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Stream/Area:
-
Subject:
-
Paper ID:
IJSREM67719
Volume:
10 |
10 |
Oct 2026
Pages:
1-3
Publication Frequency:
Monthly (12 issues per year)
License:International Journal of Scientific Research in Engineering and Management follows the Creative Commons Attribution 4.0 (CC BY 4.0) License, allowing free use and distribution with proper attribution.
Electronics And Telecommunication
Dr. Daultrao Aher College , Karad
Electronics And Telecommunication
Dr. Daultrao Aher College , Karad
Electronics And Telecommunication
Dr. Daultrao Aher College , Karad
Electronics And Telecommunication
Dr. Daultrao Aher College , Karad
Maritime security and surveillance require continuous monitoring of large and dynamic marine environments, where conventional methods can be costly, time-consuming, and dependent on manual observation. This paper proposes a Smart Multi Sensor Marine Security and Surveillance System that integrates a camera module, GPS, Inertial Measurement Unit (IMU), distance sensor, environmental sensor, and water-level sensor with an ESP32-based microcontroller for real-time data collection and processing. The system uses Wi-Fi and 4G/LTE communication to transmit sensor data, location information, video, and alerts to a centralized monitoring platform. AI/ML-based object detection is incorporated to identify and classify potentially suspicious objects, while sensor fusion improves overall situational awareness. A centralized dashboard provides real-time information regarding sensor parameters, object detection, location, threat status, and alerts, while historical data can be stored for further analysis. The proposed prototype also includes simulated response mechanisms such as a buzzer, LED indicators, servo motor, and searchlight for demonstration purposes. The system can be applied to coastal surveillance, harbour security, restricted-area monitoring, and marine infrastructure protection, providing a cost-effective and scalable approach to intelligent marine surveillance through the integration of embedded systems, IoT, sensor fusion, AI/ML, and real-time monitoring.
KEYWORDS
The proposed Smart Multi Sensor Marine Security & Surveillance System provides an integrated approach to intelligent marine monitoring by combining embedded systems, IoT communication, multi-sensor data acquisition, computer vision, and AI/ML-based object detection. The ESP32-based system collects and processes information from the camera, GPS, IMU, distance, environmental, and water-level sensors, while Wi-Fi and 4G/LTE enable real-time transmission of sensor data, location, video, and alerts to a centralized dashboard. The integration of multiple sensors and AI-based object detection can improve situational awareness and assist in identifying potentially suspicious marine objects. The proposed system also provides simulated response mechanisms using a buzzer, LED indicators, servo motor, and searchlight for demonstration purposes. Overall, the system demonstrates the potential of integrating IoT, sensor fusion, AI/ML, embedded systems, and real-time communication for marine security and surveillance. Future development can focus on extensive real-world testing, improved AI models, reliable communication under marine conditions, optimized power consumption, larger marine datasets, and advanced sensor-fusion techniques.
[1]G. Xu, Y. Shi, X. Sun, and W. Shen, “Internet of Things in Marine Environment Monitoring: A Review,” Sensors, vol. 19, no. 7, p. 1711, 2019. doi: 10.3390/s19071711. MDPI [2] W. Zhang et al., “Survey on Deep Learning-Based Marine Object Detection,” Journal of Advanced Transportation, vol. 2021, Article ID 5808206, 2021doi: 10.1155/2021/5808206. Wiley Online Library [3] G. Potamos, E. Stavrou, and S. Stavrou, “Enhancing Maritime Cybersecurity through Operational Technology Sensor Data Fusion: A Comprehensive Survey and Analysis,” Sensors, vol. 24, no. 11, p. 3458, 2024. doi: 10.3390/s24113458. MDPI [4] “Ship Detection of Unmanned Surface Vehicle Based on YOLOv8,” 2024 IEEE 13th Global Conference on Consumer Electronics (GCCE), 2024.doi: 10.1109/GCCE62371.2024.10760724. DOI [5] “YOLOv8 Neural Network Application for Noncollaborative Vessel Detection Using Sentinel-1 SAR Data: A Case Study,” IEEE Geoscience and Remote Sensing Letters, vol. 22, 2024. doi: 10.1109/LGRS.2024.3508462.IEEE Xplore [6] “Vessel Detection Leveraging Satellite Imagery and YOLO in Maritime Surveillance,” Remote Sensing Applications: Society and Environment, vol. 40, Article 101730, 2025. doi: 10.1016/j.rsase.2025.101730. ScienceDirect [7] J. Mu et al., “M2S-YOLOv8: Multi-Scale and Asymmetry-Aware Ship Detection for Marine Environments,” Sensors, vol. 26, no. 2, p. 502, 2026. doi: 10.3390/s26020502.
Publication Date
10/7/2026
Peer Review Type
Double-Blind Peer Review
Review Rounds: 2
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The authors declare no conflict of interest related to this research work.
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