Network Robustness Analysis for IoT Networks Using Regular Graphs

Dhuli, Sateeshkrishna; Kouachi, Saïd; Chhabra, Anamika; Singh, Yatindra Nath

doi:10.1109/jiot.2021.3116256

Cited by 7 publications

(7 citation statements)

References 39 publications

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“…• Impact of network topology Some basic topologies (e.g., point-to-point topology, star topology, and mesh topology) are commonly used in IoT networks [67]. Many types of real-world network topologies, which consist of different combinations of basic network topologies, are adopted and used in IoT research (e.g., grid network [68], [69], random geometric graph network [70]- [72], scale-free network [73], Fig. 9: MTTC with respect to the changing value of average degree.…”

Section: • Impact Of Network Sizementioning

confidence: 99%

IoTSecSim: A Framework for Modelling and Simulation of Security in Internet of Things

Chee¹,

Ge²,

Bai³

et al. 2023

Preprint

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The project is focusing on building a framework of IoT security. Our main goals are: <ol><li>to model IoT attacks and defences in an emulated IoT networks; and</li><li>to evaluate the impact of different defence techniques on IoT attacks by using multiple security metrics.</li></ol> This framework can create IoT networks with different settings and configurations on IoT devices and network topologies, model malware attacks against the emulated IoT networks and evaluate relevant defences. The simulation software is built with the aim to offer flexibility and adaptability to users. Also, it can be extended with new features and functionalities to simulate recent discovered malware attacks and defences.

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Section: • Impact Of Network Sizementioning

confidence: 99%

IoTSecSim: A Framework for Modelling and Simulation of Security in Internet of Things

Chee¹,

Ge²,

Bai³

et al. 2023

Preprint

View full text Add to dashboard Cite

show abstract

“…• Impact of network topology Some basic topologies (e.g., point-to-point topology, star topology, and mesh topology) are commonly used in IoT networks (Beaulah Soundarabai and Chelliah, 2017). Many types of real-world network topologies, which consist of different combinations of basic network topologies, are adopted and used in IoT research (e.g., grid network (Yildiz et al, 2016;Anzola et al, 2017), random geometric graph network (Dhuli et al, 2021;Qurashi and Angelopoulos, 2020;Kenniche and Ravelomananana, 2010), scale-free network (Usman et al, 2020;Sohn, 2017), and smallworld network (Sohn, 2017)). In order to confirm the impact of network topologies in our simulator, we use our baseline scenario based on an office topology and consider four other topologies for comparison, including a grid network, random geometric graph (RGG) network, scale-free (SF) network, and smallworld (SW) network (as shown in Fig.…”

Section: • Impact Of Non-vulnerable Node Populationmentioning

confidence: 99%

IoTSecSim: A Framework for Modelling and Simulation of Security in Internet of Things

Chee¹,

Ge²,

Bai³

et al. 2023

Preprint

View full text Add to dashboard Cite

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“…Initially, the aim was to establish connections among a few computers; however, with the dawn of automation and the advent of modern devices, the Internet now possesses the capability to connect more devices and equipment than the number of human beings on earth. The Internet of Things (IoT) facilitates the implementation of a wide range of applications by linking multiple intelligent devices via the Internet [1]. A prediction of fifty million new connections (and a net number of 35 billion) was made till 2020 as a result of the massive increase of IoT devices [2], with a mention of 180% growth rate alone for industrial IoT devices [3].…”

Section: Contextmentioning

confidence: 99%

Topology Robustness Modelling against Targeted Attacks for Smart Healthcare System using Go-GA

Changazi

Bakhshi

Yousaf

et al. 2023

Preprint

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The Internet of Things (IoT) has become a pivotal component of modern technology, enabling the interconnection of numerous intelligent devices. It is envisioned to revolutionize various fields through the realization of numerous applications across industries, including healthcare, precision farming, transportation, and smart cities. However, the practical problem of node and link failures due to various constraints, such as low power backup, adverse environmental conditions, fragile metallurgy, and targeted attacks, can negatively impact the overall functionality of IoT networks. To address these challenges, soft and intelligent computing techniques, such as classical heuristic and genetic algorithms, can be utilized to improve the topology robustness of future IoT networks. In this study, we present a system model for a smart healthcare network and evaluate the effectiveness of optimization techniques, including the newly proposed Optimization of Topology for Efficient Convergence (OTEC), which combines a geometric approach in a genetic algorithm with two mechanisms, Efficient Edge Swap (EES) and Node Removal based on Threshold (NRT), to address key limitations in existing techniques. The metric of Schneider R is used to assess the robustness of the topology, with geographic information about IoT nodes and their neighbors stored on a central big data server. The OTEC approach utilizes a nested approach, demonstrating significant improvement over classical genetic algorithms by achieving a 21% increase in Schneider R. Additionally, OTEC effectively addresses limitations present in traditional heuristic algorithms such as ROSE, Simulated Annealing and Hill Climbing. The concept of trustworthiness is also considered from an overall perspective of system functionality. The optimized topology not only improves the robustness of the network but also enhances its trustworthiness, ensuring that the system can operate reliably and ethically. By leveraging soft and intelligent computing techniques, this study provides a valuable contribution to the development of future IoT networks that can handle the challenges of node and link failures while remaining trustworthy and dependable.

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Network Robustness Analysis for IoT Networks Using Regular Graphs

Cited by 7 publications

References 39 publications

IoTSecSim: A Framework for Modelling and Simulation of Security in Internet of Things

IoTSecSim: A Framework for Modelling and Simulation of Security in Internet of Things

IoTSecSim: A Framework for Modelling and Simulation of Security in Internet of Things

Topology Robustness Modelling against Targeted Attacks for Smart Healthcare System using Go-GA

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