Search

Edge–cloud coordination offers the chance to mitigate the enormous storage and processing load brought on by a massive increase in traffic at the network's edge. Though this paradigm has benefits on a large scale, outsourcing the sensitive data from the smart devices deployed in an Internet of Things (IoT) application may lead to privacy leakage. With an attribute-based keyword search (ABKS), the search over ciphertext can be achieved; this reduces the risk of sensitive data explosion. However, ABKS has several issues, like huge computational overhead to perform multi-keyword searches and tracing malicious users. To address these issues and enhance the performance of ABKS, we propose a novel traceable and lightweight attribute-based keyword search technique in an Edge–cloud-assisted IoT, named TL-ABKS, using edge–cloud coordination. With TL-ABKS, it is possible to do effective multi-keyword searches and implement fine-grained access control. Further, TL-ABKS outsources the encryption and decryption computation to edge nodes to enable its usage to resource-limited IoT smart devices. In addition, TL-ABKS achieves tracing user identity who misuse their secret keys. TL-ABKS is secure against modified secret keys, chosen plaintext, and chosen keyword attacks. By comparing the proposed TL-ABKS with the current state-of-the-art schemes, and conducting a theoretical and experimental evaluation of its performance and credibility, TL-ABKS is efficient. [ABSTRACT FROM AUTHOR]

IoT enables the emergence and implementation of smart devices to address real-world problems and challenges. Today we are surrounded by various smart devices, such as smart phones, smart homes, smart cars, smart televisions, and smartwatches that assist us in making our lives easier and smoother. IoT is a conglomeration of multiple technologies at various layers to impart the best of ubiquitous and pervasive computing to deliver several benefits in a variety of application sectors such as medicine, agriculture, and industry. In an IoT setting, blockchain technology is used for addressing security challenges and eradicating third-party participation. Utilizing public networks for storing or transmitting health care images poses risks of eavesdropping, data breaches, and unauthorized access. Before uploading medical data to the decentralized network, encryption is required to prevent unauthorized access. The aim of this study is to integrate technologies to ensure transactions are conducted safely and securely. We proposed an IoT-Blockchain system based on chaos encryption scheme using Tinkerbell mapping to ensure medical data integrity and authenticity. The suggested approach is examined to assess performance parameters such as, key space analysis, key sensitivity analysis, Information Entropy (IE), histogram, correlation of adjacent pixels, Number of Pixel Change Rate (NPCR), Unified Average Changing Intensity (UACI), Peak Signal to Noise Ratio (PSNR), Mean Square Error (MSE) and Structural Similarity Index (SSIM). These findings demonstrated that the suggested method is extremely efficient in avoiding security breaches and guaranteeing information integrity. [ABSTRACT FROM AUTHOR]

The Internet of Things (IoT) technology is used in smart homes to enhance comfort, security, and energy efficiency. This study outlines a theoretical framework for the deployment and dissemination of IoT technologies in intelligent residential environments. The framework of this study highlights the significance of many components that cater to people's individual needs. The study offers a systematic explanation of IoT principles, tangible devices, services, data creation, software, applications, and logistical needs. Furthermore, it contains an extensive table that assists users in choosing the most appropriate resources and components. The study points to smart home systems based on IoT, which utilize sensors, controllers, and cloud solutions to manage data and provide user control while ensuring privacy and confidentiality. The study addresses various needs of users by utilizing a semantic vision framework for smart house design that guarantees economic advantages, enhanced security, and user contentment, ultimately leading to enhanced community interactions and the overall welfare of residents. [ABSTRACT FROM AUTHOR]

There are many difficulties in growing tomatoes, such as erratic weather patterns, variable yields, and short shelf lives. Tomatoes are a staple food that is grown all throughout the world, particularly in climate-friendly areas such as Morocco. These areas are not free from the difficulties that present major obstacles for farmers, though. Modern agricultural techniques are increasingly using cutting-edge technologies, such as Internet of Things (IoT)-enabled smart greenhouses, to address these problems. In order to improve tomato output, this study presents an enhanced smart greenhouse model that incorporates the zoning principle. The method provides optimal plant growth and resource efficiency by separating the greenhouse into various zones, each tailored for specific growth stages and environmental circumstances. Precision farming and sustainable practices are made possible by the suggested systems real-time monitoring and control, which integrates sensors, actuators, and data analytics. The automatic irrigation system of the intelligent greenhouse prototype is additionally set up to offer the best possible support for the growth of tomato plants. The functionality and efficacy of the prototype are investigated through extensive laboratory testing and experimentation, providing insight into its potential influence on agricultural practices and practical practicality. The greenhouse that has been created with the help of IoT capabilities and the zoning concept has proven to be an effective instrument in assisting farmers in their attempts to grow tomatoes in an efficient and sustainable manner. Our comprehensive testing yielded data that demonstrate the zoning principle-integrated IoT-enabled smart greenhouse greatly improves tomato quality and production. This system demonstrates the benefits and practical applicability of sophisticated data-driven automation in agriculture by providing a scalable and sustainable solution to the problems encountered by tomato producers. [ABSTRACT FROM AUTHOR]

In the age of high-tech automation and wireless technology, it is possible to make various devices come together in a given environment, enhancing comfort, energy savings, security, and affordability. We can observe environmental indicators remotely and manage relevant equipment with embedded systems' tiny, low-energy consumption. These devices' power consumption and network bandwidth utilization are critical. Thus, a low-power device must transfer data using minimal redundant protocol. Since Wi-Fi is so widely available, linking all devices within the space through one standard gateway makes sense. This study gives an overview of Message Queuing Telemetry Transport (MQTT), an ideal lightweight protocol for such applications because of its low power and bandwidth requirements. The proposed prototype uses MQTT on the ESP8266, a Wi-Fi-based expansion board. In the case of ESP8266, sensors and actuators are connected directly using Cayenne-based MQTT brokers to provide remote control and monitoring. It allows for efficient and effective communication within the network, ensuring stable data transmission and device management while minimizing power consumption and bandwidth usage. The project has shown how possible it is to use MQTT in the ESP8266 to support smart environments, leading to better IoT-integrated solutions. [ABSTRACT FROM AUTHOR]

