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Project Report prepared by Shayok Chakraborty Weijia Che

Arizona State University Department of Computer Science and Engineering CSE 535 : Mobile ComputinG Indoor localization in kids’ network. Project Report prepared by Shayok Chakraborty Weijia Che. Agenda. Introduction Data Collection Proposed Approach Results Comparison References

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Project Report prepared by Shayok Chakraborty Weijia Che

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  1. Arizona State UniversityDepartment of Computer Science and EngineeringCSE 535 : Mobile ComputinGIndoor localization in kids’ network Project Report prepared by ShayokChakraborty WeijiaChe

  2. Agenda • Introduction • Data Collection • Proposed Approach • Results • Comparison • References • Questions

  3. Introduction • Goal – To localize kids in a room using a fixed set of beacon nodes installed in the walls • Challenges - Random movements of the kids - Presence of obstacles in the room

  4. Data Collection

  5. Data Collection

  6. Proposed Approach - Weijia FingerPrinting Points near each other have Similar RSSI Manhattan distance a, basic algorithm—assign mote to the anchor with the smallest distance b, improved sampling --drop samples exceeds mean(RSSI)+-1 c, improved calculation—static/dynamic threshold algorithm d, improved calculation – k (k=4) nearest algorithm e, weighted calculation – static/dynamic threshold/Knearest (did not improve accuracy)

  7. Proposed Approach - Weijia f, proposed calculation – check and feed back—Knearest (improves accuracy for points find the right anchor point and degrades accuracy for points did not) Results: • a. avg. ERROR=1.4342m • b. avg. ERROR=1.1229m • c. avg. ERROR=0.62435m/0.96065m • d. avg. ERROR=0.91275m • e. avg. ERROR=0.86755m/0.8475m/1.03447m • f. avg. ERROR= 0.99745m

  8. Results Defense: The algorithm is based on the assumption that the mobile host already find the right nearest anchor point and based on this to improve accuracy. (It did not generate better result in our experiments coz we only have two motes and failed to find the correct nearest point most of time) Further Improvement: combine this algorithm with static threshold algorithm instead of Knearest algorithm. introduce power levels into the calculation to improve accuracy

  9. Proposed Approach - Shayok • Triangulation Power(P) varies with distance(d) according to: Pj = kdj-α or, dj = (Pj / k)-1/α j = beacon number ( = 1,2) α = attenuation exponent k = a constant α and k are calculated from the reference points

  10. Triangulation - Illustration Beacon 2 Find distances using power equation. Then use Euclidean distance measure to find the position of the node Node to localize Beacon 1

  11. Results Average error – 0.8864 m

  12. Explanation • Radio propagation indoors is very chaotic due to the presence of obstacles, reflection etc. • More beacons can increase the accuracy of the method

  13. Comparison

  14. References [1] P. Bergamo, G. Mazzini . Localization in Sensor Networks with Fading and Mobility [2] Zang Li, Wade Trappe, Yanyong Zhang, Badri Nath. Robust Statistical Methods for Securing Wireless Localization in Sensor Networks [3] Yi Shang, Wheeler Ruml, Ying Zhang, Markus P. J. Fromherz. Localization From Mere Connectivity [4] Paramvir Bahl, Venkata N.Padmanabhan, RADAR: An In-Building RF-based User Location and Tracking System [5] K Lorincz, M Welsh,MoteTrack: a robust, decentralized approach to RF-based location tracking [6] Eiman Elnahrawy, xiaoyan Li, Richard P. Martin, The limits of localization using signal strength: a comparative study

  15. Questions ???

  16. Thank You !!!

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