IoT and Node MCU based Smart Logistics

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Year : 2025 | Volume : 12 | Issue : 02 | Page : –
    By

    Mulla Nikat,

  • Kazi Kutubuddin Sayyad Liyakat,

  1. Student, Department of Electronics and Telecommunication Engineering, Brahmdevdada Mane Institute of Technology, Solapur, Maharashtra, India
  2. Professor & Head, Department of Electronics and Telecommunication Engineering, Brahmdevdada Mane Institute of Technology, Solapur, Maharashtra, India

Abstract

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Logistics plays a key role in global economies, encompassing the planning, implementation, and control of goods movement. Traditional logistics systems often suffer from inefficiencies, such as manual tracking, delays, and lack of real-time visibility, leading to increased costs and environmental waste. The advent of IoT has revolutionized this sector by enabling interconnected devices to gather and argument data seamlessly. At the heart of many IoT applications is Node MCU. It is favored for its low cost, ease of programming, and built-in Wi-Fi capabilities, making it ideal for deploying smart solutions in resource-constrained environments. This paper delves into how Node MCU can be utilized to build a smart logistics system, focusing on key components, implementation strategies, and practical outcomes. By leveraging Node MCU’s capabilities for sensor integration, wireless connectivity, and data processing, logistics operations can achieve real-time tracking, prognostic maintenance, and effectual resources management. The discussion highlights the system’s architecture, benefits such as cost reduction and improved accuracy, and challenges like security concerns. We proposed a system using Temperature and Humidity sensor, GPS tracker, and Weight sensor. Overall, this approach demonstrates how Node MCU-based IoT solutions can transform traditional logistics into more agile and data-driven ecosystem, paving the way for sustainable supply chain innovations.

Keywords: Smart logistics, node MCU, IoT, sensors, humidity sensor, GPS

[This article belongs to Recent Trends in Sensor Research & Technology ]

How to cite this article:
Mulla Nikat, Kazi Kutubuddin Sayyad Liyakat. IoT and Node MCU based Smart Logistics. Recent Trends in Sensor Research & Technology. 2025; 12(02):-.
How to cite this URL:
Mulla Nikat, Kazi Kutubuddin Sayyad Liyakat. IoT and Node MCU based Smart Logistics. Recent Trends in Sensor Research & Technology. 2025; 12(02):-. Available from: https://journals.stmjournals.com/rtsrt/article=2025/view=0


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References

1. AO, B AV, B j B, D AB, J H , L KK. B A , , f F . A f A -B . 2024;29(2):71-86.
2. Parihar B, Kiran A, Valaboju S, Rashid SZ, Liyakat KK, DR AS. Enhancing Data Security in Distributed Systems Using Homomorphic Encryption and Secure Computation Techniques. In ITM Web of Conferences 2025 (Vol. 76, p. 02010). EDP Sciences.
3. Parihar B, Kiran A, Valaboju S, Rashid SZ, Liyakat KK, DR AS. Enhancing Data Security in Distributed Systems Using Homomorphic Encryption and Secure Computation Techniques. InITM Web of Conferences 2025 (Vol. 76, p. 02010). EDP Sciences.

4. Veena C, Sridevi M, Liyakat KK, Saha B, Reddy SR, Shirisha N. HEECCNB: An efficient IoT- cloud architecture for secure patient data transmission and accurate disease prediction in healthcare systems. In2023 Seventh International Conference on Image Information Processing (ICIIP) 2023 Nov 22 (pp. 407-410). IEEE.
5. Liyakat KK, Khadake SB, Tamboli DA, Sawant VA, HM M, Sathe S. AI-Driven-IoT (AIIoT) Based Decision-Making-KSK Approach in Drones for Climate Change Study. In2024 4th International Conference on Ubiquitous Computing and Intelligent Information Systems (ICUIS) 2024 Dec 12 (pp. 1735-1744). IEEE.

