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

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

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

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

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Localization Solution #1: Marking the location of each node as deployed 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 Solution #2: GPS capabilities on all nodes – Expensive and more energy consumption Solution #3: Anchor Nodes 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 Using Ranged-Based and Ranged-Free schemes

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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.

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Ranged-Free Localization Never tries to estimate the absolute point-to-point distance. Never tries to estimate the absolute point-to-point distance. Some available solutions: 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.

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Summary of ROCRSSI Ring Overlapping based on Comparison of Received Signal Strength Indicator Basic Procedure 1. 1.Reduces location of sensor to a rings of finite definite thickness. 2. 2.Adds rings to grid. (increments counter in these positions). 3. 3.Takes region of grid with highest values. 4. 4.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

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

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

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Simulation Timeline 1. All anchors send a broadcast message with its location. 2. Other anchors upon receiving broadcast messages, store the locations and RSSI of the message in a list of their neighboring anchors. 3. After a predetermined interval of time, each anchor then broadcast its location, and its list of neighbors and RSSIs. 4. This broadcast is heard from sensor nodes, received, and used to compute its location.

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Preliminary Simulations Real loc = [ 350, 250 ] Real loc = [ 350, 250 ] Estimated loc = [ 374, 258 ] Estimated loc = [ 374, 258 ] Sensor #Real Location Estimated Location 0350,250331,257 1450,200457,195 2375,150374,152 3375,275331,257 4180,250170,220 5300,200299,192 6550,200516,165

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

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

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

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Further simulations #Anchors/#Sensors #Anchors/#Sensors Overall increase in accuracy with more anchors. Overall increase in accuracy with more anchors. Spike in Centriod at 60%. 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.

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

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

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Result Summary Lack of improvement to estimation accuracy in many cases. 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 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. Sensor nodes situated outside the perimeter of the anchor nodes, will obtain a more accurate location estimation using the S-ROCRSSI algorithm.

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Conclusion / Possible Future Work Despite the lack of improvement in some cases, the project did still demonstrate the effectiveness of the ROCRSSI algorithm. 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 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. 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. 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.

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