Authors : Om Kadam; Kunal Mane; Anup Pimpalkar; Mangesh Munde; A. A. Chaphadkar

Volume/Issue : Volume 11 - 2026, Issue 4 - April

Google Scholar : https://tinyurl.com/yyumpf33

Scribd : https://tinyurl.com/4dd8n86s

DOI : https://doi.org/10.38124/ijisrt/26apr1899

Note : A published paper may take 4-5 working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and ResearchGate.

Abstract : This paper provides a comprehensive review of existing AI- and IoT-based systems for autonomous detection and tracking of enemy tanks to improve situational awareness on the battlefield and provide convoy protection. Traditional manual surveillance involves high-risk and time-consuming operations for personnel; however, IoT integrated with embedded devices like the Raspberry Pi and ESP32, provides real-time acquisition, processing, and transmission of data with sensors, social as ultrasonic, infrared, metal, and vibration sensors. The identified studies in this review highlight the performance of several IoT-driven robotic systems that utilize wireless communication methods (Wi-Fi, GSM, LoRa) for remote observation/monitoring, and remote-control applications. Although several advances have been made, the majority of all identified systems are semi-autonomous, without AI decision-making based on multisensory fusion in addition to cybersecurity. This study identifies gaps in energy-efficient methods, secure methods for networking, and autonomous threat recognition. Potential pathways to address the gaps identified within this study include the incorporation of AI- enabled edge computing, blockchain-based communications, and swarm robotics for intelligent, resilient, and adaptive military surveillance systems.

Keywords : Internet of Things (IoT), Raspberry Pi, Military Convoy Protection, Sensor Networks, Real-Time Surveillance.

References :

1. H. Li, X. He, Y. Zhang and W. Guan, "Attack Detection in Cyber- Physical Systems Using Particle Filter: An Illustration on Three-Tank System," 2018 IEEE 8th Annual International Conference on CYBER Technology in Automation, Control, and Intelligent Systems (CYBER), Tianjin, China, 2018
2. F. Qi, P. Huang, Y. Chai, X. Li, Y. Liu and Z. Zhu, "Sensor fault detection based on fractional-order chaotic system under strong noise and disturbance," 2021 CAA Symposium on Fault Detection, Supervision, and Safety for Technical Processes (SAFEPROCESS), Chengdu, China, 2021.
3. Q. Li, Y. Zhang, H. Chen and G. Zhou, "Object Detection for High- Resolution Sar Images Under the Spatial Constraints of Optical Images," IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium, Valencia, Spain, 2018
4. W. Budiharto, Y. Anderas, J. S. Susroso, A. A. S. Gunawan and E. Irwansyah, "Development of Tank-Based Military Robot and Object Tracker," 2019 4th Asia- Pacific Conference on Intelligent Robot Systems (ACIRS), Nagoya, Japan, 2019.
5. S. Liu, H. Shi and Z. Guo, "Remote sensing image object detection based on improved SSD," 2022 3rd International Conference on Computer Vision, Image and Deep Learning & International Conference on Computer Engineering and Applications (CVIDL & ICCEA), Changchun, China, 2022
6. R. Girshick, J. Donahue, T. Darrell, and J. Malik, "Region-Based Convolutional Networks for Accurate Object Detection and Segmentation," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 38, no. 3, pp. 142–158, Mar. 2016
7. Wu, H. Zhang, J. Zhang and F. Xu, "Typical Target Detection in Satellite Images Based on Convolutional Neural Networks," 2015 IEEE International Conference on Systems, Man, and Cybernetics, Hong Kong, China, 2015.
8. W. Budiharto, A. S. A. Gunawan, E. Irwansyah and J. S. Susroso, "Android-Based Wireless Controller for Military Robot Using Bluetooth Network," 2019 2nd World Symposium on Communication Engineering (WSCE), Nagoya, Japan, 2019.
9. Gupta and U. Gupta, "Military Surveillance with Deep Convolutional Neural Network," 2018 International Conference on Electrical,Electronics, Communication, Computer, and Optimization Techniques (ICEECCOT), Mysuru, India, 2018.
10. Krizhevsky, I. Sutskever, and G. E. Hinton, "ImageNet Classification with Deep Convolutional Neural Networks," Proc. 25th International Conference on Neural Information Processing Systems (NIPS), vol. 1, pp. 1097–1105, 2012.