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Perpendicular Intersection: Locating Wireless Sensors with Mobile Beacon

Perpendicular Intersection: Locating Wireless Sensors with Mobile Beacon. Zhongwen Guo, Ying Guo, Feng Hong, Xiaohui Yang, Yuan He, Yuan Feng, Yunhao Liu. Ocean University of China. Hong Kong University of Science and Technology. Outline. Introduction Observations on RSSI

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Perpendicular Intersection: Locating Wireless Sensors with Mobile Beacon

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  1. Perpendicular Intersection: Locating Wireless Sensors with Mobile Beacon Zhongwen Guo, Ying Guo, Feng Hong, Xiaohui Yang, Yuan He, Yuan Feng, Yunhao Liu Ocean University of China Hong Kong University of Science and Technology

  2. Outline • Introduction • Observations on RSSI • Our Scheme: PI (Perpendicular Intersection) • Design of Optimal Trajectory • Experiments • Conclusion and Future Work

  3. Introduction • Locating sensor nodes is a crucialissue in WSN applications. • OceanSense • https://www.cse.ust.hk/~liu/Ocean • http://osn.ouc.edu.cn

  4. Existing Approaches (1) • Range-Based Approaches • TOA, TDOA, AOA • Require additional hardware support • Expensive in manufactory cost and energy consumption • RSSI-based (Received Signal Strength Index) • Easy to implement; • Based on the log-normal shadowing model • Rely on absolute RSSI values (unstable & irregular) • Inaccurate due to channel noise, interference, attenuation, reflection, and environmental dynamics Environment-dependent !

  5. Existing Approaches (2) • Range-Based Approaches • Mobile-assisted • Avoid cumulative errors of coordinate calculations and unnecessary communication overhead • Still rely on absolute RSSI values • Range-Free Approaches • Rely on connectivity measurements (e.g. hop-count) • Accuracy and precision affected by node density and network conditions

  6. More about OceanSense • Sparsely deployed restricted floating sensors • Unstable wireless communications • Varying network connectivity The existing localization approaches cannot well support such a practically complex scenario.

  7. Design Goals A localization approach • which is easy to implement in practice • which has better accuracy, especially under dynamic and complex environments. • which is time and energy efficient in locating a network of wireless sensors.

  8. Observations on RSSI Outdoor observation The closer a node is to the signal sender, the larger RSSI value it perceives.

  9. Observations on RSSI Indoor observation The closer a node is to the signal sender, the larger RSSI value it perceives.

  10. PI (Perpendicular Intersection) P2(x2,y2) No longer absolute RSSI values! H’(x’, y’) stop start H’’(x’’, y’’) Virtual Triangle (VT) N (x, y)  start stop P1(x1,y1) P3(x3,y3) M

  11. Theoretical Estimation Error • Given the velocity of the mobile beacon as V and the broadcast frequency as F, the distance between two beacon points is V/F. or

  12. Optimal Mobile Trajectory (1) • The optimal mobile trajectory to locate a network of sensor nodes satisfies the following requirements: • All the sensor nodes can be located. • The optimal trajectory consists of multiple joint VTs, which cover the entire deployment area. • It is the shortest trajectory. • The mobile beacon traverses the entire area in the shortest time and consumes the minimum energy cost. • The localization latency of a sensor node is minimized. • The node should be located as soon as the mobile beacon traverses along the two sides of the VT.

  13. Optimal Mobile Trajectory (2) • The node is in the VT which has largest sum of RSSI values. • Side length of a VT = R (transmission range)

  14. Optimal Mobile Trajectory (3) • Trajectory length: • Localization latency:

  15. Analysis on Overhead • Communication Cost • Zero communication cost among the sensor nodes to be located. • The number of beacon messages receivedby a sensor node when the mobile beacon traverses one VTside is FR/V. • A sensor node receives the beacon messages from at most 6 sides.The upper bound of communication cost of a sensor node is 6FR/V . • Computation Overhead • The computation overhead on a sensor node is O(FR/V). • Storage Overhead • PI only needs to store at most 14 vertices with their corresponding RSSI values, which cost 70 bytes.

  16. Experiments (1) • A prototype system with 100 TelosB motes • Various environments:library hall, laboratory, racket court, parking lots, and the sea. • The mobile beacon is also a TelosB mote. • A base station is deployed to collect the localization results. • PI is compared with two existing approaches • TRL [1]: a range-based approach using trilateration. (3 vertices) • BI [2]: a mobile-assisted localization approach that exploits Bayesian inference to improve the estimation accuracy. (3 vertices + 3 random) [1] J. Hightower and G. Borriello. Location systems for ubiquitous computing. IEEE Computer, 34(8):57 – 66, August 2001. [2] M. Sichitiu and V. Ramadurai. Localization of wireless sensor networks with a mobile beacon. Proceedings of IEEE MASS, 2004.

  17. Experiments (2) • Hall Experiment • In a hall of our library, which is about 450m2. • The side length of a VT R=15m. • The moving velocity V=0.1m/s • The broadcast frequency F=1time/sec.

  18. Experiments (3) • Hall Experiment – Group 1 • 14 sensor nodes are deployed randomly. Ave. Estimation error: PI 1.2175m BI 2.4921m TRL 3.3631m RSSI values perceived by node N6

  19. Experiments (4) • Hall Experiment – Group 2 • 35 sensor nodes are deployed randomly. Ave. Estimation error: PI 1.3213m BI 1.6978m TRL 3.6617m

  20. Experiments (5) • Laboratory Experiment • In the laboratory of computer software center, a room of 324m2 with 120 computers and desks inside. People sit, stand, or walk in the room. • R=9m. V=0.1m/s. F=1time/sec.

  21. Experiments (6) • Laboratory Experiment – Group 1 • 12 sensor nodes are deployed. Ave. Estimation error: PI 1.7655m BI 2.9775m TRL 4.0880m

  22. Experiments (7) • Laboratory Experiment – Group 2 • 100 sensor nodes are deployed. Ave. Estimation error: PI 2.5645m BI 3.5829m TRL 4.7299m

  23. Experiments (8) • Racket Court Experiments • R=15m. V=0.1m/s. F=1time/sec. Ave. Estimation error: PI 1.2174m BI 2.2313m TRL 3.6942m

  24. Experiments (9) • Parking Lots Experiments • R=15m. V=0.1m/s. F=1time/sec. Ave. Estimation error: PI 1.0952m BI 2.2079m TRL 3.7324m

  25. Experiments (10) • Offshore Experiment • V=1.5m/s. F=1time/sec. Ave. Estimation error: PI 7.3391 m BI 7.9094 m TRL 9.1620 m

  26. Experiments (11) • Impact of Different Factors • Evaluated with the hall experiments. V is set at 0.05m/s, 0.1m/s, 0.2m/s and 0.4m/s, F is set at 0.5, 1, 2, and 4 times per second, respectively. The intersection point of the two curves in the right figure represents a good setting in practice, which sets appropriate trade-off between the localization accuracy and the communication cost.

  27. Conclusion and Future Work • Conclusion • We propose a mobile-assisted localization approach: Perpendicular Intersection (PI). • Easy to implement • Accurate in dynamic and complex environments • Time and energy efficient • We examine the performance of PI by implementing a prototype system. • Further Work • Improve the prototype system, introducing an automatic mobile beacon. • Large-scale field tests on the OceanSense platform. • Extend PI in the underwater acoustic sensor networks.

  28. Thanks !

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