Demystifying the Fog: Rapid and Interoperable Computing for the Internet of Things

Presentation at the IoT Observatory Workshop, July 2017, Keble College, Oxford, UK

Transcript

1. Demystifying THE FOG EUGENE SIOW RAPID AND INTEROPERABLE COMPUTING FOR THE INTERNET OF THINGS
2. DATA OWNERSHIP & PRIVACY WITH LIGHTWEIGHT COMPUTERS A Smart Home Scenario implementing a Personal IoT Repository Smart Home Dashboard Personal IoT Repository Environmental Sensors Energy Meters Data Stream Energy Saving Analytics Stream & Historical Queries Motion Sensors Data ownership Own and store your data at home ↓PUBLIC Cloud ENCRYPTION BETTER PERFORMANCE SPECIFIC POLICIES/CONTROL ONLINE/OFFLINE, TRUST, ACCESS CONTROL
3. LIGHTWEIGHT COMPUTERS WITHIN A FOG COMPUTING INFRASTRUCTURE Fog Computing utilises the space between the “Ground” and “Cloud” Irrigation Application Soil Moisture Analytics Lightweight Computer Hub Data Stream Environmental Sensors GROUND National Disaster Monitoring Application Weather Data City Inclement Weather Planning Application CLOUD Distributed Queries
4. LOTS of data, lots of private data “The Internet of Things is currently beset by product silos.” W3C Web of Things Interest Group CURRENT STATE OF THE INTERNET OF THINGS DATA SILOS: we don’t agree on standards EMERGING IDEAS like FOG COMPUTING PERFORMANCE of APPLICATIONS & ANALYTICS
5. INTRODUCING GRAPH DATA MODELS LIKE LINKED DATA OR PROPERTY GRAPHS FOR INTEROPERABILITY Ontologies AND DATA MODELS Establish common data structures & References http://thing.io/1 is a http://ont/weather_sensor CLASS produces http://thing.io/obs/1 http://ont/temp_observation is a 13.0 has value CLASS ℃ unit ENABLES RICH METADATA what, where, WHEN, HOW of DATA located at http://thing.io/loc/1 latitude longitude -1.41 50.9 PERFORMANCE CHALLENGES STORES DON’T SCALE & PERFORM WELL ON WEB YET Buil-Aranda, C., Hogan, A.: SPARQL Web-Querying Infrastructure: Ready for Action? ISWC 2013
6. THE SHAPE OF IOT TIME-SERIES DATA 1 2,3 4 5 6+ Width { timestamp : 1467673132, temperature : 32.0, humidity : 10.5, pressure : 1016, light: 120.0, } 1 2 3 4 { timestamp : 1467673132, temperature : { max: 22.0, min: 15.0, current: 17.0, error: { percentage: 5.0 } } } FLAT { timestamp : 1467673132, temperature : 32.0, wind_speed : 10.5, pressure : 1016 } COMPLEX 20k UNIQUE DEVICES dweet.io 99.5% FLAT SCHEMATA 80.0% WIDE SCHEMATA 87.2% NUMERICAL FIELDS Siow, E., Tiropanis, T., Hall, W. (2016) A Study of Dweet.io and Sparkfun Internet of Things Device Schemata. http://dx.doi.org/10.5258/SOTON/D0076
7. INTEROPERABILITY WITH A RICH DATA MODEL THING TEMPERATURE OBS HUMIDITY OBS WIND SPEED OBS 13.0 2016-01-01 06:00:00 CELCIUS 93.0 2016-01-01 06:00:00 PERCENT 10.5 2016-01-01 06:00:00 MPH LOCATION produces produces located produces has value unit time GRAPH MODEL Siow, E., Tiropanis, T. and Hall, W. (2016) Interoperable & Efficient: Linked Data for the Internet of Things. INSCI2016: The 3rd Internet Science Conference
8. OBSERVATION DATA OBSERVATION METADATA SENSOR METADATA THING TEMPERATURE OBS HUMIDITY OBS WIND SPEED OBS 13.0 LOCATION produces produces located produces has value THING THING THING TEMPERATURE OBS time TEMPERATURE OBS 2016-01-01 06:00:00 unit TEMPERATURE OBS celcius 93.0 has value HUMIDITY OBS time HUMIDITY OBS 2016-01-01 06:00:00 unit HUMIDITY OBS PERCENT 10.5 has value WIND SPEED OBS time WIND SPEED OBS 2016-01-01 06:00:00 unit WIND SPEED OBS MPH RDF TRIPLES Siow, E., Tiropanis, T. and Hall, W. (2016) Interoperable & Efficient: Linked Data for the Internet of Things. INSCI2016: The 3rd Internet Science Conference INTEROPERABILITY WITH A RICH DATA MODEL
