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Paper ID:IJSREM67796

NexusGuard AI System: An AI and IoT-Enabled Integrated Smart Infrastructure for Campus Safety and Sustainability

Stream/Area:

-

Subject:

-

Paper ID:

IJSREM67796

Volume:

10 |

10 |

Oct 2026

Pages:

1-5

Publication Frequency:

Monthly (12 issues per year)

DOI
Digital Object Identifier
10.55041/IJSREM67796

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.

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Authors

Neil Khude

Neil Khude

Electronic & Telecommunication Engineering.

JSMP Bhivrabai Sawant Polytechnic

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SK

Shraddha Koymahale

Electronic & Telecommunication Engineering.

JSMP Bhivrabai Sawant Polytechnic

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SK

Siddharth Kusgal

Electronic & Telecommunication Engineering.

JSMP Bhivrabai Sawant Polytechnic

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DL

Durgesh Lohar

Electronic & Telecommunication Engineering.

JSMP Bhivrabai Sawant Polytechnic

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VBT

V. B. Takale

Electronic & Telecommunication Engineering.

JSMP Bhivrabai Sawant Polytechnic

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Abstract

NexusGuard AI is an Artificial Intelligence (AI) and Internet of Things (IoT)-based integrated smart infrastructure system designed to improve safety, monitoring, emergency response, and energy efficiency in college campuses. The proposed system combines multiple independent campus facilities such as surveillance, environmental monitoring, emergency warning, smart lighting, and communication into a unified smart pole. An ESP32 microcontroller collects sensor data and performs device-level control, while a Raspberry Pi 4 processes camera data for AI-based object detection and event analysis. The system incorporates an AI camera, PIR motion sensor, smoke and gas sensors, flame sensor, temperature and humidity sensor, air-quality sensor, sound sensor, LDR, and emergency SOS button. Abnormal conditions can trigger a siren, speaker, warning lighting, display, and remote notification. Sensor and event data are communicated using Wi-Fi and MQTT, stored through Firebase, and presented on a dashboard or mobile application. Solar power with battery backup supports autonomous operation. The proposed system provides a scalable approach for campus safety, environmental monitoring, energy management, and future smart-campus integration.

KEYWORDS

Artificial intelligence Internet of Things smart campus edge computing campus safety smart infrastructure

Cite This Article

Neil Khude, Shraddha Koymahale, Siddharth Kusgal, Durgesh Lohar, V. B. Takale, "NexusGuard AI System: An AI and IoT-Enabled Integrated Smart Infrastructure for Campus Safety and Sustainability," International Journal of Scientific Research in Engineering and Management, vol. 10, no. 10, pp. 1-5, Oct 2026. https://doi.org/10.55041/IJSREM67796

Full Article/Conclusion

NexusGuard AI presents an integrated approach to campus safety and sustainability by combining artificial intelligence, Internet of Things technologies, embedded controllers, environmental sensors, camera-based surveillance, smart lighting, emergency response, cloud monitoring, and renewable power into a unified smart-pole architecture. The ESP32 functions as the main sensor and control controller, while the Raspberry Pi 4 performs AI-based camera processing. A collection of sensors provides information about movement, smoke, gases, flame, temperature, humidity, sound, and ambient light, while the actuator section enables local warning and control through LEDs, siren or buzzer, speaker, OLED display, and relay devices. Wi-Fi and MQTT provide network communication, Firebase provides real-time cloud management, and the dashboard or mobile application allows remote monitoring. Solar power, battery storage, and DC-DC conversion support autonomous operation. Overall, the proposed architecture aims to overcome the limitations of fragmented campus systems by providing a centralized, intelligent, scalable, and energy-conscious infrastructure for safety and sustainability.

References

[1]       M. A. Ahmed, S. A. Chavez, A. M. Eltamaly, H. O. Garces, A. J. Rojas, and Y.-C. Kim, “Toward an intelligent campus: IoT platform for remote monitoring and control of smart buildings,” Sensors, vol. 22, no. 23, Art. no. 9045, 2022. [2]       A. G. Putrada, M. Abdurohman, D. Perdana, and H. H. Nuha, “CIMA: A novel classification-integrated moving average model for smart lighting intelligent control based on human presence,” Complexity, Art. no. 4989344, 2022. [3]       A. Omar et al., “Smart city: Recent advances in intelligent street lighting systems based on IoT,” J. Sensors, Art. no. 5249187, 2022. [4]       T. Anagnostopoulos, “Smart campus safety systems survey,” in IoT-Enabled Unobtrusive Surveillance Systems for Smart Campus Safety, 2022. [5]       Z. Chen, C. B. Sivaparthipan, and B. Muthu, “IoT based smart and intelligent smart city energy optimization,” Sustain. Energy Technol. Assessments, vol. 49, Art. no. 101724, 2022. [6]       P. Neis, D. Warch, and M. Hoppe, “Testing and evaluation of low-cost sensors for developing open smart campus systems based on IoT,” Sensors, vol. 23, no. 20, Art. no. 8652, 2023. [7]       M. Takruri, K. P. Thulasingam, H. Attia, A. Omar, A. Altunaiji, and S. Almaeeni, “Design and implementation of a real-time street light dimming system based on a hybrid control architecture,” Int. J. Distrib. Sens. Netw., Art. no. 6641563, 2023. [8]       M. Kanthi and R. Dilli, “Smart streetlight system using mobile applications: Secured fault detection and diagnosis with optimal powers,” Wireless Netw., vol. 29, pp. 2015–2028, 2023. [9]       M. H. Kabir, A. Al Noman, A. Al Afiq, R. H. Raju, M. N. Hasan, and Ahmad, “Design and implement IoT-based intelligent manageable smart street lighting systems for future smart city,” Eng. Proc., vol. 56, no. 1, Art. no. 147, 2023. [10]     T. Domínguez-Bolaño, V. Barral, C. J. Escudero, and J. A. García-Naya, “An IoT system for a smart campus: Challenges and solutions illustrated over several real-world use cases,” Internet Things, vol. 25, Art. no. 101099, 2024.

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10/10/2026

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