Enhanced Data Security and Transmission for IoT-Driven Smart Agriculture Using Hybrid Optimization and Cryptograph

Precision agriculture is a transformative approach in modern farming that leverages advanced technologies such as the Internet of Things (IoT), GPS mapping, and big data analytics to optimize crop yield and reduce resource wastage. This paper presents a secure and efficient model for precision agriculture that integrates Wireless Sensor Networks (WSNs), remote sensing, and information technology. The proposed system comprises two primary components: sensor node clustering and secure data transmission. In the clustering phase, sensor nodes are grouped into clusters, with an optimal Cluster Head (CH) selected using a novel hybrid optimization technique called Reptile Search Customized Lichtenberg Optimization (RSCLO). This algorithm combines the strengths of the Reptile Search Algorithm (RSA) and the Lichtenberg Algorithm (LA) for effective CH selection, ensuring energy efficiency and reliable data aggregation. For secure data transmission, collected agricultural data is encrypted using a hybrid cryptographic scheme termed BlowRSA, which fuses the Blowfish and RSA algorithms to enhance data confidentiality. The encrypted data is uploaded to a cloud server, where blockchain technology ensures secure transaction verification, immutability, and trust. Upon reaching the receiver, the data is decrypted using the BlowRSA model. This study addresses critical challenges in data security and efficiency in precision agriculture networks. By incorporating advanced clustering, encryption, and blockchain mechanisms, the proposed model enhances data integrity, reduces operational costs, and supports intelligent agricultural decision-making.