OFFM-ANFIS analysis for flood prediction using mobile IoS, fog and cloud computing

Khanna, Nitin; Sachdeva, Monika

doi:10.1007/s10586-019-03033-w

Cited by 9 publications

(7 citation statements)

References 29 publications

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“…They also employed the adaptive neuro-fuzzy system-genetic algorithm for cloud calculation of health vulnerability indices. Khanna and Sachdeva [42] introduced a framework for forecasting and predicting flood. The structure utilized fog computing, mobile edge computing, and cloud computing, together with a sensing network based on IOS.…”

Section: B Iot Big Data Analytics Infrastrucuresmentioning

confidence: 99%

Optimized ANFIS Model Using Hybrid Metaheuristic Algorithms for Parkinson’s Disease Prediction in IoT Environment

2020

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Throughout recent years, the progress of telemonitoring and telediagnostics devices for evaluating and tracking Parkinson's (PD) disease has become increasingly important. The early detection of PD increases the consistency of the treatment of patients and ultimately allows it possible to achieve a rapid diagnostic decision from an experienced clinician. In this paper, a proposed fog-based ANFIS+PSOGWO model provided for Parkinson's disease prediction. The proposed model exploits the advantages of the grey wolf optimization (GWO) and the particle swarm optimization (PSO) for adjusting the adaptive neuro-fuzzy inference system (ANFIS) parameters with the use of chaotic tent map for the initialization. The fog processing utilized for gathering and analyzing the data at the edge of the gateways and notifying the local community instantly. Compared to other optimization methods, many evaluation metrics used like the root mean square error (RMSE), the mean square error (MSE), the standard deviation (SD), and the accuracy and five standard datasets from repository of UCI machine learning that demonstrated the superiority of the model proposed against the grey wolf optimization (GWO), the particle swarm optimization (PSO), the differential evolution (DE), the genetic algorithm (GA), the ant colony optimization (ACO), and the standard ANFIS model. Moreover, the proposed ANFIS+PSOGWO applied for Parkinson's disease prediction and achieved an accuracy of 87.5%. The proposed ANFIS+PSOGWO compared in producing positive outcomes better than PSO, GWO, GA, ACO, DE, and some recent literature for Parkinson's disease prediction. The proposed model produced accuracy for the Parkinson's disease prediction has outperformed its closest competitors in all algorithms by 7.3%.

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Section: B Iot Big Data Analytics Infrastrucuresmentioning

confidence: 99%

Optimized ANFIS Model Using Hybrid Metaheuristic Algorithms for Parkinson’s Disease Prediction in IoT Environment

2020

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“…When it comes to retrieving time series data, WSN is an information retrieval component that yields the best results possible [8] . Fog servers can be used to analyze data from mobile edge nodes, which then pass it on to the sensing layer, which contains a variety of static and mobile sensors IoS nodes [9] . A similar approach combining IIoT and fog is also used to sample flood prevention and causation properties [10] .…”

Section: Literature Reviewmentioning

confidence: 99%

“…The OFFM-ANFIS features seven modified ANFIS models that anticipate floods using training data and sensory data. The flow of raw and analysed data from OFFM-ANFIS is tiered so that more influential parameters have a large impact [9] . SVD-based characteristics reduction strategy for K-mean clustering algorithm is used to estimate the current condition of flood and flood rating in any place [10] .…”

Section: Literature Reviewmentioning

confidence: 99%

Design an efficient data driven decision support system to predict flooding by analysing heterogeneous and multiple data sources using Data Lake

H V,

Rao,

et al. 2023

MethodsX

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“…The output of the fuzzy logic system is produced by the sixth subsystem. Additionally, [180] suggested an IoS-based sensing network for flood forecasting and prediction that is driven by mobile edge computing (MEC), FC, and CC following analysis through a modified multi-ANFIS architecture called OFFM-ANFIS. The OFFM-ANFIS consists of seven modified ANFIS models that analyse the sensory data received and trained data to forecast floods.…”

Section: Fuzzy Inference Systemmentioning

confidence: 99%

Fuzzy Theory in Fog Computing: Review, Taxonomy, and Open Issues

et al. 2022

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Geographically dispersed Fog Computing architecture ubiquitously connected to a range of heterogeneous nodes at the edge of the network can provide cooperative flexible, and variable computations, communications, and storage services. Several fog computing methods, models, and techniques have been used to solve cloud issues. The fuzzy theory has also been used in many aspects of fog computing. Objectives: This work presents a systematic literature review of the use of fuzzy theory in Fog Computing, highlighting the main practical motivations, classification types in research approaches, fuzzy methods used, popular evaluation tools, open issues, and future trends. Methods: The investigations were systematically performed using fuzzy theory in fog computing, and four databases which are ScienceDirect, Web of Science (WoS), Scopus, and IEEE Xplore Digital Library from 2015 to 2022, were used to analyse their performance evaluation, architecture, and applications. Results: 94 articles were selected based on fuzzy theory in fog computing using different methods, models, and techniques, based on the proposed exclusion and inclusion criteria. The results of the taxonomy were divided into five major classes: task and resource management, intrusion detection systems, trust management, and healthcare services. Discussion: Applications requiring real-time, low latency, and quick responses are well suited for fog computing. These studies show that resource sharing improves the fog computing architecture by delivering reduced latency, distributed processing, improved scalability, better security, fault tolerance, and privacy. Conclusion: The majority of the time, research areas on fuzzy theory in fog computing are crucially significant. We conclude that this review will enhance research capacity, thereby expanding and creating new research domains.

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OFFM-ANFIS analysis for flood prediction using mobile IoS, fog and cloud computing

Cited by 9 publications

References 29 publications

Optimized ANFIS Model Using Hybrid Metaheuristic Algorithms for Parkinson’s Disease Prediction in IoT Environment

Optimized ANFIS Model Using Hybrid Metaheuristic Algorithms for Parkinson’s Disease Prediction in IoT Environment

Design an efficient data driven decision support system to predict flooding by analysing heterogeneous and multiple data sources using Data Lake

Fuzzy Theory in Fog Computing: Review, Taxonomy, and Open Issues

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