Reliable critical nodes detection for Internet of Things (IoT)

The 3D critical node (C-N) detection can play a vital role in algorithm development of security, surveillance, monitoring, topology detection, and situation-aware emergency navigation for the Internet of Things (IoT). However, 3D C-N detection problem in IoT raises some issues and also introduces new challenges. The existing state of the art in 3D C-N detection shows that rely on prior known anchor node, known coordinate, embedding of the 3D situation on a 2D geometrical structure like circles and presence of unreliable node and ignores the energy constraint in Low Power and Lossy Networks IoT. In this paper, we present a practical, distributed, and energy-efficient algorithm for reliable 3DC-N detection. The goal of the proposed mechanism is twofold, firstly a 3D critical nodes (C-N) detection algorithm is proposed which uses only Received Signal Strength Indicator information of neighbor. Secondly, a correlation-based algorithm for the reliability approach is proposed to increases the node resilience against malicious IoT nodes. The complexity of our proposed algorithms has a time complexity of O(log(N))\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\mathcal {O}(\log (N))$$\end{document} and computation cost O(δ(logN))\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\mathcal {O}(\delta (\log N))$$\end{document} where N is the number of nodes in networks, and δ\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\delta $$\end{document} is the total number of forward and the backward message from an individual node. To validate our work, we implemented our proposed approach with the IPv6 Routing Protocol for Low-Power and Lossy Networks (RPL) based IoT routing protocol compare it with RPL and cryptographic approach Version Number and Rank Authentication (VeRa). The result shows that the proposed approach can detect 10–15% more C-N nodes. Result also shows that our proposed algorithm has better PDR than RPL based approach by 12% and less than VeRa (cryptographic approach) by 8% however our proposed approach consumes almost 50% less power than the VeRa.