Ensuring the security of networks is a significant hurdle in the rollout of the Internet of Things (IoT). A widely used protocol in the IoT ecosystem is message queuing telemetry transport (MQTT), which is based on the published-subscribe model. IoT manufacturers are expected to expand their usage of the MQTT protocol, which is expected to increase the number of cyber security threats against the protocol. IoT settings are crucial to overcoming scalability and computing resource issues and minimizing the characteristics needed for categorization. Machine learning (ML) is extensively used in traffic categorization and intrusion detection. This study proposes a ML-based network traffic detection model (MLNTDM) to enhance IoT application layer attack detection. The proposed architecture for the MQTT protocol is evaluated based on its effectiveness in detecting malicious attacks and how these affect various MQTT brokers. This study focuses on low-power-consuming ML algorithms for detecting IoT botnet offenses and identifying typical attacks and their responses. With this framework, each network flow provides information that can help identify the source of generated traffic and network assaults. Results from our approach, as shown in the experiment, prove more accuracy. [ABSTRACT FROM AUTHOR]

With the increasing demand for energy in urban areas, there is a pressing need to manage energy consumption more effectively. Buildings, especially commercial and industrial ones, are major consumers of energy. Implementing AI-powered solutions can help monitor, predict, and reduce energy usage, leading to substantial cost savings and more efficient energy use. Integrating Industry 4.0 and the Internet of Things (IoT) into smart buildings presents a significant opportunity for enhancing energy management through advanced technologies. The increasing demand for energy in urban areas necessitates the development of more efficient energy management strategies, particularly within smart buildings, which are significant energy consumers. This study proposes an AI-powered deep learning framework utilizing Convolutional Neural Networks (CNNs) to enhance energy management in smart buildings, leveraging the ASHRAE - Great Energy Predictor III dataset. The proposed framework leverages deep learning techniques to analyze historical energy data, weather conditions, and building characteristics to forecast future energy usage accurately. Additionally, the framework includes anomaly detection mechanisms to identify inefficiencies and faults in the energy management system. By optimizing energy consumption in real-time and implementing demand response strategies, the framework aims to reduce energy costs and enhance the overall efficiency of smart buildings. The proposed CNN-IoT-based energy management framework in smart buildings demonstrates significant advancements over existing methods, achieving an accuracy of 88 %. These performance metrics indicate a substantial improvement in prediction accuracy and efficiency compared to existing approaches such as SVM, ELM, and LSTM. [ABSTRACT FROM AUTHOR]

One of the biggest challenges for the medical professionals is spotting cardiovascular issues in the earliest stages. Around the world, Cardiovascular Diseases (CVD) are a major cause of death for almost 18 million people each year. Heart disease is therefore a serious concern that needs to be treated. The numerous elements that affect health, such as excessive blood pressure, elevated cholesterol, aberrant pulse rate, and many other factors, might make it challenging to detect heart disease. Consequently, early disease detection and the development of effective treatments can benefit greatly from the field of artificial intelligence. The purpose of this work is to develop a new IoT based healthcare monitoring framework for the prediction of CVD using machine learning algorithm. Here, the data preprocessing has been performed to create the normalized dataset for improving classification. Then, an Artificial Gorilla Troop Optimization (AGTO) algorithm is deployed to choose the most pertinent features from the normalized dataset. Moreover, the Multi-Linear Regression Classification (MLRC) model is also implemented for accurately categorizing the medical information as whether healthy or CVD affected. The results of the proposed AGTO-MLRC mechanism is validated and compared using the popular benchmarking datasets. [ABSTRACT FROM AUTHOR]

With the rise in size of the elderly population suffering from urinary incontinence and the number of bedridden patients, individuals providing assistance to the aforementioned groups suffer from the "caregiving burden" due to the time-demanding nature of home health care. The current study proposes a low-cost Internet of Things (IoT)-based urine monitoring system for detecting a critical urine level in a urine collection container and sending an SMS text message to alert the caregiver. Remotely informing the caregiver of the patient's urine level provides freedom of mobility. The proposed system consists of an Arduino microcontroller connected to a sensor measuring system and a SIM800L module with a SIM card. Three sensor measuring systems are suggested: Water Level Sensor, Ultrasonic Distance Sensor, and Weighing Scale Sensor. To compare the performance of the systems, 110 detection recordings from each system are used to construct a confusion matrix. The accuracy, precision, specificity, recall, and F-measure are computed for each system. Numerical and graphical analysis of the measurements show that the Water Level Sensor measurement system (accuracy: 0.96, precision: 0.98, specificity: 0.98, recall: 0.95, F-measure: 0.96) is the most reliable system, followed by the Weighing Scale Sensor (accuracy: 0.93, precision: 1, specificity: 0.936, recall: 0.88, F-measure: 0.93). [ABSTRACT FROM AUTHOR]

These days, transport and logistics are undergoing a major transition due to the introduction of smart technologies (STs). STs use data science and artificial intelligence (AI) technologies, like big data and machine learning, to develop cognitive awareness of an object with the help of IoT and blockchain-based information and communication technologies. Currently, several ST applications have shown promise in raising the efficacy and efficiency of diverse transport and logistical systems. Furthermore, the modeling challenges posed by these new, cutting-edge technologies to conventional optimization techniques provide a wealth of fresh research opportunities for the development of novel optimization procedures in the field of logistics and transportation studies. The most effective system was discovered to be the human-AI one. The ultimate objective is to empower users with more knowledge and encourage safer, more organized, and "smarter" usage of transportation networks on mobile technologies. The Internet of Things (IoT) offers countless prospects for improved transportation management and new services related to logistics and data integration. [ABSTRACT FROM AUTHOR]

Smart grid represents an advanced and interconnected system that incorporates modern technologies to enhance efficiency, reliability and sustainability. In comparison to the conventional grid, the smart grid (SG) uses many cutting-edge technologies. This research introduces a fog computing encryption-based model for privacy preservation in the smart grid model. By using different advanced technologies, our model addresses the balance between privacy, security, effectiveness and functionality. The model facilitates efficient communication and function inquiry while mitigating challenges posed by massive Internet of Things (IoT) systems in the smart grid environment. Specifically, it tackles the secure data consolidation challenge by employing encryption-based techniques for transmitting private data from smart meters to fog devices. These devices consolidate the data before updating to cloud. Conventional data consolidation approaches for SGs have high computation and communication costs and suffer from high storage requirement. The proposed model resolves these issues; algorithms for data consolidation and extraction of data at fog device and cloud respectively to obtain the secure communication has also been included. The performance of the developed mechanism has been computed against existing data consolidation mechanisms GCEDA (Grouping of Clusters for Efficient Data Aggregation), SPPDA (Secure Privacy-Preserving Data Aggregation) and LPDA (Lightweight Privacy-preserving Data Aggregation) for numerous performance parameters. And the results proves that the performance of developed mechanism with respect to bytes of storage, communication cost and ratio of transmission is better than the existing ones. [Display omitted] • Encryption-based data consolidation strategy for 5G in fog computing is presented. • Data consolidation and data extraction algorithm at fog devices and cloud servers. • A comparative analysis based on storage, communication and transmission cost is done. [ABSTRACT FROM AUTHOR]

