Smart Environments Research Articles

Decoding Urban Intelligence: Clustering and Feature Importance in Smart Cities

The rapid urbanization trend underscores the need for effective management of city resources and services, making the concept of smart cities increasingly important. This study leverages the IMD Smart City Index (SCI) dataset to analyze and rank smart cities worldwide. Our research has a dual objective: first, we aim to apply a set of unsupervised learning models to cluster cities based on their smartness indices. Second, we aim to employ supervised learning models such as random forest, support vector machines (SVMs), and others to determine the importance of various features that contribute to a city’s smartness. Our findings reveal that while smart living was the most critical factor, with an importance of 0.259014. Smart mobility and smart environment also played significant roles, with the importance of 0.170147 and 0.163159, respectively, in determining a city’s smartness. While the clustering provides insights into the similarities and groupings among cities, the feature importance analysis elucidates the critical factors that drive these classifications. The integration of these two approaches aims to demonstrate that understanding the similarities between smart cities is of limited utility without a clear comprehension of the importance of the underlying features. This holistic approach provides a comprehensive understanding of what makes a city ’smart’ and offers a robust framework for policymakers to enhance urban living standards.

Trajectory mapping through channel state information by triangulation method and fine-tuning

Trajectory mapping techniques have widespread applications in diverse fields, including robotics, localization, smart environments, gaming, and tracking systems. However, existing free devices encounter challenges in representing trajectories, thereby limiting the effectiveness of applications such as robotics, localization, and tracking systems. The imprecise mappings generated by these methods lead to suboptimal performance and unreliable results. The proposed approach leverages WiFi sensing through channel state information (CSI), triangulation techniques, and a fine-tuning mechanism to enhance trajectory precision within indoor environment trajectory mapping. The proposed solution employs a domain adapter fine-tuning technique to enable location-independent tracking via CSI, minimizing errors. The use of CSI MIMO signals for trajectory mapping offers enhanced spatial resolution, robust multipath handling, and improved accuracy in tracking movement by leveraging multiple antenna channels and exploiting the rich information embedded in signal reflections and scattering, while triangulation aids in accurately determining the location of objects or targets. Furthermore, incorporating a fine-tuning mechanism refines the generated trajectories. The findings demonstrate substantial enhancements in mapping precision, with an accuracy of 95.5% in tracking 13 paths within the new domain. These results underscore the effectiveness of the proposed approach in overcoming the limitations of existing methods and achieving highly accurate trajectory mapping.

Ensem-DeepHAR: Identification of human activity in smart environments using ensemble of deep learning methods and motion sensor data

Recognizing human activity plays a crucial role in many applications such as medical care services in smart healthcare environments. Inertial or motion sensors can measure physiognomies such as acceleration and angular velocity of body movement while performing the activities and we can use them to learn the models capable of activity recognition. Over the past decades, many state-of-the-art activity recognition systems have been developed but there is still room to improve. In this paper, we have proposed a novel approach to identify human activity from motion sensor data by employing an enormous analysis of sensor data. Based on data analysis, we yielded quality data by preprocessing using a preprocessing chain for human activity recognition (PC-HAR) which also includes the Synthetic Minority Over-sampling Technique to balance the data of the dataset. As a recognition model, we proposed an ensemble of three different deep learning algorithms, namely, modified DeepConvLSTM, modified InceptionTime, and modified ResNet which is named ‘Ensem-DeepHAR’. The outcome of the proposed model is carried out by stacking predictions from each of the mentioned models and then a Random Forest as a meta-model uses those predictions to recognize the final activity. We evaluated our method on both person-dependent and person-independent cases and achieved 99.31 %, 99.08 %, and 97.52 % accuracies for the former case and 97.95 %, 98.11 %, and 99.51 % accuracies for the latter case using three common benchmark datasets: WISDM_ar_v1.1, PAMAP2, and UCI-HAR respectively. The various performance metrics and measures of experimental results establish the supremacy of the proposed model over the state-of-the-arts.

