WITHDRAWN: Fog computing based secured mobile cloud for cumulative integrity in smart environment and Internet of Things

Janga, Vijaykumar; Rajkumar, P.; Kandan, Saranya

doi:10.1016/j.matpr.2020.12.1134

Cited by 3 publications

(1 citation statement)

References 11 publications

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“…However, Fog Computing paradigm was evolved to support the Cloud in order to mitigate latency intolerance of real-time IoT applications in smart environments [102], such as autonomous vehicles. Similarly, Edge Computing utilizes available computational resources in smart environments, such as the resources in smart vehicles or smart phones, in order to reduce the latency even further [103]. Blockchain was again able to support these technologies by securing and protecting the privacy of this big data in smart environments [104].…”

Section: Blockchain and Cloud Fog And Edge Computingmentioning

confidence: 99%

Blockchain as privacy and security solution for smart environments: A Survey

Ebrahim¹,

Hafid²,

Elie³

2022

Preprint

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Blockchain was always associated with Bitcoin, cryptocurrencies, and digital asset trading. However, the benefits of Blockchain are far beyond that. It has been recently used to support and augment many other technologies, including the Internet-of-Things (IoT). IoT, with the help of Blockchain, paves the way for futuristic smart environments, like smart homes, smart transportation, smart energy trading, smart industries, smart supply chains, and more. To enable these smart environments, IoT devices, machines, appliances, and vehicles, will need to intercommunicate without the need for a centralized trusted party. Blockchain can replace third trusted parties by providing secure means of decentralization in such trustless environments. They also provide security enforcement, privacy assurance, authentication, and other important features to IoT ecosystems. Besides the benefits of Blockchain-IoT integration for smart environments, other technologies also have important features and benefits that attracted the research community. Software-Defined Networking (SDN), Fog, Edge, and Cloud Computing technologies, for example, play an important role in enabling realistic IoT applications. Moreover, the integration of Machine Learning and Artificial Intelligence (AI) algorithms provides smart, dynamic, and autonomous decisionmaking capabilities for IoT devices in smart environments. To push the research further in this domain, we provide in this paper a comprehensive survey that includes state-of-the-art technological integration, challenges, and solutions for smart environments, and the role of Blockchain and IoT technologies as the building blocks of such smart environments. We also demonstrate how the level of integration between these technologies has increased over the years, which brings us closer to the futuristic view of smart environments. We further discuss the current need to provide general-purpose Blockchain platforms that can adapt to different design requirements of different applications and solutions. Finally, we provide a simplified architecture of futuristic smart environments that integrates all these technologies, showing the advantage of such integration.

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Section: Blockchain and Cloud Fog And Edge Computingmentioning

confidence: 99%

Blockchain as privacy and security solution for smart environments: A Survey

Ebrahim¹,

Hafid²,

Elie³

2022

Preprint

View full text Add to dashboard Cite

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Impacts of blockchain in software‐defined Internet of Things ecosystem with Network Function Virtualization for smart applications: Present perspectives and future directions

Rahman

Islam

Kundu

et al. 2023

Int J Communication

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SummaryAs an emerging technology, blockchain (BC) has been playing a promising role in today's software‐defined networking (SDN)‐enabled Internet of Things (IoT) applications. Because of the salient feature of the network function virtualization (NFV) techniques, SDN can ensure an IoT system runs efficiently and smoothly in a cloud‐driven ecosystem. When cloud‐enabled systems encounter immense security and operational challenges caused mainly by third‐party dependency, large‐scale data communication, and maintenance, BC offers effective and robust data transfer solutions without incorporating intermediaries over the distributed network. With the increased SDN‐BC convergence in the IoT domain, the underlying challenges and perspectives deserve proper attention methodically and structurally. From the motivation of addressing such issues, this study provides necessary insights to combine those for successful plug‐and‐play. Therefore, the study includes purposefully investigating current state‐of‐the‐art to extract the research trends, future directions, and perspectives in this domain. This study provides a comprehensive survey of IoT, SDN, NFV, and BC‐enabled emerging technologies. More importantly, the authors intelligently integrated the four different technologies—IoT, SDN, BC, and NFV based on characteristics, scopes, challenges, taxonomies, and tables in numerous areas. Initially, the authors introduce the SDN‐IoT ecosystem in brief and address the features and applications. We took a close look at the SDN's overall taxonomy based on security, environment, scopes, and challenges. We also briefly describe the integration of SDN‐IoT with the NFV ecosystems. Moreover, we review the prospect of BC technology from security perspectives, its extent, challenges of practical implementation, and the possible integration of IoT regarding smart applications. Finally, this study highlights several future directions based on these technologies.

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Intrusion Detection Using Chaotic Poor and Rich Optimization with Deep Learning Model for Smart City Environment

et al. 2023

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Artificial intelligence (AI) techniques play a vital role in the evolving growth and rapid development of smart cities. To develop a smart environment, enhancements to the execution, sustainability, and security of traditional mechanisms become mandatory. Intrusion detection systems (IDS) can be considered an effective solutions to achieve security in the smart environment. This article introduces intrusion detection using chaotic poor and rich optimization with a deep learning model (IDCPRO-DLM) for ubiquitous and smart atmospheres. The IDCPRO-DLM model follows preprocessing, feature selection, and classification stages. At the initial stage, the Z-score data normalization system is exploited to scale the input data. Additionally, the IDCPRO-DLM method designs a chaotic poor and rich optimization algorithm-based feature selection (CPROA-FS) approach for selecting feature subsets. For intrusion detection, butterfly optimization algorithm (BOA) with a deep sparse autoencoder (DSAE) is used. The simulation analysis of the IDCPRO-DLM technique is studied on the benchmark CICIDS dataset and the comparison results show the better performance of the IDCPRO-DLM algorithm over recent state-of-the-art approaches with a maximum accuracy of 98.53%.

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WITHDRAWN: Fog computing based secured mobile cloud for cumulative integrity in smart environment and Internet of Things

Cited by 3 publications

References 11 publications

Blockchain as privacy and security solution for smart environments: A Survey

Blockchain as privacy and security solution for smart environments: A Survey

Impacts of blockchain in software‐defined Internet of Things ecosystem with Network Function Virtualization for smart applications: Present perspectives and future directions

Intrusion Detection Using Chaotic Poor and Rich Optimization with Deep Learning Model for Smart City Environment

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