Enhanced WSN security based on Trust-Based Secure Intelligent Opportunistic Routing Protocol in agriculture data transmission sector

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In recent years agriculture is a developing environment through remote sensing environment with support of Wireless Sensor Network (WSN). Increasing communication and Internet of things make a communication behind to share the information all over the monitoring fields However, due to the open nature of WSNs, they are vulnerable to various security attacks, which can lead to serious consequences, such as data integrity breaches, denial-of-service attacks, and identity theft. Machine Learning (ML) can be used to improve the security of WSNs to detect and prevent security attacks. ML algorithms analyze the normal behavior of WSNs and to detect anomalies that may indicate an attack. To propose a TBSIOP (Trust-Based Secure Intelligent Opportunistic Routing Protocol) for improving the security in WSN-agriculture environment. Initially the Weighted Trust Mechanism (WTM) is evaluated to find behaviors aspects of nodes in the communication medium. Then the data packets are secured through Elliptic Diffie-Hellman Encryption Integrated Cryptography (EDHEIC) on each handover to the communication nodes. In addition, the Substitutive Padding Key Verification (SPKV) is applied to securely handover the data by getting key authentication policy. Which prevents non-legitimate users from accessing the network. The security can be carried out by flooding the network with packets, by sending malicious code to the network, or by overloading the network's resources. Finally, the TBSIOP works on the principle of best candidate selection model to ensure the routing to improve the security. The proposed system achieves high performance to ensure the security in the level of trust-based routing, node authentication, secure data handover with proof of knowledge to deliver the data. This improves the security as well in higher data transmission rate, low level packet drop ratio with redundant time complexity compared to the other system.