College Students’ Learning Experience and Engagement in the Smart Classroom: The Mediating Role of Self-Efficacy in the Background of COVID-19

Previous studies suggest that learning experience influences students’ academic engagement and self-efficacy mediates the association between them, and the sub-types of self-efficacy are distinct and play different roles. However, the different roles of self-efficacy sub-types, especially in blended learning environments, have yet to be explored. The present study investigated the potential mechanism of sub-types of self-efficacy on the relationship between the learning experience and academic engagement in a smart classroom environment in the background of COVID-19. Three hundred-eight participants volunteered in this study and finished the survey, including the learning experience, self-efficacy, and academic engagement questionnaire. The results showed that learning experience significantly influences academic engagement, and sub-types of self-efficacy play different roles in the relationship between learning experience and academic engagement. Furthermore, only academic and behavioral self-efficacy mediated the relationship between them. The results of this study have specific implications for creating a sustainable blended learning environment and promoting students’ academic engagement.

A Personalized Teaching System for College English Based on Big Data and Artificial Intelligence

In the ultra-modern digital age, the schooling sector is experiencing a transformative shift driven by the convergence of massive records and artificial Intelligence (AI). This paper offers a singular initiative—a ”personalised teaching machine for university English” that harnesses the electricity of these technologies to revolutionise how English language practice is delivered at the college level. The system is designed to address the various ways of gaining knowledge of the desires of students and adapting dynamically to their talent degrees, styles, and possibilities. The centre of this device lies in its capability to collect, technique, and analyse extensive amounts of records associated with students’ language acquisition journey. By leveraging massive statistics techniques, the machine captures and interprets college students’ interactions with direct materials, assignments, exams, and peer interactions. Concurrently, the latest AI algorithms, which include herbal Language Processing (NLP) and gadget learning (ML), are employed to create a responsive and smart learning environment. The system tailors mastering pathways for personal college students, ensuring they get hold of content material and sporting events aligned with their cutting-edge scalability stages. Ordinary formative checks are performed to gauge college students’ progress, taking into consideration timely interventions and changes in teaching techniques. This progressive customised coaching device for university English now not only enhances language proficiency but additionally promotes self-directed getting-to-know and empowers educators with information-pushed insights. The amalgamation of large facts and AI promises to reshape English language education’s panorama, paving the way for an extra personalised, green, and effective pedagogical method in university settings. In conclusion, this study illuminates the transformative ability of harnessing massive records and AI in schooling, with precise relevance to language instruction. The machine is a pioneering model for personalised, adaptive, and information-centric teaching methodologies in higher training.

Efficient wastewater management for smart cities using Internet of Things (IoT) and blockchain technology

Access to clean and sufficient water is considered a basic right for humans and other living organisms sharing the environment. However, factors like rapid urban growth, climate change, population increase, and the overproduction of industrial and agricultural goods have contributed to water shortages and quality problems in many communities worldwide. Digital technologies, such as blockchain, machine learning, and the Internet of Things (IoT), offer opportunities to develop new solutions for creating smart and sustainable environments. In this paper, we introduce a unified water management system (IB-WMS) using IoT and blockchain technologies to monitor water quality, level, temperature, pressure, and consumption. This system is designed to be reliable, scalable, and transparent due to the integration of blockchain and IoT. Simulations indicate that the IB-WMS system achieves 89% efficiency, a 90% wastewater reuse rate, and a 95% water recycling rate.

Lure Monitoring for Mediterranean Fruit Fly Traps Using Air Quality Sensors.

Effective pest population monitoring is crucial in precision agriculture, which integrates various technologies and data analysis techniques for enhanced decision-making. This study introduces a novel approach for monitoring lures in traps targeting the Mediterranean fruit fly, utilizing air quality sensors to detect total volatile organic compounds (TVOC) and equivalent carbon dioxide (eCO2). Our results indicate that air quality sensors, specifically the SGP30 and ENS160 models, can reliably detect the presence of lures, reducing the need for frequent physical trap inspections and associated maintenance costs. The ENS160 sensor demonstrated superior performance, with stable detection capabilities at a predefined distance from the lure, suggesting its potential for integration into smart trap designs. This is the first study to apply TVOC and eCO2 sensors in this context, paving the way for more efficient and cost-effective pest monitoring solutions in smart agriculture environments.

