OTMCL: Orientation Tracking-based Localization for Mobile Sensor Networks

OTMCL: Orientation Tracking-based Localization for Mobile Sensor Networks

Location 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

How 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

The case of mobility in localization

Our goal • We are interested in positioning 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

Probabilistic 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)‏

Sequential 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

MCL: Initialization Node’s actual position Node’s estimate Initialization: Node has no knowledge of its location. L0 = { set of N random locations in the deployment area }

MCL: Prediction Prediction: New particles based on previous estimated location and maximum velocity, vmax Node’s actual position Node’s last estimate

Filtering a a Indirect Anchor Within distance (r, 2r] of anchor Direct Anchor Node is within distance r of anchor

MCL : Filtering Node’s actual position Binary filtering: Samples which are not inside the communication range of anchors are discarded r Anchor Invalid samples

Re-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)

Other 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)

Problem 1: Predicted samples with wrong direction or velocity Problem 2: Previous location estimate is not well-localized Issue: Sample degradation Why don’t we tell where samples should be generated?

Proposal: Orientation Tracking-based Monte Carlo Localization (OTMCL)

Sensor 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 β • High-precision sensors ( β= 10o) • Medium-precision sensors ( β= 30o, β= 45o) • Low-precision sensors ( β = 90o)

Analysis – Convergence time relative to communication range stabilization phase ~ 7m OTMCL achieves a decent performance even when the inertial sensor is under heavy bias

Analysis – 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

Analysis – Anchor density OTMCL is robust even when the anchor network is sparse

Analysis – Speed variance As speed increases, the larger is the sampling area  lower accuracy

Analysis – Communication Irregularity OTMCL is robust to radio irregularity. Dead reckoning is responsible for maintaining accuracy

Conclusion • 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

Thank you for your attention martins@mcl.iis.u-tokyo.ac.jp

appendix

OTMCL: Necessary number of samples Estimate error fairly stable when N > 50

Analysis – Regular node density OTMCL is robust even when the anchor network is sparse

Is it feasible? (On computational overhead) • Impact of sampling (trials until fill sample set)

Radio 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])

OTMCL: Orientation Tracking-based Localization for Mobile Sensor Networks