Hybrid Energy Regulated Constant Gain Kalman-Filter for Optimized Target Detection and Tracking in Wireless Sensor Networks

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Urvashi Saraswat 1,* Anita Yadav 1 Abhishek Bhatia 2

1. Department of Computer Science and Engineering, Harcourt Butler Technical University, Kanpur-208002, India

2. Department of Applied Economics and Statistics, University of Delaware, Newark-19716, United States of America

* Corresponding author.

DOI: https://doi.org/10.5815/ijcnis.2023.05.04

Received: 21 Jul. 2022 / Revised: 1 Nov. 2022 / Accepted: 13 Jan. 2023 / Published: 8 Oct. 2023

Index Terms

Wireless Sensor Networks, Target Detection, Target Tracking, Kalman Filter


Wireless Sensor Networks (WSNs) are one of the most researched areas worldwide as the wide-scale networks possess low cost, are small in size, consume low power, and can be deployed in various environments. Among various applications of WSNs, target tracking is a highly demanding and broadly investigated application of wireless sensor networks. The parameter of accurate tracking is restricted because of the limited resources present in the wireless sensor networks, noise of the network, environmental factors, and faulty sensor nodes. Our work aims to enhance the accuracy of the tracking process as well as energy utilization by combing the mechanism of clustering with the prediction. Here, we present a hybrid energy-regulated constant gain Kalman filter-based target detection and tracking method, which is an algorithm to make the best use of energy and enhance precision in tracking. Our proposed algorithm is compared with the existing approaches where it is observed that the proposed technique possesses efficient energy utilization by decreasing the transference of unimportant data within the sensor network, achieving accurate results.

Cite This Paper

Urvashi Saraswat, Anita Yadav, Abhishek Bhatia, "Hybrid Energy Regulated Constant Gain Kalman-Filter for Optimized Target Detection and Tracking in Wireless Sensor Networks", International Journal of Computer Network and Information Security(IJCNIS), Vol.15, No.5, pp.41-49, 2023. DOI:10.5815/ijcnis.2023.05.04


