scholarly journals Incentive Mechanism of Data Storage Based on Blockchain for Wireless Sensor Networks

2018 ◽  
Vol 2018 ◽  
pp. 1-10 ◽  
Author(s):  
Yongjun Ren ◽  
Yepeng Liu ◽  
Sai Ji ◽  
Arun Kumar Sangaiah ◽  
Jin Wang

In this paper, the blockchain technology is utilized to build the first incentive mechanism of nodes as per data storage for wireless sensor networks (WSNs). In our system, the nodes storing the data are rewarded with digital money. The more the data stored by the node, the more the reward it achieves. Moreover, two blockchains are constructed. One is utilized to store data of each node and another is to control the access of data. In addition, our proposal adopts the provable data possession to replace the proof of work (PoW) in original bitcoins to carry out the mining and storage of new data blocks, which greatly reduces the computing power comparing to the PoW mechanism. Furthermore, the preserving hash functions are used to compare the stored data and the new data block. The new data can be stored in the node which is closest to the existing data, and only the different subblocks are stored. Thus, it can greatly save the storage space of network nodes.

2010 ◽  
Vol 159 ◽  
pp. 29-34
Author(s):  
Shu Ming Xiong ◽  
Xiao Qian Qu ◽  
Yong Zhao Zhan ◽  
Xin Sheng Wang ◽  
Liang Min Wang

Due to the node failures incurred by intrusion threat, a wireless sensor networks will initiate topology re-generation, which is based on correct availability evaluation of current intrusion-tolerant topology. The paper proposes an availability evaluation model based on semi-Markov process (SMP) to estimate topology availability of the intrusion-tolerant topology concerning the effects from intrusion behaviors. In view of some limitations of node computation ability and storage ability, this model reduces the complexities resulting from modeling the different intrusion threats and is set up on the uniform intruding results to simplify the model design. Using the DTMC model embedded in SMP topology availability is computed and finally we analyze the sensitivity to parameters in the model.


2009 ◽  
Vol 26 (5) ◽  
pp. 335 ◽  
Author(s):  
Norbert Siegmund ◽  
Marko Rosenmuller ◽  
Guido Moritz ◽  
Gunter Saake ◽  
Dirk Timmermann

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