Neuro‐Fuzzy‐Based Bidirectional and Biobjective Reactive Routing Schema for Critical Wireless Sensor Networks

Raghunath, K. M. Karthick; Raman, G. Venkat

doi:10.1002/9781119682806.ch4

Cited by 2 publications

(1 citation statement)

References 21 publications

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“…Using the input data, the neural network trains its neurons to identify patterns and formulate predictions. In contrast, fuzzy logic is used when exact information is lacking, and assessments must be made using approximate criteria [8,13].…”

Section: Overview Of H-nf Approachmentioning

confidence: 99%

Multi-Criteria Decision-Making for Energy Management in Smart Homes Using Hybridized Neuro-Fuzzy Approach

Anbazhagu,

Koti,

Muthukumaran

et al. 2023

DGAEJ

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The necessity for smart energy oversight solutions has arisen in response to the rising popularity of energy-efficient home automation and other energy-saving technologies. Optimizing smart home energy use using multi-criteria decision-making (MCDM) is a proven methodology. However, the procedure for making decisions and MCDM’s capacity to handle various criteria are typically limiting factors. Hybrid methods, which integrate multiple decision-making approaches like Fuzzy Logic (FL) and Modular Neural Networks (MNN), could potentially be able to circumvent these restrictions and boost energy management systems’ efficacy and precision. This investigation presents a hybrid Neuro-Fuzzy (H-NF) method for MCDM in regulating energy for smart homes by combining FL with an MNN. The suggested approach would optimize energy use in smart homes by considering several parameters, notably cost, ease of use, and environmental effects. In addition, this study aims to examine how the H-NF model fares in comparison to other methods of making important decisions in terms of several performance metrics. The suggested hybridized approach has the potential to deliver more precise and effective decision-making processes for energy management in smart homes, allowing users to optimize their energy consumption while preserving comfort and lowering environmental impact.

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Section: Overview Of H-nf Approachmentioning

confidence: 99%

Multi-Criteria Decision-Making for Energy Management in Smart Homes Using Hybridized Neuro-Fuzzy Approach

Anbazhagu,

Koti,

Muthukumaran

et al. 2023

DGAEJ

View full text Add to dashboard Cite

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Smart Decision-Making and Communication Strategy in Industrial Internet of Things

Gunasekaran¹,

Kumar

Kaladevi

et al. 2023

IEEE Access

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Smart machine-machine (M2M) interactions, such as those enabled by the Internet of Things (IoT), have enabled people and machines to communicate and make decisions together. Furthermore, these systems have become increasingly important in the commercial and industrial sectors over the previous two decades. The Industrial Internet of Things (IIoT) is a smart system comprising engineering equipment which can connect to one another to improve manufacturing operations. This task would become more complicated if the amount of energy used by the IIoT ecosystems, as well as the amount of network traffic they generate, increased dramatically. Consequently, decision-making processes during communication are essential for autonomous interaction in critical IoT infrastructure. Smart factories employ communication technology to track and gather information in real-time to enhance the output, effectiveness, and predictability while lowering the overall cost of vital operations. In this context, Industry 4.0 not only limits to addresses the issues of integrating technologies, but it also focuses on data collection, dissemination, utilization, and organization and also improves the delivery of the solution or services quicker with more sustainability. This study intends to create an NF-based communication system for IIoT platforms to leverage those benefits. The proposed model includes smart decision-making procedures to deal with communication issues. Compared with the many methods already in use, the suggested mechanism's functional viability in the automated system is found to be optimal. Outcomes from simulations reveal that the suggested method has improved the accuracy and communication reliability of the IIoT platforms in comparison with the previous methods. Aside from these, the suggested model keeps the throughput of the local automation unit at 96.03% and the throughput of the production hall at 95.58% on average while maintaining the lowest average PLR of about 26.48% across different data rates.

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Neuro‐Fuzzy‐Based Bidirectional and Biobjective Reactive Routing Schema for Critical Wireless Sensor Networks

Cited by 2 publications

References 21 publications

Multi-Criteria Decision-Making for Energy Management in Smart Homes Using Hybridized Neuro-Fuzzy Approach

Multi-Criteria Decision-Making for Energy Management in Smart Homes Using Hybridized Neuro-Fuzzy Approach

Smart Decision-Making and Communication Strategy in Industrial Internet of Things

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