Intelligent Asset Tracking System for Logistics Industry using IoT and Big Data

Abstract

The logistics industry is a complex and dynamic ecosystem that requires efficient and reliable asset tracking systems (IATS) to optimize operations and reduce costs. To address these challenges, an IATS is proposed in this paper that leverages the power of IoT and big data technologies to collect real-time data on the location, condition, and status of assets such as trucks, containers, and shipments. The system is designed to provide end-to-end visibility and control of assets throughout the logistics value chain. It uses a combination of RFID, GPS, and other tracking technologies to collect data on asset location, temperature, humidity, vibration, and other relevant parameters. The data is then transmitted to a cloud-based platform for storage, processing, and analysis using big data analytics and machine learning algorithms. The platform enables logistics companies to monitor and manage their assets in real-time, optimize routes and schedules, and improve delivery times. It also provides machine learning tools for predictive modeling of asset price movement, enabling companies to identify potential price changes before they occur and minimize loss. The efficiency and effectiveness of our system were shown through simulation studies using data from real-world assets; as a result, it is an attractive option for the tracking and management of assets in real-world logistic businesses.