This paper introduces the intelligent interconnected healthcare system (IIHS), an innovative fusion of the Internet of Things (IoT) and big data analytics technologies designed to revolutionize proactive and personalized healthcare. IIHS facilitates the integration of real-time data from various devices, ambient sensors, and hospital equipment, creating a continuous stream of comprehensive healthcare data. Leveraging advanced data analysis, IIHS offers actionable insights for ongoing patient health monitoring, trend prediction through machine learning, and rapid information access via a user-friendly interface. The system architecture features a combination of centralized cloud storage and edge storage at healthcare facilities, enhancing both efficiency and security in data management. The effectiveness of IIHS has been demonstrated in two healthcare facilities, which reported significant reductions in patient length of stay and readmission rates. This indicates the system's potential to improve patient care while seamlessly integrating with existing healthcare infrastructures. IIHS represents the future of digital and personalized medicine, offering a scalable, patient-centric solution that supports the ongoing transformation towards data-driven healthcare. [ABSTRACT FROM AUTHOR]

The design of a smart water monitoring and control system in urban areas plays a pivotal role in providing efficient distribution mechanisms to reduce leakage, especially in regions facing water scarcity and limited resources. The convergence of the Internet of Things (IoT) and blockchain technology to improve the system's performance, enhance its security, and provide a decentralized and tamper-proof environment presents an excellent opportunity to evolve the system further and form a state-of-the-art Water 4.0 ecosystem. The proposed Blockchain of Things (BCoT) water system is introduced as a pilot to explore its potential in delivering and managing Water 4.0 applications. An Ethereum platform formed the heart of the BCoT system, while a Raspberry Pi 4 acted as a node to the blockchain that collected data from various sensors and microcontrollers via MQTT programmed by Node-Red. LabVIEW software also provided supervisory control and data acquisition (SCADA). The BCoT system was tested, and its functionality was verified, showing good promise to take smart water systems to a new level of innovation that may resolve the many challenges faced by countries with limited water resources and address the challenges of the 21st-century "Water 4.0" ecosystem. [ABSTRACT FROM AUTHOR]

The Internet of Things (IoT) is growing rapidly in an exponential manner due to its versatility in technology. This has led to many challenges in securing the IoT environment. Devices in IoT environments are vulnerable to various cyberattacks. Botnet-based attacks are predominant and widespread in nature. Due to insufficient memory and computational power, the IoT environment cannot handle the botnet attack that affects security. Identifying intrusions in IoT environments is another challenge for researchers. Finding unknown patterns in the data generated through IoT networks helps improve security in the IoT environment. Machine learning (ML) is a platform that helps identify patterns in the provided data. In this study, we present our research on classifying incoming data from the IoT as malicious or benign using machine learning techniques. We propose an ML-based botnet attack detection framework for nine commercial IoT devices that primarily target BASHLITE and Mirai botnet attacks. Rigorous pragmatic research was conducted on the N-BaIoT dataset, which was extracted from realtime IoT devices connected to a network. Using this framework, the results have been depicted, which can efficiently detect botnet attacks and can also be applied to any other types of attacks. [ABSTRACT FROM AUTHOR]

Internet of Things (IoT) in recent times offers a greater amount of flexible design during the deployment of it in the form of network. The interfacing of IoT with a wireless sensor network (WSN) requires an optimal solution to preserve the consumption of energy while the data has been transmitted via nodes. In this paper, we develop machine learning (ML)-assisted routing to route the data packets of IoT sensors from the real-time environment over WSN. In this study, we use three different planes for optimal routing of packets from the source node to the destination node. The source node is the IoT sensors that involve data collection, and the intermediate nodes are the WSN sensor nodes that route the packets. Optimal routing decisions based on the pre-trained data in an artificial neural network (ANN) stabilize the routing of data packets without congestion. The simulation is conducted to test the efficacy of the ANN routing in the integrated network, and the results show that the ANN-based routing achieves higher energy efficiency and throughput than other models. [ABSTRACT FROM AUTHOR]

This research and development project aimed to create learning materials in the form of an Internet of Things (IoT) trainer kit specifically designed for the Digital Circuit course. The goal was to enhance student motivation and learning outcomes by facilitating a comprehensive understanding of the subject matter. The research methodology employed was research and development (R&D) utilizing the Instructional Development Institute (IDI) development model, which consists of define, develop, and evaluate stages. Primary data were gathered from experts in instrumentation, media, and materials, as well as feedback from lecturers and students. The findings revealed a high level of validity for the IoT trainer kit learning media. Media experts assigned a validity value of 0.93, while material experts provided a validity value of 0.86. The practicality of the media was confirmed by favorable responses from lecturers, indicating a practicality score of 87.31%. Additionally, students' responses after the trial reflected a high practicality rating of 83.25%. Regarding effectiveness, the IoT trainer kit learning media demonstrated success in improving student learning outcomes, with a gain score of 77.25%, affirming its effectiveness. In conclusion, the IoT trainer kit Learning Media is considered valid, practical, and effective for teaching digital circuits within the Electrical Engineering Study Program at Muhammadiyah University of West Sumatra. Its utilization is expected to boost student engagement, creativity, and learning outcomes, potentially improving the overall quality of education. [ABSTRACT FROM AUTHOR]

Over the last decade, health information systems (HIS) have undergone significant changes, particularly in embracing flexible frameworks for ongoing development. This evolution underscores the necessity for information technology (IT) infrastructures that rapidly align with clinical processes. The paper investigates the transition of hospital information systems to comprehensive strategies, examining the benefits and challenges involved. It also examines the increasing demands on health data management systems (HDMS) for patient care and biomedical research. The focus is on how the integration of the Internet of Things (IoT) and open-source enterprise resource planning (ERP) systems, such as Odoo, impacts health information management. The study evaluates the effectiveness and implications of combining these technologies. It provides examples of these integrated systems in action, particularly in resource-limited settings, and evaluates their potential to improve care. The document provides a comprehensive review of the current status and evolution of HIS and HDMS, highlighting the importance of integrating IoT and other cutting-edge technologies in healthcare. This is a crucial aspect for developing countries, where these advancements can significantly improve healthcare outcomes. [ABSTRACT FROM AUTHOR]

