SpecSense: Crowdsensing for Efficient Querying of Spectrum Occupancy Md. Shaifur Rahman, Ayon Charkraborty, Himanshu Gupta and Samir R. Das 2017

Outline Spectrum Occupancy Query Interpolation Sensor Selection System Architecture & Benchmarking Conclusion WINGS Lab

Spectrum Occupancy Query Goal: Build infrastructure to respond to spectrum occupancy query Spectrum occupancy query: Is channel f at location (x, y) in use? Why? To identify spectrum sharing opportunities Can help in spectrum patrolling Deeper understanding of spectrum usage for policy formulation How?: Crowdsensing Enables large-scale monitoring via many low-cost low-power sensors. Incentive mechanism. Goal is to build infra,,,,, to respond to this query WINGS Lab

Spectrum Sensing Trade-off VS. High Accuracy Expensive Sensor ThinkRF Realtime Spectrum Analyzer Low Accuracy Inexpensive Sensor RTL-Dongle connected to Cellphone WINGS Lab

Spectrum Sensing Trade-off Challenge: Cost of large-scale deployment of spectrum sensors For a given budget: Small no. of high-accuracy expensive sensors Or Large no. of low-accuracy inexpensive sensors? WINGS Lab

Estimated signal value at (x, y) High Level Overview Sensor SpecSense System Sensor Selection Interpolation Query Location Label sensors, query…..not occupied => signal values Query for Channel: f Location: (x, y) Estimated signal value at (x, y) WINGS Lab

Outline Spectrum Occupancy Query Interpolation Sensor Selection System Architecture & Benchmarking Conclusion WINGS Lab

Interpolation Techniques Inverse Distance Weighting (IDW): Predicted value = distance-weighted average of neighboring values Ordinary Kriging (OK): Takes into accounts structure of the spatial correlation via a “Variogram” function Predicted value = a weighted average of neighboring values, where the weights come from the variogram Minimizes the prediction variance In this work, we improve OK for our context in two ways: Detrending Partitioning WINGS Lab

Interpolation: Ordinary Kriging Predicted value = weighted average of neighboring values Considers spatial correlation Distance: ~ 1 meter Difference: ~ 1 unit Distance: ~ 1 meter Difference: ~ 1 unit WINGS Lab

Variogram Y (30, 625) 30 15 x13 = 50 3 yij 1 (50, 225) x12 = 20 x23 = 65 2 (20, 100) y12 = (15 – 5)2 = 100 5 y13 = (30 – 15)2 = 225 y23 = (30 – 5)2 = 625 (0, 0) h X WINGS Lab

OK Interpolation Z4 = λ1.Z1+ λ2.Z2+ λ3.Z3 = 15λ1+5λ2+30λ3 Find λ1, λ2, λ3 such that: λ1+λ2+λ3 = 1 Expected prediction error = 0 Expected prediction variance is minimum λ1 λ2 λ3 α = 𝑦11 𝑦12 𝑦13 1 𝑦21 𝑦22 𝑦23 1 𝑦31 𝑦32 𝑦33 1 1 1 1 0 −1 𝑦14 𝑦24 𝑦34 1 Z4 = 15(0.2)+5(0.15)+30(0.65) = 23.25 30 15 3 1 x14 = 50 x34 = 20 4 2 x24 = 45 ? 5 WINGS Lab

Improving OK by Detrending OK requires mean of signal values to be constant across all locations But that is not the case in our context WINGS Lab

Improving OK by Detrending Solution: Decompose sensed signal values Signal value = Path-loss + Shadowing Path-loss Estimation Assume TX at the sensor with max. signal Estimate path loss exponent α at sensors Estimate path-loss at query using α. Has zero-mean Use OK interpolation WINGS Lab

Improving Ordinary Kriging by Partitioning WINGS Lab

Improving Ordinary Kriging by Partitioning Issue with OK: It assumes that “variance” depends only on distance. Not true in our context (i.e. signal variance depends on terrain) Solution: Partition the region into sub-region such that: each sub-region has similar path-loss characteristics Algorithm (OK with Partitioning): Divide the region into small cells Initially, each cell is a separate sub-region Iteratively merge neighboring sub-regions with similar estimated path-loss exponent. WINGS Lab

Results: Kriging Improvement Dataset Sensor: RTL-SDR connected to smartphones Spectrum: AT&T’s LTE Downlink 1500 locations Spanning indoors and outdoors 15000 m^2 area Change axis title WINGS Lab

Outline Spectrum Occupancy Query Interpolation Sensor Selection System Architecture & Benchmarking Conclusion WINGS Lab

Sensor Selection Problem: Given locations of sensors and potential queries, select at most k sensors to minimize “interpolation prediction error” NP-hard. Two algorithms: Nearest Query Cover: In each iteration, for each query, select the closest remaining sensor. Iterative Query Cover: In each iteration, select the sensor that “covers” most number of queries. Here, “coverage” is based on the variogram. Above generalize to weighted versions (e.g., weight may be battery-level, precision etc. ) Diagram to ILLUSTRATE the DIFFERENCE between the two algorithms. WINGS Lab

Results: Sensor Selection Pvg prior work.. WINGS Lab

System Architecture WINGS Lab

Results: Benchmarking Requirements: Sensing + Query Latency has to be small enough to serve in real time Sensing Latency: Very low latency even for high FFT size Query Latency: Negligible compared to sensing latency MOTIVATION: … Sensing Latency Query Latency WINGS Lab

Conclusion Crowdsensing based large scale deployment Address two problems- interpolation & sensor selection Improvement of interpolation by detrending and partitioning Performance comparison on real varied dataset Benchmarking of the implemented system WINGS Lab

End of Presentation WINGS Lab