Blockchain-based authentication and authorization for smart city applications

Esposito, Christian; Ficco, Massimo; Gupta, Brij B.

doi:10.1016/j.ipm.2020.102468

Cited by 312 publications

(123 citation statements)

References 51 publications

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“…Current work could be realized by enabling optimal tracking [ 38 ] and price control [ 39 ] strategies for heterogeneous loads. Secured control can be guaranteed using blockchain-based authentication and authorization [ 40 ], and convolutional neural network [ 41 ]. Consequently, climate change as a critical governing factor in the global hydrological cycle could be relieved [ 42 ].…”

Section: Discussionmentioning

confidence: 99%

Handling Data Heterogeneity in Electricity Load Disaggregation via Optimized Complete Ensemble Empirical Mode Decomposition and Wavelet Packet Transform

Chui

Gupta

Liu

et al. 2021

Sensors

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Global warming is a leading world issue driving the common social objective of reducing carbon emissions. People have witnessed the melting of ice and abrupt changes in climate. Reducing electricity usage is one possible method of slowing these changes. In recent decades, there have been massive worldwide rollouts of smart meters that automatically capture the total electricity usage of houses and buildings. Electricity load disaggregation (ELD) helps to break down total electricity usage into that of individual appliances. Studies have implemented ELD models based on various artificial intelligence techniques using a single ELD dataset. In this paper, a powerline noise transformation approach based on optimized complete ensemble empirical model decomposition and wavelet packet transform (OCEEMD–WPT) is proposed to merge the ELD datasets. The practical implications are that the method increases the size of training datasets and provides mutual benefits when utilizing datasets collected from other sources (especially from different countries). To reveal the effectiveness of the proposed method, it was compared with CEEMD–WPT (fixed controlled coefficients), standalone CEEMD, standalone WPT, and other existing works. The results show that the proposed approach improves the signal-to-noise ratio (SNR) significantly.

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Section: Discussionmentioning

confidence: 99%

Handling Data Heterogeneity in Electricity Load Disaggregation via Optimized Complete Ensemble Empirical Mode Decomposition and Wavelet Packet Transform

Chui

Gupta

Liu

et al. 2021

Sensors

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“…The former provides big data storage capability for low-latency, high-throughput access. The latter is Hadoop's computing framework and programming framework [ 4 , 5 ]. Applications developed using the MapReduce programming framework can get the powerful computing power of the Hadoop cluster to handle storage.…”

Section: Proposed Methodsmentioning

confidence: 99%

“…Section 5 summarizes and emphasizes the promotion of computer technology and information processing technology to the medical industry and further looks forward to the intelligent diagnosis system for human beings, as well as contributions made. e latter is Hadoop's computing framework and programming framework [4,5]. Applications developed using the Map-Reduce programming framework can get the powerful computing power of the Hadoop cluster to handle storage.…”

Section: Introductionmentioning

confidence: 99%

Research on Diagnostic Information of Smart Medical Care Based on Big Data

Shi

Zhi-wei

2021

Journal of Healthcare Engineering

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The medical field has gradually become intelligent, and information and the research of intelligent medical diagnosis information have received extensive attention in the medical field. In response to this situation, this paper proposes a Hadoop-based medical big data processing system. The system first realized the ETL module based on Sqoop and the transmission function of unstructured data and then realized the distributed storage management function based on HDFS. Finally, a MapReduce algorithm with variable key values is proposed in the data statistical analysis module. Through simulation experiments on the system modules and each algorithm, the results show that because the traditional nondistributed big data acquisition module transmits the same scale of data, it consumes more than 3200 s and the transmission time exceeds 3000 s. The time consumption of smart medical care under the 6G protocol is 150 s, the transmission time is 146 s, and the time is reduced to 1/10 of the original. The research of intelligent medical diagnosis information based on big data has good rationality and feasibility.

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“…In the latest research topic, deep learning has become a popular field in natural language processing, computer vision, etc. [18][19][20]. Moreover, in many types of research, the usage of multi-task learning shows the improvement of application performance.…”

Section: Related Workmentioning

confidence: 99%

A Framework of Vehicular Security and Demand Service Prediction Based on Data Analysis Integrated with Blockchain Approach

Shahbazi

Byun

2021

Sensors

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The prediction of taxi demand service has become a recently attractive area of research along with large-scale and potential applications in the intelligent transportation system. The demand process is divided into two main parts: Picking-up and dropping-off demand based on passenger habit. Taxi demand prediction is a great concept for drivers and passengers, and is designed platforms for ride-hailing and municipal managers. The majority of research has focused on forecasting the pick-up part of demand service and specifying the interconnection of spatial and temporal correlations. In this study, the main focus is to overcome the access point of non-registered users for having fake transactions using taxi services and predicting taxi demand pick-up and drop-off information. The integration of machine learning techniques and blockchain framework is considered a possible solution for this problem. The blockchain technique was selected as an effective technique for protecting and controlling the real-time system. Historical data analysis was processed by extracting the three higher related sections for the intervening time, namely closeness and trend. Next, the pick-up and drop-off taxi prediction task was processed based on constructing the components of multi-task learning and spatiotemporal feature extraction. The combination of feature embedding performance and Long Short-Term Memory (LSTM) obtain the pick-up and drop-off correlation by fusing the historical data spatiotemporal features. Finally, the taxi demand pick-up and drop-off prediction were processed based on the combination of the external factors. The experimental result is based on a real dataset in Jeju Island, South Korea, to show the proposed system’s efficacy and performance compared with other state-of-art models.

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Blockchain-based authentication and authorization for smart city applications

Cited by 312 publications

References 51 publications

Handling Data Heterogeneity in Electricity Load Disaggregation via Optimized Complete Ensemble Empirical Mode Decomposition and Wavelet Packet Transform

Handling Data Heterogeneity in Electricity Load Disaggregation via Optimized Complete Ensemble Empirical Mode Decomposition and Wavelet Packet Transform

Research on Diagnostic Information of Smart Medical Care Based on Big Data

A Framework of Vehicular Security and Demand Service Prediction Based on Data Analysis Integrated with Blockchain Approach

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