Evaluating Polynomial, and Gaussian Approaches for Temperature Change Sub Index of Water Quality Index for Smart Environmental Management

Evaluating Polynomial, and Gaussian Approaches for Temperature Change Sub Index of Water Quality Index for Smart Environmental Management

Energy-Efficient Industrial Internet of Things in Green 6G Networks

The research problem of this systematic review was whether green 6G networks can integrate energy-efficient Industrial Internet of Things (IIoT) in terms of distributed artificial intelligence, green 6G pervasive edge computing communication networks and big-data-based intelligent decision algorithms. We show that sensor data fusion can be carried out in energy-efficient IoT smart industrial urban environments by cooperative perception and inference tasks. Our analyses debate on 6G wireless communication, vehicular IoT intelligent and autonomous networks, and energy-efficient algorithm and green computing technologies in smart industrial equipment and manufacturing environments. Mobile edge and cloud computing task processing capabilities of decentralized network control and power grid system monitoring were thereby analyzed. Our results and contributions clarify that sustainable energy efficiency and green power generation together with IoT decision support and smart environmental systems operate efficiently in distributed artificial intelligence 6G pervasive edge computing communication networks. PRISMA was used, and with its web-based Shiny app flow design, the search outcomes and screening procedures were integrated. A quantitative literature review was performed in July 2024 on original and review research published between 2019 and 2024. Study screening, evidence map visualization, and data extraction and reporting tools, machine learning classifiers, and reference management software were harnessed for qualitative and quantitative data, collection, management, and analysis in research synthesis. Dimensions and VOSviewer were deployed for data visualization and analysis.

Early prediction of grape disease attack using a hybrid classifier in association with IoT sensors

Machine learning with IoT practices in the agriculture sector has the potential to address numerous challenges encountered by farmers, including disease prediction and estimation of soil profile. This paper extensively explores the classification of diseases in grape plants and provides detailed information about the conducted experiments. It is important to keep track of each crop's current environmental conditions because different environmental conditions, such as humidity, temperature, moisture, leaf wetness, light intensity, wind speed, and wind direction, can affect or sustain the quality of a crop. IoT will increasingly be used in precision agriculture and smart environments to detect, gather, and share data about environmental occurrences. The environmental factor that is active at all times and has an effect on a crop from its cultivation to harvest. With the aid of an IoT, we will monitor the following factors: temperature, humidity, and leaf wetness, all of which have an impact on the overall quality and lifespan of grapes. A Self-created database of weather parameter using sensors is introduced in this article. It consists of 5 categories with a total of 10,000 records. Here, experiment has been carried out using our dataset to predict grape diseases on various machines learning algorithm. The system receives overall accuracy of 98.25 % for Powdery Mildew, 98.85 % for Downy Mildew and 93.95 % for Bacterial Leaf Spot.

Malware containment with immediate response in IoT networks: An optimal control approach

The exponential growth of Internet of Things (IoT) devices has triggered a substantial increase in cyber-attacks targeting these systems. Recent statistics show a surge of over 100 percent in such attacks, underscoring the urgent need for robust cybersecurity measures. When a cyber-attack breaches an IoT network, it initiates the dissemination of malware across the network. However, to counteract this threat, an immediate installation of a new patch becomes imperative. The time frame for developing and deploying the patch can vary significantly, contingent upon the specifics of the cyber-attack. This paper aims to address the challenge of pre-emptively mitigating cyber-attacks prior to the installation of a new patch. The main novelties of our work include: (1) A well-designed node-level model known as Susceptible, Infected High, Infected Low, Recover First, and Recover Complete (SIHILRFRC). It categorizes the infected node states into infected high and infected low, according to the categorization of infection states for IoT devices, to accelerate containment strategies for malware propagation and improve mitigation of cyber-attacks targeting IoT networks by incorporating immediate response within a restricted environment. (2) Development of an optimal immediate response strategy (IRS) by modeling and analyzing the associated optimal control problem. This model aims to enhance the containment of malware propagation across IoT networks by swiftly responding to cyber threats. Finally, several numerical analyses were performed to fully illustrate the main findings. In addition, a dataset has been constructed for experimental purposes to simulate real-world scenarios within IoT networks, particularly in smart home environments.

Simulation of Malfunctions in Home Appliances’ Power Consumption

Predicting errors in home appliances is crucial for maintaining the reliability and efficiency of smart homes. However, there is a significant lack of such data on appliance malfunctions that can be used in developing effective anomaly detection models. This research paper presents a novel approach for simulating errors of heterogeneous home appliance power consumption patterns. The proposed model takes normal consumption patterns as input and employs advanced algorithms to produce labeled anomalies, categorizing them based on the severity of malfunctions. One of the main objectives of this research involves developing models that can accurately reproduce anomaly power consumption patterns, highlighting anomalies related to major, minor, and specific malfunctions. The resulting dataset may serve as a valuable resource for training algorithms specifically tailored to detect and diagnose these errors in real-world scenarios. The outcomes of this research contribute significantly to the field of anomaly detection in smart home environments. The simulated datasets facilitate the development of predictive maintenance strategies, allowing for early detection and mitigation of appliance malfunctions. This proactive approach not only improves the reliability and lifespan of home appliances but also enhances energy efficiency, thereby reducing operational costs and environmental impact.

