Title: OTMCL: Orientation Trackingbased Localization for Mobile Sensor Networks
1OTMCL Orientation Tracking-based Localization
for Mobile Sensor Networks
2Location awareness
- Localization is an important component of WSNs
- Interpreting data from sensors requires context
- Location and sampling time?
- Protocols
- Security systems (e.g., wormhole attacks)
- Network coverage
- Geocasting
- Location-based routing
- Sensor Net applications
- Environment monitoring
- Event tracking
- Mapping
3How can we determine location?
- GNSS receiver (e.g., GPS, GLONASS)
- Consider cost, form factor, inaccessibility, lack
of line of sight - Cooperative localization algorithms
- Nodes cooperate with each other
- Anchor-based case
- Reference points (anchors) help other nodes
estimate their positions
4The case of mobility in localization
5Our goal
- We are interested on positioning of low-powered,
resource-constrained sensor nodes - A (reasonably) accurate positioning system for
mobile networks - Low-density, arbitrarily placed anchors and
regular nodes - Range-free no special ranging hardware
- Low communication and computational overhead
- Adapted to the MANET model
6Probabilistic methods
- Classic localization algorithms (DV-Hop,
Centroid, APIT, etc.) compute the location
directly and do not target mobility - Probabilistic approach explicitly considers the
impreciseness of location estimates - Maximum Likelihood Estimator (MLE)?
- Maximum A Posteriori (MAP)?
- Least Squares
- Kalman Filter
- Particle Filtering (Sequential Monte Carlo or
SMC)?
7Sequential Monte Carlo Localization
- Monte Carlo Localization (MCL)? Hu04
- Locations are probability distributions
- Sequentially updated using Monte Carlo sampling
as nodes move and anchors are discovered
8MCL Initialization
Nodes actual position
Nodes estimate
Initialization Node has no knowledge of its
location. L0 set of N random locations in
the deployment area
9MCL Prediction
Prediction New particles based on previous
estimated location and maximum velocity, vmax
Nodes actual position
Nodes last estimate
10Filtering
a
a
Indirect Anchor Within distance (r, 2r of that
anchors location.
Direct Anchor Within distance r of the anchors
location
11MCL Filtering
Nodes actual position
Binary filtering Samples which are not inside
the communication range of an anchor are discarded
r
Anchor
Invalid samples
12Re-sampling
- Repeat prediction and filtering until we obtain a
minimum number of samples N. - Final estimate is the average of all filtered
samples - If no samples found, reposition at the center of
deployment area (initialization)
13Other SMC-based Works
- MCB Baggio08
- Better prediction smaller sampling area using
neighbor coordinates - MSL Rudhafshani07
- Better filtering use information from non-anchor
nodes after they are localized - Samples are weighted according to reliability of
neighbors (non-binary filter)
14Sample Degradation
- Problem 1 Predicting samples with the wrong
direction or velocity
- Problem 2 Previous location estimate is not
well-localized
Why dont we tell where samples should be
generated?
15Proposal Orientation Tracking-based Monte Carlo
Localization (OTMCL)
16Sensor bias
- Inertial sensor is subject to bias due to
- Magnetic interference
- Temperature variation
- Erroneous calibration
- Affects velocity and orientation estimation
during movement - Lower localization accuracy
- No assumptions about hardware
- Analyses use 3 categories of nodes for OTMCL
based on ß - High-precision sensors ( ß 10o)
- Medium-precision sensors ( ß 30o, ß 45o)
- Low-precision sensors ( ß 90o)
17Analysis Convergence time
relative to communication range
stabilization phase
7m
OTMCL achieves a decent performance even when the
inertial sensor is under heavy bias
18Analysis Communication overhead
- Reducing power consumption is a primary issue in
WSNs - Limited batteries
- Inhospitable scenarios
- Assumes no data aggregation, compression
- OTMCL needs less information to achieve similar
accuracy to MSL
19Analysis Anchor density
OTMCL is robust even when the anchor network is
sparse
20Analysis Speed variance
As speed increases, the larger is the sampling
area ? lower accuracy
21Analysis Communication Irregularity
OTMCL is robust to radio irregularity. Dead
reckoning is responsible for maintaining accuracy
22Conclusion
- Monte Carlo localization
- Achieves accurate localization cheaply with low
anchor density - Orientation data promotes higher accuracy even on
adverse conditions (low density, communication
errors) - Our contribution
- A positioning system with limited communication
requirements, improved accuracy and robustness to
communication failures - Future work
- Adaptive localization (e.g., variable sampling
rate, variable sample number) - Feasibility in a real WSN
23Thank you for your attention
- martins_at_mcl.iis.u-tokyo.ac.jp
24appendix
25OTMCL Necessary number of samples
Estimate error fairly stable when N gt 50
26Analysis Regular node density
OTMCL is robust even when the anchor network is
sparse
27Is it feasible? (On computational overhead)
- Impact of sampling (trials until fill sample set)
28Radio Model
- Upper lower bounds on signal strength
- Beyond UB, all nodes are out of communication
range - Within LB, every node is within the comm. range
- Between LB UB, there is (1) symmetric
communication, (2) unidirectional comm., or (3)
no comm. - Degree of Irregularity (DOI) (Zhou04)