Large-Scale Crowdsourcing of Solar Data via Roadside WiFi Beacons
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The main objective of this paper is to show that large-scale sensor data collection is feasible while relying on vehicles to collect and deliver data to a relatively small number of static nodes on the roadside infrastructure. The proposed technology is based on simple 802.11 beacons (as opposed to 802.15 WAVE and 802.11p and others) which are accessible on common smartphones and are comparatively easier to program on commodity 802.11 hardware. While the proposal is not limited to a particular type of collected data, this paper showcases the proposal on smart power grids, where power plants serve as sources of information, vehicles collect the information while passing on the road near the plants, and roadside nodes finally collect the aggregated information from passing vehicles.
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Large-Scale Crowdsourcing of Solar Data via Roadside WiFi Beacons
- 1. #p2pwifi #groupconnect #beacon #IoT #roadside #solarbubble #crowdsourcing Large-Scale Crowdsourcing maratishe@gmail.com maratishe.github.io 2017/11/23＠SOCA＠Kanazawa PDF: bit.do/171123 Zhanikeev Marat by Vehicular Data Packets in a Sparse Roadside Infrastructure Tokyo Univ. of Science
- 2. Solar Bubble A large solar power plant • with heavily subsidized solar panels, Japan already experiences solar energy bubble (at the have-already-burst stage) • makes sense to offload power subsumption, why not as part of an EV battery replacement infra? Hour Poweroutput Sunrise Sunset Threshold for a standalone plant Threshold for an in-grid plant Wasted energy Marat Zhanikeev – maratishe@gmail.com Large-Scale Crowdsourcing by Vehicular Data Packets in a Sparse Roadside Infrastructure – bit.do/171123 2/18 2/18
- 3. Vehicular Connectivity WiFi 3G 3G 802.11p (WAVE) GroupConnect Beacons 802.11a -n,ac Beacon Frame Marat Zhanikeev – maratishe@gmail.com Large-Scale Crowdsourcing by Vehicular Data Packets in a Sparse Roadside Infrastructure – bit.do/171123 3/18 3/18
- 4. Vehicular Services Vehicles Rarely Meet Meeting Likely Travel Together Park Together DTN MANETs Connected Cars Sensor Cloud Storage Cloud Information Support Roadside + Crowdsourcing Marat Zhanikeev – maratishe@gmail.com Large-Scale Crowdsourcing by Vehicular Data Packets in a Sparse Roadside Infrastructure – bit.do/171123 4/18 4/18
- 5. Fog Clouds and Edge Connectivity • this paper goes for Beacon Stuffing, with the backup SSID messaging option WiFi 3G Connectivity WebApp Cloud Client A Client B Service API Service API Service Orchestration Clients can be people and/or IoT devices Marat Zhanikeev – maratishe@gmail.com Large-Scale Crowdsourcing by Vehicular Data Packets in a Sparse Roadside Infrastructure – bit.do/171123 5/18 5/18
- 6. Intended Scenario (right side) • main assumption: solar farms do not have internet connectivity, only WiFi beacons Cloud, BigData Cloud, BigDataNetworked roadside infra Data exchange Data sources Infra nodes Data sources Marat Zhanikeev – maratishe@gmail.com Large-Scale Crowdsourcing by Vehicular Data Packets in a Sparse Roadside Infrastructure – bit.do/171123 6/18 6/18
- 7. Intended Scenario (specifically) Roadside beacon • beacon messaging: ❶ solar farm → ❷ car → ❸ roadside node • most of the network is crowdsourced = covered by ❶❷ • few roadside infra nodes ❸ installed only at key/popular roads Marat Zhanikeev – maratishe@gmail.com Large-Scale Crowdsourcing by Vehicular Data Packets in a Sparse Roadside Infrastructure – bit.do/171123 7/18 7/18
- 8. Modeling Space Traces Travel Time Density Centers Mobility Traces Synthesis Context Voronoi Plots Hotspot Model Marat Zhanikeev – maratishe@gmail.com Large-Scale Crowdsourcing by Vehicular Data Packets in a Sparse Roadside Infrastructure – bit.do/171123 8/18 8/18
