1. Structure-Free Data Aggregation in Sensor Networks

2. Outline Introduction Background DAA & RW Performance Evaluation Conclusion

3. Introduction Because of structured approaches may incur high maintenance overhead in dynamic scenarios for event-based application. The author wish to design techniques and protocols that lead to efficient data aggregation without explicit maintenance of a structure. So the author propose the structure-free data aggregation for the event-based sensor networks.

4. Outline Introduction Background DAA & RW Performance Evaluation Conclusion

5. Background What is Data Aggregation Why Data Aggregation How to do Data Aggregation Structure data aggregation

6. What is Data Aggregation  When sensors generate raw data packets, before these packets transfer to the sink, we can do the process combining and compressing data coming from different sensors in order to reduce the packets to be sent over the network. A B aggregator SinkA B aggregator Sink

7. Why to do Data Aggregation  The main purpose of data aggregation is in order to conserve energy to extend the lifetime of sensor node.  So we reduce the packet length and the transmission times to conserve energy by data aggregation.

8. How to do Data Aggregation Two approaches:  Aggregation with size reduction (e.g. local temperature)  Aggregation without size reduction (e.g. temperature and humidity) Two consideration factor:  Temporal Convergence - These packets must meet in the same node at the same time  Spatial Convergence - These packets must meet in the same node at the same time

9. Spatial Convergence  How to transfer packet to the node with the packet which can be aggregated. A B C S E D A B C S A B C S E D (b) D E A B C S E D A B C S E D A B C S E D (a)  Fig (a) shows the packet transmissions using opportunistic aggregation.  Fig(b) shows how information about the existence of data in neighboring nodes can be exploited to make dynamic forwarding decisions to achieve higher aggregation. Node have packet to send Routing path by routing protocol Wireless link

10. Structure data aggregation Centralized structured approaches - These approaches are suited for data gathering application Distributed structured approaches - These approaches are proposed for event-based application.  Structured approaches have several limitations for event-based application. - For dynamic scenarios, the overhead of construction and maintenance of the structure may outweigh the benefits of data aggregation.

11. Outline Introduction Background DAA & RW Performance Evaluation Conclusion

12. DAA Assumption  Node know the geographic location of their one-hop neighbors and the sink.  The Interference range is at least twice the transmission range.  We aggregate packets that are generated at the same time, so nodes must be time-synchronized. a b c r r Node a’s Interference range

13. RTS CTS RTS Data-Aware Anycast(DAA)  DAA is based on anycasting at the MAC layer to determine the next-hop for each transmission. DAA Base-approach (use RTS/CTS)  We define the Aggregation ID(AID) to associate packets that can be aggregated.  RTS contains the AID of the transmitting packet and any neighbor that has a packet with the same AID can respond with a CTS. a e c b d f sink

14. Enhanced DAA (1/2) CTS Priority  Class A: The receiver has a packet with the same AID as specified in RTS and ids closer to the sink than the sender.  Class B: The receiver has a packet with the same AID as specified in RTS but is farther away from the sink than the sender.  Class C: The receiver does not have a packet with the same AID but is closer to the sink than the sender. Class C1: nodes are on the shortest path to the sink. Class C2: nodes are at least closer to the sink by half of the transmission range than the sender. Class C3: Remaining nodes in Class C.

15. Enhanced DAA (2/2)  It shows packets are still aggregated when they have the chance to meet; otherwise, the packets are forwarded greedily toward the sink. RTS sende r Class A Class B Class C Mini- slot CTS slot CTS Canceled CTS Class A receiver 1 Class A receiver 2 Class B receiver 3 Class C receiver 4 a e c b d f Class A Class C2 Class C3 Class B Shortest path

16. Goals Early aggregation - Packets must get aggregated as early as possible on their journey to the sink. Tolerance to event dynamics - If the event region changes, the overhead must not increase and the aggregation performance must remain unchanged. Robust to interference - Intermittent link failures should not affect the aggregation performance. Fault tolerance - The aggregation performance must not be affected by node failures.

17. Without Temporal Convergence  In DAA, packets may not get aggregated if they are spatially separated and if they are be forwarded in lock-step by MAC layer. A Sink B C D Interference range A Sink B C D With packet Without packet - Hence A must set a delay time.

18. How to choose delay time Concept:  The difference between two delays must larger then the transmission time between two nodes. A Sink B C D 3ms 2ms 12ms Delay time transmission time 6ms9ms 2ms … A Sink B C D 3ms 2ms 12ms 1ms2ms … How to decide A’s delay ?

19. Sink sensor Event-detected sensor Event Size

20. Randomized Waiting (RW)  Each source delays its transmission by an interval chosen from 0 to γ, where γ is the maximum delay. How to choose γ?  Because the optimum value of γ depends on the size of the event and the time to transmit a packet. - if event size increases, the max delay should increase. Weak point -Random delay is chosen too close.

21. Outline Introduction Background DAA & RW Performance Evaluation Conclusion

22. Experiment Simulation Environment description:  Topology is a 15 x 7 grid network with 105 nodes.  Sink locate at corner of the network and other 104 node generate packets when they detect an event.  Each node can communicate with their two-hop grid neighbors directly.  We compare DAA+RW to Aggregation Tree (AT) and Opportunistic Aggregation (OP).

23. Result

24. Conclusion  The proposed approach for data aggregation that do not use any explicit structures.  For spatial convergence, we proposed Data-Aware Anycast (DAA)  For temporal convergence, we proposed Randomized Waiting (RW)  In simulation, DAA with RW approach can improve the normalized load by as much as 73 percent compared to opportunistic aggregation.  Based on the experimental study, DAA + RW can significantly reduce the normalized overhead in terms of number of transmissions.  This shows that structure-free data aggregation techniques have great potential for event-based applications.