Comparison and Evaluation of Real Time Reservation Technologies in the Intelligent Public Transport System

Mewada, Akshay; Tanwar, Sudeep; Narmawala, Zunnun

doi:10.1109/pdgc.2018.8745888

Cited by 5 publications

(4 citation statements)

References 12 publications

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“…The analysis of these data through ML techniques can contribute to improving the density of smart cities. (iv) Smart Vehicular Traffic: Vehicular traffic data with a suitable analysis will benefit the government and citizens to a great extent [62]. Everyone can decide the arrival time to a destination by using these data.…”

Section: F Smart Citiesmentioning

confidence: 99%

Machine Learning Adoption in Blockchain-Based Smart Applications: The Challenges, and a Way Forward

et al. 2020

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In recent years, the emergence of blockchain technology (BT) has become a unique, most disruptive, and trending technology. The decentralized database in BT emphasizes data security and privacy. Also, the consensus mechanism in it makes sure that data is secured and legitimate. Still, it raises new security issues such as majority attack and double-spending. To handle the aforementioned issues, data analytics is required on blockchain based secure data. Analytics on these data raises the importance of arisen technology Machine Learning (ML). ML involves the rational amount of data to make precise decisions. Data reliability and its sharing are very crucial in ML to improve the accuracy of results. The combination of these two technologies (ML and BT) can provide highly precise results. In this paper, we present a detailed study on ML adoption for making BT-based smart applications more resilient against attacks. There are various traditional ML techniques, for instance, Support Vector Machines (SVM), clustering, bagging, and Deep Learning (DL) algorithms such as Convolutional Neural Network (CNN) and Long short-term memory (LSTM) can be used to analyse the attacks on a blockchain-based network. Further, we include how both the technologies can be applied in several smart applications such as Unmanned Aerial Vehicle (UAV), Smart Grid (SG), healthcare, and smart cities. Then, future research issues and challenges are explored. At last, a case study is presented with a conclusion. INDEX TERMS Blockchain, machine learning, smart grid, data security and privacy, data analytics, smart applications.

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Section: F Smart Citiesmentioning

confidence: 99%

Machine Learning Adoption in Blockchain-Based Smart Applications: The Challenges, and a Way Forward

et al. 2020

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“…Due to the continuous expansion of smart devices [104], smart city [105], [106], sensors, smart agriculture [107], smart healthcare [108], [109], and many more applications, IoT became a widespread technology since last decade [110]. This application communicates among themselves through sensor nodes.…”

Section: A Overview Of Iotmentioning

confidence: 99%

A Survey on Decentralized Consensus Mechanisms for Cyber Physical Systems

et al. 2020

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Modern industry 4.0 applications are shifting towards decentralized automation of computing and cyber-physical systems (CPS), which necessitates building a robust, secure, and efficient system that performs complex interactions with other physical processes. To handle complex interactions in CPS, trust and consensus among various stakeholders is a prime concern. In a similar direction, consensus algorithms in blockchain have evolved over the years that focus on building smart, robust, and secure CPS. Thus, it is imperative to understand the key components, functional characteristics, and architecture of different consensus algorithms used in CPS. Many consensus algorithms exist in the literature with a specified set of functionalities, performance, and computing services. Motivated from these facts, in this survey, we present a comprehensive analysis of existing state-of-the-art consensus mechanisms and highlight their strength and weaknesses in decentralized CPS applications. In the first part, we present the scope of the proposed survey and identify gaps in the existing surveys. Secondly, we present the review method and objectives of the proposed survey based on research questions that address the gaps in existing studies. Then, we present a solution taxonomy of decentralized consensus mechanisms for various CPS applications. Then, open issues and challenges are also discussed in deploying various consensus mechanisms in the CPS with their merits and demerits. The proposed survey will act as a road-map for blockchain developers and researchers to evaluate and design future consensus mechanisms, which helps to build an efficient CPS for industry 4.0 stakeholders.

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“…The popular approach is to optimize the plan to enhance the strengths of a firm. Wang et al introduced an AutoML architecture for a blockchain-focused approach [8][9][10]. It comprises of six strands: (i) Association layer (ii) Clients layer, (iii) Implementation layer (iv) AutoML layer (vi) Blockchain layer (v) Data transmission layer.…”

Section: Client Supportmentioning

confidence: 99%

“…(ii) Smart Parking Method: Here, the entrance and exit of automobiles can be monitored in a supervised way inside the smart city for the various park areas feasible [9]. (iii) Smart Vehicle Traffic: Vehicle traffic information with an effective review would be of considerable importance to the community and customers [10]. By employing this information, everybody can determine the period of entry to a location.…”

Section: Smart Citiesmentioning

confidence: 99%

Review of Deployment of Machine Learning in Blockchain Methodology

Solanki¹,

Solanki²

2020

IRJASH

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The evolution of blockchain methodology has been a remarkable, highly transformative and trend-setting platform in current years. BT's accessible platform reinforces data protection and confidentiality. In addition, the consensus framework in it ensures system is protected and accurate. Nevertheless, it introduces additional security challenges such as invasion by the majority and double consumption. Data analysis on encrypted data centered on blockchain is crucial to manage the existing challenges. Insights on these results elevates the value of emerging of Machine Learning technique. It covers the fair quantity of data needed to make specific choices. Consistency of data and its distribution are very critical in ML to increase findings reliability. The fusion of these two techniques will produce extremely accurate outcomes. In this article, we describe a thorough analysis on ML implementation to make smart applications based on BT further robust to threats. There are numerous standard ML approaches such as Support Vector Machines (SVM), Clustering, Bagging, and Deep Learning (DL) algorithms such as Convolutional Neural Network (CNN) and Long-Term Memory (LSTM) that can be employed to evaluate the threats on a block chain network. Finally, we discuss how two different techniques can be implemented in a number of smart applications like Unmanned Aerial Vehicle (UAV), Smart Grid (SG), medical care and Smart cities.

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Comparison and Evaluation of Real Time Reservation Technologies in the Intelligent Public Transport System

Cited by 5 publications

References 12 publications

Machine Learning Adoption in Blockchain-Based Smart Applications: The Challenges, and a Way Forward

Machine Learning Adoption in Blockchain-Based Smart Applications: The Challenges, and a Way Forward

A Survey on Decentralized Consensus Mechanisms for Cyber Physical Systems

Review of Deployment of Machine Learning in Blockchain Methodology

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