The Inability Of The Network To Restrict Node Jamming Across The Path Ability Fron End To End

A RAJANI, Y RAJESH BABU

Abstract


The right approach is to add a complete routing path to each packet. The problem with this approach is that its message may be large for packets with long routing routes. A multidimensional and diagnostic approach by routing each packet can provide effective control and protocol optimization for deployed WSNs with a large number of uncontrolled sensor nodes. Path includes a new design of lightweight hash functions for checking bad roads. To improve application features and performance, iPath includes a fast-loading algorithm to restore the original tracks. For iterative growth to be effective and efficient, two problems need to be addressed. The hash function should be easy and efficient, since the resource should be launched on the limited nodes of the sensor. A multidimensional and diagnostic approach by routing each packet can provide effective control and protocol optimization for deployed WSNs with a large number of uncontrolled sensor nodes. We also introduce iPath and evaluate performance with large-scale WSN deployments and extensive modeling. The results show that iPath achieves higher recovery rates with different network settings compared to other modern approaches. Compared to Path Zip, iPath uses a high degree of similarity between multiple packages for faster results and scales better.


References


Yi Gao, Student Member, IEEE, Wei Dong, Member, IEEE, Chun Chen, Member, IEEE,Jiajun Bu, Member, IEEE, ACM, Wenbin Wu, and Xue Liu, Member, IEEE, “iPath: Path Inference in Wireless Sensor Networks”, ieee/acm transactions on networking, vol. 24, no. 1, february 2016.

L. Ma, T. He, K. K. Leung, A. Swami, and D. Towsley, “Identifiability of link metrics based on end-to-end path measurements,” in Proc. IMC, 2013, pp. 391–404.

R. Lim, C. Walser, F. Ferrari, M. Zimmerling, and J. Beutel, “Distributed and synchronized measurements with FlockLab,” in Proc. SenSys, 2012, pp. 373–374.

Y. Yang, Y. Xu, X. Li, and C. Chen, “A loss inference algorithm for wireless sensor networks to improve data reliability of digital ecosystems.,” IEEE Trans. Ind. Electron., vol. 58, no. 6, pp. 2126–2137, Jun. 2011.

J. Wang, W. Dong, Z. Cao, and Y. Liu, “On the delay performance analysis in a large-scale wireless sensor network,” in Proc. IEEE RTSS, 2012, pp. 305–314.

M. Ceriotti et al., “Monitoring heritage buildings with wireless sensor networks: The Torre Aquila deployment,” in Proc. IPSN, 2009, pp. 277–288.

R. Lim, C. Walser, F. Ferrari, M. Zimmerling, and J. Beutel, “Distributed and synchronized measurements with FlockLab,” in Proc. SenSys, 2012, pp. 373–374.


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