6. Prasad KR, Karanam SR, Ganesh D, Liyakat KK, Talasila V, Purushotham P. AI in public-private partnership for IT infrastructure development. The Journal of High Technology Management Research. 2024 May 1;35(1):100496.
7. Liyakat KK. Detecting malicious nodes in IoT networks using machine learning and artificial neural networks. In2023 International Conference on Emerging Smart Computing and Informatics (ESCI)2023 Mar 1 (pp. 1-5). IEEE.
8. Liyakat KK. Malicious node detection in IoT networks using artificial neural networks: A machine learning approach. InIntelligent Networks (pp. 182-197). CRC Press.
9. Kasat K, Shaikh N, Rayabharapu VK, Nayak M, Liyakat KK. Implementation and recognition of waste management system with mobility solution in smart cities using Internet of Things. In2023 Second International Conference on Augmented Intelligence and Sustainable Systems (ICAISS)2023 Aug 23 (pp. 1661-1665). IEEE.
10. Kazi K. AI-driven IoT (AIIoT) in healthcare monitoring. InUsing Traditional Design Methods to Enhance AI-Driven Decision Making 2024 (pp. 77-101). IGI Global Scientific Publishing.
11. Kazi K. Modelling and simulation of electric vehicle for performance analysis: BEV and HEV electrical vehicle implementation using Simulink for E-mobility ecosystems. InE-Mobility in Electrical Energy Systems for Sustainability 2024 (pp. 295-320). IGI Global.
12. Kazi KS. Machine Learning-Powered IoT (MLIoT) for Retail Apparel Industry. InSustainable Practices in the Fashion and Retail Industry 2025 (pp. 345-372). IGI Global Scientific Publishing.
13. Kazi KS. Braille-Lippi Numbers and Characters Detection and Announcement System for Blind Children Using KSK Approach: AI-Driven Decision-Making Approach. InDriving Quality Education Through AI and Data Science 2025 (pp. 531-556). IGI Global Scientific Publishing.
14. Kazi KS. AI-Driven IoT (AIIoT)-Based Decision-Making System for High BP Patient Healthcare Monitoring: KSK1 Approach for BP Patient Healthcare Monitoring. InOptimization, Machine Learning, and Fuzzy Logic: Theory, Algorithms, and Applications 2025 (pp. 71-102). IGI Global Scientific Publishing.
15. Kazi KS. Advancing Towards Sustainable Energy With Hydrogen Solutions: Adaptation and Challenges. InGeopolitical Landscapes of Renewable Energy and Urban Growth 2025 (pp. 357-394). IGI Global Scientific Publishing.
16. Kazi KS. AI-Driven-IoT (AIIoT) Decision-Making System for Hepatitis Disease Patient Healthcare Monitoring: KSK1 Approach for Hepatitis Patient Monitoring. InNavigating Innovations and Challenges in Travel Medicine and Digital Health 2025 (pp. 431-450). IGI Global Scientific Publishing.
17. Kazi KS. Machine learning-based pomegranate disease detection and treatment. In Revolutionizing Pest Management for Sustainable Agriculture 2024 (pp. 469-498). IGI Global.
18. Kazi KS. Computer-aided diagnosis in ophthalmology: A technical review of deep learning applications. Transformative Approaches to Patient Literacy and Healthcare Innovation. 2024:112-35.
19. Kazi KS. IoT driven by machine learning (MLIoT) for the retail apparel sector. InDriving Green Marketing in Fashion and Retail 2024 (pp. 63-81). IGI Global.
20. Liyakat KK, Khadake SB, Tamboli DA, Sawant VA, HM M, Sathe S. AI-Driven-IoT (AIIoT) Based Decision-Making-KSK Approach in Drones for Climate Change Study. In2024 4th International Conference on Ubiquitous Computing and Intelligent Information Systems (ICUIS)2024 Dec 12 (pp. 1735-1744). IEEE.

Regular Issue Subscription Review Article
Volume 12
Issue 02
Received 13/05/2025
Accepted 16/05/2025
Published 26/06/2025
Publication Time 44 Days
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