In-network Surface Simplification for Sensor Fields

1. In-network Surface Simplification for Sensor Fields Brian Harrington and Yan Huang University of North Texas {brh,huangyan}@cs.unt.edu

2. In-network Surface Simplification for Sensor Fields Self forming wireless network gateway Backbone network Local Monitoring detection detection cameras Monitor human and structural health Measure environment variables Track inventories Detect ground vibrations Identify toxic chemical spills. Satellites Remote monitoring Brian Harrington, Yan Huang, University of North Texas

3. that “take the Earth’s pulse” National Ecology Observatory Network (NEON) RiverNet EarthScope GEOSS Interoperability Standardization efforts Heterogeneity Video cameras connected to wide band network High ends nodes, e.g. Intel XScale Motes with small storage and low processing ability Scalability Large scale deployment of small motes In-network Surface Simplification for Sensor Fields Example Ecology Sensor Networks Issues in Sensor Networks Brian Harrington, Yan Huang, University of North Texas

4. In-network Surface Simplification for Sensor Fields Sensor Mote Peculiarities: • Small storage, low processing ability • A typical sensor is equipped with a processor of a few MHz and a few kilobytes of RAM. • transmitting 1 bit of data to a distance of 10 meters consumes as much power as 220 to 2,900 instructions • Battery powered, short wireless communication range • A Berkeley Mica Mote operates on two AA batteries • communication range between a few to a few hundred feet depending on transmission power and environmental conditions. • Lasting for a few days in full duty cycle mode, months to years if energy is budgeted CrossBow MICA2/DOT Professional Kit (MOTE-KIT 5x4x) Brian Harrington, Yan Huang, University of North Texas

5. Sensors form a fine grained distributed database Use declarative language, e.g. to interact with sensor database SELECT {attributes, aggregates} FROM {Sensordata S}, {EnvironmentalData E}, {HistoricalSensorData H} WHERE {predicate} GROUP BY {attributes} HAVING {predicate} DURATION time interval EVERY time span e In-network Surface Simplification for Sensor Fields Sensor Databases Brian Harrington, Yan Huang, University of North Texas

6. Sensor database prototypes ([YaoCIDR03], [MaddenSIGMOD03]) In-network aggregation ([SharifzadehGIS04], [KrishnamachariICDCS02] , [FangTRP02], [SamuelOSDI02] , [VuranCN04], [ConsidineICDE04] ) Surface Simplification ([HeckbertTRP97]) In-network Surface Simplification for Sensor Fields Related Work Brian Harrington, Yan Huang, University of North Texas

7. Many phenomena in natural science are continuous and thus best represented as fields temperature, precipitation, hydraulic head, soil moisture, and ocean current velocity, Requiring all sensors to send back readings are too expensive Flat surfaces need less readings to represent Reduce communication cost By reducing the number of sensors to report Rationale Use simple in-network calculation to save more expensive messaging cost In-network Surface Simplification for Sensor Fields Field Model In-network Surface Simplification Brian Harrington, Yan Huang, University of North Texas

8. In-network Surface Simplification for Sensor Fields Surface Simplification Example Dots represent sensors. Dot in (0,0) may be the gateway sensor with long-haul communication capacity Brian Harrington, Yan Huang, University of North Texas

9. A hierarchical quad tree based simplification algorithm A triangulation based decimation algorithm In-network Surface Simplification for Sensor Fields Problem Definition Proposed Approach • Given: • A set of randomly deployed resource and communication constrained sensors S in a physical field. • Find: • Algorithms to select a subset SD of all the sensors in S to report to the central site so that the central site can reconstruct the surface using SD • Objective: • Reducing the message cost • Bounding the error. Brian Harrington, Yan Huang, University of North Texas

10. In-network Surface Simplification for Sensor Fields Hierarchical Approach Actual Surface • Parents send average value for its children with homogeneous readings • Readings far off from average are sent individually • An incremental top-down refinement process during reconstruction • using increasingly finer levels of detail sent by selected sensors • Guarantees the reading received by the central site is within ε of the real sensor readings Reconstructed Surface Level 0 Level 1 Level 2 Level 3 Brian Harrington, Yan Huang, University of North Texas

11. In-network Surface Simplification for Sensor Fields Analysis on Energy Consumption • Hierarchical approach is useful if the following inequality holds: P × N + F × L × N < L × N F < 1 - P/L • P: average number of hops from a sensor to its parent • N: total number of sensors • F: fractional of sensors that need to report individually • L: average number of hops to the query origination • If P is 10 and L= 1000, then for F < 99% we save! P must be less than L for this technique to be beneficial. Brian Harrington, Yan Huang, University of North Texas

12. In-network Surface Simplification for Sensor Fields Decimation Approach • A localized Vonoroi cell construction is used to create the initial triangulation • The initial surface is incrementally refined • We propose a probabilistic approach to select sensors not to report • Concurrent error calculation and deletion by all sensors may result in error accumulation • No guarantees the reading received by the central site is within ε of the real sensor readings Brian Harrington, Yan Huang, University of North Texas

13. In-network Surface Simplification for Sensor Fields Localized Vonoroi Cell Calculation • p’s voronoi cell: convex polygon that contains all of the points that are closer to p than any other sensor • Theorem: all sensors which may clip the initial voronoi cell must be in c(p) • We propose an acquisitional approach • Build a broadcasting tree rooted with radius c(p) routed at p • Collect information from the tree and refine the voronoi cell Brian Harrington, Yan Huang, University of North Texas

