In-network Surface Simplification for Sensor Fields

In-network Surface Simplification for Sensor Fields

Brian Harrington and Yan Huang

University of North Texas {brh,huangyan}@cs.unt.edu

Brian Harrington, Yan Huang, University of North Texas

204/19/23

Satellites Remote monitoring

Local Monitoring

detection

detection

Self forming wireless networkgateway

Backbone network

cameras

Monitor human and structural healthMeasure environment variables

Track inventoriesDetect ground vibrations

Identify toxic chemical spills.



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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

Example Ecology Sensor Networks

Issues in Sensor Networks



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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)


Sensor Mote Peculiarities:


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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

Sensor Databases



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Sensor database prototypes ([YaoCIDR03], [MaddenSIGMOD03]) In-network aggregation ([SharifzadehGIS04],

[KrishnamachariICDCS02] , [FangTRP02], [SamuelOSDI02] , [VuranCN04], [ConsidineICDE04] )

Surface Simplification ([HeckbertTRP97])

Related Work



704/19/23

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


Field Model In-network Surface Simplification


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Surface Simplification Example

Dots represent sensors. Dot in (0,0) may be the gateway sensor with long-haul communication capacity


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A hierarchical quad tree based simplification algorithm

A triangulation based decimation algorithm


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.

Problem Definition


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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


Hierarchical Approach Actual Surface

Reconstructed Surface

Level 0

Level 1

Level 2

Level 3


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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.


Analysis on Energy Consumption


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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


Decimation Approach


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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


Localized Vonoroi Cell Calculation


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Error Estimation

Error Accumulation

Propose a probabilistic node deletion scheme

p(s_i) = min(ε_i/ ε,1)

where ε is the error threshold


A Probabilistic Node Deletion Scheme


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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!


Analysis on Energy Consumption


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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


Experiment Setup and Results


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# of messages w.r.t. density:

Density increases savings increase


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# of messages w.r.t. ε:

Error thresholds increase savings increase


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% of points outside ε w.r.t. density:

Decimation has low error rate of less than 4% for density between 2% and 10%


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% of points outside threshold w.r.t. ε:

Decimation has low error rate of less than 4% for density between 2% and 10%


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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


Conclusion


2204/19/23

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


Future Work


2304/19/23

[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.

Sensor Database and Data Mining – Sensor Network as a Field

References


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[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.

Sensor Database and Data Mining – Sensor Network as a Field

References

Documents

In-network Surface Simplification for Sensor Fields