5G ready optical fog‐assisted cyber‐physical system for IoT applications

Singh, Kiran Deep; Sood, Sandeep K.

doi:10.1049/iet-cps.2019.0037

Cited by 25 publications

(9 citation statements)

References 33 publications

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“…A practical gas turbine engine, named GE-F404 engine is used to illustrate the effectiveness of the proposed estimators. Based on the discrete-time model in Reference 39 the GE-F404 engine system subject to mixed DoS and Deception attack is introduced as follows: attacks are as follows: , C [3] = [ 0.42 2.31 0 ] R [1] = 0.035I n 1 , R [2] = 0.15I n 2 , R [3]…”

Section: Numerical Examplementioning

confidence: 99%

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Centralized approximate optimal estimation for cyber‐physical systems under joint cyber‐attacks

Li,

Lin,

Zhao

et al. 2023

Intl J Robust & Nonlinear

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This paper studies the state estimation for cyber‐physical systems with multiple channels under joint Denial‐of‐Service and deception attacks. We show that the optimal estimator (OE) cannot be implemented in practice, as its computational complexity exponentially grows with respect to time. Then, we develop a centralized approximate optimal estimator (AOE) by using a generalized pseudo‐Bayesian algorithm. This AOE is not only computationally efficient, but also features theoretically guaranteed performance and stability. Specifically, it is proved when the attack success rate is less than a threshold value, both the OE and the AOE are stable, and the averaged performance deviation between the OE and the proposed AOE remains bounded. Finally, simulation examples are provided to illustrate the obtained results.

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Section: Numerical Examplementioning

confidence: 99%

“…Channel 1 is under DoS attacks with 𝛾 [1] = 0.36. Channels 2 and 3 are under deception attacks with 𝛾 [2] = 0.33, 𝛾 [3] = 0.28, R [2] d = 0.08I n 2 , R [3] d = 0.04I n 3 . The joint DoS and Deception attacks over three channels are generated as in Figure 2.…”

Section: Numerical Examplementioning

confidence: 99%

Centralized approximate optimal estimation for cyber‐physical systems under joint cyber‐attacks

Li,

Lin,

Zhao

et al. 2023

Intl J Robust & Nonlinear

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“…Fog computing provides the concept of processing at the network edges with cloud computing features. Fog nodes are capable of computing and storing data with the support of neighboring edge devices [12] . It also helps to reduce the load of clouds and refer to as mini clouds.…”

Section: Introductionmentioning

confidence: 99%

“…CPS uses fog computing and IoT features that integrate various cyber components such as smartphones, wireless sensors, and network devices. CPS has significant applications in various fields such as healthcare, automatic transportation, industry, and so on [12] . Hence, panic wellbeing of the human being in the disaster is decisive for timely evacuation with effectiveness to keep away from any attacks due to panic and infection caused by COVID-19.…”

Section: Introductionmentioning

confidence: 99%

“…The proposed approach utilized the characteristics of cloud, real-time monitoring, strategy based evacuation, and data reduction. Singh and Sood [12] proposed the fog-assisted CPS system in the 5G network for various applications based on IoT. The framework uses an optical fog network to facilitate real-time delivery with low delay and high scalability in the 5G network.…”

Section: Introductionmentioning

confidence: 99%

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A fog assisted intelligent framework based on cyber physical system for safe evacuation in panic situations

Sood

Rawat

2021

Computer Communications

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In the current scenario of the COVID-19 pandemic and worldwide health emergency, one of the major challenges is to identify and predict the panic health of persons. The management of panic health and on-time evacuation prevents COVID-19 infection incidences in educational institutions and public places. Therefore, a system is required to predict the infection and suggests a safe evacuation path to people that control panic scenarios with mortality. In this paper, a fog-assisted cyber physical system is introduced to control panic attacks and COVID-19 infection risk in public places. The proposed model uses the concept of physical and cyber space. The physical space helps in real time data collection and transmission of the alert generation to the stakeholders. Cyberspace consists of two spaces, fog space, and cloud-space. The fog-space facilitates panic health and COVID-19 symptoms determination with alert generation for risk-affected areas. Cloud space monitors and predicts the person’s panic health and symptoms using the SARIMA model. Furthermore, it also identifies risk-prone regions in the affected place using Geographical Population Analysis. The performance evaluation acknowledges the efficiency related to panic health determination and prediction based on the SARIMA with risks mapping accuracy. The proposed system provides an efficient on time evacuation with priority from risk-affected places that protect people from attacks due to panic and infection caused by COVID-19.

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Energy‐efficient fog computing in Internet of Things based on Routing Protocol for Low‐Power and Lossy Network with Contiki

Kumar

Sharma

Goyal

et al. 2021

Int J Communication

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Summary The traditional centralized cloud computing (CC) model faces a range of problems with the exponential growth of the Internet of Things (IoT) applications, like high latency, reduced bandwidth, and network instability. Fog computing (FC) takes the cloud closer to IoT computers to overcome these problems. Rather than moving them to the cloud, the FC provides local IoT data processing and storage on IoT computers. This paper focuses on the Routing Protocol for Low‐Power and Lossy Network (RPL), a universally routing protocol for a static environment that is used for reducing energy consumption, delay, and packet loss with data aggregation at the border router using a fog simulation model in Contiki Cooja. The objective function (OF) choice has an impact on network topology, as each node selects a set of potential parents to send to the destination. However, no systematic analysis of the effects of OF behavior in the RPL environment has been undertaken. Here, three different OFs, Objective Function Zero (OF0), Advanced Objective Function Zero (AOF0), and Minimum Rank with Hysteresis Objective Function (MRHOF) for RPL in the static environment for different node numbers, have been compared. The findings demonstrate that altering all three OFs has a significant impact on RPL. The energy consumption is reduced in the case of the AOF0 in the fog node by 50.86%, which is less than the case of the OF0 and MRHOF function. Extensive simulations show that AOF0 outperforms the existing OFs.

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5G ready optical fog‐assisted cyber‐physical system for IoT applications

Cited by 25 publications

References 33 publications

Centralized approximate optimal estimation for cyber‐physical systems under joint cyber‐attacks

Centralized approximate optimal estimation for cyber‐physical systems under joint cyber‐attacks

A fog assisted intelligent framework based on cyber physical system for safe evacuation in panic situations

Energy‐efficient fog computing in Internet of Things based on Routing Protocol for Low‐Power and Lossy Network with Contiki

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