Abstract
The Routing Over Low power and Lossy network (ROLL) working group of IETF proposed the IPv6 Routing Protocol over Low Power and Lossy network (RPL) to fulfill multiple traffic requirements of the Internet of Things (IoT) environments. RPL structures ubiquitous IoT networks in the form of Directed Acyclic Graphs (DAGs) that comprises basic sensor nodes (motes), motes with routing capability, and one or more root (sink) nodes. RPL ensures optimal topological organization of IoT networks by its rank and version number properties. However, vulnerabilities in the RPL routing process pose threats to several protocol violations in the IoT environment. In RPL, information solicitation is the process by which sensor nodes seek network configuration information required to join an IoT network. The paper investigates the vulnerabilities associated with the information solicitation process in RPL-based IoT networks that leads to flooding of control messages. Consequently, there is energy and queue buffer depletion of sensor nodes in IoT networks. On the one hand, queue buffer depletion may lead to the dropping of packets by sensor nodes. On the other hand, energy depletion of sensor nodes may altogether make them non-functioning. Therefore, IoT network administrators must pay attention to the device’s power consumption. Recent research focuses on energy depletion due flooding of control packets. Nevertheless, flooding also affects other significant parameters like, the network convergence time, beacon interval and packet arrival rate. In this work, we perform an in-depth evaluation of the performance of RPL-based IoT networks under the threat of vulnerabilities associated with information solicitation process. The results of our analysis will assist in devising mitigation mechanisms.
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Sahay, R., Sushant, C.G. (2023). Investigating Vulnerabilities of Information Solicitation Process in RPL-Based IoT Networks. In: Bhateja, V., Yang, XS., Ferreira, M.C., Sengar, S.S., Travieso-Gonzalez, C.M. (eds) Evolution in Computational Intelligence. FICTA 2023. Smart Innovation, Systems and Technologies, vol 370. Springer, Singapore. https://doi.org/10.1007/978-981-99-6702-5_54
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DOI: https://doi.org/10.1007/978-981-99-6702-5_54
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