Abstract
The Internet of Things (IoT) is a two-decade-old technology that allows the interconnection of various cyber-physical objects located anywhere in the world over the internet. A cyber-physical object is an inanimate object with the ability to connect itself to the internet and communicate with other similar objects. This communication is enabled by communication protocols that bridge the gap between hardware devices and the user by ensuring secure communication with optimum efficiency. As the number of IoT devices is increasing exponentially every year with the current estimation of more than 35 billion IoT devices according to a study conducted by findstack.com, there is a need for a lightweight and highly secure communication protocol for IoT devices. This creates a need for handling all the dynamic data exchanges in real-time. Hence, Big Data and IoT technologies are often proposed together for processing and analyzing data streams. Incorporating Big Data analytics in IoT applications enables real-time information transfer, data monitoring and processing easily. To facilitate efficient communication in IoT networks, the Routing Protocol for Low-Power and Lossy Networks (RPL) comes into the picture which is a low power consuming routing protocol that operates on IEEE 802.15. This paper aims to encrypt the RPL Protocol using cryptographic algorithms and compare the results with a non-encrypted RPL Protocol using Cooja Simulator so that the data is securely communicated for Big Data analysis. The (Secure Hash Algorithm) SHA3 algorithm had been used to encrypt the sink nodes of the RPL protocol and the parameters such as average power consumption, network graph, latency is analyzed and compared. The results of this experiment show a considerable drop in latency and power consumption after deploying cryptographic encryption.
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Patel, A., Shanmugapriya, D., Srivastava, G., Lin, J.CW. (2021). Routing Protocol Security for Low-Power and Lossy Networks in the Internet of Things. In: Srirama, S.N., Lin, J.CW., Bhatnagar, R., Agarwal, S., Reddy, P.K. (eds) Big Data Analytics. BDA 2021. Lecture Notes in Computer Science(), vol 13147. Springer, Cham. https://doi.org/10.1007/978-3-030-93620-4_11
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