As proficiency carries on to progress and gain ground, cybersecurity continues to exist crucial in safeguarding digital devices and private data from cyber threats and attacks. Current advances in low-power wide area network (LPWAN) protocols have changed how information is gathered through Internet of Things (IoT) across food supply chain. The focus of the research is to bridge the identified gap by integrating LPWAN protocols into the NS2 simulator and evaluating their performance in the preview of food supply management. The study investigates how LPWAN protocols can enhance detection and supervision in food supply chain, using an NS2 simulator to implement and evaluate the performance. The study carried out by the simulations which were the most important. Some were energy consumption, latency, and packet delivery ratio. The revealed vital effect of the LPWAN protocol is the improved efficiency and reliability of data transmission in IoT-powered supply chain systems. This information is vital for the preference of the most suitable communication protocol for the advancement of the food supply chain. Despite challenges like coverage, low power consumption, and battery efficiency in IoT devices used in the food supply chain, Low-Power Wide Area Network (LPWAN) offers an affordable and energy-efficient solution. It provides extensive coverage, making it ideal for IoT systems that require long-range communication while ensuring efficient energy usage.

This study successfully integrated and evaluated LPWAN protocols using the NS2 simulator, demonstrating improved energy efficiency and reduced latency in food supply chain monitoring. The results highlight the significance of protocol selection in enhancing IoT network performance. The findings provide a baseline for future research on optimizing LPWAN-based food tracking systems. Future research can explore hybrid LPWAN models, integrate advanced blockchain protocols for enhanced data security, and simulate large-scale networks to validate performance under real-world conditions. Additionally, incorporating machine learning models could optimize resource management and anomaly detection within the network.

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