9. SENSOR METADATA OBSERVATION DATA OUR APPROACH THING TEMPERATURE OBS WIND SPEED OBS CELCIUS PERCENT MPH LOCATION produces located HUMIDITY OBS unit TEMPERATURE HUMIDITY WIND SPEED 13.0 93.0 10.5 TIME 2016-01-01 06:00:00 OBSERVATION METADATA Siow, E., Tiropanis, T. and Hall, W. (2016) Interoperable & Efficient: Linked Data for the Internet of Things. INSCI2016: The 3rd Internet Science Conference INTEROPERABILITY WITH A RICH DATA MODEL
10. EFFICIENT QUERYING: MAP-MATCH-OPERATE THING TEMPERATURE OBS WIND SPEED OBS CELCIUS PERCENT MPH LOCATION produces located HUMIDITY OBS unit TEMPERATURE HUMIDITY WINDSPEED 13.0 93.0 10.5 TIME 2016-01-01 06:00:00 Table1 TABLE1.TEMPERATURE has value has value TABLE1.HUMIDITY has value TABLE1.WINDSPEED Siow, E., Tiropanis, T. and Hall, W. (2016) Interoperable & Efficient: Linked Data for the Internet of Things. INSCI2016: The 3rd Internet Science Conference
11. THING TEMPERATURE OBS WIND SPEED OBS CELCIUS PERCENT MPH LOCATION produces located HUMIDITY OBS unit TEMPERATURE HUMIDITY WINDSPEED 13.0 93.0 10.5 TIME 2016-01-01 06:00:00 Table1 TABLE1.TEMPERATURE has value has value TABLE1.HUMIDITY has value TABLE1.WINDSPEED EFFICIENT QUERYING: MAP-MATCH-OPERATE Siow, E., Tiropanis, T. and Hall, W. (2016) Interoperable & Efficient: Linked Data for the Internet of Things. INSCI2016: The 3rd Internet Science Conference
12. THING TEMPERATURE OBS CELCIUS PERCENT produces loc HUMIDITY OBS unit TEMPERATURE HUMID 13.0 93.0 TIME 2016-01-01 06:00:00 TABLE1.TEMPERATURE has value has va TABLE1.H MAX( ) ?TEMPERATURE SELECT ?OBS TEMPERATURE OBS a has value ?OBS ?TEMPERATURE has unit ?OBS ?uom { } SELECT MAX( ) ?TEMPERATURE ?OBS TEMPERATURE OBS a has value ?OBS ?TEMPERATURE has unit ?OBS ?uom EFFICIENT QUERYING: MAP-MATCH-OPERATE Siow, E., Tiropanis, T. and Hall, W. (2016) Interoperable & Efficient: Linked Data for the Internet of Things. INSCI2016: The 3rd Internet Science Conference
13. TEMPERATURE OBS CELCIUS TEMPERATURE 13.0 TABLE1.TEMPERATURE has value MAX( ) ?TEMPERATURE SELECT ?OBS TEMPERATURE OBS a has value ?OBS ?TEMPERATURE has unit ?OBS ?uom { } SELECT MAX( ) ?TEMPERATURE ?OBS TEMPERATURE OBS a has value ?OBS ?TEMPERATURE has unit ?OBS ?uom Siow, E., Tiropanis, T. and Hall, W. (2016) Interoperable & Efficient: Linked Data for the Internet of Things. INSCI2016: The 3rd Internet Science Conference EFFICIENT QUERYING: MAP-MATCH-OPERATE
14. MAX( ) ?TEMPERATURE SELECT ?OBS TEMPERATURE OBS a has value ?OBS ?TEMPERATURE has unit ?OBS ?uom { } SELECT MAX( ) ?TEMPERATURE ?OBS TEMPERATURE OBS a has value ?OBS ?TEMPERATURE has unit ?OBS ?uom SELECT MAX( ) ?TEMPERATURE ?OBS ?TEMPERATURE ?uom TABLE1.TEMPERATURE CELCIUS NODE_TEMP EFFICIENT QUERYING: MAP-MATCH-OPERATE Siow, E., Tiropanis, T. and Hall, W. (2016) Interoperable & Efficient: Linked Data for the Internet of Things. INSCI2016: The 3rd Internet Science Conference