- 9. Map Dataset • comes from a separate project on crowdsourcing aggregation of large all-to-all graphs from unit Google Maps route queries • node density and road=leg density are added (node/link betweenness) A Marat Zhanikeev – maratishe@gmail.com Large-Scale Crowdsourcing by Vehicular Data Packets in a Sparse Roadside Infrastructure – bit.do/171123 9/18 9/18
- 10. Beacon Frame and Scenario Roadside beacon • Beacon Stuffing method: use 256 bytes withing the Beacon or Reply frames in the WiFi protocol • accessible via a relatively simple hack on Linux (including RaspberryPi) • cars and farms broadcast at an interval, roadside infra listens/collects Frame header Beacon payload 256 bytes Location A State A … 1 tuple 2kbps at 1s interval Marat Zhanikeev – maratishe@gmail.com Large-Scale Crowdsourcing by Vehicular Data Packets in a Sparse Roadside Infrastructure – bit.do/171123 10/18 10/18
- 11. Results: 1-frame limit 0 10 20 30 40 50 60 Distribution (descending order of frames) 0 5 10 15 20 25 30 Numberofframes 1 frame 1 frame 1 frame 50 nodes 100 nodes 200, 300 nodes • technically important to stay within single-frame broadcasts – cars simply pass py a roadside infra • outcome: drastic difference going from 50 to 100 nodes • a bit unintuitive: with fewer infra nodes, more 1-frame cases and more congested infra nodes Marat Zhanikeev – maratishe@gmail.com Large-Scale Crowdsourcing by Vehicular Data Packets in a Sparse Roadside Infrastructure – bit.do/171123 11/18 11/18
- 12. Results: overall • when selecting by node/link betweenness, selected nodes vary drastically between 50, 100 and 200 cases 0 5 10 15 20 25 30 Number of frames 0.42 0.48 0.54 0.6 0.66 0.72 0.78 0.84 0.9 ytiralupopgeL 0 5 10 15 20 25 30 Number of frames 0.42 0.48 0.54 0.6 0.66 0.72 0.78 0.84 0.9 Legpopularity 0 5 10 15 20 25 30 Number of frames 0.42 0.48 0.54 0.6 0.66 0.72 0.78 0.84 0.9 Legpopularity 50 legs 100 legs 200 legs Popular leg, within 1 frame 4 frames, unpopular leg Mid-popularity, large volume Mid-popularity, very high volume Marat Zhanikeev – maratishe@gmail.com Large-Scale Crowdsourcing by Vehicular Data Packets in a Sparse Roadside Infrastructure – bit.do/171123 12/18 12/18
- 13. Wrapup • large-scale collection of local data is possible using beacons and vehicles as delivery packets • control is extremely flexible, one can control congestion at popular locations by installing infra as less popular ones • with relatively few (50 out of 400) infra nodes, half of the infra nodes work at 1-frame mode – no need for cars to slow down Marat Zhanikeev – maratishe@gmail.com Large-Scale Crowdsourcing by Vehicular Data Packets in a Sparse Roadside Infrastructure – bit.do/171123 13/18 13/18
- 14. That’s all, thank you ... Marat Zhanikeev – maratishe@gmail.com Large-Scale Crowdsourcing by Vehicular Data Packets in a Sparse Roadside Infrastructure – bit.do/171123 14/18 14/18
- 15. Advanced WiFi Beacons … 802.11* Channels TX Channels RX Channels Partial or even complete overlap allowedDevice (beacon) Channel Center Freq. (2.484GHz) 20MHz 1 2 3 4 5 6 7 8 9 10 11 12 13 Marat Zhanikeev – maratishe@gmail.com Large-Scale Crowdsourcing by Vehicular Data Packets in a Sparse Roadside Infrastructure – bit.do/171123 15/18 15/18
- 16. Cloud Orchestration and GroupConnect Service Provider Roadside beacon Marat Zhanikeev – maratishe@gmail.com Large-Scale Crowdsourcing by Vehicular Data Packets in a Sparse Roadside Infrastructure – bit.do/171123 16/18 16/18
- 17. Data Center Extension Scenario 4 5G Vehicular Group 4 5G End Users Data Center (DC) Marat Zhanikeev – maratishe@gmail.com Large-Scale Crowdsourcing by Vehicular Data Packets in a Sparse Roadside Infrastructure – bit.do/171123 17/18 17/18
- 18. EV Battery Replacement Ecosystem nG E H Station V2HSecondLife B1 A4 A5 H2 B2 H1 Solar/wind Power plant B3 Roadside beacon Internet connection Marat Zhanikeev – maratishe@gmail.com Large-Scale Crowdsourcing by Vehicular Data Packets in a Sparse Roadside Infrastructure – bit.do/171123 18/18 18/18