Managing plastic bottle waste is a significant challenge. Manual systems often result in waste accumulation and environmental harm. Effective system analysis and design are needed to address this issue. One proposed solution is implementing IoT-based smart trash bins and the CLEIN mobile application. Using the Scrum product development method aims to ensure project efficiency and effectiveness. This project aims to (1) describe the development of the CLEIN app and IoT-based smart bins, (2) outline development challenges and strategies, and (3) present findings on the solution's acceptability and usability in waste management. The agile Scrum methodology was used to develop the CLEIN app and IoT-based smart bins, involving concept development, mock-up creation, stakeholder feedback, technical development, and initial testing. The team included core developers, mobile app developers, hardware developers, and potential end users. The development process followed seven Scrum phases: (1) concept design, (2) software and hardware development, (3) stakeholder feedback, (4) IoT solution development, (5) final version creation, (6) initial field testing, and (7) evaluation of acceptability and usability by end users. [ABSTRACT FROM AUTHOR]

The concept of the Internet of Things (IoT) is significant in today's world and opens up new opportunities for several organizations. IoT solutions are proliferating in fields such as self-driving cars, smart homes, transportation, and healthcare, and new services are constantly being created. Over the previous decade, society has seen a significant expansion in IoT connectivity. In reality, IoT connectivity will expand in a variety of domains over the next few years. Various problems must be overcome to permit effective and secure operations. However, growing connections increase the potential for cyber-attacks since attackers can exploit the broad network of linked devices. Artificial intelligence (AI) detects and prevents cyber assaults by constantly developing and adjusting to new threats and weaknesses. In this study, we offer a novel cyber-detection model for IoT networks based on convolutional neural networks (CNN) transformers. The study aims to enhance the system's ability to identify and detect cyberattacks, new and sophisticated assaults, and its performance. The experimental study findings, using a new cybersecurity CICIoT2023 dataset, show that the CNN-Transformer model can detect IoT hazards with an overall accuracy of 99.49%. In identifying hazardous activity, MLP accuracy is 99.39%, while XGBoost-pipeline accuracy is 99.40%. [ABSTRACT FROM AUTHOR]

Regarding the contemporary economic trend marked by emerging technologies, novel industries, evolving formats, and innovative modes. engineering education on the Internet of Things C loT) should actively respond to challenges and align with global development strategies. To this end, this work proposes a closed-loop teaching model based on OBE-CDIO to resolve the main issues in this field, including the incongruities between teaching content and industry demands, fragmented curriculum provisions, and limitations on students' technical capabilities. In this closed-loop teaching model. the instructional design is guided by Outcome-Based Education (OBE), and the curriculum implementation is based on Conceive-Design-Implement-Operate (CDIO). Besides, to further enhance the quality of instruction for better cultivating high-quality students. the model can iteratively adjust the teaching design based on feedback. The method validation is performed by administering questionnaires. tests. and controlled experiments to 40 students in terms of their attitudes. knowledge, and abilities. The results demonstrate that the proposed model beneficially advances the quality of teaching and enhances the student's skills. Particularly. the average score for the experimental class is 87%, whereas the traditional class attains an average score of 52.5%. Consequently, it can be concluded that the model significantly contributes to fostering teaching innovation in an loT program. Such experiences provide insightful reference cases for developing and implementing IoT-related courses. [ABSTRACT FROM AUTHOR]

The integration of blockchain technology with the rapid growth of Internet of Things (IoT) devices has enabled secure and decentralised data exchange. However, security vulnerabilities and performance limitations remain significant challenges in IoT blockchain networks. This work proposes a novel approach that combines transaction representation and machine learning techniques to address these challenges. Various clustering techniques, including k-means, DBSCAN, Gaussian Mixture Models (GMM), and Hierarchical clustering, were employed to effectively group unlabelled transaction data based on their intrinsic characteristics. Anomaly transaction prediction models based on classifiers were then developed using the labelled data. Performance metrics such as accuracy, precision, recall, and F1-measure were used to identify the minority class representing specious transactions or security threats. The classifiers were also evaluated on their performance using balanced and unbalanced data. Compared to unbalanced data, balanced data resulted in an overall average improvement of approximately 15.85% in accuracy, 88.76% in precision, 60% in recall, and 74.36% in F1-score. This demonstrates the effectiveness of each classifier as a robust classifier with consistently better predictive performance across various evaluation metrics. Moreover, the k-means and GMM clustering techniques outperformed other techniques in identifying security threats, underscoring the importance of appropriate feature selection and clustering methods. The findings have practical implications for reinforcing security and efficiency in real-world IoT blockchain networks, paving the way for future investigations and advancements. [ABSTRACT FROM AUTHOR]

IoT techniques have transformative potential across domains, particularly in precision agriculture, where they enable farmers to optimize crop production while minimizing environmental impact. This paper presents an IoT-based real-time monitoring and control system for hydroponic tomato cultivation. The system integrates various hardware components, including sensors for monitoring temperature, humidity, pH levels, and gas concentrations. Arduino microcontrollers collect and process sensor data, enabling automated control of water, nutrient supply, and ventilation. Wi-Fi and GSM modules facilitate remote monitoring and data transmission, empowering farmers to monitor and control their tomato crops remotely. The system has been successfully tested for tomato cultivation, demonstrating promising results. Although tailored for tomatoes, its design principles can be adapted for other crops. Moreover, it shows significant water and nutrient efficiency, highlighting its economic feasibility. [ABSTRACT FROM AUTHOR]

Fog computing has emerged as an essential technology for enabling real-time, low-latency responses in cloud-based applications within Internet of Things (IoT) systems. This study explored the impact of the number of fog devices (NFDs) on the performance of IoT-fog-cloud systems. Through comparative analysis, two scheduling algorithms—First Come First Serve (FCFS) and Priority—were evaluated across different scales of system deployment. The results indicated that the FCFS algorithm was optimal for systems with fewer NFDs, whereas the Priority algorithm proved advantageous in larger settings. These findings not only establish guidelines for selecting appropriate scheduling strategies based on the scale of fog device deployment but also provide strategic insights for businesses on the optimal distribution of computational resources. This research aids companies in deciding whether to centralize resources in fewer branches with more employees or to decentralize into more branches with fewer employees, thereby optimizing operational efficiency and responsiveness. [ABSTRACT FROM AUTHOR]

This paper presents a monitoring, control, and management system that allows the interoperability of multiple smart gateways in a greenhouse to bolster automatic decision making on water supplied to the crops in real-time. The system consists of several sensor nodes interconnected by Zigbee to measure ambient and soil parameters to control the greenhouse irrigation process. In addition, a web service is included to help the system manager to visualize and operate the greenhouse on a web platform. The system also supports human decision making to cope with undesirable and unexpected environmental events. Smart gateways enable data reading, storage and edge computing using automatic rules to manage actions in the greenhouse, as well as data transmission for centralized processing in cloud services. The service communication system layer is supported by choreography middleware for messaging distribution, which enables agile scalability. This research proposes a hybrid scheme and compares to a centralized scheme. The latency times in the choreography system were analyzed to find a suitable solution adapted to infrastructure deployment features. Numerical results shows that such an adaptive deployment of the irrigation services among edge and fog layers can reduce the computational load on the gateways, improve system response times, and enhance device performance. [ABSTRACT FROM AUTHOR]

