Structure-free Data Aggregation

1. Structure-freeData Aggregation Kaiwei Fan, Sha Liu, and Prasun Sinha (speaker)The Ohio State UniversityDept of Computer Science and Engineering

2. Outline • Introduction • Structure-free Data Aggregation • Simulation Results • Experiments on a testbed • Conclusion

3. Introduction • Data Aggregation • In-network processing • Reduces communication cost • Approaches • Static Structure • [LEACH, TWC ’02] • [PEGASIS, TPDS ’02] • Dynamic Structure • [Directed Diffusion, Mobicom ‘00] • [DCTC, Infocom ‘04]

4. Static Structure • Pros • Low maintenance cost • Good for unchanging traffic pattern • Cons • Unsuitable for event triggered network • Long link-stretch • Long delay sink

5. Static Structure • Pros • Low maintenance cost • Good for unchanging traffic pattern • Cons • Unsuitable for event triggered network • Long link-stretch • Long delay sink

6. Dynamic Structure • Pros • Reduces communication cost • Cons • High maintenance overhead sink

7. Structure-free Data Aggregation • Challenge • Routing: who is the next hop? • Waiting: who should wait for whom? • Approach • Spatial Convergence • Temporal Convergence • Solution • Data Aware Anycast • Randomized Delay Routing? Waiting? sink

8. Data Aware Anycast • Improve Spatial Convergence • Anycast • One-to-Any forwarding scheme • Anycast for Immediate Aggregation • To neighbor nodes having packets for aggregation • Keep Anycasting for Immediate Aggregation sink

9. Data Aware Anycast 50 nodes in 200mx200m sink

10. Data Aware Anycast • Forward to Sink • To neighbor nodes closer to the sink • Using Anycast for possible Immediate Aggregation sink

11. Data Aware Anycast • Forwarding and CTS replying priority • Class A: Nodes for Immediate Aggregation • Class B: Nodes closer to the sink • Class C: Otherwise, do not reply mini-slot CTS slot Class A Class B Sender RTS Class A Nbr CTS Class A Nbr Canceled CTS Class B Nbr Canceled CTS Class C Nbr

12. Randomized Waiting • Improve Temporal Convergence • Naive Waiting Approach • Use delay based on proximity to sink (closer to sink => higher delay) • Long delay for nodes close to the sink in case the event is near the sink • Our Approach: Random Delay at Sources

13. Sink … … h=n/k Analysis • Y: Number of hops a packet is forwarded before being aggregated • Assumptions: • Each node has k choices for next hops closer to sink • All n nodes have packets to send • E[Y] = • x : random delay in [0,1] picked up by a node • dh :random delay chosen by a node h hops away from sink • Total Number of Transmissions =

14. Analysis vs. Simulation • Results matches up to 40 hops • Gap increases as network size increases • Reason: transmission delay is ignored in analysis

15. Evaluated Protocols Opportunistic (OP) Optimum Aggregation Tree (AT) Data Aware Anycast (DAA) Randomized Waiting (RW) DAA+RW Evaluated Metric Normalized Number of Transmissions Parameters Studied Maximum Delay Event Size Aggregation Function Network Size Simulation Results

16. Simulation Results – Maximum delay • Configuration • 33 x 33 grid network • event moves at 10m/s • event radius: 200m • 140 nodes triggered by the event • data rate: 0.2 pkt/s • data payload: 50 bytes • AT-2: Aggregation tree approach with varying delay • DAA+RW improve OP by 70%

17. AT is sensitive to delay AT has best performance with highest delay Simulation Results – Maximum delay

18. Configuration event radius: 50m ~ 300m 8 ~ 260 nodes triggered by the event event radius: 200m Key Observations DAA+RW is much better than OP DAA+RW is close to AT (optimal tree) Simulation Results – Event Size

19. Configuration Aggregation Ratio ρ:0 ~ 1 Packet size:max(50, 50* (1-ρ)* n) Max packet size:400 bytes Key Observation DAA+RW performs better than AT Following the best tree is not optimum if the packet size is limited Simulation Results – Aggregation Ratio

20. event distance to the sink: 300m ~ 700m event radius: 200m Key Observation Improvement is higher for events farther from the sink Simulation Results – Network Size

21. Linear network with 5 sources and 1 sink 0.2 pkt/s data payload: 29 bytes Key Observation Delay as low as 0.1 is sufficient for optimizing performance Experiment – Randomized Waiting

22. Conclusion • Data Aware Anycast for Spatial Convergence • Randomized Waiting for Temporal Convergence • Efficient Aggregation without a Structure • High Aggregation • No maintenance overhead