Performance evaluation of mobile RPL-based IoT networks under version number attack

Abstract

The Internet of Things (IoT) has a vital role in communication and has many cross-platform applications which generate a massive volume of data. IoT interconnects various devices from small to big without the direct intervention of humans. The resource-constrained environment poses a significant problem in IoT applications, and it is challenging to develop secure applications. The Internet community endeavours to cope with such challenges by developing different internet protocols. IETF ROLL working group standardized a mechanism called IPv6 over Low-Power Wireless Personal Area Networks (6LoWPAN) to carry IPv6 packets over IEEE 802.15.4. 6LoWPAN which supports the constrained environment uses the Routing Protocol for Low Power and Lossy Networks (RPL) as a routing protocol. It is essential to secure such applications since the malicious attacker can breach the privacy and security of humans through a small device. Traditional security mechanisms are not prominent in a resource-constrained context. Version attack is one of the most common attacks in RPL based 6LoWPAN. The network becomes unstable due to the version attack, which results in a Denial of Service attack. The integrity of the version number is not provided by RPL specifications, leading to threats for IoT applications. The impact of a version number attack on an RPL-based network is demonstrated in this study. The implications on the constrained network when the nodes are mobile is the main objective of this paper. In many IoT applications nodes move and it is vital to address the impact of mobility in a constrained environment. This paper investigates the network’s performance in terms of packet delivery, delay, and power consumption in RPL based IoT when there is version attack. Version attacks must be prevented as quickly as possible since they have the potential to significantly disrupt mobile networks. The main contribution of this research is a performance metric-based analysis of mobile RPL-based IoT networks under attack.