Network Coding Data Collecting Mechanism based on Prioritized Degree Distribution in Wireless Sensor Network Wei Zhang, Xianghua Xu, Qinchao Zhang, Jian Wan, Naixue Xiong 2011 Ninth IEEE/IFIP International Conference on Embedded and Ubiquitous Computing 1

Outline Introduction Network Model PLTCDS Algorithm PLTCDS Analysis PLTCDS TYPE Ⅱ Result and Performance Conclusion and Future Work 2

Introduction Goal – To develop an effective data collecting mechanism based on network coding to quickly recover source data while collecting. i.e., cliff effect. Main idea – The predefined node broadcasts a beacon to stimulate the nodes to form the network with degree distribution priority. – Introducing a class of cumulative counter scheme to avoid empty storage. 3

Network Model 4

The k source nodes disseminate the sensed data to the storage nodes by simply replication or coding, improving the network data persistency and reliability. 5

Network Model Description of our scheme ： 1)The position of collector S is known. 2)The source nodes act both a sensing and storage node. 3)Each node has limited knowledge of global information, such as neighbor nodes, n, k but does not know the maximum degree of the network, and no routing table is maintained. 4)Each node has limited storage, assuming it can only store one encoded packet. 6

PLTCDS Algorithm We present a prioritized LT Codes based distributed storage (PLTCDS) algorithm to improve decoding efficiency while collecting. The source packets are disseminated throughout the network by simple random walks, and nodes use the coding strategy to do its encoding operation. We use a simple way to grade the network finding out the nodes with prioritized degree level. The PLTCDS algorithm consists of 4 main phases: initialization, encoding, storage and decoding. 7

PLTCDS Algorithm 8

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2)Encoding phase 10

PLTCDS Algorithm 3)Storage phase The node(recoding all of sources’ ID) finishes its encoding phase would not update the storage. When all nodes satisfied these conditions, network coding phase is completed. 4)Decoding phase The collector gets into the network from the entry of the network and visits the nodes on a certain path. 11

PLTCDS Analysis In this scenario, the collector firstly visits the nodes around it, it can quickly gather the encode packets with low degree, accelerating the decoding speed. Considering the worst case, that is, all of prioritized nodes failed before the collector get into the network, the performance would decline, but it can still maintain the performance of directly application of LT codes. 12

PLTCDS TYPE Ⅱ The nodes with degree priority are distributed around the network instead of concentrated together near the entry of the network. Compared to PLTCDS, this algorithm is particularly effective for data collecting in disaster-prone areas and suitable for those areas that are difficult to access to the interior of the network. 13

PLTCDS TYPE Ⅱ There are some differences from the previous PLTCDS in the initialization phase and decoding phase. 14

PLTCDS TYPE Ⅱ  Initialization phase Constructing network with degree level:  Initial : the degree level of every node is 0.  The node nearest to the middle of the network broadcasts a beacon.  All nodes hear it mark the degree level to one, and rebroadcast it to their neighbors.  A node hears the beacon with i but its degree is still zero, update itself to i+1.  We should find nodes with priority, so we define that the nodes with the highest two degree level are prioritized nodes. 15

PLTCDS TYPE Ⅱ  Decoding phase  The collector moves a circle around the network first and then gets into the interior zone. 16

Result and Performance The nodes are uniformly distributed in a field at random, and the hop count coefficient of the source packet C = 3. The location of the collector is (0,0). 17

Data collecting in a random network 18

Data collecting in a random network 19

Data collecting in a disaster network 20

Network connectivity 21

PLTCDS TYPE Ⅱ 22

CONCLUSION AND FUTURE WORK Unlike previous schemes, PLTCDS considered the data decoding efficiency. PLTCDS can efficiently collect source data in disaster scenarios, especially when the collector only can gather a small part of all nodes. we will improve this algorithm in more disaster scenarios and ease the influence on data persistence. 23