Presentation on theme: "Self-Configurable Positioning Technique for Multi-hop Wireless Networks To appear in IEEE Transaction on Networking Chong Wang Center of Advanced Computer."— Presentation transcript:
Self-Configurable Positioning Technique for Multi-hop Wireless Networks To appear in IEEE Transaction on Networking Chong Wang Center of Advanced Computer Studies University of Louisiana at Lafayette
Introduction Geographic location information – Reduce routing overhead – Improve scalability – Intelligent coordination Global vs. local positioning Our goal – Self-configurability – Robustness – High accuracy
Related Work Global Positioning Techniques –Global Positioning System (GPS) –Signpost Navigation System –Global Navigation Satellite System –Cellular Geolocation System –Drawbacks: hardware, signal obstruction Local Positioning Techniques – GPS-free positioning  Not robust – Connectivity-based positioning  Inaccurate – GPS-less , Fine-grain , APS  Convex, Recursive  Not self-configurable
Euclidean Distance Estimation Crucial for positioning Proposed scheme –Given node distribution (a) (b) Fig. 1. The Euclidean distance estimation model.
Euclidean Distance Estimation (2) First hop – within Ss range & closest to D – s coordinates where – 1-hop length Shortest path length – Apply 1-hop estimation recursively – Total path length
Coordinates Establishment Two steps: landmarks & regular nodes Landmarks –Estimate distance to each other –Exchange distance information –Define error function –Minimize by using Simplex method where
Coordinates Establishment (2) Regular nodes – May be considered as landmarks, but not scalable – Estimate dist. to landmarks – Define error function – Minimize p A B C D A B C D LABLAB L CD L BC L AD LACLAC LBDLBD p (a)(b)
Selection of Landmarks Number of landmarks –The more landmarks, the higher the accuracy. Location of landmarks –Separated as far as possible Algorithm of identifying corner nodes –Degree of center:
Simulation And Discussion Simulation Model – Simulator: Matlab – Variable parameters – Number of nodes: 50 – 400 – Number of landmarks: 3 – 8 – Measurement inaccuracy: 0 – 40% – Performance criteria – Coordinates error – Computing time
Examples GPS tuning N=50, no translationN=100, no translationN=400, no translation N=50, center matchN=100, center matchN=400, center match N=50, GPS tuningN=100, GPS tuningN=400, GPS tuning
with node density Impact of measurement error Accuracy with more landmarksDelay with more landmarks Simulation And Discussion
Conclusion We have proposed a self-configurable positioning technique for multi-hop wireless networks. The proposed positioning technique is self-configurable and independent of global position information. The coordinates error is determined by node density, one-hop distance measurement inaccuracy, and the number of landmarks. The computing time for coordinates establishment is in the order of milliseconds, which can be accepted by most applications in the mobile ad hoc networks as well as the sensor networks.
Reference: B. Parkinson and S. Gilbert, Navstar: global positioning system -- ten years later, Proceedings of the IEEE, pp , S. Capkun, M. Hamdi, and J.P. Hubaux, Gps-free positioning in mobile ad-hoc networks, Proceedings of the 34th Annual Hawaii International Conference on System Sciences, 2001, pp D. Niculescu and B. Nath, Ad hoc positioning system (APS), Proceedings of IEEE Global Communications Conference GLOBECOM'01, 2001, pp Y. Shang, W. Ruml, and Y. Zhang, Localization from mere connectivity, Proceedings of IEEE Mobile Ad Hoc Networking & Computing (MobiHOC'03), 2003, pp N. Bulusu, J. Heidemann, and D. Estrin, GPS-less low cost outdoor localization for very small devices, IEEE Personal Communications Magazine, vol. 7, no. 5, pp , A. Savvides, C. Han, and M. B. Strivastava, Dynamic fine-grained localization in ad-hoc networks of sensors, Proceedings of ACM/IEEE the 7th Annual International Conference on Mobile Computing and Networking (MobiCom'01), 2001, pp T. Ng and H. Zhang, Predicting the internet network distance with coordinates- based approaches, Proceedings of IEEE Conference on Computer Communication (INFOCOM '02), 2002, pp J. Nelder and R. Mead, A simplex method for function minimization, Computer Journal, vol. 7, pp , 1965.