15. Benchmarks on queries SMART HOME BENCH SRBench 2 to 104 3 to 69 Siow, E., Tiropanis, T. and Hall, W. (2016) SPARQL-to-SQL on internet of things databases and streams. ISWC2016: The 15th International Semantic Web Conference 300K TRIPLES 3ok TRIPLES GraphDB (OWLIM) Ontop Our Approach (S2S) TDB G Morph O S M T Raspberry Pi 2 Model B+ 1GB RAM, 900MHz Quad Core ARM Cortex A7, Class 10 SD Cards
16. STREAM PROCESSING EFFICIENCY 1 2 3 4 5 7 8 9 10 SRBENCH 294 261 306 277k 3243k 5245 426 280k 98 Le-Phuoc, D., et al. (2011) "A native and adaptive approach for unified processing of linked streams and linked data.” The 10th International Semantic Web Conference. CQELS Performance Improvement For IoT Data Over 196 2 1 167 xImprovement Query SMART HOME Siow, E., Tiropanis, T. and Hall, W. (2016) SPARQL-to-SQL on internet of things databases and streams. ISWC2016: The 15th International Semantic Web Conference
17. STREAM PROCESSING SCALABILITY VELOCITY >99% <1ms latency increasing from 1 to 1000 rows/ms VOLUME 33.5million rows, projected ~2.5 billion triples! <1ms 10-100ms 1 2 5 10 100 1000 99% 100% Rate in rows/ms Percentage Latency in ms Bands Siow, E., Tiropanis, T. and Hall, W. (2016) SPARQL-to-SQL on internet of things databases and streams. ISWC2016: The 15th International Semantic Web Conference
18. PERSONAL IOT REPOSITORY Siow, E., Tiropanis, T. and Hall, W. (2016) PIOTRe: Personal Internet of Things Repository: The 15th International Semantic Web Conference P&D github.com/eugenesiow/piotre PIOTRE Apps sparql2stream sparql2sql Metadata
19. Eywa is like a huge biological internet; the trees being computer servers that store information and sensors being neural-connected flora and fauna AVATAR JAMES CAMERON’S
20. FOG COMPUTING FOR RDF STREAM PROCESSING Siow, E., Tiropanis, T. and Hall, W. (2016) Eywa: An Interoperable Fog Computing Infrastructure with RDF Stream Processing. INSCI2017: The 4th Internet Science Conference Sensors Node Data Stream Broker Subscribe(URI_1) Client Publish ([Query_p1,Q_p2]) Push (Select_Stream), Access Control, Bandwidth Control Inverted pub-sub Query Broadcast, Nodes manage distributed processing WORKLOAD DISTRIBUTION No single point of failure. Any RPi can serve as a broker. ‘Best effort’ for source nodes ResultSet
21. INTRODUCING A DECENTRALISED SOCIAL WEB OF THINGS Siow, E., Tiropanis, T. and Hall, W. (2017) A Decentralised Social Web of Things. PRIMARY APPLICATION AND INFRASTRUCTURE TETIARY APPLICATIONS SECONDARY APPLICATIONS APPS COLLABORATIVE WORK DECENTRALSATION SOCIAL GRAPH WOT IOT LD hubber.space
22. Siow, E., Tiropanis, T. and Hall, W. (2017) A Decentralised Social Web of Things. HANDLING VOLUME MACHINE-TRANSITION HUMAN-DRIVEN MACHINE-AUTOMATED Chatbots Messaging Client Neural Representation Messaging Friend/Follow Trustless Networks Edge Prediction Policy Game Theory Social Graph Crowd Sourcing Collaborative Editing Collaboration Web 2.0/Mobile Strong AI Algorithmic Rule-based Learned Interaction
23. TritanDB Siow, E., Tiropanis, T. and Hall, W. (2017) TritanDB: Time-series Rapid Internet of Things Analytics,
24. A hit TV-Series portraying realistic hacking and bleeding-edge technology fsociety E CORP
25. “NOTHING IS WORKING.” “UNPLUG WHAT?” “EVERYTHING IS INSIDE THE WALLS.”
26. @eugene_siow “It's a long road, it's a long and narrow way. If I can't work up to you, you'll surely have to work down to me someday.” Narrow Way by Bob Dylan EUGENE SIOW THANASSIS TIROPANIS WENDY HALL