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

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

The Urban Experience Cracking civil infrastructures I-35 W

st, 2007 Increased pollution Mexico City estimates unhealthy

commuting time caused by congestion totals ~$7 billion US

2 The Urban Experience Cracking civil infrastructures I-35 W Mississippi River bridge collapse during rush hour on August 1 st, 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,

3 Revisiting Vehicular Networks Large-scale Distributed Sensor Network Cars represent an untapped resource Large-scale Geographic coverage Diverse mobility patterns Abundant resources (energy and storage) Add sensors Environmental Traffic Local conditions 2

4 What do we do with all of that data? Traffic conditions Road conditions Weather Fuel efficiency Noise Pollution Distributed location-based services Route planning Real-time carbon footprint monitoring Live monitoring of critical infrastructure 3

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 4

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? 5

data lives DTN forwarding techniques Nodes opportunistically exchange

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 6

8 A New Challenge: Data Access Access method Query geographic area? Challenges Enabling on-line access Getting the response back to the querying node 7

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 Home Buffer Utility-based replication Utility 8

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 buffer U(peer.future) is high 9

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 10

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 11

Data Access: Response Forwarding Any matching node can send a response Target location is source node s location Responses forwarded like queries

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 12

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 13

15 Data Distance to Location 50 th Per rcentile Distance Uniq que Messages Location-based policy does keep messages near home Location-based policy preserves distance at cost of number of unique messages Time But if you can t find it, it might as well not be there! Time 14

16 Frac ction Delivered Query/Response Success Rate Location LeastSent Random Newest Oldest Fractio on Delivered Location LeastSent Random Newest Oldest 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 Query Age (minutes) 15

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 16

18 Thank you! Location-based Data Overlay for Intermittently-Connected Networks

19 Data Object Utility Function Map distance to utility value [0.0,1.0] Parameterized Sigmoid function: f e ( x) = 1 α ( β x ) α is rate of slope β is point utility = 0.5 Buffer zone = X -X according to α Bubble size = β - buffer/2 Bubble Buffer X X 18

Evaluation of Seed Selection Strategies for Vehicle to Vehicle Epidemic Information Dissemination

Evaluation of Seed Selection Strategies for Vehicle to Vehicle Epidemic Information Dissemination Richard Kershaw and Bhaskar Krishnamachari Ming Hsieh Department of Electrical Engineering, Viterbi School