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
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.
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.
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
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.
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.