Emerging IoT Applications in Sustainable Smart Cities for COVID-19: Network Security and Data Preservation Challenges with Future Directions

Smart monitoring solution for dengue infection control: A digital twin-inspired approach

BACKGROUND AND OBJECTIVE

In the realm of smart healthcare, precise monitoring and prediction services are crucial for mitigating the impact of infectious diseases. This study introduces an innovative digital twin technology-inspired monitoring architecture that employs a similarity-based hybrid modeling scheme to significantly enhance accuracy in the smart healthcare domain. The research also delves into the potential of IoT technology in delivering advanced technological healthcare solutions, with a specific focus on the rapid expansion of dengue fever.

METHODS

The proposed digital twin-inspired healthcare system is designed to proactively combat the spread of dengue virus by enabling ubiquitous monitoring and forecasting of individuals' susceptibility to dengue infection. The system utilizes digital twin technology to observe the status of healthcare and generate likely predictions about the vulnerability to the virus by employing k-means Clustering and Artificial Neural Networks.

RESULTS

The proposed system has been validated and its effectiveness has been demonstrated through experimental evaluation using carefully defined methods. The results of the experimental assessment confirm that the system performs optimally in terms of Temporal Delay (14.15 s), Classification Accuracy (92.86%), Sensitivity (92.43%), Specificity (91.52%), F-measure (90.86%), and Prediction Effectiveness. Moreover, by integrating a hybrid model that corrects errors in physics-based predictions employing a model for error correction driven by data, this approach has demonstrated a noteworthy 48% reduction in prediction errors, particularly in health monitoring scenarios.

CONCLUSIONS

The digital twin-inspired healthcare system proposed in this study can assist healthcare providers in assessing the health vulnerability of the dengue virus, thereby reducing the likelihood of long-term or catastrophic health consequences. The integration of a hybrid modeling approach and the utilization of IoT technology has shown promising results in enhancing the accuracy and effectiveness of smart health monitoring and prediction services.

A deep convolutional neural network approach using medical image classification

The epidemic diseases such as COVID-19 are rapidly spreading all around the world. The diagnosis of epidemic at initial stage is of high importance to provide medical care to and recovery of infected people as well as protecting the uninfected population. In this paper, an automatic COVID-19 detection model using respiratory sound and medical image based on internet of health things (IoHT) is proposed. In this model, primarily to screen those people having suspected Coronavirus disease, the sound of coughing used to detect healthy people and those suffering from COVID-19, which finally obtained an accuracy of 94.999%. This approach not only expedites diagnosis and enhances accuracy but also facilitates swift screening in public places using simple equipment. Then, in the second step, in order to help radiologists to interpret medical images as best as possible, we use three pre-trained convolutional neural network models InceptionResNetV2, InceptionV3 and EfficientNetB4 and two data sets of chest radiology medical images, and CT Scan in a three-class classification. Utilizing transfer learning and pre-existing knowledge in these models leads to notable improvements in disease diagnosis and identification compared to traditional techniques. Finally, the best result obtained for CT-Scan images belonging to InceptionResNetV2 architecture with 99.414% accuracy and for radiology images related to InceptionV3 and EfficientNetB4 architectures with the accuracy is 96.943%. Therefore, the proposed model can help radiology specialists to confirm the initial assessments of the COVID-19 disease.

Smart cities software applications for sustainability and resilience

To transform urban areas into smart cities, various technologies-including software, user interfaces, communication networks, and the Internet of Things (IoT)-must tackle complex sustainability and resilience issues. This study aims to investigate the challenges of rapid urban population growth and explore how Information and Communication Technologies (ICT) can be utilized to foster the development of smart cities. Specifically, it seeks to understand how the integration of ICT can contribute to enhancing urban resilience, promoting urban sustainability, and improving citizens' quality of life. The study relied on a literature review, appraisals of fifteen (15) different Smart City software applications and their characteristics (spanning various domains, including data analytics, the Internet of Things (IoT), urban mobility, energy management, and citizen engagement platforms, all related to sustainability and resilience), and thirty (30) case studies cutting across sustainability and resilience. Furthermore, thematic analysis from the case studies was used to evaluate the benefits of smart city applications mapped to the six (6) action areas of Smart City. Based on the findings from case studies and smart city software analysis, rapid urbanisation presents multifaceted challenges like traffic congestion, disaster management, environmental degradation, community engagement, economic disparities, and so on. However, adopting Smart City software applications and aligning with various domains, including data analytics, the Internet of Things (IoT), urban mobility, energy management, and citizen engagement platforms, play pivotal roles in addressing these challenges. Further findings reveal that the benefits of smart city software align with the action areas of smart cities, including Governance, Mobility, Economy, Environment, Living, and People. The research offers practical application of smart city software for Urban designs and planners. It highlights the influence of contextual factors across countries on Smart City effectiveness. The study advances ICT-driven urban transformation, enhancing the quality of life in fast-growing cities.

Study on the mechanism of public attention to a major event: The outbreak of COVID-19 in China

This study focuses on public attention to major events, which has become an important topic in the context of the COVID-19 pandemic. In the background of the global transmission of COVID-19, this study discusses the relationship between information shock and sustainable development, which is rarely mentioned before. By developing an appropriate theoretical model, we discuss how the level of public attention changes over time and with the severity of events. Then we use data on the daily clicks on a popular Chinese medical website to indicate public attention to the pandemic. Our analysis shows that, in the first half of 2020, the level of public attention is closely related to the scale of domestic transmission. The marginal effect of the domestic cases in the first wave is 1% to 0.217%. After the pandemic was largely under control in China, people still followed the latest news, but the scale of public attention to regional transmission diminished. And when the pandemic quickly and severely worsened in other countries, people in China were very attentive, that is, public attention increased. The time interval of social reaction we calculate is fairly stable, with a value of between 0 and 5 most of the time. The average time interval from January 2020 to May 2021 ranges from 1.76 days to 1.94 days, depending on the choice of models and parameters. This study suggests that raising public participation in dealing with the crisis over the long term would be enhanced in China by media encouragement to pay more attention to small-scale regional transmission and the course of the pandemic in other countries. The goal of sustainable development requires dealing with health and economic crises much better in the long term. Thus, the model and method used in the paper serve to enhance general interest.

