Two Layer data Prediction and secured data transmission in WSN

  IJETT-book-cover  International Journal of Engineering Trends and Technology (IJETT)          
© 2017 by IJETT Journal
Volume-54 Number-4
Year of Publication : 2017
Authors : Shashikumar R, Dr. Anupama A Deshponde, Dr. B. Mohankumar Naik
DOI :  10.14445/22315381/IJETT-V54P231


Shashikumar R, Dr. Anupama A Deshponde, Dr. B. Mohankumar Naik "Two Layer data Prediction and secured data transmission in WSN", International Journal of Engineering Trends and Technology (IJETT), V54(4),216-222 December 2017. ISSN:2231-5381. published by seventh sense research group

This Wireless Sensor Network (WSN) is one of the advance technologies for transmitting and receiving the sensor related information. The development of cyber-physical system (CPSs) can reduce the gap between physical world and the cyber world. The sensors sense the environmental data periodically. In continuous data sensing there are more redundant data, which leads to transmission of unnecessary bits and consumes energy. The prediction based approach has been implemented to reduce the data redundancy in the network and save the energy in the sensor nodes. It is a challenge to design a system which supports efficient method to sense and predict the data from different WSNs. Least Mean Square (LMS) and Kalman filters are used to predicate the data based on the actual value. Before transmitting the data has to be encoded. Blowfish Algorithm (BA) is implemented to encode the data. By encoding, data can be transmitted across wide range. The proposed system with LMS-Kalman filter can achieve better prediction accuracy, increase in network lifetime and privacy.

[1] Naveed Ilyasa, Turki Ali Alghamdib, Muhammad Nauman Farooqa, Bilal Mehbooba, Abdul Hannan Sadiqa, Umar Qasimc, Zahoor Ali Khand and Nadeem Javaida, ?AEDG AUV-Aided Efficient Data Gathering Routing Protocol for Underwater Wireless Sensor Networks, Procedia Computer Science. Elsevier, Vol. 52, pp. 568-575, 2015.
[2] Ravinesh C. Deo and Mehmet ?ahin, ?Application of the Extreme Learning Machine Algorithm for the Prediction of Monthly Effective Drought Index in Eastern Australia, Atmospheric Research. Elsevier, Vol. 152, pp. 512 – 525, 2015.
[3] Samer Samarah, ?A Data Predication Model for Integrating Wireless Sensor Networks and Cloud Computing?, Procedia Computer Science. Elsevier, Vol. 52, pp. 1141 – 1146, 2015.
[4] Mariam Alnuaimia, Khaled Shuaiba, Klaithem Alnuaimia and Mohammed Abdel-Hafez, ?Ferry-Based Data Gathering in Wireless Sensor Networks with Path Selection, Procedia Computer Science. Elsevier, Vol. 52, pp. 286 – 293, 2015.
[5] Lahouari Ghouti, Tarek R. Sheltami and Khaled S. Alutaibi, ?Mobility Prediction in Mobile Ad Hoc Networks Using Extreme Learning Machines, Procedia Computer Science. Elsevier, Vol. 19, pp. 305 – 312, 2013.
[6] Huang Lu, Jie Li, and Mohsen Guizani, ?Secure and Efficient Data Transmission for Cluster-Based Wireless Sensor Networks, IEEE, Vol. 25, No. 3, pp. 750 – 761, 2014.
[7] Yanjun Yao1 and Qing Cao, ?EDAL: an Energy-efficient, Delay-aware and Lifetime-balancing Data Collection Protocol for Wireless Sensor Networks, Mobile ad-hoc and sensor systems (MASS), 2013 IEEE 10th international conference. IEEE, Vol. 23, issue 3, pp. 810 – 823, 2015.
[8] Mou Wua, Liansheng Tan and Naixue Xiong, ?Data Prediction, Compression, and Recovery In Clustered Wireless Sensor Networks For Environmental Monitoring Applications, Elsevier, Vol. 329, pp. 800-818, 2016.
[9] Guiyi Wei, Yun Ling, Binfeng Guo, Bin Xiao and Athanasios V. Vasilakos, ?Prediction-Based Data Aggregation in Wireless Sensor Networks: Combining Grey Model and Kalman Filter?, Elsevier, Vol. 34, pp. 793 – 802, 2011.
[10] Liansheng Tan and Mou Wu, ?Data Reduction in Wireless Sensor Networks A Hierarchical LMS Prediction Approach, IEEE, Vol. 16, No. 6, pp. 1708 – 1715, 2016.
[11] Luo X, Zhang D, Yang L T, Liu J, Chang X and Ning H, ?A Kernel Machine-Based Secure Data Sensing and Fusion Scheme in Wireless Sensor Networks for the Cyber-Physical Systems, Future Generation Computer Systems. Elsevier, Vol. 61, pp. 85-96, 2016.

Wireless Sensor Network (WSN), Cyber-Physical System (CPS), Least Means Square (LMS), Kalman Filter and Blowfish Algorithm (BA).