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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)

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Outline•Introduction•Related Works•Network Model and Problem Statement•PBDP

▫The Probability broadcast mechanism(PBM)

▫Algorithm of PBDP•Simulations•Conclusions

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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.

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Related Works - Growth codes[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

R1 R3R2 R4

d=1 d=2 d=3 d=4

Time ->

http://www.powercam.cc/slide/17704

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Related Works - Growth codes[4]

• 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: rNumber of decoded original data: r

Impo

rtanc

e of

Imm

edia

tely

D

ecod

able

Pac

ket

: Low Degree

: High Degree

http://www.powercam.cc/slide/284

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Related Works – DFCNS[5]

1. each node should store an information of the path from it to the destination.

2. Cost storage space3. Assume grid topology

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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: Encoding1. Require global information2. 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.

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K=1000N=2000

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Related Work – LTCDS-I[7]

1. Local-cluster effect may happen.

http://www.powercam.cc/slide/16907

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

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Related Works – DSA-I[8]

http://www.powercam.cc/slide/23057

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1. The transmissions of CF mechanism cost large amount of energy.

2. Each node’s storage reach about 10% of network size.

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Related Works – rateless packet[*]

13Fig. 3. Example of rateless packet initialization, encoding and dispersion phase.

http://www.powercam.cc/slide/16047

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Node-centric Packet-centric

Growth codes EDFC LTCDS-I DSA-I Rateless

packetSink o x x x xSynchronous o x x x x

Degree New degree RSD RSD RSD RSD

dissemination

- Probabilistic forwarding table

Simple random walk

Simple random walk

Simple random walk

Global information - N>>K, M N>>k N=K N=KBuffer size - large One data -

# of copies - b (by formula) 1 1

b (by experiment)

Mixing time - >500

Network Model• V = {} randomly distributed in a field of M*M.• The working time of the network is broken up into

time intervals.

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Tim

e in

terv

al Sensing

Inference

Storage

Collection

1. use EP[9] technology to estimate the number n of nodes in the network.1. compute parameters according

to .2. disseminate and store data.

1. wake up to sense its vicinity and generate data.

1. into sleep state.2. a collector enter the

network to collect data.

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Network Model

𝑣1 𝑣8

𝑣3𝑣7

𝑣6

𝑣5𝑣4

Sensor network 𝑥8 5 storage units 𝑠3• node generates 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

M

M

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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

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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.

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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

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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 .

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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

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The Probability broadcast mechanism

, (5) A is the event that receives the packet.

(6)

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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)

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The Probability broadcast mechanism

(10)

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Performance of PBM

100m*100mr = 25m = 50 nJ/bit = 100 nJ/bitPacket size =100 bits

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Algorithm of PBDP

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Simulations

100m*100mr = 25m2 storage units

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Conclusion• PBDP can achieve higher decoding performance

and energy efficiency than existing schemes.

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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

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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.