A Novel Approach to Optimizing Anchor Point Placement for Wireless Rechargeable Sensor Networks
DOI:
https://doi.org/10.48001/978-81-980647-6-9-1Keywords:
Mobile charger, Wireless Rechargeable Sensor Network, Optimizing Anchor points, Placement ClusteringAbstract
In wireless sensor networks, energy efficiency has a major impact on the lifespan and functionality of the network. The scheduling of mobile cars, or mobile chargers (MC), to recharge sensor nodes via wireless energy transfer technologies has been the subject of several recent research. Regretfully, a lot of these recent studies overlooked important factors like the energy that the cars used while moving and the limitations of their capacity to recharge. In order to lower the MC's energy usage, academics have suggested that the network's topological structure be improved. Therefore, in order to construct the network topology of Wireless Sensor Networks (WSNs), we have created a grouping technique. In this study, we suggest an approach for structuring the network to produce a nearly optional number of anchor points. Keep in mind that an MC travels to anchor points in order to recharge neighbouring sensor nodes. The best feasible set of anchor points is found using a modified version of the affinity propagation clustering technique. Our suggested solution beats current techniques the number of RPs required and the length of the MS path, as shown by extensive simulations.
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References
Barker, A., Brown, D., & Martin, W. (1995). Bayesian estimation and the Kalman filter. Computers Mathematics with Applications, 30, 55–77. https://doi.org/10.1016/0898-1221(95)00156-S
Bi, J., Huang, L., Cao, H., & Yao, G. (2021). Improved indoor fingerprinting localization method using clustering algorithm and dynamic compensation. International Journal of Geo-Information (IJGI), 10(9), 613. https://doi.org/10.3390/ijgi10090613
Boubrima, A., Bechkit, W., & Rivano, H. (2017). Optimal WSN deployment models for air pollution monitoring. IEEE Transactions on Wireless Communications, 16, 2723–2735. https://doi.org/10.1109/TWC.2017.2671032
Caso, G., De Nardis, L., & Di Benedetto, M.-G. (2015). A mixed approach to similarity metric selection in affinity propagation-based WiFi fingerprinting indoor positioning. Sensors, 15, 27692–27720. https://doi.org/10.3390/s151127692
Han, G., Yang, X., Liu, L., Zhang, W., & Guizani, M. (2017). A disaster management-oriented path planning for mobile anchor node-based localization in wireless sensor networks. IEEE Transactions on Emerging Topics in Computing, 8, 115–125. https://doi.org/10.1109/TETC.2017.2724550
Kenniche, H., & Ravelomananana, V. (2010). Random geometric graphs as model of wireless sensor networks. Proceedings of the 2010 2nd International Conference on Computer and Automation Engineering (ICCAE), 103–107. https://doi.org/10.1109/ICCAE.2010.5451442
Luo, J., Zhang, C., & Wang, C. (2020). Indoor multi-floor 3D target tracking based on the multi-sensor fusion. IEEE Access, 8, 36836–36846. https://doi.org/10.1109/ACCESS.2020.2972962
Radmand, P., Talevski, A., Petersen, S., & Carlsen, S. (2010). Comparison of industrial WSN standards. Proceedings of the 4th IEEE International Conference on Digital Ecosystems and Technologies, 632–637. https://doi.org/10.1109/DEST.2010.5610631
Wang, Y.-M., Luo, Y., & Hua, Z. (2008). On the extent analysis method for fuzzy AHP and its applications. European Journal of Operational Research, 186, 735–747. https://doi.org/10.1016/j.ejor.2007.01.050
Zheng, Y., Ozdemir, O., Niu, R., & Varshney, P. (2012). New conditional posterior Cramér-Rao lower bounds for nonlinear sequential Bayesian estimation. IEEE Transactions on Signal Processing, 60, 5549–5556.
