International Research Journal of Engineering and Technology (IRJET)
e-ISSN: 2395-0056
Volume: 12 Issue: 04 | Apr 2025
p-ISSN: 2395-0072
www.irjet.net
IoT-Based Smart Logistics and Transportation Management System Omkar Thombre1, Piyush Thakur2, Sanjana Tikone3, Prof. Sachin Chavan4 1,2,3Student at Mahatma Gandhi Missions College of Engineering and Technology, Mumbai, Maharashtra, India.
4Assistant professor at Mahatma Gandhi Missions College of Engineering and Technology, Mumbai, Maharashtra,
India. ---------------------------------------------------------------------***---------------------------------------------------------------------
Abstract - With the rapid advancements in Industry
1.
4.0, the Internet of Things (IoT) has transformed logistics and supply chain management by integrating real-time monitoring, automation, and predictive analytics. This paper presents an IoT-based Advanced Logistics and Transportation Management System that incorporates GPS tracking, RFID-based authentication, cloud storage, predictive maintenance, and alternative distribution methods such as autonomous vehicles and drones. The system enhances supply chain visibility, improves efficiency, and reduces operational costs by utilizing IoT-enabled sensors, connectivity modules, and cloud computing. A detailed cost estimation and implementation strategy are also outlined.
2. 3. 4.
1.3 Emerging Technologies in Smart Logistics To overcome these challenges, logistics providers are adopting cutting-edge IoT technologies, such as: GPS & Telematics: Real-time fleet tracking and driver behavior analysis. Blockchain: Ensures tamper-proof tracking of goods to prevent fraud. Edge Computing: Enables faster data processing near IoT devices, reducing cloud dependency. AI & Machine Learning: Optimizes delivery routes and predicts maintenance needs. 5G Networks: Enhances real-time communication for vehicle-to-vehicle (V2V) interactions. Autonomous Drones & Vehicles: Improves lastmile delivery solutions.
Keywords: IoT, Smart Logistics, RFID, GPS, Predictive Maintenance, Cloud Computing, Autonomous Vehicles, Supply Chain Optimization
1. INTRODUCTION The logistics and transportation industry plays a crucial role in global commerce, facilitating the movement of goods across supply chains. However, traditional logistics systems face several inefficiencies, including lack of realtime tracking, security risks, high operational costs, and poor inventory management【1】. With the rise of Logistics 4.0, the Internet of Things (IoT) has emerged as a key enabler, transforming supply chain operations through automation, predictive analytics, and real-time monitoring【2】.
1.4 Real-World Applications of IoT in Logistics Several leading companies have successfully implemented IoT-based logistics solutions: Amazon: Uses AI-powered robots for warehouse automation. DHL: Deploys smart RFID tracking for supply chain optimization. FedEx & UPS: Utilize AI-based route optimization to minimize delivery delays. Tesla & Waymo: Are testing autonomous delivery trucks to revolutionize freight transportation.
1.1 The Role of IoT in Smart Logistics IoT enables logistics providers to integrate smart sensors, cloud computing, and AI-driven analytics for end-to-end supply chain visibility. Studies show that: RFID tracking and smart warehousing have improved inventory accuracy by over 95%【3】. AI-based predictive analytics have optimized delivery routes, reducing fuel costs by 20%【4】. Real-time fleet monitoring using IoT-based GPS and telematics has minimized transportation delays by 30%【5】.
1.5 Research Objective This paper presents the design and implementation of an IoT-based Smart Logistics and Transportation Management System, integrating ESP32-CAM, RFID readers, GPS modules, and cloud computing to improve efficiency, security, and tracking. The system aims to: 1. Enable real-time tracking of goods and vehicles.
1.2 Challenges in Traditional Logistics Systems Despite advancements, logistics providers still encounter several issues:
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Impact Factor value: 8.315
Limited Supply Chain Visibility – Manual tracking leads to inefficiencies and lost shipments【3】. High Fuel Consumption – Inefficient route planning increases carbon emissions【5】. Theft and Counterfeit Risks – Weak authentication measures allow unauthorized access【2】. Data Overload – Handling vast IoT-generated data requires advanced edge computing solutions【4】.
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