IoT-Blockchain Enabled Optimized Provenance System for Food Industry 4.0 Using Advanced Deep Learning

Khan, Prince Waqas; Byun, Yung-Cheol; Park, Namje

doi:10.3390/s20102990

Cited by 182 publications

(116 citation statements)

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“…Equations (2) and 3show functions for output gate and forget gate, respectively. h t−1 serves the hidden state as current time in these Equations [8]. Figure 11 shows the process flow chart for prediction using machine learning.…”

Section: Machine Learningmentioning

confidence: 99%

“…Equations ( 2 ) and ( 3 ) show functions for output gate and forget gate, respectively.

serves the hidden state as current time in these Equations [ 8 ].

…”

Section: Architecturementioning

confidence: 99%

“…Blockchain technology is developing rapidly, and practical applications have begun in many fields such as logistics [6], medical [7], and food safety [8]. It is foreseeable that more and more industries will apply them in the future.…”

Section: Introductionmentioning

confidence: 99%

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Smart Contract Centric Inference Engine For Intelligent Electric Vehicle Transportation System

Byun

2020

Sensors

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The provision of electric vehicles (EVs) is increasing due to the need for ecological green energy. The increment in EVs leads to an intelligent electric vehicle transportation system’s need instead of cloud-based systems to manage privacy and security issues. Collecting and delivering the data to current transportation systems means disclosing personal information about vehicles and drivers. We have proposed a secure and intelligent electric vehicle transportation system based on blockchain and machine learning. The proposed method utilizes the state of the art smart contract module of blockchain to build an inference engine. This system takes the sensors’ data from the vehicle control unit of EV, stores it in the blockchain, makes decisions using an inference engine, and executes those decisions using actuators and user interface. We have utilized a double-layer optimized long short term memory (LSTM) algorithm to predict EV’s stator temperature. We have also performed an informal analysis to demonstrate the proposed system’s robustness and reliability. This system will resolve the security issues for both information and energy interactions in EVs.

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Section: Machine Learningmentioning

confidence: 99%

“…Equations ( 2 ) and ( 3 ) show functions for output gate and forget gate, respectively.

serves the hidden state as current time in these Equations [ 8 ].

…”

Section: Architecturementioning

confidence: 99%

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Smart Contract Centric Inference Engine For Intelligent Electric Vehicle Transportation System

Byun

2020

Sensors

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“…Currently, in the era of the IoT [1], communication technologies have significantly expanded to reach a variety of industries. Consequently, providing low-power and long-distance communication networks has become an essential component of many applications within smart cities, such as waste management [2], supply chain [3], Industrial Internet of Things [4], smart metering [5], and traffic control. Low Power Wide Area Networking (LPWAN) is an effective way for such applications to overcome these cost, energy, and complexity challenges, especially when such applications require covering large geographic areas.…”

Section: Introductionmentioning

confidence: 99%

Clustering Optimization of LoRa Networks for Perturbed Ultra-Dense IoT Networks

et al. 2021

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Long Range (LoRa) communication is widely adapted in long-range Internet of Things (IoT) applications. LoRa is one of the powerful technologies of Low Power Wide Area Networking (LPWAN) standards designed for IoT applications. Enormous IoT applications lead to massive traffic results, which affect the entire network's operation by decreasing the quality of service (QoS) and minimizing the throughput and capacity of the LoRa network. To this end, this paper proposes a novel cluster throughput model of the throughput distribution function in a cluster to estimate the expected value of the throughput capacity. This paper develops two main clustering algorithms using dense LoRa-based IoT networks that allow clustering of end devices according to the criterion of maximum served traffic. The algorithms are built based on two-common methods, K-means and FOREL. In contrast to existing methods, the developed method provides the maximum value of served traffic in a cluster. Results reveal that our proposed cluster throughput model obtained a higher average throughput value by using a normal distribution than a uniform distribution.

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“…The authors of [ 6 ] propose a hybrid model based on recurrent neural networks (RNN) in the context of secure IoT–blockchain data for Industry 4.0 in the food sector. The authors adopt advanced deep learning (ADL) techniques, long short-term memory (LSTM), and gated recurrent units (GRU) as a prediction model, together with a genetic algorithm (GA) in order to optimize the parameters of the hybrid model.…”

mentioning

confidence: 99%