1 / 35

Wei WU 1 , Hock Beng LIM 2 , Kian-Lee TAN 1 1 National University of Singapore

Query Driven Data Collection and Data Forwarding in Intermittently Connected Mobile Sensor Networks. Wei WU 1 , Hock Beng LIM 2 , Kian-Lee TAN 1 1 National University of Singapore 2 Nanyang Technological University DMSN 2010, 2010-09-13. Outline. Sparse mobile sensor network

roland
Download Presentation

Wei WU 1 , Hock Beng LIM 2 , Kian-Lee TAN 1 1 National University of Singapore

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Query Driven Data Collection and Data Forwarding in Intermittently Connected Mobile Sensor Networks Wei WU1, Hock Beng LIM2, Kian-Lee TAN1 1National University of Singapore 2Nanyang Technological University DMSN 2010, 2010-09-13

  2. Outline • Sparse mobile sensor network • Problem: long query answering time • Proposal: query-driven data collection and location-based data forwarding • System model and assumptions • Query-driven data collection • Location-based data forwarding • Simulation results • Conclusion

  3. Mobile Sensor Networks (MSNs) • Dynamic networks formed by mobile sensors. • Mobile sensors • On ground, aerial, wearable UTK NASA Nokia NASA UAV

  4. Applications of MSNs • Reconnaissance • Disaster rescue • Environment monitoring

  5. Sparse MSNs • A MSN is sparse when • The number of sensors is small, • Mobile sensors are more expensive than stationary sensors • Task field is big, • Wireless communication range is limited. • Characteristics • The topology is dynamic. • The connection between nodes is intermittent.

  6. Problem • The query response time at the base station can be long. • Data forwarding to BS is done in a carry-and-forward manner. • Only a small portion of data objects are forwarded to the base station. • Limited data availability at the base station. • The base station cannot disseminate queries to the sensor nodes.

  7. Mobile Data Collector • Mobile data collector • A mobile node that moves to the sensors to collect data objects and returns to the base station. • Can be a normal mobile sensor or a special mobile unit.

  8. Our Study • Purpose: reduce the query response time at the base station. • Basic idea: use a mobile data collector (MDC) to do query-driven data collection. • The base station sends a pending query to a mobile data collector (MDC). • The MDC moves to the sensors to find query answer and returns to the base station.

  9. Challenge • Challenge • The sensor net is disconnected. • How can the MDC find the sensor that has query result? • Solution • Use spatial predicate in the query to direct the MDC’s movement. • The mobile sensors collaborate with the MDC, forward data objects to neighbors along data objects’ collection path.

  10. Sparse MSNs System Model • One base station • Receives queries from users; • Receives data objects from mobile nodes. • A number of mobile sensors • Move in a task field; • acquire data objects periodically; • Forward data towards BS when connected. • One mobile data collector (MDC) • Gets query from BS; • Moves to collect data objects from mobile sensors; • Returns to BS.

  11. Assumptions • Mobile sensors have GPS units. • Data objects have location metadata. • Queries have spatial predicates. • A query requests for a data object acquired at a specific location. • A data object can be used to answer the query if the data object was acquired at (or very close to) query location. • Sensors spend most energy on movement. • Sensors have enough storage.

  12. Collection Path and Forwarding Region • Qp: a query requesting for a data object acquired at (near) p. • Dp: a data object acquired at location p. • Collection Path for Dp. Path(Dp) • The shortest physical path in the field from the base station to p. • Forwarding Region for Dp. Region(Dp) • The union of the points in the field whose distances to Path(Dp) are shorter than sensors’ communication range r. • The area covered by the MDC’s wireless signal when it moves on Path(Dp).

  13. Collection Path and Forwarding Region (Cont’d) • Each data object has a collection path and a forwarding region. • The MDC moves along Path(Dp) to collect data for Qp. • Mobile sensors facilitate the MDC’s data collection. • Mobile sensors (best effort) forward data objects to neighbors in data objects’ forwarding regions. • Increase the chance that MDC will meet a sensor that carries Dp. • Forward data objects towards the base station. • Reduce the MDC’s move distance.