Artificial intelligence-inspired comprehensive framework for Covid-19 outbreak control

COVID-19 is a life-threatening contagious virus that has spread across the globe rapidly. To reduce the outbreak impact of COVID-19 virus illness, continual identification and remote surveillance of patients are essential. Medical service delivery based on the Internet of Things (IoT) technology backed up by the fog-cloud paradigm is an efficient and time-sensitive solution for remote patient surveillance. Conspicuously, a comprehensive framework based on Radio Frequency Identification Device (RFID) and body-wearable sensor technologies supported by the fog-cloud platform is proposed for the identification and management of COVID-19 patients. The J48 decision tree is used to assess the infection degree of the user based on corresponding symptoms. RFID is used to detect Temporal Proximity Interactions (TPI) among users. Using TPI quantification, Temporal Network Analysis is used to analyze and track the current stage of the COVID-19 spread. The statistical performance and accuracy of the framework are assessed by utilizing synthetically-generated data for 250,000 users. Based on the comparative analysis, the proposed framework acquired an enhanced measure of classification accuracy, and sensitivity of 96.68% and 94.65% respectively. Moreover, significant improvement has been registered for proposed fog-cloud-based data analysis in terms of Temporal Delay efficacy, Precision, and F-measure.

A Comprehensive Survey on the Internet of Things with the Industrial Marketplace

There is no doubt that new technology has become one of the crucial parts of most people's lives around the world. By and large, in this era, the Internet and the Internet of Things (IoT) have become the most indispensable parts of our lives. Recently, IoT technologies have been regarded as the most broadly used tools among other technologies. The tools and the facilities of IoT technologies within the marketplace are part of Industry 4.0. The marketplace is too regarded as a new area that can be used with IoT technologies. One of the main purposes of this paper is to highlight using IoT technologies in Industry 4.0, and the Industrial Internet of Things (IIoT) is another feature revised. This paper focuses on the value of the IoT in the industrial domain in general; it reviews the IoT and focuses on its benefits and drawbacks, and presents some of the IoT applications, such as in transportation and healthcare. In addition, the trends and facts that are related to the IoT technologies on the marketplace are reviewed. Finally, the role of IoT in telemedicine and healthcare and the benefits of IoT technologies for COVID-19 are presented as well.

Detecting Cybersecurity Attacks in Internet of Things Using Artificial Intelligence Methods: A Systematic Literature Review

In recent years, technology has advanced to the fourth industrial revolution (Industry 4.0), where the Internet of things (IoTs), fog computing, computer security, and cyberattacks have evolved exponentially on a large scale. The rapid development of IoT devices and networks in various forms generate enormous amounts of data which in turn demand careful authentication and security. Artificial intelligence (AI) is considered one of the most promising methods for addressing cybersecurity threats and providing security. In this study, we present a systematic literature review (SLR) that categorize, map and survey the existing literature on AI methods used to detect cybersecurity attacks in the IoT environment. The scope of this SLR includes an in-depth investigation on most AI trending techniques in cybersecurity and state-of-art solutions. A systematic search was performed on various electronic databases (SCOPUS, Science Direct, IEEE Xplore, Web of Science, ACM, and MDPI). Out of the identified records, 80 studies published between 2016 and 2021 were selected, surveyed and carefully assessed. This review has explored deep learning (DL) and machine learning (ML) techniques used in IoT security, and their effectiveness in detecting attacks. However, several studies have proposed smart intrusion detection systems (IDS) with intelligent architectural frameworks using AI to overcome the existing security and privacy challenges. It is found that support vector machines (SVM) and random forest (RF) are among the most used methods, due to high accuracy detection another reason may be efficient memory. In addition, other methods also provide better performance such as extreme gradient boosting (XGBoost), neural networks (NN) and recurrent neural networks (RNN). This analysis also provides an insight into the AI roadmap to detect threats based on attack categories. Finally, we present recommendations for potential future investigations.

Design Optimization and Comparison of Cylindrical Electromagnetic Vibration Energy Harvesters

Investigating the coil–magnet structure plays a significant role in the design process of the electromagnetic energy harvester due to the effect on the harvester’s performance. In this paper, the performance of four different electromagnetic vibration energy harvesters with cylindrical shapes constrained in the same volume were under investigation. The utilized structures are (i) two opposite polarized magnets spaced by a mild steel; (ii) a Halbach array with three magnets and one coil; (iii) a Halbach array with five magnets and one coil; and (iv) a Halbach array with five magnets and three coils. We utilized a completely automatic optimization procedure with the help of an optimization algorithm implemented in Python, supported by simulations in ANSYS Maxwell and MATLAB Simulink to obtain the maximum output power for each configuration. The simulation results show that the Halbach array with three magnets and one coil is the best for configurations with the Halbach array. Additionally, among all configurations, the harvester with two opposing magnets provides the highest output power and volume power density, while the Halbach array with three magnets and one coil provides the highest mass power density. The paper also demonstrates limitations of using the electromagnetic coupling coefficient as a metric for harvester optimization, if the ultimate goal is maximization of output power.