Wireless Sensor Data Fusion Techniques in Estimating Temporal Resource Attributes in Scenarios of Intermittent Connectivity

Yıl 2022, Cilt: 9 Sayı: 2, 413 - 423, 31.05.2022

Ashokkumar Nagarajan Kavıtha A Devı S

https://doi.org/10.31202/ecjse.948125

Cited By: 3

Öz

In data fusion process the fusion centre which lies intermediate distance aggregates the data and forwards to the sink. In scenario of data aggregation at fusion centre an imbalance may occur due to the potential forwarding process of other fusion centres or other sensor nodes to sink. Hence, this back log of time results in forwarding large chunks of data resulting in link imbalance where the associated classical time interval of reporting varies. The problem balancing and coordinating among fusion centres has been achieved in this work using Data Fusion using Generalized Interval Probability Protocol (DFGIPP). DFGIPP is developed considered the duality principle with proper and improper intervals of reporting to provide coherence among the links. Thus allocating and de-allocating links with the quality of fusion metrics in cooperation between fusion centre and sensor nodes within the terrain is being achieved. The simulation using discrete event network simulator-2 provides better fusion capability under data transfer rates and simulation scenarios.

Anahtar Kelimeler

Sensor data fusion, generalized interval probability, Quality of fusion.

Kaynakça

• 1. Durrant-Whyte, H. F. (1990). Sensor models and multisensor integration. In Autonomous robot vehicles (pp. 73-89). Springer, New York, NY.
• 2. Dasarathy, B. V. (1997). Sensor fusion potential exploitation-innovative architectures and illustrative applications. Proceedings of the IEEE, 85(1), 24-38.
• 3. Castanedo, Federico. "A review of data fusion techniques." The Scientific World Journal 2013 (2013). Article ID 704504, https://doi.org/10.1155/2013/704504.
• 4. Wu, J., Su, Y., Cheng, Y., Shao, X., Deng, C., & Liu, C. (2018). Multi-sensor information fusion for remaining useful life prediction of machining tools by adaptive network based fuzzy inference system. Applied Soft Computing, 68, 13-23.
• 5. Wang, J., Gao, Y., Liu, W., Sangaiah, A. K., & Kim, H. J. (2019). An intelligent data gathering schema with data fusion supported for mobile sink in wireless sensor networks. International Journal of Distributed Sensor Networks, 15(3), 1550147719839581.
• 6. Pfeuffer, A., & Dietmayer, K. (2019, July). Robust semantic segmentation in adverse weather conditions by means of sensor data fusion. In 2019 22th International Conference on Information Fusion (FUSION) (pp. 1-8). IEEE.
• 7. Panigrahi, S. R., Björsell, N., & Bengtsson, M. (2019). Data Fusion in the Air With Non- Identical Wireless Sensors. IEEE Transactions on Signal and Information Processing over Networks, 5(4), 646-656.
• 8. Cao, L., Cai, Y., Yue, Y., Cai, S., & Hang, B. (2020). A Novel Data Fusion Strategy Based on Extreme Learning Machine Optimized by Bat Algorithm for Mobile Heterogeneous Wireless Sensor Networks. IEEE Access, 8, 16057-16072.
• 9. Nakamura, E. F., Loureiro, A. A., & Frery, A. C. (2007). Information fusion for wireless sensor networks: Methods, models, and classifications. ACM Computing Surveys (CSUR), 39(3), 9-es.
• 10. Pinto, A. R., Montez, C., Araújo, G., Vasques, F., & Portugal, P. (2014). An approach to implement data fusion techniques in wireless sensor networks using genetic machine learning algorithms. Information fusion, 15, 90-101.
• 11. Patil, S., Das, S. R., & Nasipuri, A. (2004, October). Serial data fusion using space-filling curves in wireless sensor networks. In 2004 First Annual IEEE Communications Society Conference on Sensor and Ad Hoc Communications and Networks, 2004. IEEE SECON 2004. (pp. 182-190). IEEE.
• 12. Wang, Y. (2010). Imprecise probabilities based on generalised intervals for system reliability assessment. International Journal of Reliability and Safety, 4(4), 319-342.
• 13. Mostafaei, H., Montieri, A., Persico, V., & Pescapé, A. (2017). A sleep scheduling approach based on learning automata for WSN partial coverage. Journal of Network and Computer Applications, 80, 67-78.
• 14. Tan, R., Xing, G., Liu, B., Wang, J., & Jia, X. (2011). Exploiting data fusion to improve the coverage of wireless sensor networks. IEEE/ACM Transactions on networking 20(2), 450-462.
• 15. Wang, J., Tawose, O. T., Jiang, L., & Zhao, D. (2019). A New Data Fusion Algorithm for Wireless Sensor Networks Inspired by Hesitant Fuzzy Entropy. Sensors, 19(4), 784.
• 16. Luo, L. X. (2020). Information fusion for wireless sensor network based on mass deep auto- encoder learning and adaptive weighted D–S evidence synthesis. Journal of Ambient Intelligence and Humanized Computing, 11(2), 519-526.