Comparison with related work Conclusions - Future Work

Master Thesis, University of Crete – ICS-FORTH, Hellas 3 Pervasive computing century Pervasive computing  enhances computer use by making many computers available throughout the physical environment but effectively invisible to the user

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

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

Master Thesis, University of Crete – ICS-FORTH, Hellas 6 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 infrastructure, physical absolute, triangulation Ladd et al. IEEE infrastructure, physical, relative Cricket ultrasound, RF from IEEE Savarese et al. ad hoc networks

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

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

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

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

Master Thesis, University of Crete – ICS-FORTH, Hellas 11 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 idPositionTimeRangeWeightDistanceVote A(x A,y A )tntn RARA wAwA (d u,A - e, d u,A + e)Positive C(x C,y C )tktk RCRC wCwC (R C,  )Negative CLS table of host u with entries for peers A and C Positioning entry Distance estimation CLS entries Communication protocol

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

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

Master Thesis, University of Crete – ICS-FORTH, Hellas 14 Evaluation of CLS Impact of several parameters on the accuracy:  ST and LECT thresholds  Range error  Density of peers and landmarks

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

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

Master Thesis, University of Crete – ICS-FORTH, Hellas 17 Extension of CLS Incorporation of:  signal strength maps  information about the environment (e.g., floorplan)  confidence intervals  topological information  pedestrian speed

Master Thesis, University of Crete – ICS-FORTH, Hellas 18 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 LB i [c] ≤ ŝ i ≤ UB i [c]: the cell c accumulates a vote from APi  final position: centroid of all the cells with maximal values

Master Thesis, University of Crete – ICS-FORTH, Hellas 19 CLS with signal strength map 95% - confidence intervals  no CLS : 80% hosts ≤ 2 m  extended CLS : 80% hosts ≤ 1 m

Master Thesis, University of Crete – ICS-FORTH, Hellas 20 Impact of mobility Movement of mobile nodes Speed of the mobile nodes Frequency of CLS runs

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

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

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

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

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

Master Thesis, University of Crete – ICS-FORTH, Hellas 26 Use of signal strength maps 'extended mobile CLS with walls & SS':  80% of the nodes have 30% location error (%R)

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

Master Thesis, University of Crete – ICS-FORTH, Hellas 28 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 error i [c] = max{|d(i,c) - d(i,c’)|},  c' such that: CI i [c]∩CI i [c’] ≠ Ø 90% cells ≤ 4 meters range error (10% R) Maximum range error due to the topology ≤ 9.4 meters

Master Thesis, University of Crete – ICS-FORTH, Hellas 29 Conclusions Evaluation and extension of the CLS algorithm Evaluation of the system under mobility Good accuracy with mobility without additional hardware, training and infrastructure

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

Master Thesis, University of Crete – ICS-FORTH, Hellas 31 RADAR vs. CLS RADAR : 3 APs 90% hosts: 6 m sampling density: 1 sample every 13.9 m 2 Extended static CLS: 5 APs 90% hosts: 2 m sampling density: 1 sample every 14.8 m 2

Master Thesis, University of Crete – ICS-FORTH, Hellas 32 Ladd et al. vs. CLS Static localization Ladd et al.  9 APs  77% of hosts: 1.5 m Extended static CLS  5 APs  77% of hosts: 0.8 m Static fusion Ladd et al.  9 APs  64% of hosts: 1 m Extended mobile CLS  5 APs  45% of hosts: 1 m

Master Thesis, University of Crete – ICS-FORTH, Hellas 33 Savarese et al. vs. CLS Savarese et al. better with very small connectivity degree (4) or less than 5 landmarks Extended static CLS better with connectivity degree of at least 8 and 10% or more landmarks

Master Thesis, University of Crete – ICS-FORTH, Hellas 34 Impact of ST and LECT thresholds Terminate the iteration process  ST: the num of votes in a cell must be above it  LECT: the num of cells with max value must be below it LECT Host h defined solely from host g  not acceptable: the possible cells of host h correspond to a ring Host h defined from host g and k  1 case: not acceptable  2 case: location error max = √ [D max 2 – (D min + e) 2 ] Host h defined from host g, k and m  Possible area: (2· ε +1) 2  location error max : √ [(2· ε +1) 2 / 2] ST  eventually each host will receive votes from every landmark and every other host (CLS updates)  w all_landmarks +w all_hosts

Master Thesis, University of Crete – ICS-FORTH, Hellas 35 ST and LECT Simulation setting  10 landmarks and 90 nodes  avg connectivity degree = 10  range error = 10% R Best values  ST = 800  LECT = 5