Load balance and efficient hierarchical data centric storage in sensor networks
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Load Balance and Efficient Hierarchical Data-Centric Storage in Sensor Networks. Yao Zhao, List Lab, Northwestern Univ Yan Chen, List Lab, Northwestern Univ Sylvia Ratnasamy, Intel Research. Outline. Background and Motivation Hierarchical Voronoi Graph based Routing Basic routing algorithm

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Load balance and efficient hierarchical data centric storage in sensor networks

Load Balance and Efficient Hierarchical Data-Centric Storage in Sensor Networks

Yao Zhao,List Lab, Northwestern Univ

Yan Chen, List Lab, Northwestern Univ

Sylvia Ratnasamy, Intel Research


Outline
Outline in Sensor Networks

  • Background and Motivation

  • Hierarchical Voronoi Graph based Routing

    • Basic routing algorithm

    • Practical design issues

  • Evaluation

  • Conclusions and Future Work


Generic storage schemes
Generic Storage Schemes in Sensor Networks

  • External Storage

  • Local Storage

  • Data-Centric Storage (DCS)


Generic storage schemes1

Event in Sensor Networks

Generic Storage Schemes

  • External Storage

    • Hotspot problem (if no need to store all events )


Generic storage schemes2

Event in Sensor Networks

Generic Storage Schemes

  • Local Storage

    • Overhead of flooding


Generic storage schemes3

Event in Sensor Networks

Generic Storage Schemes

  • Data-Centric Storage [CCR03]

    • Good to avoid hotspots and flooding overhead in some scenarios


Motivation
Motivation in Sensor Networks

  • Routing Primitive for Data-Centric Storage vs Any-to-any Routing

    • DCS doesn’t require any-to-any routing

      • E.g. in pathDCS [NSDI06], not all nodes are routable

    • Any-to-any routing may not be suitable for DCS

      • E.g. BVR[NSDI05] and S4[NSDI07]

    • Only a few any-to-any routing can be DCS routing

      • E.g. VRR [Sigcomm06], GEM[Sensys03]


Motivation1
Motivation in Sensor Networks

  • Routing Primitive for Data-Centric Storage vs Any-to-any Routing

  • Desirable Properties of DCS Routing

    • No GPS (or other location device)

    • Scalability

    • Efficiency

      • Path stretch (routing path length / shortest path length)

    • Load Balancing

      • In routing (forwarding overhead)

      • In Storage

  • Our Goal

    • Design routing primitive for DCS with the above properties


Outline1
Outline in Sensor Networks

  • Background and Motivation

  • Hierarchical Voronoi Graph based Routing

    • Basic routing algorithm

    • Practical design issues

  • Evaluation

  • Conclusions and Future Work


Hierarchical voronoi graph based routing
Hierarchical Voronoi Graph based Routing in Sensor Networks

  • Basic Routing Algorithm

    • Hierarchical coordinate

    • Region oriented routing

    • Name based routing for DCS

  • Practical Issues

    • Landmark selection

    • Path stretch reduction

    • Handling dynamic changes


Voronoi graph
Voronoi Graph in Sensor Networks


Hierarchical coordinate
Hierarchical Coordinate in Sensor Networks

  • Divide the network based on the hop distance to landmarks

Irregular borderline in realilty


Hierarchical coordinate1
Hierarchical Coordinate in Sensor Networks

  • Divide the network based on the hop distance to landmarks

In smallest region, nodes know each other


Overhead of building coordinate
Overhead of Building Coordinate in Sensor Networks

  • Initialization Overhead

    • Each Layer

      • O(mN) messages where m is the number landmarks splitting a region, and N is the number of nodes

    • K Layers

      • K ~ O(log N)

    • Total Overhead

      • O(mN·log N) messages

  • Memory Usage

    • Km ~ O(m·log N)


