Scalable Data Aggregation for Dynamic Events in Sensor Networks Kai-Wei Fan Kai-Wei Fan, Sha Liu, and Prasun Sinha Dept of Computer Science and Engineering The Ohio State University

2 Wireless Sensors  Genesis of Wireless Sensors  Miniaturization of sensing devices and actuators  Miniaturization of computing platforms  Miniaturization of wireless component  Applications  Data Collection Networks  Environment Monitoring, Habitat Monitoring  Event Triggered Networks (focus of this work)  Military Applications, National Asset Protection  Challenges  Battery power  Limited bandwidth Berkeley MicaDot

3 Data Aggregation  Motivation  Communication cost is higher than computation cost  In-network processing reduces number/size of packets  Challenges  Rare & dynamic events  Protocol must use low energy for long network lifetime  Related Work  Static Structures  Dynamic Structures  Structure-Free

4 Data Aggregation Approaches Static Structure  Routing on a pre-computed structure  Suitable for unchanging traffic pattern  Inappropriate for dynamic event  Long link stretch – avg / worst: O(log n) / O(n) [Alon et al., SIAM 95]  [LEACH, TWC ’02], [PEGASIS, TPDS ’02], [GIST, DCOSS ’06], SMT, MST…

5 Data Aggregation Approaches Dynamic Structure  Create a structure dynamically  Optimization for a subset of nodes  High control overhead for dynamic events  [Directed Diffusion, Mobicom ‘00], [GIT, ICDCS ’02], [DCTC, Infocom ‘04]

6 Data Aggregation Approaches Structure-Free  Improve aggregation without any structure  Suitable for dynamic event scenarios  No guarantee of aggregation for all packets  [DAA, Infocom ’06]

7 Our Proposed Approach: Tree on Directed Acyclic Graph  Combine benefits of structured and structure-free approaches  Properties  Structure-free data aggregation  Packet forwarding on an implicit structure  Guarantee early aggregation irrespective of network size  Advantages  Low overhead of structure construction & maintenance  Suitable for dynamic event scenarios  Scalable in large scale sensor networks

8 ToD - Tree on DAG  One-Dimension illustration  Definition  Cell: Cell size is the maximum diameter of events  F-cluster: First-level Cluster. Composed of multiple cells  S-cluster: Second-level Cluster. Composed of multiple cells  Interleaved with F-clusters …… …………………… …… network one row instance of the network Cell F-clusterS-cluster

9 ToD - Tree on DAG sink S-cluster S-cluster-head sink F-clusters F-cluster-head

10  Rule 0: forward packets to F-cluster-head by structure-free data aggregation protocol [Infocom ’06]  Rule 1: event spans two cells, forward to sink  Rule 2: event spans one cell, forward to S-cluster-head Dynamic Forwarding sink

11 C1 A4B3 B1C2 A3 A1A2B2 B4 C3C4 D3 D1D2 D4E3 E1E2 E4F3 F1F2 F4 G1G2H1H2I1I2 Two-Dimension ToD Construction ABC D GHI EF S1S2 S3S4 G3G4H3H4I3I4 2Δ F-ClustersCellsS-Clusters Δ: Maximum Diameter of an event

12 Cluster-head Selection  Assumptions  Each node knows all nodes and their locations in its F-cluster  Time synchronization – Low precision.  Approach  Sort list of nodes based on node id: N  Hash current time to a node in the F-cluster  F-cluster = N[k] where k = H(current time);  F-cluster-heads play the role of S-cluster-heads  Benefits  No cluster-head election/update overhead  Local synchronization – sync only within an F-cluster

13 Dynamic Forwarding: Aggregating Cluster  Sharing cluster-head  F-cluster-head also takes the role of S- cluster-head  Benefits  Avoids maintenance of S-cluster-heads  Nodes only need to know the F-cluster- head in their F-cluster  Illustration  Assume sink is at bottom left corner S-cluster F-cluster S-cluster head F-cluster head F-cluster & S-cluster head F-cluster, aggregating cluster for the S-cluster

14 Dynamic Forwarding Rules  Nodes send data to their F-cluster-head  F-cluster-head forwards data to one/two S-cluster-heads  depends on which cells sent data to F-cluster-head  only need to consider packets from one or two cells  Guarantee aggregation in constant number of steps  independent of network size

15 Dynamic Forwarding: Example One cell scenario S-cluster Aggregating Cluster

16 Dynamic Forwarding: Example Two cells scenario S-cluster (S1) Aggregating Cluster for S1 S-cluster (S2) Aggregating Cluster for S2

17 Dynamic Forwarding Rules

18 Experimental Results  Evaluated Protocols  ToD  Data Aware Anycast (DAA) (includes RW)  Shortest Path Tree (SPT)  SPT with Delay (SPT-D)  Testbed Configuration  105 Mica2-based motes  15 * 7 grid network  TX Range: 2 grid-neighbor (max 12 neighbors)  Evaluated Metric  Normalized Number of Transmissions  Parameters  Maximum Delay  ToD, DAA, SPT-D  Event Size

19 Experiment Results - Delay  All nodes are sources  Data rate: 0.1 pkt/s  Data payload: 20 bytes  2 F-clusters in ToD  Key observations  ToD performs better than DAA  SPT-D is sensitive to the delay

20 Experiment Results – Event Size  12 ~ 78 sources  Data rate: 0.1 pkt/s  Data payload: 20 bytes  SPT-D delay: 6s  Key observations  ToD performs best  High variation of SPT-D: Long stretch problem

21 Simulation Results  Evaluated Protocols  ToD  Data Aware Anycast (DAA)  Shortest Path Tree (SPT)  Optimal Aggregation Tree (OPT)  Evaluated Metric  Normalized Number of Transmissions  Parameters  Event Size  Network Size  Cell Size

22 Simulation Results – Event Size  2000m X 1200m (35 X 58 grid network)  TX Range: 50m (8 neighbors)  Event moves at 10m/s  Data rate: 0.2 pkt/s  Data payload: 50 bytes  Key Observations  TOD performs close to OPT

23 Simulation Results – Network Size  Vary the distance from the event to sink: 400 ~ 1600m  Key Observations  SPT & DAA performance goes down with distance  ToD & OPT remain steady 2000m 1200m 400m

24 Simulation Results – Cell Size  Event Size: 200m, 400m, 600m in diameter  Vary cell size from 50m to 800m  Key Observations  ToD performs best on average when the cell size is smaller than the event size  Larger cell size: bad for traffic from sources to cluster-heads  Smaller cell size: bad for traffic from cluster-heads to sink

25 Conclusion  Structure-Free Aggregation  Dynamic Forwarding on ToD for Scalability  Efficient Aggregation without overhead of structure computation and maintenance  Future Work  Dynamic Forwarding for irregular network topology  Early aggregation irrespective of event size

26 Q&A