ICTACT Journal on Communication Technology

Open Access Open Access  Restricted Access Subscription Access
Open Access Open Access Open Access  Restricted Access Restricted Access Subscription Access

Improved Scheme for Cluster Based Fault Tolerant Data Aggregation in Wireless Sensor Networks


Affiliations
1 School of Computing, Shanmugha Arts, Science, Technology and Research Academy, India
2 Department of Computer Science and Engineering, RMK Engineering College, India
3 Department of Electronics and Communication Engineering, PET Engineering College, India
     

   Subscribe/Renew Journal


Wireless Sensor Networks (WSNs) are used widely in many mission critical applications like battlefield surveillance, environmental monitoring, forest fire monitoring etc. A lot of research is being done to reduce the energy consumption, enhance the network lifetime and fault tolerance capability of WSNs. In this paper, we propose an energy aware routing algorithm for cluster based WSNs along with an ANFIS estimator based data aggregation scheme called Neuro-Fuzzy Optimization Model (ANFIS-NFO) for the design of fault-tolerant. The algorithm is based on a clever strategy of cluster head (CH) selection, residual energy of the CHs and the intra-cluster distance for cluster formation. To facilitate data routing, a directed virtual backbone of CHs is constructed which is ischolar_mained at the sink. The proposed algorithm is also shown to balance energy consumption of the CHs during data routing process. We prove that the algorithm achieves constant message and linear time complexity and they pro-actively identify the faulty CHs by the application of the proposed ANFIS estimator and perform inter-cluster fault tolerant data aggregation.

Keywords

Wireless Sensor Networks, Neuro-Fuzzy Optimization Model, Fault- Tolerant, ANFIS.
Subscription Login to verify subscription
User
Notifications
Font Size

  • Mohammad A. Haque, Ramin Irani, Kamal Nasrollahi and Thomas B. Moeslund, “Heart Beat Rate Measurement from Facial Video”, IEEE Intelligent Systems, Vol. 31, No. 3, pp. 1-6, 2016.

  • H. Rahman and M.U. Ahmed, S. Begum and P. Funk, “Real Time Heart Rate Monitoring from Facial RGB Color Video using Webcam”, Proceedings of 29th Annual Workshop of the Swedish Artificial Intelligence Society, pp. 1-8, 2016.

  • Simmi Dutta, Hiteshwar, Abhimanyu Dev Jamwal and Azhar Ud Din Guroo, “Heart Rate Detection using Independent Component Analysis and Multivariate Adaptive Regression Splines”, Imperial Journal of Interdisciplinary Research, Vol. 2, No. 10, pp. 1-7, 2016.

  • M. Kumar, A. Veeraraghavan and A. Sabharwal, “Distance PPG: Robust Non-Contact Vital Signs Monitoring using A Camera”, Biomedical Optics Express, Vol. 6, No. 5, pp. 1565-1588, 2015.

  • Hussain A. Jaber, A.L. Ziarjawey and Ilyas Cankaya, “Heart Rate Monitoring and PQRST Detection Based on Graphical User Interface with Matlab”, International Journal of Information and Electronics Engineering, Vol. 5, No. 4, pp. 311-317, 2015.

  • J. Moreno, J. Ramos-Castro, J. Movellan, E. Parrado, G. Rodas and L. Capdevila, “Facial Video-based Photoplethysmography to Detect HRV at Rest”, International Journal of Sports Medicine, Vol. 36, No. 6, pp. 474-480, 2015.

  • Larissa Carvalho, H.G. Virani and S. Kutty, “Analysis of Heart Rate Monitoring Using a Webcam”, International Journal of Advanced Research in Computer and Communication Engineering, Vol. 3, No. 5, pp. 1-7, 2014.

  • X. Li, J. Chen, G. Zhao, and M. Pietikainen, “Remote Heart Rate Measurement from Face Videos under Realistic Situations”, Proceedings of IEEE International Conference on Computer Vision and Pattern Recognition, pp. 4264-4271, 2014.

  • R. Irani, K. Nasrollahi and T.B. Moeslund, “Improved Pulse Detection from Head Motions using DCT”, Proceedings of 9th International Conference on Computer Vision Theory and Applications, pp. 124-129, 2014.

  • S. Thulasi Prasad and S. Varadarajan, “Heart Rate Detection using Hilbert Transform”, International Journal of Research in Engineering and Technology, Vol. 2, No. 8, pp. 12-18, 2013.

  • Gerard De Haan and Vincent Jeanne, “Robust Pulse Rate from Chrominance-Based rPPG”, IEEE Transactions on Biomedical Engineering, Vol. 60, No. 10, pp. 94-128, 2013.

  • G. Balakrishnan, F. Durand and J. Guttag, “Detecting Pulse from Head Motions in Video”, Proceedings of IEEE International Conference on Computer Vision and Pattern Recognition, pp. 3430-3437, 2013.

  • X. Yu, J. Huang, S. Zhang, W. Yan and D. Metaxas, “Posefree Facial Landmark Fitting Via Optimized Part Mixtures and Cascaded Deformable Shape Model”, Proceedings of IEEE International Conference on Computer Vision and Pattern Recognition, pp. 1944-1951, 2013.

  • Isayiyas Nigatu Tiba and Li Li, “Image-Based Automatic Pulse Rate Monitoring System Using PC Webcam”, International Journal of Engineering Research and Technology, Vol. 2, No. 12, pp. 841-847, 2013.

  • M. Soleymani, J. Lichtenauer, T. Pun and M. Pantic, “A Multimodal Database for Affect Recognition and Implicit Tagging”, IEEE Transactions on Affective Computing, Vol. 3, No. 1, pp. 42-55, 2012.


Abstract Views: 351

PDF Views: 0




  • Improved Scheme for Cluster Based Fault Tolerant Data Aggregation in Wireless Sensor Networks

Abstract Views: 351  |  PDF Views: 0

Authors

T. Suriya Praba
School of Computing, Shanmugha Arts, Science, Technology and Research Academy, India
Venkatesh Veeramuthu
Department of Computer Science and Engineering, RMK Engineering College, India
R. D. Harshitha
Department of Electronics and Communication Engineering, PET Engineering College, India
T. Sethukarasi
Department of Electronics and Communication Engineering, PET Engineering College, India

Abstract


Wireless Sensor Networks (WSNs) are used widely in many mission critical applications like battlefield surveillance, environmental monitoring, forest fire monitoring etc. A lot of research is being done to reduce the energy consumption, enhance the network lifetime and fault tolerance capability of WSNs. In this paper, we propose an energy aware routing algorithm for cluster based WSNs along with an ANFIS estimator based data aggregation scheme called Neuro-Fuzzy Optimization Model (ANFIS-NFO) for the design of fault-tolerant. The algorithm is based on a clever strategy of cluster head (CH) selection, residual energy of the CHs and the intra-cluster distance for cluster formation. To facilitate data routing, a directed virtual backbone of CHs is constructed which is ischolar_mained at the sink. The proposed algorithm is also shown to balance energy consumption of the CHs during data routing process. We prove that the algorithm achieves constant message and linear time complexity and they pro-actively identify the faulty CHs by the application of the proposed ANFIS estimator and perform inter-cluster fault tolerant data aggregation.

Keywords


Wireless Sensor Networks, Neuro-Fuzzy Optimization Model, Fault- Tolerant, ANFIS.

References