Abstract
The wireless sensor networks (WSN) provides advancement of number of revolutionary applications such as localization, target tracking, etc. Most of these applications involve a numerous sensor device that are connected to the base station which behaves as a gateway to connect internally and cloud computing environments. The key operation of WSNs is data collection, data sensing and transmission. However, the sensor devices gather data and is communicated over the intermediate node in an episodic manner for smart decisions periodically. Enhancing the tracking prediction accuracy, reliability of network and lifetime performance for data gathered is the important objective of target tracking applications using WSNs. This work presents Reliable Target Tracking (RTT) model employing WSNs. First, in achieving higher prediction accuracy a Modified Kalman Filter (MKF) is introduced. Second, improved cluster head (CH) selection and multi-objective-based route optimization are presented. Experiment results shows the RTT model achieves major outcome when compared with present target tracking model employing WSNs for improving energy efficiency, tracking accuracy, latency reduction and communication overhead.










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References
Zou X, Li L, Du H, Zhou L. Intelligent sensing and computing in wireless sensor networks for multiple target tracking. Journal of Sensors. 2022. https://doi.org/10.1155/2022/2870314. (2870314).
Feng J, Zhao H. Dynamic nodes collaboration for target tracking in wireless sensor networks. IEEE Sens J. 2021;21(18):21069–79. https://doi.org/10.1109/JSEN.2021.3093473.
Pang C, Xu G-g, Shan G-l, Zhang Y-p. A new energy efficient management approach for wireless sensor networks in target tracking. Defence Technol. 2021;17(3):932–47. https://doi.org/10.1016/j.dt.2020.05.022.
Nayak P, Devulapalli A. A fuzzy logic-based clustering algorithm for WSN to extend the network lifetime. IEEE Sens J. 2016;16(1):137–44.
Nayak P, Vathasavai B. Energy efficient clustering algorithm for multi-hop wireless sensor network using type-2 fuzzy logic. IEEE Sens J. 2017;17(14):4492–9.
Ang KLM, Seng JKP, Zungeru AM. Optimizing energy consumption for big data collection in large-scale wireless sensor networks with mobile collectors. IEEE Syst J. 2017;99:1–11.
Rani S, Ahmed SH, Talwar R, Malhotra J. Can sensors collect big data? An energy-efficient big data gathering algorithm for a WSN. IEEE Trans Industr Inf. 2017;13(4):1961–8.
Liu X, Li J, Dong Z, Xiong F. Joint design of energy-efficient clustering and data recovery for wireless sensor networks. IEEE Access. 2017;5:3646–56.
Twayej W, Khan M, Al-Raweshidy HS. Network performance evaluation of M2M with self-organizing cluster head to sink mapping. IEEE Sens J. 2017;17(15):4962–74.
Deva Sarma HK, Mall R, Kar A. E2R2: energy-efficient and reliable routing for mobile wireless sensor networks. IEEE Syst J. 2016;10(2):604–16.
Gianluigi F, Mengjia Z, Xu H, Bo Z, Xiangxiang F. A heterogeneous energy wireless sensor network clustering protocol. Wirel Commun Mob Comput. 2019. https://doi.org/10.1155/2019/7367281.
Qiu T, Zhang Y, Qiao D, Zhang X, Wymore ML, Sangaiah AK. A robust time synchronization scheme for industrial Wireless sensor networks. IEEE Trans Ind Informat. 2018;14(8):3570–80.
Liu Y, et al. QTSAC: an energy-efficient MAC protocol for delay minimization in wireless sensor networks. IEEE Access. 2018;6:8273–91.
Jurado-Lasso FF, Clarke K, Nirmalathas A. A software-defined management system for IP-enabled WSNs. IEEE Syst J. 2020;14(2):2335–46. https://doi.org/10.1109/JSYST.2019.2946781.
Xiang X, Liu W, Wang T, Xie M, Li X, Song H, Liu A, Zhang G. Delay and energy-efficient data collection scheme-based matrix filling theory for dynamic traffic WSN. EURASIP J Wirel Commun Netw. 2019.
Kulkarni PKH, MalathiJesudason P. Multipath data transmission in WSN using exponential cat swarm and fuzzy optimisation. IET Commun. 2019;13(11):1685–95.
Kumar P, Kulkarni H, Malathi P. PFuzzyACO: fuzzy-based optimization approach for energy-aware cluster head selection in WSN. J Internet Technol. 2019;20(6):1787–800.
Chauhan V, Soni S. Mobile sink-based energy efficient cluster head selection strategy for wireless sensor networks. J Ambient Intell Human Comput. 2020;11:4453–66. https://doi.org/10.1007/s12652-019-01509-6.
Sangaiah AK, et al. Energy-aware geographic routing for real-time workforce monitoring in industrial informatics. IEEE Internet of Things J. 2021;8(12):9753–62. https://doi.org/10.1109/JIOT.2021.3056419.
Pang C, Xu G, Shan G, Zhang Y. A new energy efficient management approach for wireless sensor networks in target tracking. Defence Technol. 2020. https://doi.org/10.1016/j.dt.2020.05.022.
Zhang H, Zhou X, Wang Z, Yan H. Maneuvering target tracking with event-based mixture Kalman filter in mobile sensor networks. IEEE Trans Cybern. 2020;50(10):4346–57. https://doi.org/10.1109/TCYB.2019.2901515.
Liu F, Jiang C, Xiao W. Multistep prediction-based adaptive dynamic programming sensor scheduling approach for collaborative target tracking in energy harvesting wireless sensor networks. IEEE Trans Autom Sci Eng. 2021;18(2):693–704. https://doi.org/10.1109/TASE.2020.3019567.
Shnitzer T, Talmon R, Slotine J-J. Diffusion maps Kalman filter for a class of systems with gradient flows. IEEE Trans Signal Process. 2020. https://doi.org/10.1109/TSP.2020.2987750.
Kumar S, Sudhir, Tiwari UK. Energy efficient target tracking with collision avoidance in WSNs. Wirel Pers Commun. 2018;103:2515–28. https://doi.org/10.1007/s11277-018-5944-6.
Lokesh D, Reddy NV. Energy efficient target tracking method for multi-sensory scheduling in wireless sensor networks. 2020.
Al-Karaki JN, Al-Mashaqbeh GA. SENSORIA: a new simulation platform for wireless sensor networks. In: 2007 International Conference on Sensor Technologies and Applications (SENSORCOMM 2007), Valencia, 2007; pp. 424–9.
Rayudu, DM, Naresh E, Vijaya Kumar BP. The impact of test-driven development on software defects and cost: a comparative case study. Int J Comput Eng Technol (IJCET). 2014;5(2).
Naresh E, Kalaskar SK. A Novel Testing Methodology to Improve the quality of testing a GUI application. MSR J Eng Technol Res. 2013;1(1):41–6.
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Chaitra, H.V., Patil, M., Manjula, G. et al. Reliable Target Tracking Model Employing Wireless Sensor Networks. SN COMPUT. SCI. 4, 446 (2023). https://doi.org/10.1007/s42979-023-01872-4
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DOI: https://doi.org/10.1007/s42979-023-01872-4