Name based routing

d in Sensor Networks

Name Based Routing

Bypass landmarks

  • S has an event E

    • Take a hash function H1 and get j = H1(E)%3

    • S sends E to the jth 1st level landmark and enter Lj’s region via node a

    • Node a compute H2(E)%3 to determine the next landmark

L2

L1,2

s

L1,2,3

a

L1

L3


Load balancing in storage
Load Balancing in Storage in Sensor Networks

  • Load Balancing Problem

    • In naïve name based routing, non-uniform division of regions causes non-uniform storage distribution

    • To divide regions uniformly is very hard

  • Our Approach: Non-uniform Hash Function

    • Collect the number of nodes in each region

    • Hashed value is proportional to the population of possible sub-regions


Outline2
Outline in Sensor Networks

  • Background and Motivation

  • Hierarchical Voronoi Graph based Routing

    • Basic routing algorithm

    • Practical design issues

  • Evaluation

  • Conclusions and Future Work


Evaluation
Evaluation in Sensor Networks

  • Simulation Setup

    • C++ implementation

    • Simple MAC without collision

    • Unit disk graph model in 2D space (communication range 1)

    • Baseline simulation

      • 3200 nodes

      • Density: 3π neighbors in average

    • Simulate HVGR, HVGR+ and VRR[Sigcomm06]

      • m = 6 (number of landmarks splitting a region)

  • Metrics

    • Path stretch

    • Load balancing: CDF for visualization

    • Route table size

    • Initialization overhead

    • Maintenance overhead


Efficiency
Efficiency in Sensor Networks

  • The stretch of HVGR doesn’t increase as N increase.


Scalability
Scalability in Sensor Networks

  • The route table size and initialization overhead increase logarithmically.


Routing load balancing
Routing Load Balancing in Sensor Networks

  • The routing load balancing feature of HVGR is close to that of shortest path routing.


Storage load balancing
Storage Load Balancing in Sensor Networks

  • The storage load balancing feature of HVGR is close to that of ideal hash based storage.


Conclusion
Conclusion in Sensor Networks

  • Design HVGR/HVGR+

    • Topology based routing (No GPS)

    • Good scalability (log N memory)

    • High efficiency (close to shortest path routing)

    • Balanced load in both routing and storage

  • Future Work

    • Theoretical analysis

    • Tinyos implementation


Thanks
Thanks! in Sensor Networks

Q&A?


Name based routing for dcs
Name Based Routing for DCS in Sensor Networks

  • Convert Name to Label

    • Event name S

    • A series of hash functions Hi

    • Order the m landmarks

    • Let j = Hi(S) mod m, the ith level label is the j th landmark


Voronoi graph1
Voronoi Graph in Sensor Networks


Voronoi graph2
Voronoi Graph in Sensor Networks

  • Divide the regions based on the closest landmark rule.


Number of landmark m in each level
Number of Landmark (m) in Each Level in Sensor Networks

  • m is not critical


Number of landmark m in each level1
Number of Landmark (m) in Each Level in Sensor Networks

  • The larger the m, the more overhead. We pick m=6 finally.


Desirable properties of dcs
Desirable Properties of DCS in Sensor Networks

  • DCS without Location Information

    • No GPS or other location devices

  • Scalability

    • Memory usage

    • Control message overhead

  • Efficiency

    • Path stretch (routing path length / shortest path length)

  • Load Balancing

    • In routing (forwarding overhead)

    • In Storage


Outline3
Outline in Sensor Networks

  • Background and Motivation

  • Hierarchical Voronoi Graph based Routing

    • Basic routing algorithm

    • Practical design issues

  • Evaluation

  • Conclusions and Future Work


Region oriented routing
Region Oriented Routing in Sensor Networks

  • From s to d with label (L1, L1,2, L1,2,3)

Bypass landmarks

L1,2

s

d

L1,2,3

a

L1


Hierarchical coordinate2
Hierarchical Coordinate in Sensor Networks

  • Divide the network based on the hop distance to landmarks


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