Locating Sensors in the Forest: A Case Study in GreenOrbs

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/ • 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. Preliminary experiments I • 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

10. Preliminary experiments II

11. Preliminary experiments II • 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 9th to 14th 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 Xjwith respect to Xi • 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 smallneighbor 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 • Pi 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 ~