The Architecture, Classification, and Unsolved Research Issues of Big Data extraction as well as decomposing the Internet of Vehicles (IoV)

Bangare, Sunil L.; Prakash, Sunny; Gulati, Kamal; Veeru, None Dr. B.; Dhiman, Gaurav; Jaiswal, Sushma

doi:10.1109/ispcc53510.2021.9609451

Cited by 24 publications

(17 citation statements)

References 10 publications

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“…The critical area of interest is to provide remote monitoring and data collection. The sensors are made as small and energy efficient as possible; they do not have the computing power to preprocess any data, and so send all collected data to the fog subsystem [35][36][37]. The fog subsystem processes the data, and finally, it is stored in cloud repositories placed in the cloud subsystem.…”

Section: User Subsystemmentioning

confidence: 99%

A Fog-Cluster Based Load-Balancing Technique

et al. 2022

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The Internet of Things has recently been a popular topic of study for developing smart homes and smart cities. Most IoT applications are very sensitive to delays, and IoT sensors provide a constant stream of data. The cloud-based IoT services that were first employed suffer from increased latency and inefficient resource use. Fog computing is used to address these issues by moving cloud services closer to the edge in a small-scale, dispersed fashion. Fog computing is quickly gaining popularity as an effective paradigm for providing customers with real-time processing, platforms, and software services. Real-time applications may be supported at a reduced operating cost using an integrated fog-cloud environment that minimizes resources and reduces delays. Load balancing is a critical problem in fog computing because it ensures that the dynamic load is distributed evenly across all fog nodes, avoiding the situation where some nodes are overloaded while others are underloaded. Numerous algorithms have been proposed to accomplish this goal. In this paper, a framework was proposed that contains three subsystems named user subsystem, cloud subsystem, and fog subsystem. The goal of the proposed framework is to decrease bandwidth costs while providing load balancing at the same time. To optimize the use of all the resources in the fog sub-system, a Fog-Cluster-Based Load-Balancing approach along with a refresh period was proposed. The simulation results show that “Fog-Cluster-Based Load Balancing” decreases energy consumption, the number of Virtual Machines (VMs) migrations, and the number of shutdown hosts compared with existing algorithms for the proposed framework.

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Section: User Subsystemmentioning

confidence: 99%

A Fog-Cluster Based Load-Balancing Technique

et al. 2022

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“…There isn't such a tool, which is a shame language independent [12]. Bangare et al [13,14] worked on machine learning. Shelke et al [15] worked on emotion analysis.…”

Section: Figure 2 Generic Model For Language Processingmentioning

confidence: 99%

Recursive LSTM for the Classification of Named Entity Recognition for Hindi Language

Shelke¹,

Vanjale²

2022

ISI

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NER assumes a key part in Information Extraction from reports (for example email), conversational information, and so forth. Many tongue handling applications, for example, data recovery, question responding to, and machine interpretation, depend on NER. It tends to be challenging to determine the ambiguities of lexical components utilized in a text arrangement. There is too much work has been already done in English language but there is a need to improve accuracy for the NER in Hindi language. In this research researcher are minimize chances of misclassification by using different classifier namely location, name, weather etc. BiLSTM Development of a NER framework for Indian languages is a similarly troublesome task. In this paper, Researcher have done the different research to contrast the aftereffects of NER and typical implanting and quick text implanting layers to examinations the exhibition of word installing with various bunch sizes to prepare the profound learning models. In this paper, Researcher have done the different examinations to contrast the consequences of NER and typical implanting and quick text installing layers to investigations the presentation of word inserting with various group sizes to prepare the profound learning models. The value of the precision of proposed system architecture is 76.13% which is way more than other system architectures. Also, the value of recall and F1-score of proposed system architecture is 71.49 and 74.26 respectively. So, by comparing proposed system architecture with existing SpaCy, CoreNLP and NLTK it is easy to conclude that proposed system architecture is reliable in all the sense.

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“…Studies in IoT machine learning have been presented by S. L. Bangare et al [15][16]. Researchers J. Surve et al [17] focused on utilising IoT to improve medical care. In the field of humancomputer interaction, J. Alanya-Beltran et al [18] have made contributions.…”

Section: Literature Surveymentioning

confidence: 99%

Predictive Models for ABS and TPMS based on Gaussian Naïve Bays

Vanjire

Patil²

2022

IJRITCC

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The car industry is currently preoccupied with the issue of safety. The increasing number of accidents occurring around the world as a result of automobile problems is a major contributing factor to these incidents. The amount of complicated electronics that is used in vehicles is becoming more prevalent every day. A great effort has been made in evaluating vehicle features in relation to vehicle components. Through such systems, a smart architecture and complex function designs are involved. During all of this vehicle transformation and evolution, the automotive industry recognises a high demand for vehicle safety. While designing and manufacturing this system, automotive experts understand a need for a strict monitoring and feedback system for complex vehicle architecture, which can notify the end user if there is any indication of a failure ahead of time. In order to effectively participate in vehicle design activities, it is critical to grasp the significance of safety features. Tire system failures and braking system failures have played a large role in several recent traffic accidents. The failures of the tyre system and the braking system in the vehicle are addressed in this study. While investigating this system, it is discovered that it is supported by complex electrical systems, which include an ECU (electronic controller unit), sensors, and a wire system. Through the use of these technologies, censored data can be processed in a timely manner and made available for diagnostic purposes. Nevertheless, car diagnostics is needed after any vehicle failure but that does not serve the aim of maintaining vehicle safety. As a result, predictive analysis or predictive diagnostics may be a viable option for informing the driver about the health of a particular vehicle component in advance. In this study, the author discusses the concepts of vehicle prognostics for the tyre pressure monitor system and the antilock braking system, which are accomplished using a statistical method of machine learning. In today's world, machine learning is expanding in breadth, and the world is becoming more aware of its enormous potential in the field of data analytics. It is the purpose of this study to introduce methodologies by which machine learning can assist vehicle predictive analytics to attain the intended goal of vehicle safety.The author of this article discusses how Bayesian statistics may be used to produce predictions in the form of probability estimation. The prediction's outcome is thoroughly analysed.

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The Architecture, Classification, and Unsolved Research Issues of Big Data extraction as well as decomposing the Internet of Vehicles (IoV)

Cited by 24 publications

References 10 publications

A Fog-Cluster Based Load-Balancing Technique

A Fog-Cluster Based Load-Balancing Technique

Recursive LSTM for the Classification of Named Entity Recognition for Hindi Language

Predictive Models for ABS and TPMS based on Gaussian Naïve Bays

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