The Message Queuing Telemetry Transport (MQTT) protocol, widely used in various industries for Machine-to-Machine (M2M) communication, plays a central role in the IoT architecture. However, despite its widespread adoption, an analysis conducted on the shodan search engine revealed up to 540531 vulnerabilities worldwide as of April 2024, underscoring the security challenges facing this protocol. How can an innovative cyber range system be designed to assess and improve the security of MQTT protocols, focusing on in-depth vulnerability analysis, authentication strategy development, and targeted countermeasures against specific attacks? The study includes an analysis of relevant articles, followed by classification into five different categories. Furthermore, the presentation of a cyber range that exposes various attacks, such as replay attacks, Man-In-The-Middle (MITM), Denial of Service (DoS)/Distributed DoS(DDoS), and cam-hackers, enhances the understanding of MQTT protocol vulnerabilities, emphasizes the critical importance of strengthening security, and serves as a reference for new researchers in the field. [ABSTRACT FROM AUTHOR]

With the continuous advancement of Internet of Things (IoT) intelligence, IoT security issues have become more and more prominent in recent years. The research on IoT security has become a hot spot. A lightweight IoT intrusion detection model fusing a convolutional neural network, bidirectional long short-term memory network is proposed. It aims to improve processed data security and attack detection accuracy. First, sampling is performed by a hybrid sampling algorithm fusing SMOTE and ENN. Its aim is to minimize the impact of imbalanced-data and ensure data quantity in the process. Then, the data features are extracted by 2-dimensional convolutional neural network (2dCNN), and the effect of useless information is reduced by mean pooling and maximum pooling, so it can be adapted to the demanding resource environment of the IoT. On this basis, long-range dependent temporal features are extracted using bidirectional long short-term memory (BiLSTM), which aims to fully extract data features to improve detection accuracy in the limited resource environment. Finally, the algorithm is validated on the UNSW_NB15 dataset, and the results of the experiments reaches 93.5% at Accuracy, 86.4% at Precision, 85.3% at Recall and 85.8% at F1-Score. According to the results, the proposed algorithm can generate higher-quality samples, achieve higher detection rate with faster inference time and spend lower memory costs. [ABSTRACT FROM AUTHOR]

Conventional maritime communication struggles to provide high data rate services for Internet of Things (IoT) devices due to the variability of maritime environments, making it challenging to ensure consistent connectivity for onboard sensors and devices. To resolve this, we perform mathematical modeling of the maritime channel and compare it with real measurement data. Through the modeled channel, we verify the received beam gain at buoys on the ocean surface. Additionally, leveraging the modeled wave motions, we estimate future angles of the buoy to use the Extended Kalman Filter (EKF) for design beamforming strategies that adapt to the evolving maritime environment over time. We further validate the effectiveness of these strategies by assessing the results from an outage probability perspective. focuses on improving maritime communication by developing a dynamic model of the maritime channel and implementing a Kalman filter-based buoy motion tracking system. This system is designed to enable precise beamforming, a technique used to direct communication signals more accurately. By improving beamforming, the aim is to enhance the quality of communication links, even in challenging maritime conditions like rough seas and varying sea states. In our simulations that consider realistic wave motions, you've observed significant improvements in link quality due to the enhanced beamforming technique. These improvements are particularly notable in environments with high sea states, where communication challenges are typically more pronounced. The progress made in this area is not just a technical achievement; it has broad implications for the future of maritime communication technologies. This paper promises to revolutionize the way we approach communication in maritime environments, paving the way for more reliable and efficient information exchange on the seas. [ABSTRACT FROM AUTHOR]

IoT techniques have transformative potential across domains, particularly in precision agriculture, where they enable farmers to optimize crop production while minimizing environmental impact. This paper presents an IoT-based real-time monitoring and control system for hydroponic tomato cultivation. The system integrates various hardware components, including sensors for monitoring temperature, humidity, pH levels, and gas concentrations. Arduino microcontrollers collect and process sensor data, enabling automated control of water, nutrient supply, and ventilation. Wi-Fi and GSM modules facilitate remote monitoring and data transmission, empowering farmers to monitor and control their tomato crops remotely. The system has been successfully tested for tomato cultivation, demonstrating promising results. Although tailored for tomatoes, its design principles can be adapted for other crops. Moreover, it shows significant water and nutrient efficiency, highlighting its economic feasibility. [ABSTRACT FROM AUTHOR]

Fog computing has emerged as an essential technology for enabling real-time, low-latency responses in cloud-based applications within Internet of Things (IoT) systems. This study explored the impact of the number of fog devices (NFDs) on the performance of IoT-fog-cloud systems. Through comparative analysis, two scheduling algorithms—First Come First Serve (FCFS) and Priority—were evaluated across different scales of system deployment. The results indicated that the FCFS algorithm was optimal for systems with fewer NFDs, whereas the Priority algorithm proved advantageous in larger settings. These findings not only establish guidelines for selecting appropriate scheduling strategies based on the scale of fog device deployment but also provide strategic insights for businesses on the optimal distribution of computational resources. This research aids companies in deciding whether to centralize resources in fewer branches with more employees or to decentralize into more branches with fewer employees, thereby optimizing operational efficiency and responsiveness. [ABSTRACT FROM AUTHOR]

This paper presents a monitoring, control, and management system that allows the interoperability of multiple smart gateways in a greenhouse to bolster automatic decision making on water supplied to the crops in real-time. The system consists of several sensor nodes interconnected by Zigbee to measure ambient and soil parameters to control the greenhouse irrigation process. In addition, a web service is included to help the system manager to visualize and operate the greenhouse on a web platform. The system also supports human decision making to cope with undesirable and unexpected environmental events. Smart gateways enable data reading, storage and edge computing using automatic rules to manage actions in the greenhouse, as well as data transmission for centralized processing in cloud services. The service communication system layer is supported by choreography middleware for messaging distribution, which enables agile scalability. This research proposes a hybrid scheme and compares to a centralized scheme. The latency times in the choreography system were analyzed to find a suitable solution adapted to infrastructure deployment features. Numerical results shows that such an adaptive deployment of the irrigation services among edge and fog layers can reduce the computational load on the gateways, improve system response times, and enhance device performance. [ABSTRACT FROM AUTHOR]

