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Range-Free Sensor Localization Simulations with ROCRSSI-based Algorithm

Range-Free Sensor Localization Simulations with ROCRSSI-based Algorithm. Matt Magpayo Matthew.magpayo@tufts.edu. Presentation Outline. Introduction to WSN The Localization Problem ROCRSSI and Signed-ROCRSSI Implementation and Simulation Results Future Work. Why Wireless Sensors Networks?.

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Range-Free Sensor Localization Simulations with ROCRSSI-based Algorithm

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  1. Range-Free Sensor Localization Simulations with ROCRSSI-based Algorithm Matt Magpayo Matthew.magpayo@tufts.edu

  2. Presentation Outline • Introduction to WSN • The Localization Problem • ROCRSSI and Signed-ROCRSSI • Implementation and Simulation Results • Future Work

  3. Why Wireless Sensors Networks? • WSNs involve the use of numerous small, wireless sensors that are inexpensive and easily deployed. • Various applications • Habitat monitoring • (forest fire detection, water pollutants) • Military surveillance • (enemy tracking, sniper detection) • Medical care • (smart hospitals, patient monitoring) • Introduces Design Challenges • Limited storage capacity, limited energy supply, limited communication bandwidth • All designs must take each into consideration.

  4. WSN Research Areas • Tracking • Detection and tracking in a sensor network • Routing • Routing protocols of the sensor network. • Localization • Location information of sensor nodes.

  5. Localization • Solution #1: Marking the location of each node as deployed • Impractical for large number of nodes, limited mobility • Solution #2: GPS capabilities on all nodes • Expensive and more energy consumption • Solution #3: Anchor Nodes • Have a small subset of nodes have GPS. Sensors use them to find relative location. • Using Ranged-Based and Ranged-Free schemes

  6. Range-Based Localization • Distance estimation • Time of Arrival (TOA) • measure signal propagation time to obtain range information • Angel of Arrival (AOA) • estimate and map relative angles between anchors • Received Signal Strength Indicator (RSSI) • use theoretical or empirical model to translate signal strength into distance (RADAR, SpotOn) • Distance estimation done by • Most methods require complex hardware.

  7. Ranged-Free Localization • Never tries to estimate the absolute point-to-point distance. • Some available solutions: • Centroid Algorithm • After receiving location information of several anchors node, use centroid formula to estimate its location • DV-HOP • Anchor node flood their location and hop count throughout the network. Nodes calculate their position based on the received anchor location, hop count and average-distance per hop. • Ring Overlapping based on Comparison of Received Signal Strength Indicator (ROCRSSI) • Reduces location of sensor to a ring of finite definite thickness by comparing RSSI values.

  8. Summary of ROCRSSI • Ring Overlapping based on Comparison of Received Signal Strength Indicator • Basic Procedure • Reduces location of sensor to a rings of finite definite thickness. • Adds rings to grid. (increments counter in these positions). • Takes region of grid with highest values. • Center of gravity of region = sensor location. • All the sensor needs • a list of its neighboring anchors and relative RSSI, and, for each anchor in that list, a list of their neighboring anchors and relative RSSI. • Does not require sensor nodes to send out control messages

  9. Improving ROCRSSI : (Signed-ROCRSSI) • Improvement • Adding of rings to the grid where sensor cannot be (negative rings) • Original Algorithm • Allowing Negative Rings

  10. Implementation and Simulation • TinyOs and TOSSIM • NesC programming • Lacked signal strength simulation • OMNet++ : Mobility Framework • C++ programming • Open source network simulator • Layer by layer implementation

  11. Simulation Timeline • All anchors send a broadcast message with its location. • Other anchors upon receiving broadcast messages, store the locations and RSSI of the message in a list of their neighboring anchors. • After a predetermined interval of time, each anchor then broadcast its location, and its list of neighbors and RSSIs. • This broadcast is heard from sensor nodes, received, and used to compute its location.

  12. Preliminary Simulations • Real loc = [ 350 , 250 ] • Estimated loc = [ 374 , 258 ]

  13. First extensive simulation • Ten simulations • 15 anchor nodes and 45 sensor nodes randomly placed in a 2000x2000 playground • Error Percentage = (distance error/sensor radio distance) • Poor results; increase in error

  14. Grid Scan Algorithm and Negative Rings • Increase of error must be attributed to the grid scan portion of the algorithm. • Highest block sum approach • High negative values near or around the area of intersection can throw off the grid scan, causing the algorithm to search elsewhere

  15. Alleviating shifting • No degrees of exclusion • Once ALL rings were added to the grid. Negative values are taken as zero. • Ten simulations of random placement were performed again and the results recorded. • However an improvement from the first set of simulations, no overall improvement. • Not a lot of negative rings produced.

  16. Further simulations • #Anchors/#Sensors • Overall increase in accuracy with more anchors. • Spike in Centriod at 60%. This could be attributed to the shifting of a centroid that an additional anchor provides, ruining an otherwise accurate estimation.

  17. Average Number of Neighboring Anchors • Overall increase in accuracy with more neighboring anchors • ROCRSSI and S-ROCRSSI significantly better than Centroid

  18. Varying Anchor Placement • Simulations on how anchor topology effects the estimation accuracy • Overall decrease in accuracy • S-ROCRSSI outperforms by 20% • Negative Rings Produced 88% of the time

  19. Result Summary • Lack of improvement to estimation accuracy in many cases. • lack of cases where the information negative rings gave actually came into use • Usually the negative rings only reinforced information that the original ROCRSSI algorithm already knew. • Substantial Difference in Unattractive Topologies • Where the negative rings actually made a substantial difference was when anchors were not placed along the perimeter. • This caused a large amount of negative rings to be produced, giving the S-ROCRSSI algorithm more information and a better location estimate. • Sensor nodes situated outside the perimeter of the anchor nodes, will obtain a more accurate location estimation using the S-ROCRSSI algorithm.

  20. Conclusion / Possible Future Work • Despite the lack of improvement in some cases, the project did still demonstrate the effectiveness of the ROCRSSI algorithm. • A 16% estimation error percentage is better than most range-based approaches out there. • This project also helped uncover an improving algorithm for sensor nodes located outside the anchor perimeter • Test algorithms with actual motes in real world conditions. • The sensor node could alternate which location algorithm it uses by somehow estimating its general location in respect to the perimeter of the network anchor nodes.

  21. Questions?

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