Multi-objectives reinforcement federated learning blockchain enabled Internet of things and Fog-Cloud infrastructure for transport data

For the past decade, there has been a significant increase in customer usage of public transport applications in smart cities. These applications rely on various services, such as communication and computation, provided by additional nodes within the smart city environment. However, these services are delivered by a diverse range of cloud computing-based servers that are widely spread and heterogeneous, leading to cybersecurity becoming a crucial challenge among these servers. Numerous machine-learning approaches have been proposed in the literature to address the cybersecurity challenges in heterogeneous transport applications within smart cities. However, the centralized security and scheduling strategies suggested so far have yet to produce optimal results for transport applications. This work aims to present a secure decentralized infrastructure for transporting data in fog cloud networks. This paper introduces Multi-Objectives Reinforcement Federated Learning Blockchain (MORFLB) for Transport Infrastructure. MORFLB aims to minimize processing and transfer delays while maximizing long-term rewards by identifying known and unknown attacks on remote sensing data in-vehicle applications. MORFLB incorporates multi-agent policies, proof-of-work hashing validation, and decentralized deep neural network training to achieve minimal processing and transfer delays. It comprises vehicle applications, decentralized fog, and cloud nodes based on blockchain reinforcement federated learning, which improves rewards through trial and error. The study formulates a combinatorial problem that minimizes and maximizes various factors for vehicle applications. The experimental results demonstrate that MORFLB effectively reduces processing and transfer delays while maximizing rewards compared to existing studies. It provides a promising solution to address the cybersecurity challenges in intelligent transport applications within smart cities. In conclusion, this paper presents MORFLB, a combination of different schemes that ensure the execution of transport data under their constraints and achieve optimal results with the suggested decentralized infrastructure based on blockchain technology.

Industrial Internet of Water Things architecture for data standarization based on blockchain and digital twin technology☆

INTRODUCTION

The Industrial Internet of Water Things (IIoWT) has recently emerged as a leading architecture for efficient water distribution in smart cities. Its primary purpose is to ensure high-quality drinking water for various institutions and households. However, existing IIoWT architecture has many challenges. One of the paramount challenges in achieving data standardization and data fusion across multiple monitoring institutions responsible for assessing water quality and quantity.

OBJECTIVE

This paper introduces the Industrial Internet of Water Things System for Data Standardization based on Blockchain and Digital Twin Technology. The main objective of this study is to design a new IIoWT architecture where data standardization, interoperability, and data security among different water institutions must be met.

METHODS

We devise the digital twin-enabled cross-platform environment using the Message Queuing Telemetry Transport (MQTT) protocol to achieve seamless interoperability in heterogeneous computing. In water management, we encounter different types of data from various sensors. Therefore, we propose a CNN-LSTM and blockchain data transactional (BCDT) scheme for processing valid data across different nodes.

RESULTS

Through simulation results, we demonstrate that the proposed IIoWT architecture significantly reduces processing time while improving the accuracy of data standardization within the water distribution management system.

CONCLUSION

Overall, this paper presents a comprehensive approach to tackle the challenges of data standardization and security in the IIoWT architecture.

Multiobjective optimization of a hybrid electricity generation system based on waste energy of internal combustion engine and solar system for sustainable environment

In recent years, the interest in generating power through hybrid power generation systems has increased. In this study, a hybrid power generation system including an internal combustion engine (ICE) and a solar system based on flat plate collectors to generate electricity is investigated. To benefit from the thermal energy absorbed by solar collectors, an organic Rankine cycle (ORC) is considered. In addition to the solar energy absorbed by the collectors, the heat source of the ORC is the wasted heat through exhaust gases and the cooling system of the ICE. A two-pressure configuration for ORC is proposed for optimal heat absorption from the three available heat sources. The proposed system is installed to produce power with a capacity of 10 kW. A bi-objective function optimization process is carried out to design this system. The objective of the optimization process is to minimize the total cost rate and maximize the exergy efficiency of the system. The design variables of the present problem include the ICE rated power, the number of solar flat plate collectors (SFPC), the pressure of the high-pressure (HP) and low-pressure (LP) stage of the ORC, the degree of superheating of the HP and LP stage of the ORC, and its condenser pressure. Finally, it is observed among the design variables the most impact on total cost and exergy efficiency is related to the ICE rated power and the number of SFPCs.

Machine learning algorithms for high-resolution prediction of spatiotemporal distribution of air pollution from meteorological and soil parameters

This study uses machine learning (ML) models for a high-resolution prediction (0.1°×0.1°) of air fine particular matter (PM_2.5) concentration, the most harmful to human health, from meteorological and soil data. Iraq was considered the study area to implement the method. Different lags and the changing patterns of four European Reanalysis (ERA5) meteorological variables, rainfall, mean temperature, wind speed and relative humidity, and one soil parameter, the soil moisture, were used to select the suitable set of predictors using a non-greedy algorithm known as simulated annealing (SA). The selected predictors were used to simulate the temporal and spatial variability of air PM_2.5 concentration over Iraq during the early summer (May-July), the most polluted months, using three advanced ML models, extremely randomized trees (ERT), stochastic gradient descent backpropagation (SGD-BP) and long short-term memory (LSTM) integrated with Bayesian optimizer. The spatial distribution of the annual average PM_2.5 revealed the population of the whole of Iraq is exposed to a pollution level above the standard limit. The changes in temperature and soil moisture and the mean wind speed and humidity of the month before the early summer can predict the temporal and spatial variability of PM_2.5 over Iraq during May-July. Results revealed the higher performance of LSTM with normalized root-mean-square error and Kling-Gupta efficiency of 13.4% and 0.89, compared to 16.02% and 0.81 for SDG-BP and 17.9% and 0.74 for ERT. The LSTM could also reconstruct the observed spatial distribution of PM_2.5 with MapCurve and Cramer's V values of 0.95 and 0.91, compared to 0.9 and 0.86 for SGD-BP and 0.83 and 0.76 for ERT. The study provided a methodology for forecasting spatial variability of PM_2.5 concentration at high resolution during the peak pollution months from freely available data, which can be replicated in other regions for generating high-resolution PM_2.5 forecasting maps.

