Enhancing Food Outbound Logistics With Event-Driven and Service-Oriented IoT Middleware (EDSOA-OLP-IoT)

Abstract

Traceability and visibility of outbound logistics are crucial for companies aiming to enhance customer satisfaction and ensure product quality and reliability. The Internet of Things (IoT) offers promising solutions by enabling real-time tracking and intelligent decision-making in supply chains. However, processing and interpreting heterogeneous IoT data (sensors, actuators) remain challenging, as timely and accurate information dissemination is required. In this paper, we propose EDSOA-OLP-IoT; novel semantic middleware architecture based on the OLP-IoT ontology and designed to optimize outbound logistics operations. Our approach integrates a service-oriented event-driven architecture with a Publish-Subscribe communication model, complex event processing (CEP), and ontology-based reasoning. Unlike traditional IoT frameworks, our system enhances anomaly detection, improves decision-making accuracy, and optimizes resource management by leveraging semantic reasoning. Through experimental simulations, we demonstrate that EDSOA-OLP-IoT effectively reduces response time to critical events and enhances supply chain efficiency. To validate our approach, we conducted simulations based on real-world-inspired scenarios, including temperature monitoring in refrigerated trucks and warehouses. These scenarios showcase the system’s ability to detect anomalies and trigger appropriate responses, highlighting the potential of semantic reasoning and event-driven architectures for real-time logistics optimization.