Distributed Storage Coding for Flexible andEfficient Data Dissemination and Retrieval in

Wireless Sensor Networks

Ning Cao, Qian Wang, Kui Ren, and Wenjin Lou

IEEE ICC 2010 Proceedings

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Outline• Introduction

• Related Works

• Network model

• Efficient Dissemination of Source Data

• Proposed Distributed Storage Coding Scheme

• Scheme Evaluation

• Conclusion

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Introduction• The technique of distributed storage coding has been widely

used in WSN for increasing the data persistence and efficiency of data retrieval.

• Centralized storages such as sink cause performance bottleneck.• Sensed data should be distributed over the whole network.

• How to disseminate data efficiently is a problem.• Many exiting works utilize the random walk to disseminate data.• However, traditional random walk algorithm is inefficient.

• The scheme proposed in this paper ensures the efficient dissemination and flexible recovery of data of interest.• Based on a variant of Metropolis Algorithm• Taking advantage of broadcast transmission.

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Goal• Improving the efficiency of traditional random walk algorithm.

• Querying storage nodes at the t-th time slot to recover the up-to date data generated by the target source nodes.• each storage node encodes all the source packets received from

the same source node into one packet.

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Related works• A decentralized coding algorithm based on Reed-Solomon code.

[1]

• ML decoding algorithm makes the decoding process inefficient.

• Taking advantage of the linear coding property of Fountain codes.[2]-[5]

• The drawback of decoding all packets or nothing.

• Addressing partial data recovery problem.[6]-[9]

• Not flexible

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Related Works[5]

• The probabilistic forwarding tables for random walks are computed by the Metropolis algorithm based on the required steady-state distribution of the random walks, which in turn is derived from the initially assigned RSD.

() the code degree of node i > node j ‘s

• The transmission cost is the product of and .

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[5] Y. Lin, B. Liang, and B. Li, “Data persistence in large-scale sensor networks with decentralized fountain codes,” in INFOCOM, May 2007

Related Works[5]

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Step 1 : Degree generation• Choose degree independently from RSD.

Step 2 : Compute steady-state distribution

Step 3 : Compute probabilistic forwarding table• By the Metropolis algorithm

Step 4 : Compute the number of random walkStep 5 : Block disseminationStep 6: Encoding

• Choose d distinct packets from received packets to be encoded.

This paper,

Network model• Random geometric graph, denoted G( n, r).

• k source nodes.

• The sensing time is divided into K time slots.

• Each source node produces one packet per time slot.

• Adopt the same asynchronous time model as that used in [11].

8[11] S. Boyd, A. Ghosh, B. Prabhakar, and D. Shah, “Gossip algorithms: design, analysis and applications,” in INFOCOM, 2005, pp. 1653–1664.

Efficient Dissemination of Source Data

• Ideal case• No duplicate source packet arrives at the same storage node.• A storage node x should receive d(x) packets from each source

node over K time slot.• n storage nodes will receive packets totally.

• The transmission cost is the product of and .

• Reduce

• employ the broadcast nature of wireless transmission.

• If one packet is buffered on a node for more than one copy, broadcast the packet to all neighbors through one transmission. 9

Efficient Dissemination of Source Data

• To meet the design needs, propose a packet structure

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• A variant of Metropolis algorithm

Proposed Distributed Storage Coding Scheme

1) Initialization• code degree d(x) • transition probability

2) Data Generation• Generate one data at each time slot t.• Initialize , • according to (1)

3) Data Dissemination4) Data Encoding5) Data Querying and Decoding

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Data Dissemination• First, the source nodes broadcast source packets to their

neighbor nodes.• Then all the storage nodes use proposed forwarding

algorithm.

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Example

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4

1

5

8

9

23

7

611

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, M = 4L = 75 =23

= 23*1/4 = 5= 23*1/4 = 5= 23*1/4 = 5= 23*1/4 = 5= 3

d=1

d=4

d=2

d=2d=1

d=2

d=3d=3

d=2

d=4

d=1

=5= 5*1/16 = 0= 5*2/16 = 0= 5

=5= 5*1/4 = 1= 4

=5= 5*1/4 = 1= 4

=5= 5*1/4 = 1= 5*1/4 = 1= 3

Data Dissemination

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Data Encoding• Coded packet consists of

• Encode when a random walk stops at a node with the coded packet of same NID.

• Do not encode the packet

• same PID as the storage node has already encoded

• code degree d(x) is reached

• a new random walk is launched such that .

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Data Querying and Decoding• Using a mobile collector instead of a sink .

• It can recover all the data in the specific area of interest through querying any storage nodes.• t is the current time slot.• is recovery overhead.

• The collector specifies the NIDs of source nodes deployed in that subarea, and every queried storage node returns a coded packet with specified NID if available.

• Utilize BP decoder to decode all the t source packets of every specified source node.

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Scheme Evaluation• n = 1000• k = 100• r = 4, avg. neighbors of each node = 4.• 88 time slots, i.e., K =88

• In each time slot, the simulator first disseminates new source packets, and then performs data querying and decoding.

• All results are an average of 50 runs.

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Recovery Overhead

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Recovery overhead affected by the length of random walk and the time slot, instead of the fraction of recovered source nodes.

Fig.3(a)Recover 0.2kt through querying storage nodes.

Dissemination Cost

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Conclusion• A mobile collector can recover data of any subset of source

nodes at any time via querying any storage nodes.

• Enjoy the broadcast nature of wireless transmission while achieving similar stationary distribution.

• Simulation results show that proposed coding algorithm does not introduce much more overhead to recover data of larger subset of source nodes.

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