Energy Efficient Secure & Privacy Preserving Data Aggregation for WSNs
Abstract
The aim of this research work is to enhance wireless sensor network life time via reducing communication overhead. Sensor nodes have limited resources specially energy resource which is difficult or impossible to change/replace. As communication is by far the most energy consuming aspect in WSNs, one of the main goals to save energy is therefore to reduce communication overhead. Data aggregation techniques reduce number of transmitted messages and enhance WSNs lifetime. In many WSNs applications, data aggregation while preserving data security and privacy becomes hot issue because of the personal data. In the paper, we present an approach to aggregate data in energy efficient and secure manner for WSNs, which is called Energy efficient Secure & Privacy Preserving data Aggregation for WSNs (ESPPA). The technique “slicing and mixing’’, is implemented to provide privacy. To show the superiority of our proposed ESPPA scheme, we compare it with an existing “slicing and mixing" based scheme (i.e., SMART (Slice-Mix-AggRegaTe) scheme). Through simulation results, we demonstrate that our presented approach ESPPA scheme effectively preserve data privacy, and has significantly less communication overhead than the SMART.References
H. Sarfraz, S. Hussain, R. Bokhari, A. A. Raza, I. Ullah, Z. Sarfraz, S. Pervez, A. Mustafa, I. Javed, R. Parveen “Speech Corpus Development for a Speaker Independent Spontaneous Urdu Speech Recognition System”, Oriental COCOSDA 2010 conference, Nov. 24-25, 2010, Katmandu, Nepal.
S. Irtza, S. Hussain, “Error Analysis of Single Speaker Urdu Speech Recognition System”, in CLT-12, University of Engineering and Technology, Lahore, Pakistan, 2012.
Irtza, S. and Hussain, S. "Minimally Balanced Corpus for Speech Recognition", in the Proceedings of 1st International Conference on Communications, Signal Processing, and their Applications (ICCSPA'13), IEEE, Sharjah, 2013.
J. Daniel & J. H. Martin, Speech and Language Processing: An introduction to natural language processing computational linguistics, and speech recognition, 2005.
A. Raza, S. Hussain, H. Sarfraz, I. Ullah and Z. Sarfraz, “An ASR System for Spontaneous Urdu Speech”, In the Proc. of Oriental COCOSDA, Kathmandu, Nepal. 24-25 November 2010.
A. Samoulian, Knowledge based approach to speech recognition, Department of Electrical and Computer Engineering University of Wollongong.
L. Deng , H. Strik, Structure-Based and Template-Based Automatic Speech Recognition, Comparing parametric and non-parametric approaches”.
Nirav S. Uchat, Hidden Markov Model and Speech Recognition.
HTK, http://htk.eng.cam.ac.uk, accessed July 2010.
S. T. Abate, W. Menzel, and B. Tafila, “An amharic speech corpus for large vocabulary Continuous speech recognition,” ISCA, 2005. Ninth European Conference on Speech Communication and Technology.
L. Villase.or-Pineda, M. Montes-y Gomez, D. Vaufreydaz, and J. F. Serignat, “Experiments on the construction of a phonetically balanced corpus from the web,” Lecture notes in computer science, pp. 416–419, 2004.
A. Li, F. Zheng, W. Byrne, P. Fung, T. Kamm, Y. Liu, Z. Song, U. Ruhi, V. Venkataramani, and X. Chen, “Cass: A phonetically transcribed corpus of mandarin spontaneous speech,” ISCA, 2000. Sixth International Conference on Spoken Language Processing.
A. L. Ronzhin, R. M. Yusupov, I.V. Li, and A. B. Leontieva, “Survey of Russian speech recognition systems”.
A. Li, F. Zheng, W. Byrne, P. Fung, T. Kamm, Y. Liu, Z. Song, U. Ruhi, V. Venkataramani, and X. X. Xhen, “ Cass: A phonetically transcribed corpus of mandrain spontaneous speech, ISCA, 2000. Sixth International Conference on Spoken Language Processing.
S. T Abate, W. Menzel, and B. Tafila, “An Amharic speech corpus for large vocabulary continuous speech recognition,” ISCA, 2005. Ninth European Conference on Speech.
P. A Heeman, “The American English sala-II data collection,” 2004. Proceedings LREC.
G. Raskinis, “Building medium vocabulary isolated word Lithuanian HMM speech recognition system,” Informatica, vol. 14, no. 1, pp.75-84, 2003.
G. Anumanchipalli, R. Chitturi, S. Joshi, R. Kumar, S. P. Singh, R. N. V. Sitaram, and S. P. Kishore, “Development of Indian language speech database for large vocabulary speech recognition system”.
V. Chourasia, K. Samudravijaya, and M. Chandwant, “Phonetically rich Hindi sentence corpus for creation of speech database,” Proc. O-Cocosda,p. 132-137, 2005.
Y. C. Yio, M. S Liang, Y.C. Chiang, and R. Y. Lyu, “Biphone rich versus triphone rich: a comparison of speech corpora in automatic speech recognition,” pp.194-197, 2005. Cellular Neural Networks and their applications, 2005 9th International workshop.
A. C. Kelly, H. Berthelsen, N. Campbell, A. Chasaide, C. Gobl , “Corpus Design Techniques for Irish Speech Synthesis Phonetics and Speech Laboratory”, SLSCS, Trinity College Dublin, Ireland, 2006.
W. Chai, P. Cotsomrong, S. Suebvisai, S. Kanokphara, Information Research and Development Unit National Electronics and Computer Technology Center, Phonetically Distributed Continuous Speech Corpus for Thai Language, COCOSDA, 2003.
