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Location-based Data Overlay for Intermittently-Connected Networks

Location-based Data Overlay for Intermittently-Connected Networks. Nathanael Thompson, Riccardo Crepaldi , Robin Kravets. The Urban Experience. Cracking civil infrastructures – I-35 W Mississippi River bridge collapse during rush hour on August 1 st , 2007.

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Location-based Data Overlay for Intermittently-Connected Networks

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  1. Location-based Data Overlay for Intermittently-Connected Networks Nathanael Thompson, RiccardoCrepaldi, Robin Kravets

  2. The Urban Experience Cracking civil infrastructures – I-35 W Mississippi River bridge collapse during rush hour on August 1st, 2007 Increased pollution – Mexico City estimates unhealthy ozone emissions nearly 85% of the year Traffic delays – Extra commuting time caused by congestion totals ~$7 billion US dollars of loss for the greater Chicago J. Hilkevitch, Chicago Tribune August 5, 2008 Location-based Data Overlay for Intermittently-Connected Networks

  3. Revisiting Vehicular Networks • Cars represent an untapped resource • Large-scale • Geographic coverage • Diverse mobility patterns • Abundant resources (energy and storage) • Add sensors • Environmental • Traffic • Local conditions Large-scale Distributed Sensor Network Location-based Data Overlay for Intermittently-Connected Networks

  4. What do we do with all of that data? • Distributed location-based services • Route planning • Real-time carbon footprint monitoring • Live monitoring of critical infrastructure Traffic conditions Road conditions Weather Fuel efficiency Noise Pollution Location-based Data Overlay for Intermittently-Connected Networks

  5. Challenge: Too Much Data! • Existing approaches • Upload data to centralized databases • Overwhelms wide-area infrastructure • In dense environments, users get ~ 5 – 50 Kbps • Need uploads and downloads • Cellular network already overloaded • Delayed uploads don’t support live/real time applications • Main Problem • Centralized solutions do not consider location when storing data Location-based Data Overlay for Intermittently-Connected Networks

  6. Locality of Information • Observation • Data is tied to location at which it was sensed • Data storage • Maintain at sensed location • Challenges • On which nodes should the data live? • How are the nodes populated with the data? Location-based Data Overlay for Intermittently-Connected Networks

  7. Locus: a Location-based Data Overlay • Data tied at specific location, not device • Home location: where data was created • Creates bubbles where data lives • DTN forwarding techniques • Nodes opportunistically exchange data • Keep data as close to home location as possible • Limited encounter time Location-based Data Overlay for Intermittently-Connected Networks

  8. A New Challenge: Data Access • Access method • Query geographic area • Challenges • Enabling on-line access • Getting the response back to the querying node ? Location-based Data Overlay for Intermittently-Connected Networks

  9. There’s No Place Like Home • Store data objects near home location • Maximum utility in home area • Distance-based utility near home area (buffer) • Within buffer, increased distance decreases utility • Utility-based replication Buffer Home Utility Location-based Data Overlay for Intermittently-Connected Networks

  10. Data Storage: Location-based Utility • If node is in home area • Copy object to all nearby peers • U(node.curr) is high • If in the buffer • Copy objects to nodes within the home area • U(peer.current) is high • If leaving the buffer • Predict future location • Copy objects to nodes entering the bufferU(peer.future) is high Location-based Data Overlay for Intermittently-Connected Networks

  11. Data Access: Query • Need to send queries to target location • Existing DTN forwarding focuses on connecting two users • Queries start far from target bubble • No utility for distant objects • Will not be forwarded • Will be dropped immediately if buffers are full • Need to assign utilities that move queries through void Location-based Data Overlay for Intermittently-Connected Networks

  12. Data Access: Query Forwarding • Queries compete with data objects • Incorrect utilities lead to starvation • Distant queries: • Seed message to peers using quota • Move message as fast as possible toward home location • Forward with high utility if:(dist(peer.fut) > dist(node.fut)) • Queries close to home location treated like data objects Location-based Data Overlay for Intermittently-Connected Networks

  13. Data Access: Response Forwarding • Any matching node can send a response • Target location is source node’s location • Responses forwarded like queries • Query source node might be moving • Send response to predicted location • Adjust bubble size to account for prediction error • Flood once within range Location-based Data Overlay for Intermittently-Connected Networks

  14. Evaluating Locus • Compare against other object copying policies combined with basic DTN forwarding • Least sent policy, newest, oldest, random • Metrics • Distance to home location • Number of unique data messages • Query success rate • Evaluated in a simulated vehicle network • 150 Cars move along 5km X 5km area (map of Chicago) • Fixed bubble size of 500m, buffer 300m • 1KB data objects every 10s at every node • One query every 5s network-wide Location-based Data Overlay for Intermittently-Connected Networks

  15. Data Distance to Location • Location-based policy does keep messages near home • Location-based policy preserves distance at cost of number of unique messages • But if you can’t find it, it might as well not be there! 50th Percentile Distance Unique Messages Time Time Location-based Data Overlay for Intermittently-Connected Networks

  16. Query/Response Success Rate • Keeping data near home location increases query success rate • Location-based forwarding increases response success rate • Fewer unique messages leads to lower historical queries success Location-based Data Overlay for Intermittently-Connected Networks

  17. Conclusion • Location-based data can enable a new class of applications • Data overlay on top of mobile devices is promising approach • By building on DTN forwarding techniques with location-awareness high query success rate can be achieved • Future directions • Should the data live where is it sensed or where it is needed? Or both? • Accuracy vs. proximity • Expedited access for frequently queried data • Managing accuracy and response time Location-based Data Overlay for Intermittently-Connected Networks

  18. Thank you! Location-based Data Overlay for Intermittently-Connected Networks rcrepal2@illinois.edu http://mobius.cs.illinois.edu/

  19. Data Object Utility Function • Map distance to utility value [0.0,1.0] • Parameterized Sigmoid function: •  is rate of slope •  is point utility = 0.5 • Buffer zone = X’’-X’ according to  • Bubble size =  - buffer/2 Bubble Buffer X’ X’’ Location-based Data Overlay for Intermittently-Connected Networks

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