The Message Queuing Telemetry Transport (MQTT) protocol, widely used in various industries for Machine-to-Machine (M2M) communication, plays a central role in the IoT architecture. However, despite its widespread adoption, an analysis conducted on the shodan search engine revealed up to 540531 vulnerabilities worldwide as of April 2024, underscoring the security challenges facing this protocol. How can an innovative cyber range system be designed to assess and improve the security of MQTT protocols, focusing on in-depth vulnerability analysis, authentication strategy development, and targeted countermeasures against specific attacks? The study includes an analysis of relevant articles, followed by classification into five different categories. Furthermore, the presentation of a cyber range that exposes various attacks, such as replay attacks, Man-In-The-Middle (MITM), Denial of Service (DoS)/Distributed DoS(DDoS), and cam-hackers, enhances the understanding of MQTT protocol vulnerabilities, emphasizes the critical importance of strengthening security, and serves as a reference for new researchers in the field. [ABSTRACT FROM AUTHOR]

Electronic learning (e-learning) has become one of the most influential trends in providing enhanced and easily accessible learning to users. However, in contrast to traditional e-learning systems that offer uniform content to all learners, personalized learning systems tailor educational materials and assessments to individual learners, ensuring a customized learning experience. Personalizing e-learning through artificial intelligence (AI) is beneficial in today’s diverse environment, where beginners can tailor the learning experience to suit their preferences and requirements. This paper focuses on the personalization of e-learning, exploring future trends, opportunities, and challenges through an in-depth survey of recent literature on the personalization of e-learning technologies such as extended reality (XR), virtual reality (VR), augmented reality (AR), and mixed reality (MR). Findings identify and propose that such tools should be included in curricula that support e-learning through personalized, adaptive, and delivery models. [ABSTRACT FROM AUTHOR]

The Society 5.0 era emphasizes the use of technology and innovation to address social challenges, such as the utilization of Internet of Things (IoT) technology. Creative thinking plays a vital role in generating new ideas and approaches to problem-solving. Science, technology, engineering, and mathematics (STEM)-based education can help individuals develop creative thinking skills by engaging in problem-solving and creating innovative solutions. This study aims to enhance the creative thinking abilities of junior high school students on the topic of temperature through the development of STEM-based instructional media. This is achieved by creating the fire detector system (FDS) educational kit and testing its validity and practicality in the learning process. The study model used in this study is the analysis, design, development, implementation, and evaluation (ADDIE) model. The ADDIE model consists of five stages: analysis, design, development, implementation, and evaluation. The research instruments utilized include structured questionnaires for expert validation and application testing. The participants involved in this study include several seventh-grade students and one science teacher from a junior high school. The collected data is then analyzed using qualitative and quantitative descriptive data analysis techniques. By utilizing the FDS educational kit, it is hoped that it can effectively foster creative thinking skills among junior high school students. [ABSTRACT FROM AUTHOR]

This study delves into the adoption of Internet of Things (IoT) technology in the agriculture industry, which has the potential to revolutionize resource allocation and operational efficiency and meet the growing global demand for food and water. Despite its potential, the adoption of IoT technology in agriculture remains generally low. Hence, this research aims to explore the motivating factors influencing IoT adoption, assess the impacts of adoption on agriculturists, and identify the challenges that need to be addressed to promote future IoT adoption in the agricultural sector. Semi-structured interviews with Norwegian agriculturists who have adopted IoT technologies were conducted using an exploratory qualitative approach. Our findings reveal several key factors that influence IoT adoption, including the availability of technical support, governmental policies, and the intentions of agriculturists' children to carry on farming traditions. The adoption of IoT has led to improved decision-making, operational efficiency, and control for agriculturists, although they may not be fully aware of all the consequences associated with adoption. Additionally, the study highlights a lack of support and knowledge among industry stakeholders, which could hinder the diffusion of IoT adoption in agriculture. These findings contribute to our understanding of the complexities surrounding IoT adoption in agriculture and provide insights for policymakers, industry stakeholders, and agriculturists seeking to maximize the benefits of IoT technology. Addressing the challenges identified, such as enhancing technical support systems, developing supportive policies, and increasing awareness among agriculturists, can foster a conducive environment for greater IoT adoption in agriculture in Norway. [ABSTRACT FROM AUTHOR]

This paper presents the realization and assessment of a compact metamaterial (MTM)-integrated wireless information and power transfer (WIPT) system. Developed WIPT is tailored to facilitate numerous Internet of Things (IoT) sensors operating at the low-power regime. Generally, WIPT is accomplished with a system possessing two operating bands. Therefore, the dual-band system working at frequencies of 405 MHz and 900 MHz was proposed. WIPT is achieved by coupling two defected ground structure-based resonators that are designed by cascading two single-band resonating structures. Then, MTM with the double negative characteristics at WIPT's operating frequencies was incorporated, which led to the performance enhancement. The power transfer efficiency of WIPT was increased by 14% and 7% at 405 MHz and 900 MHz, respectively, when the resonators were coupled at 20 mm. It is worth mentioning that WIPT and MTM share the same area of 25 × 25 mm2 to keep the compactness of the overall system. The WIPT system was evaluated in terms of charging time for two low-power IoT sensors. The powering time was approximated considering two scenarios: using only one band and two bands simultaneously. The provided analysis in both cases reveals that MTM-integrated WIPT aids in the significant reduction of a charging cycle. [ABSTRACT FROM AUTHOR]

This paper shows an agricultural irrigation alternative using a low-cost Internet of Things (IoT) application. This technology allows the automation and optimization of processes by monitoring, processing, and analyzing large volumes of related data, including variables related to the soil (moisture), fertilization, and irrigation, which can be visualized through the internet to enable sustainable and efficient agriculture. The study is centered on the Los Angeles, Biobío, Chile case. The main objective of the prototype is to optimize water consumption for agricultural irrigation by using only the amount that is strictly necessary for each crop by allowing real-time visualization of irrigation status, moisture data, and irrigation times while providing a history of data that can be used for further analysis. [ABSTRACT FROM AUTHOR]

