Using Area Hierarchy for MultiResolution Storage and Search in Large Wireless Sensor Networks

1
Using Area Hierarchy for Multi-Resolution Storage
and Search in Large Wireless Sensor Networks

• Konrad Iwanicki and Maarten van Steen
• Vrije Universiteit Amsterdam
• The Netherlands

2
Introduction

• Many sensor network applications require
• large numbers of sensor nodes
• continuously collecting data from the surrounding
  environment.
• Examples
• habitat and microclimate monitoring,
• precision agriculture,
• structural monitoring,
• asset tracking.

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Fidelity vs. scalability

• Such an amount of data forces a trade-off
  between
• system scalability
• data fidelity
• Centralized data-collection mechanisms generally
  scale poorly beyond tens of nodes.
• Better scalability requires in-network
  aggregation.
• In-network aggregation, however, precludes high
  data fidelity
• One can query the aggregate reading for the whole
  network, but not the readings of individual
  sensors.
• Low data fidelity is inadequate in many
  large-scale applications of sensor networks.

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Principal idea

• Provide scalable adaptive data fidelity
• explore the trade-off between the system
  scalability and the data fidelity.
• Employ a distributed multi-resolution storage and
  search mechanisms
• Each sensor node participates in a distributed
  storage system.
• The storage system is based on a multi-level
  recursive overlay that enables
• In-network aggregation,
• Querying.

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Multi-resolution aggregation
Source Ganesan et al. Multiresolution storage
and search in sensor networks, ACM Trans.
Comput. Syst., vol. 1, no. 3, p. 277-315, August
2005.
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Drill-down querying
Source Ganesan et al. Multiresolution storage
and search in sensor networks, ACM Trans.
Comput. Syst., vol. 1, no. 3, p. 277-315, August
2005.
7
Problems

• The current design is based on geographic
  coordinates
• the overlay is a quad tree
• aggregates and queries use geographic routing
• Problems (untrue assumption that geographic
  proximity implies connectivity)
• Special mechanisms to handle different cases.
• Difficulty of porting geographic routing to three
  dimensions.
• Special localization hardware or algorithms.
• Can we use a different network organization?

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Our approach

• Observation
• Connectivity usually implies proximity.
• Employ a network organization based on actual
  physical connectivity rather than artificial
  geographic coordinates
• Area hierarchy.

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Area Hierarchy

• Area hierarchy based on connectivity, nodes
  self-organize into a multi-level
• hierarchy of nested network areas. This is a
  basis for
• Naming
• Routing.

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Area Hierarchy

• Area hierarchy based on connectivity, nodes
  self-organize into a multi-level
• hierarchy of nested network areas. This is a
  basis for
• Naming
• Routing.

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Area Hierarchy

• Area hierarchy based on connectivity, nodes
  self-organize into a multi-level
• hierarchy of nested network areas. This is a
  basis for
• Naming
• Routing.

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Aggregation

• Each node is a head of a group at some level,
  thereby also
• the aggregator for that group.

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Level-0 group head.
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Level-2 group head.
Level-1 group head.
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Aggregation

• Each node is a head of a group at some level,
  thereby also
• the aggregator for that group.

Level-0 group head. Aggregator for G0P.
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Aggregation

• Each node is a head of a group at some level,
  thereby also
• the aggregator for that group.

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Level-1 group head. Aggregator for G0L and G1L.
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Aggregation

• Each node is a head of a group at some level,
  thereby also
• the aggregator for that group.

Level-2 group head. Aggregator for G0G, G1G and
G2G.
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Aggregation

• At level-0, temporal aggregation of local
  readings.

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Aggregation

• At higher levels, spatial aggregation at parent
  aggregators.

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Aggregation

• At higher levels, spatial aggregation at parent
  aggregators.

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Querying

• A query is first processed by the top-level
  aggregator.

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Query issuer.
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Querying

• And then by the aggregators at subsequent
  hierarchy levels.

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Query issuer.
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Aggregation

• And then by the aggregators at subsequent
  hierarchy levels.

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Query issuer.
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Querying

• And then by the aggregators at subsequent
  hierarchy levels.

The reply is found here.
P.L.G
N.H.G
Query issuer.
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In the paper,

• we discuss the details of
• How does the hierarchical naming work?
• How does the routing of aggregates and queries
  work?
• How are the naming and routing used to provide
  the aggregation and querying?
• How can one maintain the names and the routes?

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Evaluation

• Simulations (application-blind)
• Event driven.
• Unit-disk connectivity.
• No message loss.
• Experiments
• Only the hierarchy maintenance and routing
  algorithm.
• No full system prototype yet.

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Sample results
Cost of multi-resolution aggregation.
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Conclusion

• Multi-resolution storage based on area hierarchy
  is an appealing solution.
• Warrants more research.
• More real-world experimentation is necessary to
  truly evaluate its potential.
• More in-depth analysis on potential applications
  is necessary.
• Open question Will our idea remain an academic
  exercise or will it lead to real world systems?

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Thank you

• Any questions?