  14. Simple Examples Example 2 Example 3 Example 1 S2 forwards Dp to S3. S2 forwards Dp to S1. S2 forwards Dp1 to S3.

  15. Location-based Data Forwarding • Sensors forward data objects to sensors in data objects’ forwarding region. • Also forward data objects towards the base station. • Decisions are made based on sensors’ locations and data objects’ location metadata. • Sensor Si carrying data object Dp encounters a sensor Sj that does not have Dp. • If Si is in Region(Dp) • If Sj is also in Region(Dp) and Sj is closer to the BS • Si forwards Dp to Sj • Else • If Sj is in Region(Dp) or Sj is closer to Region(Dp) • Si forwards Dp to Sj

  16. Query-driven Data Collection • The BS has a pending query Qp that requests for a data object acquired at p in the field. • The BS sends Qp to the MDC. • The MDC moves along Path(Dp) to collect Dp. • When connected to a sensor, it checks whether the sensor has a data object that can answer Qp. • If so, the MDC gets the data object and moves back to BS. • The MDC also collects other data objects during the course. • Mobile sensors forward data objects to the MDC when connected. • MDC’s route is determined by query location.

  17. Performance Study • Simulation setup • Follows the system model. • 600m* 600m task field. • Mobility model: random waypoint. • Bandwidth: 2Mbps.

  18. Experiment Result • Effect of the number of sensors

  19. Experiment Result (2) • Effect of move speed

  20. Experiment Result (3) • Effect of data object size

  21. Summary • In sparse mobile sensor networks, query response time at base station may be long. • We propose • Query-driven data collection. • Make use query’s location predicate. • Location-based data forwarding to facilitate query-driven data collection. • Make use data objects’ location metadata. • Improve data availability along data collection path. • Simulation results show that our solution can help reduce the average query response time at the base station.

  22. Future Work • Take query and data objects’ temporal information also into account. • Take nodes’ trajectory into account. • Multiple path forwarding. • Data collection for multiple queries.

  23. Thank you! Questions?

  24. Query-driven Data Collection and Location-based Data Forwarding • Query-driven data collection. • The MDC collects data objects for queries that it gets from the base station. • The collection path is determined by the query. • Location-based Data forwarding for data collection • data forwarding decision based on data objects’ location metadata and sensors’ location. • Prioritize data objects for forwarding, to reduce MDC’s collection distance. • Objective: reduce the query response time at the base station by reducing the distance that the MDC needs to move before finding query answer.

  25. Prioritize Data Objects in Data Forwarding • A sensor needs to decide what data objects to forward. • A sensor may (have acquired and) carry many data objects. • It can only forward a small number of data objects to a neighbor (due to limited connection time). • Two situations: • Forward to a neighboring sensor. • Prioritize based on quantitative measure of collection distance reduction. • Forward to the MDC. • Prioritize based on collection path distance.

  26. Data caching • A sensor caches a data object locally after forwarding it to a neighboring sensor. • It does not forward it any more. • Objective: improve data availability among mobile sensors. When the MDC encounters a sensor, it is more likely to get query answer from the sensor.

  27. Forwarding to a Neighboring Sensor • A sensor’s collection distance w.r.t a data object Dp. cd(si,dp) • The distance that the MDC needs to move to get Dp. • If Circle(si, r) intersects Path(Dp), cd(si,dp) is the distance from BS to the intersection that is nearer to BS. • If Circle(si,r) does not intersect Path(Dp), cd(si,dp) is the length of Dp.

  28. Forwarding to a Neighboring Sensor • A sensor’s collection distance w.r.t a data object Dp. cd(si,dp) • The distance that the MDC needs to move to get Dp. • If Circle(si, r) intersects Path(Dp), cd(si,dp) is the distance from BS to the intersection that is nearer to BS. • If Circle(si,r) does not intersect Path(Dp), cd(si,dp) is the length of Dp.

  29. Forwarding to a Neighboring Sensor (Cont’d) • Si, Sj, Dp • Delta-collection-distance: cd(Si,Dp) – cd(Sj,Dp) • It is the moving distance of MDC that is saved by the data forwarding if the MDC goes to collect Dp. • Si prioritizes the data objects for forwarding to Sj based on their delta-collection-distances. The data object with the largest delta-collection-distance is forwarded first. • For the data objects whose delta-collection-distances are zero. Si considers the data objects that are outside their forwarding regions. Si forwards Dp to Sj if Sj is closer to Dp’ forwarding region.

  30. Forwarding to the MDC • When a sensor is connected to the MDC, the sensors forwards data objects to the MDC. • Objects are prioritized based on the lengths of their collection paths. • Objects whose locations are the furthest from the BS are forwarded to the MDC first.

  31. Effect of Sense Interval

  32. Effect of Query Interval

  33. Effect of communication range

More Related