In the dynamic landscape of technology, the intersection of Smart Buildings and Internet of Things (IoT) devices continues to hold substantial promise. The integration of billions of smart devices with computing systems empowers the identification, analysis, and influence of various actions in our daily lives. A prevalent approach in IoT implementation involves leveraging Android systems, where Android applications play a pivotal role in front-end management. This paper introduces an innovative Android application designed for the comprehensive management of smart buildings, providing users with real-time data monitoring and control capabilities. The application serves as a bridge, bringing users closer to the operational intricacies of smart building systems. Notably, the application is equipped to send timely alerts when predefined limits for humidity, temperature, or light consumption are surpassed, as well as during specific time periods with detected motion. These alerts prompt users to make informed adjustments and decisions, enhancing overall user engagement and responsiveness. The proposed system architecture comprises five distinct layers: Python scripts for sensor data generation, JSON files for data storage, a Flask-based Application Programming Interface (API) for data analysis and conversion, SQLite for efficient data storage, and the Android application for user interaction. This multi-layered framework ensures seamless integration and robust functionality throughout the smart building management process. [ABSTRACT FROM AUTHOR]

The integration of the Internet of Medical Things (IoMT) and the Internet of Things (IoT), which has revolutionized patient care through features like remote critical care and real-time therapy, is examined in this study in response to the changing healthcare landscape. Even with these improvements, security threats are associated with the increased connectivity of medical equipment, which calls for a thorough assessment. With a primary focus on addressing security and performance enhancement challenges, the research classifies current IoT communication devices, examines their applications in IoMT, and investigates important aspects of IoMT devices in healthcare. The evaluation extends so far as to examine an IoMT-based system in the healthcare space, highlighting limitations in Industry 5.0 and healthcare institutions. IoMT devices are systematically categorized according to how well they can strengthen infrastructures; this process exposes operational flaws, gaps in the literature, and real-world risks. The importance of this research is in its thorough analysis of the security and operational facets of IoMT devices, with the explicit objective of promoting secure and efficient medical practices. [ABSTRACT FROM AUTHOR]

This paper describes the use of Internet of Things (IoT) technologies, digital twins (DT), and augmented reality (AR) to raise awareness and disseminate the use of digital services within the INICTEL-UNI institutional project financed by the Inter-American Development Bank to strengthen technological services and satisfy the technological needs of companies, promoting digital transformation in Peru. Within various fields, such as technical education, construction, and manufacturing, challenges are faced related to the adoption of advanced technologies and the need to improve efficiency. The main objective of this paper is to implement an IoT control and visualization system with DT and AR in a digital transformation space. A system is shown to create a technological demonstrator environment that visualizes and monitors sensor data on physical IoT devices in real time, allowing users to interact and operate them through an ESP32 module with data transmission with the MQTT protocol and an AR application developed in Unity and Vuforia. The study results successfully demonstrated the efficiency of real-time communication between the IoT device and the AR application, as well as the efficient ability to perform tasks, validated by users with no prior experience. [ABSTRACT FROM AUTHOR]

Within the realm of sustainable agriculture and the protection of crops, the presence of intruders poses a substantial risk, leading to potential crop damages and injuries. In our paper, we conceptualize intruders as objects. Consequently, the implementation of object detection emerges as a crucial safety and field protection measure, effectively mitigating the adverse consequences of such encounters. While deep learning techniques have proven to yield superior results in object detection, their computational and parameter requirements have posed challenges in their widespread implementation. This research paper presents an alert system AgriGuard equipped with a lightweight object detection model for enhancing security in agriculture fields. The proposed embedded system utilizes EmbdYOLOv3 and TinyEmbdYOLOv3 models, modified versions of YOLOv3 and TinyYOLOv3, respectively, to overcome challenges in object detection, such as occluded or look-alike animals. The system integrates ultrasonic sensors, raspberry pi, real-time object detectors, google firebase, and an android application to detect and alert farmers for unauthorized access. Experimental results show that EmbdYOLOv3 outperforms YOLOv3 by 36.85%, and TinyEmbdYOLOv3 outperforms Tiny-YOLOv3 by 14.48% under the same embedded environment. The study highlights the effectiveness of low-power IoT devices and deep learning techniques in providing robust security solutions for the agriculture industry, offering a potential approach for mitigating crop damages and ensuring field safety. [ABSTRACT FROM AUTHOR]

Big data and its analysis are crucial for other applications like the Internet of Things (IoT), data mining, cloud computing, etc. The volume of data is expanding along with the internet and other digital technologies. Traditional systems, however, cannot process this volume of data. Analyzing this much data will take a lot of work. However, there are several difficulties in assuring data security and privacy, such as safeguarding against data leakage and security breaches, flaws in open databases, and third-party data sharing, among others. This paper's goal is to analyze the benefits of blockchain in big data as well as its drawbacks and identify potential future approaches. [ABSTRACT FROM AUTHOR]

The latest effort in delivering computing resources as a service to managers and consumers represents a shift away from computing as a product that is purchased, to computing as a service that is delivered to users over the internet from large-scale data centers. However, with the advent of the cloud-based IoT and artificial intelligence (AI), which are advancing customer experience automations in many application areas, such as recommender systems (RS), a need has arisen for various modifications to support the IoT devices that are at the center of the automation world, including recent language models like ChatGPT and Bard and technologies like nanotechnology. This paper introduces the marketing community to a recent computing development: IoT-driven fog computing (FC). Although numerous research studies have been published on FC "smart" applications, none hitherto have been conducted on fog-based smart marketing domains such as recommender systems. FC is considered a novel computational system, which can mitigate latency and improve bandwidth utilization for autonomous consumer behavior applications requiring real-time data-driven decision making. This paper provides a conceptual framework for studying the effects of fog computing on consumer behavior, with the goal of stimulating future research by using, as an example, the intersection of FC and RS. Indeed, our conceptualization of the "fog-based recommender systems" opens many novel and challenging avenues for academic research, some of which are highlighted in the later part of this paper. [ABSTRACT FROM AUTHOR]

The Internet of Things (IoT) was born from the fusion of virtual and physical space and became the initiator of many scientific fields. Economic sustainability is the key to further development and progress. To keep up with the changes, it is necessary to adapt economic models and concepts to meet the requirements of future smart environments. Today, the need for electronic commerce (e-commerce) has become an economic priority during the transition between Industry 4.0 and Industry 5.0. Unlike mass production in Industry 4.0, customized production in Industry 5.0 should gain additional benefits in vertical management and decision-making concepts. The authors' research is focused on e-commerce in a three-layer vertical IoT environment. The vertical IoT concept is composed of edge, fog, and cloud layers. Given the ubiquity of artificial intelligence in data processing, economic analysis, and predictions, this paper presents a few state-of-the-art machine learning (ML) algorithms facilitating the transition from a flat to a vertical e-commerce concept. The authors also propose hands-on ML algorithms for a few e-commerce types: consumer–consumer and consumer–company–consumer relationships. These algorithms are mainly composed of convolutional neural networks (CNNs), natural language understanding (NLU), sequential pattern mining (SPM), reinforcement learning (RL for agent training), algorithms for clicking on the item prediction, consumer behavior learning, etc. All presented concepts, algorithms, and models are described in detail. [ABSTRACT FROM AUTHOR]

