An Adaptive Probability Broadcast-based Data Preservation Protocol in Wireless Sensor Networks

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)

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

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.

4. d=2 d=3 d=4 d=1 R1 R2 R3 R4 Time -> Related Works - Growth codes[4] • Degree of a codeword “grows” with time • At each timepointcodeword 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 http://www.powercam.cc/slide/17704

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

6. Related Works – DFCNS[5] each node should store an information of the path from it to the destination. Cost storage space 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 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. K=1000 N=2000

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

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

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

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

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

16. Network Model • V = {} randomly distributed in a field of M*M. • The working time of the network is broken up into time intervals. 1. wake up to sense its vicinity and generate data. 1. use EP[9] technology to estimate the number n of nodes in the network. compute parameters according to . disseminate and store data. into sleep state. a collector enter the network to collect data.

17. Network Model Sensor network • nodegenerates a data • put in a packet packet() of c bits for transmission. • each node has m1 storage units , . • is the data stored at . • ：the energy of transmission • ：the energy of reception 5 storage units M M

18. Network Model • LNSM[10] (Log-Normal Shadowing Model) P(d) : the probability of a node receives a packet sent from another node that is located d meters away. r : communication range : the path loss exponent, r =25m =2

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.

20. The Probability broadcast mechanism[11] Lemma 1. [12] Assume that each node will rebroadcast a packet after its first reception with probability p and discard it with probability 1−p. In a sufficiently large and sufficiently dense random network, there is a bimodal behavior in the network: (1) if p ≥, the packet will be received by all nodes, where is a critical probability. (2) if p < , only a small number of nodes can receive the packet

21. The Probability broadcast mechanism • is decided by • analyzing a communication graph based on the network G. All nodes that receive the packet would form a connected sub-graph .

22. The Probability broadcast mechanism • In LNSM • Degree • A connected network with n nodes, the minimum communication range of nodes is • [13], therefore, • , when the communication graph contains all nodes in the network. • Then, is considered to contain all nodes in the network with a probability close to 1[14]when

23. The Probability broadcast mechanism A is the event that receives the packet. , (5) (6)

24. The Probability broadcast mechanism (7) Since the nodes are dispersed randomly, the degree distribution P(b) can be modeled as a Poisson point process. (8) (9)

25. The Probability broadcast mechanism (10)

26. Performance of PBM 100m*100m r = 25m = 50 nJ/bit = 100 nJ/bit Packet size =100 bits

27. Algorithm of PBDP

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

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

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

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.