Trust-Value Based Wireless Sensor Network Using Compressed Sensing
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How to Cite

Anand, JV. 2020. “Trust-Value Based Wireless Sensor Network Using Compressed Sensing”. Journal of Electronics and Informatics 2 (2): 88-95. https://doi.org/10.36548/jei.2020.2.003.

Keywords

— Wireless Sensor Networks
— Trust Management
— Compressed data
— trust management
— Optimal trusted path
Published: 02-06-2020

Abstract

Wireless sensor networks have quickly paved way to novel ways of communication between two nodes. They consist of sensor nodes that have the capacity to sense, communicate and compute. If a particular node in a WSN is not able to transmit data to the base station, routing algorithms will move into action to direct the data from the node. The proposed work deals with a routing algorithm based on trust awareness and compression sensing data, to handle data routing in a clustered WSN. In general, when sensor nodes have reduced overhead, compressed sensing is utilized for data aggregation. In order to strike a balance between number of messages transmitted, hop count, distance of transmission and the optimal trusted path, many nature inspired optimisation methods have been developed over the years. However, trust-based retrieval of compressed data is executed at the base station amidst malicious nodes.

References

  1. Ari, A.A.A., Yenke, B.O., Labraoui, N., Damakoa, I. and Gueroui, A. (2016) ‘A power efficient cluster-based routing algorithm for wireless sensor networks: honeybees swarm intelligence based approach’, Journal of Network and Computer Applications, Vol. 69, pp.77–97.
  2. Xiang, W., Wang, N. and Zhou, Y. (2016) ‘An energy-efficient routing algorithm for software-defined wireless sensor networks’, IEEE Sensors Journal, Vol. 16, No. 20, pp.7393–7400.
  3. Edwin Prem Kumar, G., Baskaran, K., Elijah Blessing, R. and Lydia, M. (2018) ‘Trust based data prediction, aggregation and reconstruction using compressed sensing for clustered wireless sensor networks’, Computer and Electrical Engg.
  4. Singh, S., Verma, V.K. and Pathak, N.P. (2015) ‘Sensors augmentation influence over trust and reputation models realization for dense wireless sensor networks’, IEEE Sensors Journal, Vol. 15, No. 11, pp.6248–6254.
  5. Bhatia, T., Kansal, S., Goel, S., Verma, A.K. (2016) ‘A genetic algorithm based distance-aware routing protocol for wireless sensor networks’, Computers and Electrical Engineering, Vol. 56, pp.441–455.
  6. Delaney, D.T., Higgs, R., O’Hare, G.M.P. (2014) ‘A stable routing framework for tree based routing structures in WSNs’, IEEE Sensors Journal, Vol. 14, No. 10, pp.3533–3547.
  7. Edwin Prem Kumar, G., Baskaran, K., Elijah Blessing, R. and Lydia, M. (2016) ‘Evaluation of hybrid trust models using ant colony optimization in wireless sensor networks’, Intl. J. Smart Sensing and Intelligent Systems, Vol. 9, No. 3, pp.1243–1260.
  8. Fang, W., Zhang, C., Shi, Z., Zhao, Q. and Shan, L. (2016) ‘BTRES: beta-based trust and reputation evaluation system for wireless sensor networks’, Journal of Network and Computer Applications, Vol. 59, pp.88–94.
  9. Nguyen, M.T. and Teague, K.A. (2017) ‘Compressive sensing based random walk routing in wireless sensor networks’, Ad Hoc Networks, Vol. 54, pp.99–110.
  10. Rahat, A.A.M., Everson, R.M. and Fieldsend, J.E. (2016) ‘Evolutionary multi-path routing for network lifetime and robustness in wireless sensor networks’, Ad Hoc Networks, Vol. 52, pp.130–145.
  11. Rathore, H., Badarla, V. and George, K.J. (2016) ‘Sociopsychological trust model for wireless sensor networks’, Journal of Network and Computer Applications, Vol. 62, pp.75–87.
  12. Raj, J. S. (2020). Machine Learning Based Resourceful Clustering With Load Optimization for Wireless Sensor Networks. Journal of Ubiquitous Computing and Communication Technologies (UCCT), 2(01), 29-38.
  13. Raj, J. S. ENERGY EFFICIENT SENSED DATA CONVEYANCE FOR SENSOR NETWORK UTILIZING HYBRID ALGORITHMS.
  14. Haoxiang, W., & Smys, S. (2020). Soft Computing Strategies for Optimized Route Selection in Wireless Sensor Network. Journal of Soft Computing Paradigm (JSCP), 2(01), 1-12.
  15. Raj, J. S. (2019). QoS optimization of energy efficient routing in IoT wireless sensor networks. Journal of ISMAC, 1(01), 12-23.