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Scalable Data Aggregation for Dynamic Events in Sensor Networks

Scalable Data Aggregation for Dynamic Events in Sensor Networks. Kai-Wei Fan, Sha Liu, Prasun Sinha Computer Science and Engineering, Ohio State University ACM SenSys 2006. Outline. Introduction Structure-Less Aggregation Experiments and Simulation Conclusion. Introduction.

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Scalable Data Aggregation for Dynamic Events in Sensor Networks

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  1. Scalable Data Aggregation for Dynamic Events in Sensor Networks Kai-Wei Fan, Sha Liu, Prasun Sinha Computer Science and Engineering, Ohio State University ACM SenSys 2006

  2. Outline • Introduction • Structure-Less Aggregation • Experiments and Simulation • Conclusion

  3. Introduction • Data Aggregation • Communication cost is often larger than computation cost. • Redundancy in raw data. • Aggregate packets near sources to reduce transmission cost.  Prolong the lifetime. • Aggregation Approaches • Static structure • Dynamic structure • Structure-free

  4. Static Structure for Aggregation • Routing on a pre-computed structure • Pros • Low maintenance cost • Good for unchanged traffic pattern • Cons • Long stretch problem • Unsuitable for event-based network Sink

  5. Dynamic Structure for Aggregation • Create a structure dynamically • Pros • Optimization for source nodes • Cons • High maintenance cost Sink

  6. Structure-Free Aggregation • No structure  No structure maintenance cost • Aggregation without structure • Where to transmit? • Wait for whom? • Improve aggregating by transmitting packets to the same node at the same time • Spatial Convergence  Data Aware Anycast • Temporal Convergence  Random Waiting

  7. Data Aware Anycast • Anycast • One-to-any forwarding • Anycast to neighbor having packets for aggregating • Class A: Nodes closer to the sink with data for aggregation • Class B: Nodes with data for aggregation • Class C: Nodes closer to the sink Class A Class B Class C Sender RTS Class A Nbr CTS Class A Nbr Canceled CTS Class B Nbr Canceled CTS Class C Nbr

  8. … Sink τ=n τ=n-1 τ=n-2 τ=1 τ=0 Random Waiting • Fixed Delay • Nodes close to sink pick high delay. • Random Delay • Source nodes pick random delay between 0 and τbefore transmission.

  9. DAA and RW Example 2 1 3 4 Sink Not guarantee aggregation of all packets from a single event !!

  10. Structure-Less Aggregation • Structure-free aggregation does not guarantee all packets are completely aggregated to one. • High cost for un-aggregated or partial-aggregated packets • Structure-Less Aggregation (2 Phases) • 1st : Based on structure-free aggregation (DAA & RW) • Aggregate packets form sources to aggregators locally • 2nd : Further aggregation on an implicitly constructedstructure • Aggregate packets from aggregators to sink • Tree on Directed Acyclic Graphic (ToD)

  11. Tree on Directed Acyclic Graphic(ToD) • Definition • Contiguous events • Cell: A square area with side length greater than the diameter which an event can span • F-cluster: First cluster, composed of multiple cells • S-cluster: Second cluster, composed of multiple cells (interleaved with F-cluster) • 1D Construction of ToD F-cluster S-cluster

  12. sink sink F-cluster-head S-cluster-head Shortest Path Shortest Path sink a b c d d c a b S5 S6 F6 S-cluster F-clusters Shortest Path Tree S5 S6 F6 a c d b Tree on Directed Acyclic Graphic(ToD)

  13. sink sink Dynamic Forwarding for 1D (1) • Forwarding Rules • Rule 0: Forward packets to F-aggregator by structure-free data aggregation protocol. • Rule 1: Event spans two cells in a F-cluster, forward to sink • Rule 2: Event spans one cells, forward to appropriate S-aggregator