14. In-network Surface Simplification for Sensor Fields A Probabilistic Node Deletion Scheme • Error Estimation • Error Accumulation • Propose a probabilistic node deletion scheme p(s_i) = min(ε_i/ ε,1) where ε is the error threshold Brian Harrington, Yan Huang, University of North Texas

15. In-network Surface Simplification for Sensor Fields Analysis on Energy Consumption • To outperform the naïve algorithm, the following in-equation must hold: L × N > N × nh + L × F x N F < 1-1/L • L : average number of hops to the query origination • N : number of sensors • nh: average number of hops to reach a neighbor (typically nh=1) • F: fractional of sensors that need to report • For L = 100, if F < 99%, we will save! • L is approximately sqrt(N) • For L = 10, if F < 90%, we will save! Brian Harrington, Yan Huang, University of North Texas

16. In-network Surface Simplification for Sensor Fields Experiment Setup and Results • University of Delaware global surface monthly grids (http://www.jisao.washington.edu/data_sets/willmott) • Temperature and precipitation readings for 85794 points once a month for 50 years from 1950 through 1999 • Randomly selected 2% - 10% data • Pretty sparse data • Three approaches • Naive algorithm of having all sensors report individually • Hierarchical approach • Decimation approach • Results: • Messaging saving up-to 4 times (denser -> more saving) • Decimation method less than 4% above error thresholds Brian Harrington, Yan Huang, University of North Texas

17. In-network Surface Simplification for Sensor Fields # of messages w.r.t. density: Density increases savings increase Brian Harrington, Yan Huang, University of North Texas

18. In-network Surface Simplification for Sensor Fields # of messages w.r.t. ε: Error thresholds increase savings increase Brian Harrington, Yan Huang, University of North Texas

19. In-network Surface Simplification for Sensor Fields % of points outside ε w.r.t. density: Decimation has low error rate of less than 4% for density between 2% and 10% Brian Harrington, Yan Huang, University of North Texas

20. In-network Surface Simplification for Sensor Fields % of points outside threshold w.r.t. ε: Decimation has low error rate of less than 4% for density between 2% and 10% Brian Harrington, Yan Huang, University of North Texas

21. In-network Surface Simplification for Sensor Fields Conclusion • In-network surface simplification is useful • Proposed two approaches • Hierarchical approach • Decimation approach • Results: • The proposed two approaches have significant messaging saving • Messaging saving up-to 4 times (denser -> more saving) • Hierarchical approach has error bound • Decimation method less than 4% above error thresholds Brian Harrington, Yan Huang, University of North Texas

22. In-network Surface Simplification for Sensor Fields Future Work • Incorporating temporal auto-correlations into our model • Systematically investigate other surface simplification approaches • Grid based, feature based, refinement approaches, and hybrid approach (decimation and refinement) • Implement a prototype system on tinyOS/tinyDB • Working with domain scientists • Fault tolerance Brian Harrington, Yan Huang, University of North Texas

23. Sensor Database and Data Mining – Sensor Network as a Field References [YaoCIDR03] Y. Yao and J. E. Gehrke. Query Processing in Sensor Networks. In Proceedings of the First Biennial Conference on Innovative Data Systems Research (CIDR), 2003. [SharifzadehGIS04] M. Sharifzadeh and C. Shahabi. Supporting spatial aggregation in sensor network databases. In GIS ’04: Proceedings of the 12th annual ACM international Syposium on Geographic information systems, 2004. [HeckbertTRP97] P. S. Heckbert and M. Garland. Survey of polygonal surface simplification algorithms. Technical report,1997. [MaddenSIGMOD03] Samuel R. Madden, Michael J. Franklin, Joseph M. Hellerstein, and Wei Hong. Design of an acquisitional query processor for sensor networks. In SIGMOD, 2003. [HeckbertTRP97] Paul S. Heckbert and Michael Garland. Survey of polygonal surface simplification algorithms. Technical report, 1997. Brian Harrington, Yan Huang, University of North Texas

24. Sensor Database and Data Mining – Sensor Network as a Field References [KrishnamachariICDCS02] Bhaskar Krishnamachari, Deborah Estrin, and Stephen B. Wicker. The impact of data aggregation in wireless sensor networks. In Proceedings of the 22nd International Conference on Distributed Computing Systems, pages 575–578, 2002. [FangTRP02] Q. Fang, F. Zhao, and L. Guibas. Counting targets: Building and managing aggregates in wireless sensor networks. Technical Report P2002-10298, Palo Alto Research Center, 2002. [SamuelOSDI02] Samuel R. Madden, Michael J. Franklin, Joseph M. Hellerstein, and Wei Hong. Tag: a tiny aggregation service for ad-hoc sensor networks, 2002. OSDI. [VuranCN04] Mehmet C. Vuran, B. Akan, and Ian F. Akyildiz. Spatio-temporal correlation: theory and applications for wireless sensor networks. Comput. Networks, 45(3):245–259, 2004. [ConsidineICDE04] Jerey Considine, Feifei Li, George Kollios, and John Byers. Approximate aggregation techniques for sensor databases. In Proceedings of the 20th International Conference on Data Engineering, 2004. Brian Harrington, Yan Huang, University of North Texas