Ayon Chakraborty, Udit Gupta and Samir R. Das WINGS Lab Benchmarking Resource Usage for Spectrum Sensing on Commodity Mobile Devices Ayon Chakraborty, Udit Gupta and Samir R. Das WINGS Lab ACM HotWireless 2016

Building Radio Environment Maps Estimate the spatial distribution of signal power present in frequency F, a.k.a, Radio Environment Map (REM) The transmitters operate at a certain frequency F WINGS Lab

Building Radio Environment Maps How? WINGS Lab

Building Radio Environment Maps Need a distributed system of spectrum sensors Very Few Sensors  Poorer Estimate of the REM Spectrum Analyzer Bulky Expensive Cannot be Deployed at Scale WINGS Lab

Building Radio Environment Maps More Sensors  Better Estimate of the REM How About Mobile Spectrum Sensors? Mobility Smaller form-factor Cheaper Deployed at Scale WINGS Lab

Mobile Spectrum Sensor Prototype Powers Sensing Unit Computing Device I/Q Samples Sensing Unit Run Signal Detection Algorithms RTL-SDR BladeRF USRP B200 USRP B210 We envision that such sensors will be integrated with the computing device hardware (e.g., smartphone). Phone RPi RPi RPi ≈ $20 ≈ $400 ≈ $700 ≈ $1200 WINGS Lab

Challenges COTS Samsung Galaxy Phone Two Main Challenges Is running spectrum sensing on mobile devices energy efficient? Is the computational latency incurred in running signal detection algorithms on mobile devices prohibitive? $40K Benchmarking such resource consumption will give us better insights about feasibility of mobile spectrum sensors. ≈ $20 RTL-SDR Dongle (cheap spectrum sensor) WINGS Lab

Benchmarking Task Computing Device Sensing Unit Transmitter Measure Energy Measure Latency I/Q Samples Computing Device Sensing Unit Transmitter Signal Detection Run Signal Detection Algorithms Energy Based Feature Based Autocorrelation Based Measure Latency Measure Energy Computation WINGS Lab

Signal Detection Algorithms Energy Based (ATSC TV Signal) Feature Based (ATSC TV Signal) Autocorrelation Based (ATSC TV Signal) Prob. of Detection Prob. of False Alarm WINGS Lab

Result: Latency in Sensing Unit Change sensing parameters “Cold Boot” Changing sensing parameters incurs only a few milliseconds delay, however starting the device can incur two orders of magnitude more delay. WINGS Lab

Result: Latency in Computing Device Raspberry Pi Phone Running signal detection algorithms on CPU requires relatively longer time. A GPU-version of the implementation improved latency by a factor of 1.5X – 9X. WINGS Lab

Result: Energy Consumption in Sensing Unit Raspberry Pi Phone One minute usage: Energy used in the sensing job is approximately 5X – 6X times lower compared to very typical phone applications. WINGS Lab

Takeaways Mobile spectrum sensing enables crowdsourcing spectrum measurements resulting in more granular estimate of spectrum maps. Benchmarking results on our prototype platforms with optimization (GPU) show feasibility of such sensors. Energy consumption on our prototype platform is modest. 5X – 6X lesser than typical smartphone apps. WINGS Lab

Thanks! Questions? WINGS Lab