Evaluation on Frozen Shellfish Quality by Blockchain Based Multi-Sensors Monitoring and SVM Algorithm During Cold Storage

Feng, Huanhuan; Wang, Wensheng; Chen, Bingqi; Zhang, Xiaoshuan

doi:10.1109/access.2020.2977723

Cited by 53 publications

(33 citation statements)

References 35 publications

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“…Moreover, IoT nodes require more batteries and bandwidth for transaction validation and communication, respectively [49]. The data is sent to BSs for data processing like aggregation, which is stored on a central database that could be vulnerable to a single point of failure [50].…”

Section: ) Problemsmentioning

confidence: 99%

Securing Genetic Algorithm Enabled SDN Routing for Blockchain Based Internet of Things

Abbas¹,

Javaid

Almogren

et al. 2021

IEEE Access

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Internet of Things (IoT) is an emerging domain in which different devices communicate with each other through minimum human intervention. IoT devices are usually operated in hostile and unattended environments. Moreover, routing in current IoT architecture becomes inefficient due to malicious and unauthenticated nodes' existence, minimum network lifetime, insecure routing, etc. This paper proposes a lightweight blockchain based authentication mechanism where ordinary sensors' credentials are stored. As IoT nodes have a short lifespan due to energy depletion, few credentials are stored in the blockchain to achieve lightweight authentication. Moreover, the route calculation is performed by a genetic algorithm enabled software defined network controller, which is also used for on-demand routing to optimize the energy consumption of the nodes in the IoT network. Furthermore, a route correctness mechanism is proposed to check the existence of malicious nodes in the calculated route. Moreover, a detection mechanism is proposed to restrict the malicious nodes' activities, while a malicious node's list is maintained in the blockchain, which is used in the route correctness mechanism. The proposed model is evaluated by performing intensive simulations. The effectiveness of the proposed model is depicted in terms of gas consumption, which shows the optimized utilization of resources. The residual energy of the network shows optimized route calculation, while the malicious node detection method shows the number of packets dropped.

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Section: ) Problemsmentioning

confidence: 99%

Securing Genetic Algorithm Enabled SDN Routing for Blockchain Based Internet of Things

Abbas¹,

Javaid

Almogren

et al. 2021

IEEE Access

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show abstract

“…A multi-sensors block-chain based monitoring approach was presented to maintain the quality of frozen shellfish in cold storage. A SVM and K-means based algorithm used to evaluate the quality loss of shellfish in cold storage [37].…”

Section: Table I a Comparative Analysis Of The Existing Cold Supply Chain Monitoring Solutionsmentioning

confidence: 99%

An IoT-Based Real-Time Intelligent Monitoring and Notification System of Cold Storage

Afreen

Bajwa

2021

IEEE Access

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The intake of the perishable fruits and vegetables (FVs) in the human diet can contribute to reduce the risk of some chronic diseases. But unfortunately, FVs loss rate is high among all the food produced annually and occurs at storage stage of post-harvest life cycle. One of the key factors contributing to this high loss rate is inability to gauge vital ambient environmental parameters in cold storage. The existing monitoring solutions about cold storage are limited to only gauge temperature, relative humidity and ignore other vital ambient environmental parameters such as luminosity and concentration of gases. This is a critical issue that needs to be addressed to overcome the loss rate of FVs. This paper presents a real-time intelligent monitoring and notification system (RT-IMNS) banked on an Internet of Things (IoT)enabled approach for real-time monitoring of temperature, relative humidity, luminosity and concentration of gas in cold storage and notifies the personnel on exceeding of dangerous limits of these parameters. Moreover, decision support is implemented in the RT-IMNS using Artificial Neural Network (ANN) with forward propagation to classify the status of commodity into one of three classes i.e. good, unsatisfactory or alarming. The proposed prediction model outperforms Compress Sending (CS), Adaptive Naïve Bayes (ANB), Extreme Gradient Boosting (XGBoost) and Data Mining (DM) with respect to forecasting accuracy. We achieved 99% accuracy using forward propagation neural network model while existing models such as CS, ANB, XGBoost, DM achieved 95.60%, 87.50%, 93.59%, 90% accuracy respectively. Moreover, proposed approach achieved 100% precision, 100% recall, 100% F1-score for good class is achieved, for unsatisfactory class precision is 98%, recall is 99%, F1-score is 98% and for alarming class precision is 100%, recall is 98% and F1-score is 99%.

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“…Marine bivalves such as oyster, scallop, clam, mussel, and cockle are regarded as popular seafood by consumers all over the world and the market demand has been increasing with years (Feng et al., 2020). In 2018, the total production of bivalves reached 17.3 million tons according to statistical data of Food and Agriculture Organization of the United Nations (FAO) (FAO, 2020).…”

Section: Introductionmentioning

confidence: 99%

Mechanism of texture deterioration of cockle ( Clinocardium californiense ) during chilled storage

Liu

Rui

et al. 2021

Food Processing Preservation

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The changes in texture properties of cockle (Clinocardium californiense) meat during 4°C storage and mechanism involved were investigated. The texture indicators including hardness, springiness, chewiness, cohesiveness, gumminess, and resilience of the stored samples were decreased gradually along with the extension of storage. Pearson correlation analysis indicated that the texture indicators were positively correlated with mercapto value and water‐holding capacity (WHC), but were negatively correlated with trichloroacetic acid‐soluble peptide, myofibril fragmentation index, water‐soluble hydroxyproline, carbonyl value, disulfide bonds, surface hydrophobicity, average pore diameter, and average porosity. Thus, it can be inferred that the endogenous proteolysis and oxidation of myofibrillar proteins and collagenous proteins result in changes in protein structures, which destroy the microstructure and decrease the WHC of Clinocardium californiense meat, finally leading to the deterioration in texture properties. The present study provides a theoretical basis for establishing methods to control the texture deterioration of bivalves during cold storage. Practical application With the extension of storage time under 4°C, cockle (Clinocardium californiense) meat occurred progressive texture deterioration. The present study indicated that endogenous proteolysis and oxidation brought about the changes in structure of myofibrillar proteins and connective tissue proteins, which destroyed the microstructure and decreased the WHC of Clinocardium californiense meat, eventually leading to the deterioration in texture properties. This study provides a theoretical basis for exploring quick and safe means to maintain the texture quality of bivalves during chilled storage by inhibiting the endogenous proteolysis and oxidation of proteins.

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Evaluation on Frozen Shellfish Quality by Blockchain Based Multi-Sensors Monitoring and SVM Algorithm During Cold Storage

Cited by 53 publications

References 35 publications

Securing Genetic Algorithm Enabled SDN Routing for Blockchain Based Internet of Things

Securing Genetic Algorithm Enabled SDN Routing for Blockchain Based Internet of Things

An IoT-Based Real-Time Intelligent Monitoring and Notification System of Cold Storage

Mechanism of texture deterioration of cockle ( Clinocardium californiense ) during chilled storage

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