# IoT Services for Monitoring Food Supply Chains
## 5 Conclusions and Future Work  
In summary, the study demonstrated that implementing an IoT-based temperature and humidity monitoring system using LoRaWAN significantly enhances the efficiency and reliability of cold chain logistics. The system successfully maintained optimal storage conditions, thereby reducing temperature fluctuations and improving product quality. The findings indicate that real-time environmental monitoring can help stakeholders minimise spoilage, enhance compliance with regulatory standards, and reduce operational costs.  
Based on its implementation performance and insights from the existing literature, LoRaWAN exhibits key advantages--particularly in cost-effectiveness, energy efficiency, and scalability--that make it well-suited for real-time monitoring in agrifood logistics. Its ability to integrate hundreds of sensor nodes while maintaining high data accuracy and transmission reliability supports its applicability in large-scale cold chain systems [32]. Despite challenges such as signal interference and deployment complexity, the results confirm that the proposed system is a viable and effective solution for tracking perishable goods. These findings contribute to the growing body of research on IoT-enabled agrifood supply chains, highlighting the need for ongoing innovation in food logistics [34; 35].  
Future research holds significant promise for enhancing the proposed system. A key area of focus should be the integration of AI-driven predictive analytics, which can significantly improve anomaly detection and enable more accurate forecasting. By harnessing the power of AI, the system can learn from historical data patterns, allowing it to anticipate potential issues before they arise. Furthermore, incorporating blockchain technology will bolster data security and ensure traceability, providing an immutable record of all transactions and changes. The addition of advanced sensors, particularly gas sensors, will facilitate spoilage detection, thus enhancing the system's overall reliability. Simplifying deployment procedures is essential to ensure wider adoption. This can be achieved by creating user-friendly installation processes and incorporating automated calibration features, thereby making the system more accessible to a broader audience.  
Nevertheless, several challenges are associated with real-time data collection, such as the need for automated compliance and reporting processes. These challenges also encompass enhancing operational efficiency and the imperative to maintain the safety and quality of products sensitive to temperature fluctuations. To address these issues, we are increasingly turning to emerging technologies, including Generative AI and Large Language Models (LLMs), which offer promising solutions for navigating the complexities of real-time data management and compliance [22; 30].  
Conceptualization, D.B. and N.T.; methodology, D.B. and N.T.; software, L.P.; investigation, L.P. and D.B.; resources, N.T.; writing--review and editing, L.P. and D.B.; visualization, L.P. and N.T. supervision, D.B. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
Data Availability Statement: Data are contained within the article.  
The authors declare no conflict of interest.