Morocco has implemented circular economy principles in their waste management program to close or rehabilitate all existing landfills. However, the waste collection and transportation process remain highly inefficient and costly, contributing to a substantial environmental footprint for waste collection operations. The inefficiencies in the current waste management system highlight the need for innovative solutions to optimize collection processes and minimize environmental impact. This study responds to this imperative by proposing the use of smart bin technology combined with a vehicle routing problem tool. The integration of these technologies aims to develop an intelligent solution for appropriate waste collection, limiting container pickup only when necessary, and optimizing waste collection routes. Our approach considers several factors, including the fill level of each container, time windows, vehicle capacity, and tour completion constraints. By addressing these key factors, the system seeks to revolutionize waste management practices, improving the efficiency and effectiveness of waste collection. [ABSTRACT FROM AUTHOR]

In recent years, the number of devices being connected to the internet has grown enormously, as has the intrusive behavior in the network. Thus, it is important for intrusion detection systems to report all intrusive behavior. Using deep learning and machine learning algorithms, intrusion detection systems are able to perform well in identifying attacks. However, the concern with these deep learning algorithms is their inability to identify a suitable network based on traffic volume, which requires manual changing of hyperparameters, which consumes a lot of time and effort. So, to address this, this paper offers a solution using the extended compact genetic algorithm for the automatic tuning of the hyperparameters. The novelty in this work comes in the form of modeling the problem of identifying attacks as a multi-objective optimization problem and the usage of linkage learning for solving the optimization problem. The solution is obtained using the feature map-based Convolutional Neural Network that gets encoded into genes, and using the extended compact genetic algorithm the model is optimized for the detection accuracy and latency. The CIC-IDS-2017 and 2018 datasets are used to verify the hypothesis, and the most recent analysis yielded a substantial F1 score of 99.23%. Response time, CPU, and memory consumption evaluations are done to demonstrate the suitability of this model in a fog environment. [ABSTRACT FROM AUTHOR]

The Internet of Things (IoT) is expanding the capabilities of traditional vehicular ad-hoc networks (VANET) into the Internet of Vehicles (IoV). However, there are a few challenges that need to be addressed in order to enhance the intelligence of IoV, leading to the development of a new evolving technology called Cognitive Internet of Vehicles (CIoV). In this study, we propose a road-aware infrastructure-assisted and vehicle-to-vehicle (V2V) adaptive routing system to select the most efficient route for delivering data from the source to the destination vehicle, with the aim of reducing data delivery delay. The performance is measured and evaluated based on packet delivery ratio (PDR), average end-to-end (E2E) delay, and normalised routing overhead using MATLAB. By comparing the proposed mobile adaptive routing algorithm (MARA) with existing protocols, it has been examined and found to outperform the existing ones in terms of performance. [ABSTRACT FROM AUTHOR]

A smart city represents an advanced urban environment that utilizes digital technologies to improve the well-being of residents, efficiently manage urban operations, and prioritize long-term sustainability. These technologically advanced cities collect significant data through various Internet of Things (IoT) sensors, highlighting the crucial importance of detecting anomalies to ensure both efficient operation and security. However, real-time identification of anomalies presents challenges due to the sheer volume, rapidity, and diversity of the data streams. This manuscript introduces an innovative framework designed for the immediate detection of anomalies within extensive IoT sensor data in the context of a smart city. Our proposed approach integrates a combination of unsupervised machine learning techniques, statistical analysis, and expert feature engineering to achieve real-time anomaly detection. Through an empirical assessment of a practical dataset obtained from a smart city environment, we demonstrate that our model outperforms established techniques for anomaly detection. [ABSTRACT FROM AUTHOR]

Because of the recent development of various intrusion detection systems (IDS), which defend computer networks from security as well as privacy threats. The confidentiality, integrity and also availability of data may be compromised in the case that IDS prevention efforts fail. The amount of private, delicate and crucial data travelling over the worldwide network has expanded tremendously as a result of the recent development of Internet of Things (IoT) devices. Developing a better edge-based feature selection strategy, a deep learning technique for identifying and blocking malicious traffic, is the goal of intrusion detection. The classification method Evaluated Bird Swarm Optimization based Deep Belief Network (EBSO-DBN) has shown to be the most successful in this study. A variation of performance criteria have been used to critically assess deep learning techniques for IDS (accuracy, precision, recall, f-1 score, false alarm rate and detection rate). To ascertain the optimal performance of IDS models, this study focuses on building an ensemble classifier utilizing the suggested EBSO-DBN classification algorithm with 98.7% of accuracy, 99.4% of precision and 98.8% of recall. [ABSTRACT FROM AUTHOR]

Sustainable Development Goals (SDGs) by the United Nations have Zero Hunger and Responsible Consumption and Production as the two significant agriculture-related components. Conventional irrigation control systems, relying heavily on manual labour, exhibit shortcomings in minimizing farmer input dependency. Recognizing the critical interdependence of water resource management, crop yield optimization, and environmental preservation, this work contends that precision in water management is essential. While efforts to reuse treated wastewater offer potential water conservation solutions, they often compromise crop yield. Precisely calculating water requirements can enhance crop yield, reduce human involvement, eliminate potential decision-making errors, and address water conservation issues. Automating tasks like irrigation demands comprehensive knowledge of crop and soil characteristics, climate conditions, time factors, and physical parameters like temperature, soil moisture, and humidity. This research addresses the limitations of current agricultural practices by advocating for a paradigm shift towards sustainable farming through the integration of Artificial Intelligence of Things (AIoT). The proposed framework leverages the fusion of the Artificial Intelligence (AI) and Internet of Things (IoT) to automate irrigation by employing a comprehensive analysis of diverse parameters. Through empirical evaluation, this work demonstrates that AIoT-based precision water management not only improves crop yield but also reduces human intervention, addressing water conservation challenges, and fostering sustainable agriculture practices. [ABSTRACT FROM AUTHOR]

This research work explores the growing role of IoT devices in daily life and the increasing demand for secure data transmission in different sectors like healthcare, military, etc. Moreover, this study focuses on the recent advancements in lightweight cryptography and compares various encryption algorithms in an IoT environment using real-time data. Employing Message Queuing Telemetric Transport (MQTT) protocol, this research work assess the time of encryption, memory usage, and CPU usage as key parameters. By standardizing iterations, key width, and block length, this study aims to identify optimal encryption solutions for IoT applications. [ABSTRACT FROM AUTHOR]