Public Health Care Cybersecurity Challenges and Solutions for Cyber-Attacks on Critical Health Infrastructure

The opportunities for public health care and safety are especially high, and the healthcare industry is particularly susceptible to cybersecurity threats. Cybercriminals find healthcare facilities appealing due to their size, reliance on technology, handling of sensitive data, and susceptibility to certain disruptions. The Internet of Things, or IoT, has gained a lot of traction in recent years due to its many benefits, which include reduced costs, time savings, enhanced user comfort, and effective use of electricity. The most crucial component of the cyber-physical system is the low-capacity sensor node. These components are diverse in nature and can function as hosts or clients on the internet by connecting via a wireless network. The well-known security features found in desktop computers are inoperable on these systems because of resource constraints such low processor power, low storage capacity, and low energy backup. The suggested study uses SNMP in conjunction with an ANN classifier and secure data transmission to detect cybersecurity attacks on healthcare data and medical devices. The vulnerabilities in the security of IoT-centric systems give rise to privacy concerns that impact the use of smart environment applications.

A systematic review of non-intrusive human activity recognition in smart homes using deep learning

<p>Smart homes are a viable solution for improving the independence and privacy of elderly and dependent people thanks to IoT sensors. Reliable human activity recognition (HAR) devices are required to enable precise monitoring inside smart homes. Despite various reviews on HAR, there is a lack of comprehensive studies that include a diverse range of approaches, including sensor-based, wearable, ambient, and device-free methods. Considering this research gap, this study aims to systematically review the HAR studies that apply deep learning as their main solution and utilize a non-intrusive approach for activity monitoring. Out of the 2,171 studies in the IEEE Explore database, we carefully selected and thoroughly analyzed 37 studies for our research, following the guidelines provided by the preferred reporting items for systematic reviews and meta-analyses (PRISMA) methodology. In this paper, we explore various modalities, deep learning approaches, and datasets employed in the context of non-intrusive HAR. This study presents essential data for researchers to employ deep learning techniques for HAR in smart home environments. Additionally, it identifies and highlights the main trends, challenges, and future directions.</p>

A novel methodology for anomaly detection in smart home networks via Fractional Stochastic Gradient Descent

With the rising integration of IoT devices within smart home environments, securing these interconnected systems against unauthorized access and cyber threats has become increasingly critical. This study introduces a novel methodology utilizing an advanced Artificial Neural Network (ANN) frame-work to enhance attack and anomaly detection capabilities within smart home networks. The objective is to develop a robust model that outperforms traditional detection methods and provides high accuracy and low false positive rates in identifying potential security threats. The research employs a pioneering approach to train the ANN by incorporating a Fractional Stochastic Gradient Descent optimizer grounded in Grunwald–Letnikov fractional calculus. This method was chosen for its potential to refine learning processes and improve detection accuracy over standard optimizers. The evaluation of the model was performed using the DS2OS traffic traces dataset, applying precision, sensitivity (recall), and specificity metrics to assess performance. The proposed model demonstrated exceptional performance with an accuracy of 0.9951. It significantly surpassed traditional methods like Logistic Regression and Support Vector Machines. The precision achieved was high, indicating a low rate of false positives, while the sensitivity and specificity values underscore the model’s ability to identify both typical and unconventional behaviours within the network accurately. This study introduces a robust and efficient ANN-based methodology for enhancing security in smart home IoT networks. Using a fractional stochastic gradient descent optimizer has proven effective in improving the model’s accuracy and reliability in detecting anomalies and attacks. The findings suggest significant implications for the future of IoT security, highlighting the potential for broader applications of fractional calculus in machine learning to enhance cybersecurity measures in various domains.

