Development of IoT-based Sensor Network for Real-Time Food Freshness Monitoring

Thesis Abstract

The integrity of food products and consumer safety are increasingly threatened by the challenges associated with monitoring freshness levels during transportation, storage, and retail distribution. Traditional methods of assessing food freshness are often labor-intensive, subjective, and reactive, leading to product spoilage, economic losses, and health risks. This study aims to develop a robust Internet of Things (IoT)-based sensor network capable of providing real-time, non-invasive monitoring of food freshness, thereby enhancing supply chain management and consumer confidence. The specific objectives include designing an integrated sensor system capable of detecting key freshness indicators such as temperature, humidity, pH, and volatile organic compounds (VOCs), developing a wireless communication protocol for data transmission, and evaluating the system’s accuracy, reliability, and usability in practical food storage environments. The research employed a mixed-methods approach grounded in the technological, behavioral, and theoretical frameworks of the Technology Acceptance Model (TAM) and Sensor Integration Theory. The quantitative component involved deploying sensor nodes across 50 perishable food samples, including fresh fruits, vegetables, and dairy products, in controlled storage environments simulating real-world conditions. Data were collected over a four-week period, capturing fluctuations in freshness indicators at hourly intervals. The qualitative aspect entailed semi-structured interviews with 20 supply chain managers and retail staff to assess system usability and acceptance. Data analysis encompassed descriptive statistics, correlation analysis, multiple regression modeling to examine factors influencing sensory data, and thematic analysis of interview transcripts to elucidate user perceptions. Expected findings indicate that the IoT sensor network will accurately track key freshness parameters with a statistical significance of p < 0.01, demonstrating high sensitivity and specificity in detecting spoilage markers. The real-time data will enable predictive analytics, facilitating early intervention to prevent spoilage. Furthermore, adoption of the system is anticipated to be positively influenced by perceived ease of use, usefulness, and trustworthiness, aligning with TAM predictions. The system’s deployment is expected to reveal potential challenges such as sensor calibration drift and data security concerns, which can be mitigated through algorithmic adjustments and encryption protocols. This research contributes to the existing body of knowledge by presenting an integrated IoT solution tailored for food freshness monitoring, bridging technological innovation with practical application in food supply chains. It advances understanding of sensor network architecture, real-time data analytics, and user acceptance in the context of food technology. Additionally, the study proposes a conceptual model illustrating the interaction between sensor accuracy, system usability, and decision-making efficacy, serving as a reference for future developments in food IoT applications. In conclusion, the study demonstrates that a strategically designed IoT sensor network can revolutionize food freshness monitoring, reduce waste, and enhance food safety. Recommendations include scaling the system for commercial adoption, implementing standardized calibration procedures, and addressing data privacy issues. Future research is suggested to explore integration with blockchain technology for traceability, the use of machine learning algorithms for enhanced predictive accuracy, and longitudinal studies to assess system sustainability and impact on supply chain efficiency. Overall, this research underscores the transformative potential of IoT-enabled sensor networks in transforming traditional food quality assessment into a proactive, data-driven process.