1. Locating Sensors in the Forest: A Case Study in GreenOrbs Tsung-Yun Cheng 20120611

2. Outline Introduction Preliminary experiments System design Performance evaluation Discussion

3. Introduction GreenOrbs – http://www.greenorbs.org/ http://www.greenorbs.org/ – one of the world’s largest wireless sensor networks – monitor the forest condition Temperature Humidity Illumination Carbon dioxide

4. Introduction GreenOrbs – Potential application Canopy closure calculation Climate change observation Search and rescue in the forest – need the location information of sensor nodes – Environmental noise Illustrates Temperature Humidity canopy closure

5. Introduction Environmental aware localization (EARL) – Joint Neighbor Distance (JND) measure the distance between sensor nodes – Neighbor node relation verification technology identifies nodes with good location accuracy – Two-phases location calibration Rectify the node locations – Implementation 20% better than existing work

6. Preliminary experiments I

7. Consider the relationship between RSSI and three parameters in the forest – Temperature (0.0613) – humidity (0.0907) – Illumination(0.1325) The relationship is quite hard to capture – Taking temperature, humidity, illumination and RSSI into account, it is quite difficult to estimate the distance between nodes

8. Preliminary experiments II Experiment in different environments – Grass, Woods, Forest Exam the RSSI value in different power level – Put two nodes in three environments – Distance = 10 meter Exam the reachability of RSSI – One anchor nodes in the center – 10 nodes are deployed around in every 5 meters, ranging from 5 meters to 50 meters

9. Preliminary experiments II

11. Exam the RSSI value in different power level – The variance is large – E.g., Figure 5(a) the Grass case: -40dBm to -35dBm when the power level is 4 -29dBm could range from the 9 th to 14 th RF power level Exam the reachability of RSSI – When the RF power level increases, anchor node could reach more neighboring nodes – In the forest, many curves share the similar RSSI according to the different power level After checking the location, they are in the same area

12. Preliminary experiments RSSI is quite susceptible to environment – the distance cannot be well computed directly RSSI sensing results just can be used as an indicator for the relative “near-far” relationship

13. System design Determine Neighbor relationship – A near-far ordering relationship Obtained by RF power scanning Neighboring sequence e.g., {G, C, E, B, F, D} One-hop neighbors – Doesn’t show: how far the distance direction

14. System design Joint Neighbor Distance (JND) – estimate the distance of each pair of nodes – – = Neighbor Count of X j with respect to X i – E.g.: NC(A, B) = 4 NC(B, A) = 5 JND(A,B) = 7

15. System design Calculate the coordinate using JND – relative distance turns to the smallest accumulated JND – Choose some landmark nodes Known position Calculate JND-unit – Compute the distance to the landmark nodes Trilateration by least square estimation

16. System design Testbed in the wood – 50 nodes, 4 landmark nodes – 1.3 meter above the ground

17. System design Testbed in the wood – The boundary nodes have smaller neighbor nodes – Also the nodes near the obstacles

18. System design Calibration – Empirically, nodes with small neighbor count will lead to the great error of locations, e.g., boundary nodes – When RF power level increases, the transmission radius increases none-linearly when obstacles exist more than one neighboring nodes may be added into the neighbor sequence at same level

19. System design Calibration for boundary nodes – Detection Select every nodes as root to establish a tree Leafs is the possible boundary nodes P i larger than certain threshold => boundary node – Calibrated neighbor count Virtual NC: j is the nearest neighbor CNC = Max{VNC, NC}

20. System design Calibration for good nodes & bad nodes – Get correct neighbor sequence: Two-step process Group the neighbor nodes according to the appearance of the RF level e.g., ((A), (G), (B, C, E), (D, F)) In the same group, RSSI value is measured to get a precise neighbor nodes sequence – Get a JND scheme sequence Use JND localization scheme to compute the distance – Compare the two sequence

21. System design Calibration for good nodes & bad nodes – comparison Longest common subsequence good nodes > bad nodes Set a certain threshold – Calibrate bad nodes Don’t mention…

22. System design Calibration for good nodes & bad nodes – Calibrate good nodes: Reverse-localization Iteratively choose four of good nodes as the landmark nodes, compute the location of four original landmark nodes Find the four goods nodes with minimum error calibrate the location of good nodes using 8 landmarks nodes

23. Performance Evaluation Previous testbed (in the woods) – Compare to other works: DV-Hop, CDL – Mean error EARL: 5m, CDL: 9m, DV-Hop: Large (~=18m)

24. Performance Evaluation Testbed in the forest – 230 nodes, 4 landmark nodes – Mean error EARL: 9m, CDL: 12m, DV-Hop: Large(~=20m)

25. Performance Evaluation more landmark nodes help improve the localization accuracy

26. Discussion Network is highly affected by the complex environment factors Environmental aware localization scheme,EARL, takes the joint neighbor count to measure the distance between two nodes and compute the location of nodes EARL outperforms existing approaches in terms high accuracy and efficiency

27. Discussion Writing – Structure is weird Content – Don’t mention how they come up with these approaches and why they adopt these methods – Don’t mention how to calibrate the bad nodes

28. Q&A ~ Thank you for your attention ~