Highly isolated pin-loaded millimeter wave MIMO antenna array system for IoT-based smart environments

The Internet of Things (IoT) based wireless devices are mainly designed for rapid indoor millimeter-wave (mmW) communication systems. These systems often require high gain mmW antennas within a wideband MIMO framework. This work presents a high performance quad element pin-loaded multiple-input multiple-output (MIMO) antenna array for mmW communications that can be utilized within IoT-enabled smart environment. The novelty of these antenna elements lies in the incorporation of unique flower-shaped slots and shorting pins in each patch of a two-element antenna array. Moreover, a four-port MIMO antenna system is created by arranging the two element arrays in an orthogonal configuration. By employing this pattern diversity technique and incorporating shorting pins in each individual element, the adverse mutual coupling effects between adjacent elements are effectively mitigated. Experimental validation of the MIMO array prototype confirms the minimum isolation of 34 dB and high port isolation of 72 dB over the operating bandwidth from 26.31–30.25 GHz (3.96 GHz). Notably, these features together with lower correlation values and higher diversity gain, make it a good choice for mmW-based IoT devices in smart environments.

Re-ISSUES—Renewable Energy-Linked Interoperable Smart and Sustainable Urban Environmental Systems

Smart cities will be smart if they improve their citizens’ quality of life; to do so, it is essential to listen to citizens and collaborate with service and technological companies. For that, digitalization seems essential. Environmental management systems are complex and expensive. If their lifecycle costs are reduced, these systems would be more sustainable. This can be achieved through citizen collaboration (CS), the use of low-cost Internet of Things (IoT) devices, and collaboration with local renewable energy businesses. All this leads to a real interoperability challenge. Systems engineering offers a valid framework for managing information and knowledge for environmental systems. It offers a range of guides for processes that can improve the quality of the related information and the reusability of knowledge throughout the lifecycles of these systems. After quantifying the opportunity and the cost for a motivational case of atmospheric neighborhood odor impact and introducing trends and opportunities in energy management, the authors propose a model for renewable energy-linked interoperable smart and sustainable urban environmental systems (re-ISSUES). The model’s ontology is used to discover research trends and potential for improvements to the model itself, enabling semantic interoperability and knowledge reuse.

NeuroCamTags: Long-Range, Battery-free, Wireless Sensing with Neuromorphic Cameras

In this research, we introduce NeuroCamTags, a battery-free platform designed to detect a range of rich human interactions and activities in entire rooms and floors without the need for batteries. The NeuroCamTag system comprises low-cost tags that harvest ambient light energy and utilize high-frequency modulation of light-emitting diodes (LEDs) for wireless communication. These visual signals are captured by an available neuromorphic camera, which boasts temporal resolution and frame rates an order of magnitude higher than those of conventional cameras. We present an event processing pipeline that allows simultaneous localization and identification of multiple unique tags. NeuroCamTags offer a wide range of functionalities, providing battery-free wireless sensing for various physical stimuli, including changes in temperature, contact, button presses, key presses, and even sound cues. Our empirical evaluations demonstrate impressive accuracy at long ranges up to 200 feet. In addition to these findings, we consider a range of applications such as battery-free input devices, tracking of human movement, and long-range detection of human activities in various environments such as kitchens, workshops, etc. By reducing reliance on batteries, NeuroCamTags promotes eco-friendliness and opens doors to exciting possibilities in smart environment technology.

Research on English Classroom Teaching Interactive Strategy for Higher Vocational Students in smart Classroom Environment

This study conducted experimental teaching of vocational college English listening and speaking courses in both smart classroom and simple multimedia classroom environments. Using an improved version of the Flipped Classroom Interaction Analysis System (SCIAS) and questionnaires, supplemented by S-T analysis as a complementary research method, the study conducted a comparative analysis of classroom records in terms of classroom structure, emotional atmosphere, interactive behaviors between teachers and students, interpersonal interactions, and interactions between teachers, students, and media technology. The results showed that compared to traditional classrooms, teaching in a smart classroom environment is more conducive to stimulating students' initiative, leading to deeper and more active classroom interactions.

الاتصالات الالسلكية البصرية لشبكة انترت الاشياء : التطبيقات والقضايا والتحديات

Visible light communication (VLC) is a novel paradigm that has the potential to completely alter wireless communication in the future. In VLC, data is transferred by modifying the 400– 700 nm visible light spectrum, that is employed for lighting. It has been demonstrated via analytical and experimental study that VLC has the ability to offer high-speed data transmission with the added benefits of increased energy efficiency and communication security/privacy. As part of future smart environments, VLC with its expansive frequency spectrum and IoT integration can enable a variety of indoor and outdoor applications. An overview of optical wireless communications for the Internet of Things (OWC-IoT) is presented in this research, focusing on VLC based potential applications and challenges.