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In the modern era, prevalence of the Internet of Things (IoT) devices that have de facto protocol as IPv6 routing protocol for low power and lossy networks (RPL). Yet, RPL protocol is vulnerable to many attacks such as rank attack, password spoofing and more. To this end, most of the works have focused their research on securing the RPL-based IoT network. However, still there exist downsides such as high energy consumption, lack of effective authentication and high packet losses. Motivated by these preceding defects, this paper proposes the Novel Authentication and Secure Trust-based RPL Routing in Mobile sink-supported Internet of Things (SecRPL-MS). At first, SecRPL-MS performs a registration process where all IoT nodes in the network register themselves in the security entity. In this work, the frequent death of IoT nodes is alleviated through deploying mobile sink in the network. If any grid member (GM) node wants to transmit their data to the grid head (GH) node, then it must undergo authentication process. Secure routing is adopted in RPL by utilising the sail fish optimisation algorithm. Each GM node encrypts its sensed data using the prince algorithm before transmitting it to the GH node. The moving points are selected for the mobile sink using the Quantum Inspired Neural Network (QINN) algorithm. This proposed SecRPL-MS performance is evaluated using the Network Simulator 3 (NS3) in terms of the Packet Delivery Ratio (%), Delay (ms), Energy Consumption (mJ), Key Generation Time (ms) and Malicious Node Detection Accuracy (%). The proposed SecRPL-Ms outperforms 23% of malicious node detection accuracy when compared to existing systems, which represent the proposed SecRPL-MS system providing high security by mitigating the following attacks such as rank attack, Sybil attack, blackhole attack and man in the middle attack.

Novel Authentication and Secure Trust based RPL Routing in Mobile sink supported Internet of Things