  14. Dynamic Forwarding for 1D (2) • Property 1. Packets will be aggregated at a F-aggregator, or will be aggregated at a S-aggregator. • If only nodes in one cell are triggered and generate the packets  Aggregated at one F-aggregator (all nodes in a cell resides in the same F-cluster) • If nodes in two cells are triggered and generate the packets. • Two cells are in the same F-cluster  aggregated at the F-aggregator • Two cells are in different F-clusters  aggregated at the S-aggregator

  15. Tree on Directed Acyclic Grahpic(ToD) • 2D Construction S1 S2 A B C S1 S2 E D F S3 S4 S3 S4 G H I (a) F-clusters (b) Cells (c) S-clusters

  16. Dynamic Forwarding for 2D (1) • Event may span multiple cells in a F-cluster • Assume the region spanned by an event is contiguous. • Maximum 4 cells (a) 1 Cell (a) 2 Cells (a) 3 Cells (a) 4 Cells No other F-cluster will have packets  Forward to sink Forward to other S-aggregators

  17. Dynamic Forwarding for 2D (2) • Forwarding Rules • Rule 0: Forward packets to F-aggregator by structure-free data aggregation protocol. • Rule 1: Event spans three or four cells in a F-cluster, forwards to sink. • Rule 2: Event spans a cell in a F-cluster, forward to a S-aggregator. Corresponding S-cluster F-cluster Cell generating packets

  18. Dynamic Forwarding for 2D (2) • Rule 3: Event spans two cells, forward to two S-aggregators in order. F-cluster Y C C S-cluster I S-cluster II F-aggregator S-aggregator Sink F-cluster X  Forward to 1st S-aggregator (near sink), then forward to 2nd S-aggregator

  19. Dynamic Forwarding Example • Example Rule 0 Rule 2 Rule 3 Sink

  20. Aggregator Selections • Nodes play the role of F-aggregator in turn. • With probability based on residual energy • Hash current time to a node within that cluster • Delegate the role of S-aggregator to F-aggregator • Select the F-aggregator in the F-cluster near sink as the S-aggregator Sink Sink F-aggregator and S-aggregator (Right-top S-cluster)

  21. Dynamic Forwarding for 2D (3) • Property 2. Packets will be aggregated at the F-aggregator, at the 1st S-aggregator, or at the 2nd S-aggregator.

  22. Experiments (1) • Experiments Environment • 105 Mica2-based nodes • 7 x 15 grid network • Node spacing: 3 feet • Transmission range: 2 grid-neighbor • 2 F-clusters • Fixed event location • Protocols • Dynamic Forwarding over ToD (ToD) • Data Aware Anycast (DAA) • Shortest Path Tree (SPT) • Shortest Path Tree with Fixed Delay (SPT-D)

  23. Experiments (2) • Event Size Long Stretch Problem Better Performance: More chance of being aggregated

  24. Experiments (3) • Delay Stable: Random Delay Better Performance: Heavily depends on delay

  25. Experiments (4) • Large Simulation Environment • 2000m x 1200m area • 1938 nodes (grid network) • Node spacing: 35m • Transmission range: 50m • Cell side length = Event diameter • Event with random way-point model at 10m/s for 400 seconds • Protocols • ToD • DAA • SPT • OPT

  26. Experiments (5) • Event Size Best but not consider overhead

  27. Experiments (6) • Scalability (Event with different distance to sink) • Event Size: 400m • Event Area: 400m x 800m • Area Distance to Sink : 200m ~ 1400m

  28. Experiments (7) • Cell Size • Event Size: 200m, 400m, 600m • Best Cell Size: 200m Event 100m Cell 400m Event 200m Cell 600m Event 200m Cell • Future Work: Select appropriate cell size

  29. Conclusion • The paper proposes a semi-structured approach (ToD) that locally uses a structure-less technique followed by Dynamic Forwarding. • ToD avoids the long stretch problem in fixed structured approach and eliminates the overhead of maintenance of dynamic structure.

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