IRO Journal on Sustainable Wireless Systems
IRO Journals
Volume 2 — Issue 2 — June 2020

WSN based Improved Bayesian Algorithm Combined with Enhanced Least-Squares Algorithm for Target Localizing and Tracking

https://doi.org/10.36548/jsws.2020.2.001
Wang Haoxiang
Director and lead executive faculty member, GoPerception Laboratory, NY
S. Smys
Professor, Department of CSE, RVS Technical Campus
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Haoxiang, Wang, and S. Smys. 2020. “WSN Based Improved Bayesian Algorithm Combined With Enhanced Least-Squares Algorithm for Target Localizing and Tracking”. IRO Journal on Sustainable Wireless Systems 2 (2): 59-67. https://doi.org/10.36548/jsws.2020.2.001.
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Keywords

— Prediction position
— Improved Bayesian enhanced Least-Squares
— Localization and tracking
— Wireless sensor networks
Published: 18-05-2020

Abstract

For wireless sensor network (WSN), localization and tracking of targets are implemented extensively by means of traditional tracking algorithms like classical least-square (CLS) algorithm, extended Kalman filter (EKF) and the Bayesian algorithm. For the purpose of tracking and moving target localization of WSN, this paper proposes an improved Bayesian algorithm that combines the principles of least-square algorithm. For forming a matrix of range joint probability and using target predictive location of obtaining a sub-range probability set, an improved Bayesian algorithm is implemented. During the dormant state of the WSN testbed, an automatic update of the range joint probability matrix occurs. Further, the range probability matrix is used for the calculation and normalization of the weight of every individual measurement. Lastly, based on the weighted least-square algorithm, calculation of the target prediction position and its correction value is performed. The accuracy of positioning of the proposed algorithm is improved when compared to variational Bayes expectation maximization (VBEM), dual-factor enhanced VBAKF (EVBAKF), variational Bayesian adaptive Kalman filtering (VBAKF), the fingerprint Kalman filter (FKF), the position Kalman filter (PKF), the weighted K-nearest neighbor (WKNN) and the EKF algorithms with the values of 0.5%, 7%, 14%, 19%, 33% and 35% respectively. Along with this, when compared to Bayesian algorithm, the computation burden is reduced by the proposed algorithm by a factor of over 80%.

References

  1. Yetgin, H., Cheung, K. T. K., El-Hajjar, M., & Hanzo, L. H. (2017). A survey of network lifetime maximization techniques in wireless sensor networks. IEEE Communications Surveys & Tutorials, 19(2), 828-854.
  2. 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.
  3. 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.
  4. Ranganathan, G., & Smys, S. Smart Wireless sensors for Impairment detection of the offshore Wind Turbines.
  5. Muneera Begum H, D. A. Janeera, and AG, Aneesh Kumar. "Internet of Things based Wild Animal Infringement Identification, Diversion and Alert System", Fifth International Conference on Inventive Computation Technologies (ICICT-2020), pp. 672-676. IEEE, 2020.
  6. Lin, S., Miao, F., Zhang, J., Zhou, G., Gu, L., He, T., ... & Pappas, G. J. (2016). ATPC: adaptive transmission power control for wireless sensor networks. ACM Transactions on Sensor Networks (TOSN), 12(1), 1-31.
  7. Biswas, S., Das, R., & Chatterjee, P. (2018). Energy-efficient connected target coverage in multi-hop wireless sensor networks. In Industry interactive innovations in science, engineering and technology (pp. 411-421). Springer, Singapore.
  8. He, X., Wang, T., Liu, W., & Luo, T. (2019). Measurement data fusion based on optimized weighted least-squares algorithm for multi-target tracking. IEEE Access, 7, 13901-13916.
  9. Tomic, S., Beko, M., Dinis, R., Tuba, M., & Bacanin, N. (2017). Bayesian methodology for target tracking using combined RSS and AoA measurements. Physical Communication, 25, 158-166.
  10. Cheng, L., Feng, L., & Wang, Y. (2018). A residual analysis-based improved particle filter in mobile localization for wireless sensor networks. Sensors, 18(9), 2945.
  11. Rana, M. M., & Shuvo, M. M. R. (2018, December). Localization of Senor Nodes Using Weighted Least Squares Algorithm: Comprehensive Literature Review and Future Research Directions. In 2018 International Conference on Innovation in Engineering and Technology (ICIET) (pp. 1-6). IEEE.
  12. Tomic, S., Beko, M., Dinis, R., & Bernardo, L. (2018). On target localization using combined RSS and AoA measurements. Sensors, 18(4), 1266.
  13. Kumar, S., & Hegde, R. M. (2017). A review of localization and tracking algorithms in wireless sensor networks. arXiv preprint arXiv:1701.02080.
  14. Cheng, L., Li, Y., Wang, Y., Bi, Y., Feng, L., & Xue, M. (2019). A triple-filter NLOS localization algorithm based on fuzzy c-means for wireless sensor networks. Sensors, 19(5), 1215.
  15. Kumar, D. P., Amgoth, T., & Annavarapu, C. S. R. (2019). Machine learning algorithms for wireless sensor networks: A survey. Information Fusion, 49, 1-25.
  16. Jondhale, S. R., Shubair, R., Labade, R. P., Lloret, J., & Gunjal, P. R. (2020). Application of Supervised Learning Approach for Target Localization in Wireless Sensor Network. In Handbook of Wireless Sensor Networks: Issues and Challenges in Current Scenario's (pp. 493-519). Springer, Cham.
  17. Gumaida, B. F., & Luo, J. (2019). A hybrid particle swarm optimization with a variable neighborhood search for the localization enhancement in wireless sensor networks. Applied Intelligence, 49(10), 3539-3557.