Hybrid wavelet-gene expression programming and wavelet-support vector machine models for rainfall-runoff modeling

It is critical to use research methods to collect and regulate surface water to provide water while avoiding damage. Following accurate runoff prediction, principled planning for optimal runoff is implemented. In recent years, there has been an increase in the use of machine learning approaches to model rainfall-runoff. In this study, the accuracy of rainfall-runoff modeling approaches such as support vector machine (SVM), gene expression programming (GEP), wavelet-SVM (WSVM), and wavelet-GEP (WGEP) is evaluated. Python is used to run the simulation. The research area is the Yellow River Basin in central China, and in the west of the region, the Tang-Nai-Hai hydrometric station has been selected. The train state data ranges from 1950 to 2000, while the test state data ranges from 2000 to 2020. The analysis looks at two different types of rainy and non-rainy days. The WGEP simulation performed best, with a Nash-Sutcliffe efficiency (NSE) of 0.98, while the WSVM, GEP, and SVM simulations performed poorly, with NSEs of 0.94, 0.89, and 0.77, respectively. As a result, combining hybrid methods with wavelet improved simulation accuracy, which is now the highest for the WGEP method.

Moderating role of compassion in the link between fear of Coronavirus disease and mental health among undergraduate students

Background

The societal challenges presented by fear related to the coronavirus disease (COVID-19) pandemic may present unique challenges for an individual's mental health. However, the moderating role of compassion in the relationship between fear of COVID-19 and mental health has not been well-studied. The present study aimed to explore the association between fear of COVID-19 and mental health, as well as test the buffering role of compassion in this relationship.

Methods

The participants in this study were 325 Iranian undergraduate students (228 females), aged 18-25 years, who completed questionnaires posted on social networks via a web-based platform.

Results

The results showed that fear of COVID-19 was positively related with physical symptoms, social function, depressive symptoms, and anxiety symptoms. The results also showed that compassion was negatively associated with physical symptoms, social function, depressive symptoms, and anxiety symptoms. The interaction-moderation analysis revealed that compassion moderated the relationship between fear of COVID-19 and subscale of mental health.

Conclusion

Results highlight the important role of compassion in diminishing the effect of fear of COVID-19 on the mental health (physical symptoms, social function, depressive symptoms, and anxiety symptoms) of undergraduate students.

Smartphone-Based Techniques Using Carbon Dot Nanomaterials for Food Safety Analysis

The development of portable and efficient nanoprobes to realize the quantitative/qualitative onsite determination of food pollutants is of immense importance for safeguarding human health and food safety. With the advent of the smartphone, the digital imaging property causes it to be an ideal diagnostic substrate to point-of-care analysis probes. Besides, merging the versatility of carbon dots nanostructures and bioreceptor abilities has opened an innovative assortment of construction blocks to design advanced nanoprobes or improving those existing ones. On this ground, massive endeavors have been made to combine mobile phones with smart nanomaterials to produce portable (bio)sensors in a reliable, low cost, rapid, and even facile-to-implement area with inadequate resources. Herein, this work outlines the latest advancement of carbon dots nanostructures on smartphone for onsite detecting of agri-food pollutants. Particularly, we afford a summary of numerous approaches applied for target molecule diagnosis (pesticides, mycotoxins, pathogens, antibiotics, and metal ions), for instance microscopic imaging, fluorescence, colorimetric, and electrochemical techniques. Authors tried to list those scaffolds that are well-recognized in complex media or those using novel constructions/techniques. Lastly, we also point out some challenges and appealing prospects related to the enhancement of high-efficiency smartphone based carbon dots systems.

DCBRP: a deterministic chain-based routing protocol for wireless sensor networks

BACKGROUND

Wireless sensor networks (WSNs) are a promising area for both researchers and industry because of their various applications The sensor node expends the majority of its energy on communication with other nodes. Therefore, the routing protocol plays an important role in delivering network data while minimizing energy consumption as much as possible. The chain-based routing approach is superior to other approaches. However, chain-based routing protocols still expend substantial energy in the Chain Head (CH) node. In addition, these protocols also have the bottleneck issues.

METHODS

A novel routing protocol which is Deterministic Chain-Based Routing Protocol (DCBRP). DCBRP consists of three mechanisms: Backbone Construction Mechanism, Chain Head Selection (CHS), and the Next Hop Connection Mechanism. The CHS mechanism is presented in detail, and it is evaluated through comparison with the CCM and TSCP using an ns-3 simulator.

RESULTS

It show that DCBRP outperforms both CCM and TSCP in terms of end-to-end delay by 19.3 and 65%, respectively, CH energy consumption by 18.3 and 23.0%, respectively, overall energy consumption by 23.7 and 31.4%, respectively, network lifetime by 22 and 38%, respectively, and the energy*delay metric by 44.85 and 77.54%, respectively.

CONCLUSION

DCBRP can be used in any deterministic node deployment applications, such as smart cities or smart agriculture, to reduce energy depletion and prolong the lifetimes of WSNs.