1. An Adaptive Probability Broadcast- based Data Preservation Protocol in Wireless Sensor Networks Liang, Jun-Bin ； Wang, Jianxin; Zhang, X.; Chen, Jianer 2011 IEEE International Conference on Communications (ICC) 1

2. Outline Introduction Related Works Network Model and Problem Statement PBDP ▫The Probability broadcast mechanism(PBM) ▫Algorithm of PBDP Simulations Conclusions 2

3. Introduction Goal ▫Data preservation on harsh WSN without sink. Challenge ▫Manage the processes of data dissemination and storage effectively. Proposed method ▫PBDP (Probability Broadcast-based Data Preservation) ▫also can reduce the redundancies of data transmission to conserve the energy of nodes. 3

4. Related Works - Growth codes [4] 4 Degree of a codeword “grows” with time At each timepoint codeword of a specific degree has the most utility for a decoder (on average) This “most useful” degree grows monotonically with time R: Number of decoded symbols sink has R1R1 R3R3 R2R2 R4R4 d=1 d=2d=3d=4 Time -> http://www.powercam.cc/slide/17704

5. Related Works - Growth codes [4] 5 Consider the degree of an encoded packet: ▫Decoder has decoded r original data. ▫The probability that new received encoded packet is immediately decodable to the decoder: Number of decoded original data: r Importance of Immediately Decodable Packet : Low Degree : High Degree http://www.powercam.cc/slide/284

6. Related Works – DFCNS [5] 6 1.each node should store an information of the path from it to the destination. 2.Cost storage space 3.Assume grid topology

7. Related Works – EDFC [6] Step 1 : Degree generation ▫Choose degree independently from RSD. Step 2 : Compute steady-state distribution ▫A random walk corresponds to Markov chain model. Step 3 : Compute probabilistic forwarding table ▫By the Metropolis algorithm Step 4 : Compute the number of random walk (b copies) Step 5 : Block dissemination ▫Each node disseminate b copies of its source block with its node ID. Step 6: Encoding 7 1. Require global information 2. cost each node large amount of energy to send and receive large amount of data packet(maintain a large buffer). 3. The real node degree may not equal to the chosen degree from RSD.

8. 8 K=1000 N=2000

9. Related Work – LTCDS-I [7] 9 1. Local-cluster effect may happen. http://www.powercam.cc/slide/16907

10. 10 Fixing the ratio between n and k as 10%, k/n=0.1

11. Related Works – DSA-I [8] 11 http://www.powercam.cc/slide/23057

12. 12 1.The transmissions of CF mechanism cost large amount of energy. 2.Each node’s storage reach about 10% of network size.

13. Related Works – rateless packet [*] 13 Fig. 3. Example of rateless packet initialization, encoding and dispersion phase. http://www.powercam.cc/ slide/16047

14. 14

15. Node-centric Packet- centric Growth codes EDFCLTCDS-IDSA-I Rateless packet Sink oxxxx Synchronous oxxxx Degree New degree RSD dissemination -Probabilistic forwarding table Simple random walk Global information -N>>K, MN>>kN=K Buffer size -largeOne data- # of copies - b (by formula) 11 b (by experiment) Mixing time - >500 15

16. Network Model 16 Time interval Sensing Inference Storage Collection 1. use EP [9] technology to estimate the number n of nodes in the network. 1. wake up to sense its vicinity and generate data. 1.into sleep state. 2.a collector enter the network to collect data.

17. Network Model 17 Sensor network 5 storage units M M

18. Network Model LNSM [10] (Log-Normal Shadowing Model) 18

19. Problem statement How can each node disseminate its data to the network for effective storage at each time interval? Goal ： make the collector can recover all data even if it just visits a small number of nodes. 19

20. The Probability broadcast mechanism [11] 20

21. The Probability broadcast mechanism 21

22. The Probability broadcast mechanism 22

23. The Probability broadcast mechanism 23

24. The Probability broadcast mechanism 24 Since the nodes are dispersed randomly, the degree distribution P(b) can be modeled as a Poisson point process.

25. The Probability broadcast mechanism 25

26. Performance of PBM 26

27. Algorithm of PBDP 27

28. Simulations 28 100m*100m r = 25m 2 storage units

29. Conclusion PBDP can achieve higher decoding performance and energy efficiency than existing schemes. 29

30. Reference [4]Abhinav Kamra, Vishal Misra, Jon Feldman, and Dan Rubenstein, Growth Codes: Maximizing Sensor Network Data Persistence, in Proc. of ACM SIGCOMM, 2006. [5]Alexandros G. Dimakis, Vinod Prabhakaran, and Kannan Ramchandran, Decentralized Erasure Codes for Distributed Networked Storage, in: IEEE Transactions on Information Theory, Volume:52, Issue:6, June 2006 [6]Yunfeng Lin, Ben Liang, and Baochun Li,Data Persistence in Large-scale Sensor Networks with Decentralized Fountain Codes. In Proc. of the 26th IEEE INFOCOM07, Anchorage, Alaska, May 6-12, 2007 [7]Salah A. Aly, Zhenning Kong, and Emina Soljanin, Fountain Codes Based Distributed Storage Algorithms for Wireless Sensor Networks, Proc. 2008 IEEE/ACM Information Processing of Sensor Networks (IPSN), St. Louis, Missouri, USA, April 22-24, 2008 [8] Aly, S.A., Youssef, M., Darwish, H.S., Zidan, M., Distributed Flooding- Based Storage Algorithms for Large-Scale Wireless Sensor Networks, IEEE International Conference on Communications (ICC 2009), 2009 30

31. Reference [10] L. Quin and T. Kunz, On-demand routing in MANETs: The impact of a realistic physical layer model, in Proceedings of the International Conference on Ad-Hoc, Mobile, and Wireless Networks, Montreal, Canada, 2003 [11] Cigdem Sengul, Matthew J. Miller, Indranil Gupta, Adaptive probabilitybased broadcast forwarding in energy-saving sensor networks, ACM Transactions on Sensor Networks, 2008 [12] Raman, V., Gupta, I., Performance Tradeoffs Among Percolation-Based Broadcast Protocols in Wireless Sensor Networks, 29th IEEE International Conference on Distributed Computing Systems Workshops (ICDCS 2009), 22- 26 June 2009 [13] V. Mhatre, K. Rosenberg, Design Guidelines for Wireless Sensor Networks: Communication, Clustering and Aggregation, Ad Hoc Networks, 2004. [14] Jin Zhu, Papavassiliou, S., On the connectivity modeling and the tradeoffs between reliability and energy efficiency in large scale wireless sensor networks, IEEE Wireless Communications and Networking (WCNC 2003), 20-20 March 2003. [*]Dejan Vukobratovic´, Cˇ edomir Stefanovic´, Vladimir Crnojevic´, Francesco Chiti, and Romano Fantacci, “Rateless Packet Approach for Data Gathering in Wireless Sensor Networks,” IEEE JOURNAL ON SELECTED AREAS IN COMMUNICATIONS, VOL. 28, NO. 7, EPTEMBER 2010. 31