[1]Akyildiz IF, Su W, Sankarasubramaniam Y, Cayirci E. Wireless Sensor Networks: A survey. Computer Networks. 2002;38(4):393–422.
[2]Wang X, Nie Y. An improved distance vector-hop localization algorithm based on coordinate correction. International Journal of Distributed Sensor Networks. 2017;13(11):155014771774183.
[3]Barile G, Leoni A, Pantoli L, Stornelli V. Real-time autonomous system for structural and environmental monitoring of dynamic events. Electronics. 2018;7(12):420.
[4]Jaber AA, Bicker R. Design of a wireless sensor node for vibration monitoring of industrial machinery. International Journal of Electrical and Computer Engineering (IJECE). 2016;6(2):639.
[5]Mansouri M, Ilham O, Snoussi H, Richard C. Adaptive quantized target tracking in wireless sensor networks. Wireless Networks. 2011;17(7):1625–39.
[6]Anilkumar AK, Ananthasayanam MR, Subba Rao PV. A constant gain Kalman filter approach for the prediction of re-entry of risk objects. Acta Astronautica. 2007;61(10):831–9.
[7]Honguntikar V, Biradar GS. Frog-Based Routing Algorithm to Enhance the Network Lifetime of Wireless Sensor Networks. International Journal of Computer Network & Information Security. 2017 Aug 1;9(8).
[8]Denis S, Berkvens R, Weyn M. A survey on detection, tracking and identification in radio frequency-based device-free localization. Sensors. 2019 Dec 3;19(23):5329.
[9]Zuo H, Ke W, Chen M, Lu J, Wang Y, Jin J. An Enhanced Radio Tomographic Imaging Localization Method Based on Low-cost Wireless Sensor Networks. In2019 3rd International Conference on Circuits, System and Simulation (ICCSS) 2019 Jun 13 (pp. 197-200). IEEE.
[10]Mugunthan SR. Novel cluster rotating and routing strategy for software defined wireless sensor networks. Journal of ISMAC. 2020 Jul 6;2(02):140-6.
[11]Zhang H, Zhou X, Wang Z, Yan H, Sun J. Adaptive consensus-based distributed target tracking with dynamic cluster in sensor networks. IEEE transactions on cybernetics. 2018 Apr 24;49(5):1580-91.
[12]Leela Rani P, Sathish Kumar GA. Detecting Anonymous Target and Predicting Target Trajectories in Wireless Sensor Networks. Symmetry. 2021 Apr 19;13(4):719.
[13]Ahmadi H, Viani F, Bouallegue R. An accurate prediction method for moving target localization and tracking in wireless sensor networks. Ad Hoc Networks. 2018 Mar 1;70:14-22.
[14]Khalifeh A, Rajendiran K, Darabkh KA, Khasawneh AM, AlMomani O, Zinonos Z. On the potential of fuzzy logic for solving the challenges of cooperative multi-robotic wireless sensor networks. Electronics. 2019 Dec 10;8(12):1513.
[15]Jondhale SR, Deshpande RS. Kalman filtering framework-based real time target tracking in wireless sensor networks using generalized regression neural networks. IEEE Sensors Journal. 2018 Oct 2;19(1):224-33.
[16]Balakrishnan A, Ramana K, Nanmaran K, Ramachandran M, Bhaskar V, Kallam S. RSSI based localization and tracking in a spatial network system using wireless sensor networks. Wireless Personal Communications. 2022 Mar;123(1):879-915.
[17]Klaina H, Vazquez Alejos A, Aghzout O, Falcone F. Narrowband characterization of near-ground radio channel for wireless sensors networks at 5G-IoT bands. Sensors. 2018 Jul 26;18(8):2428.
[18]Chu SC, Dao TK, Pan JS. Identifying correctness data scheme for aggregating data in cluster heads of wireless sensor network based on naive Bayes classification. EURASIP Journal on Wireless Communications and Networking. 2020 Dec;2020(1):1-5.
[19]Tripathi RP, Singh AK, Gangwar P. Innovation-based fractional order adaptive Kalman filter. Journal of Electrical Engineering. 2020 Feb 1;71(1):60-4..
[20]Vasuhi S, Vaidehi V. Target tracking using interactive multiple model for wireless sensor network. Information Fusion. 2016 Jan 1;27:41-53..
[21]Biswas SK, Qiao L, Dempster AG. A quantified approach of predicting suitability of using the Unscented Kalman Filter in a non-linear application. Automatica. 2020 Dec 1;122:109241.
[22]Lim J, Park HM. Tracking by risky particle filtering over sensor networks. Sensors. 2020 May 31;20(11):3109.
[23]Ahmadi H, Bouallegue R, Viani F, Massa A. An improved prediction based strategy for target tracking in wireless sensor networks. International Journal of Internet Technology and Secured Transactions. 2018;8(3):453-68..
[24]Keskin ME, Yiğit V. Maximizing the lifetime in wireless sensor networks with multiple mobile sinks having nonzero travel times. Computers & Industrial Engineering. 2020 Oct 1;148:106719.
[25]A. Yadav, P. Awasthi, N. Naik, and M. R. Ananthasayanam, “A constant gain Kalman filter approach to track maneuvering targets,” in Proceedings of the IEEE International Conference on Control Applications (CCA ’13), pp. 562–567, August 2013.
[26]Li S, Li Z, Li J, Fernando T, Iu HH, Wang Q, Liu X. Application of event-triggered cubature Kalman filter for remote nonlinear state estimation in wireless sensor network. IEEE Transactions on Industrial Electronics. 2020 Apr 21;68(6):5133-45.
[27]Mahamuni CV, Jalauddin ZM. Intrusion Monitoring in Military Surveillance Applications using Wireless Sensor Networks (WSNs) with Deep Learning for Multiple Object Detection and Tracking. In2021 International Conference on Control, Automation, Power and Signal Processing (CAPS) 2021 Dec 10 (pp. 1-6). IEEE.
[28]Rahman MR, Islam MM, Pritom AI, Alsaawy Y. ASRPH: application specific routing protocol for health care. Computer Networks. 2021 Oct 9;197:108273.
[29]Biswas S, Das R, Chatterjee P. Energy-efficient connected target coverage in multi-hop wireless sensor networks. InIndustry interactive innovations in science, engineering and technology 2018 (pp. 411-421). Springer, Singapore.