Optimizing Water Resources With IoT and ML

Saudi Arabia's agriculture heavily depends on effective water management, given its limited freshwater resources and arid climate. Real-time monitoring of soil moisture levels, weather conditions, and crop watering needs, facilitated by IoT integration, plays a crucial role in conserving water and minimizing waste. The resultant improvements in crop yields and quality are essential for the long-term success of agriculture in the country. This study employs the Technique for Order of Preference by Similarity to the Ideal Solution (TOPSIS) method to investigate the transformative potential of the Internet of Things (IoT) in enhancing water management practices in Saudi Arabia's agriculture sector. The research begins by highlighting the significance of water management in agriculture, emphasizing the proportion of land in Saudi Arabia allocated to agricultural purposes. The problem statement underscores the pressing challenges in water management, encompassing issues such as water scarcity, inefficient irrigation methods, and the need for real-time data to inform decision-making. To address these challenges, the study proposes an IoT-based Agricultural Water Management System (IoT-AWMS) that leverages sensors, real-time data analytics, and machine learning algorithms. This system is designed to optimize water utilization in agriculture. Simulations conducted within the study demonstrate a significant enhancement in water usage efficiency, resulting in reduced water wastage and increased crop yields. In conclusion, this research underscores the critical importance of the proposed IoT-based water management system for Saudi Arabia. It is positioned as a valuable tool for mitigating water scarcity challenges and promoting environmentally sustainable agricultural practices in the country.

Water optimization technique for precision irrigation system using IoT and machine learning

In the 21st century, Urbanization, population growth, and climate change have created significant problems in water resource management. Recent advancements in technologies such as Internet of Things (IoT), Edge Computing (EC), Artificial Intelligence (AI), and Big Data Analytics (BDA) are changing the operations of the water resource management systems. In this study, we present a systematic review, highlighting the contributions of these technologies in water management systems. More specifically, we highlight the IoT and EC water monitoring systems that enable real-time sensing of water quality and consumption. In addition, AI methods for anomaly detection and predictive maintenance are reviewed, focusing on water demand forecasting. BDA methods are also discussed, highlighting their ability to integrate data from different data sources, such as sensors and historical data. Additionally, a discussion is provided of how Water management systems could enhance sustainability, resilience, and efficiency by combining big data, IoT, EC, and AI. Lastly, future directions are outlined regarding how state-of-the-art technologies may further support efficient water resources management.

Internet of Medical Things (IoMT) envisions the interconnection of healthcare devices and sensors to enable a new class of applications relying on medical data processing and storage. Moreover, machine learning and deep learning algorithms applied in healthcare domain allow health professionals to monitor, diagnose, focus and highlight the region of the problem and propose the required and accurate solution in the shortest duration possible. This paper surveys the state of art machine and deep learning algorithms in IoT healthcare.

This paper provides an in-depth analysis of the market status and technical implementation of intelligent water management systems in China. Firstly, the market advantages of smart water systems in improving water supply efficiency, reducing energy consumption, and reducing water waste are pointed out, and the role of policy support and new technologies such as Internet of Things and big data analytics in driving market growth is introduced. Secondly, through the data of the Bureau of Statistics and the chart of market size, this paper expounds the trend of continuous expansion of market size in China's water industry and the growth power of market demand behind it. In the industry analysis section, the main types of enterprises in the smart water industry and their product characteristics are listed, with special emphasis on the advantages of smart water management systems in improving the efficiency of water resource utilization and management. In the section of technology implementation cases, the successful cases of Quanzhijie Intelligent water management system in campus application are described in detail, including the use of Internet of Things technology to achieve the interconnection of facilities and equipment, real-time monitoring and analysis of water resources use dynamics, automatic control and intelligent scheduling and other functions. In summary, this paper comprehensively evaluates the development trend, technical implementation and future development potential of smart water management systems in the current market environment.

Today’s Machine Learning (ML) in a blend with Internet of Things (IoT)-based cloud applications plays a significant role in our everyday life. As indicated by Gartner’s recent study, there are around 25 billion devices and a gadget interfacing with IoT including wearables and automated vehicles to smart homes and smart cities applications. All such connected (smart) devices generate immense data that needs to be examined and analysed, to ensure that they continually learn from the available datasets and better themselves without any manual interference. This is where the prerequisite for machine learning comes into being. Several ML algorithms and techniques are introduced in a short time to easily evaluate big data measurements, increasing the IoT’s productivity. Similarly, special ML techniques, such as decision trees, clustering and neural and Bayesian networks, allow devices and gadgets to discern trends from various sources in different kinds of datasets and take appropriate decisions based on their analysis.It would really be difficult for smart devices to make smart decisions gradually without including and enforcing ML. The IoT helps to interconnect various hardware devices, such as houses, cars, electronic gadgets and other devices that are integrated with actuators, sensors and software, so that data can be collected and shared. As various organizations understand the progressive capability of the IoT, they have begun finding various obstructions they have to deliver to use it productively. Numerous organizations and businesses use ML to exploit the IoT’s latent capacity. This chapter evaluates the different methods of machine learning that deal with the challenges posed in the handling of IoT data. Note that this big data is generated through the communication of Internet of Things/smart devices, and this data stored at cloud. The taxonomy of machine learning algorithms is described in this chapter, explaining how different techniques are applied to data generated using IoT devices. It will also address the future problems of machine learning for IoT data analytics.KeywordsInternet of Things (IoT)-based cloud applicationsMachine learning techniquesArtificial intelligenceClassificationSmart era

The IoMT is nothing but a technology phase breakthrough in the healthcare industry that uses wearables, and other low-level digital data collection devices for better patient care and operational efficiency. Through this research, we seek to bring together the opportunities and security threats related to IoMT with a special focus on machine learning algorithms as a predictive tool for health assessment. Empirical experiments were conducted to determine the performance of four machine learning techniques (Random Forest, Support Vector Machine, K-Nearest Neighbors, and Deep Learning Neural Network) using different set of data covering medical sensor data and patient's health records. The prediction results show that the DNN-based model had higher accuracy than other algorithms, its deep learning producing the right outcomes of 0. 88, precision of 0. 89, recall of 0. With more than eighty-seventy five percent recall rate and F1-score of 0. 88. The comparison will enlighten the advantages and shortcomings of various algorithms in using the IoMT data to predict the health outcomes, showing the prospect of the DNN as one of the better algorithms in the space. Along with this, this study brings to the foreground the significance of focusing on strong cybersecurity measures to ensure that patients’ data remains protected and IoMT ecosystems are free from flaws.

Monitoring water quality metrics of ponds with IoT sensors and machine learning to predict fish species survival

The convergence of Supervisory Control and Data Acquisition (SCADA) systems with the Internet of Things (IoT) and Machine Learning (ML) is redefining automation, security, and operational efficiency across industries. Traditional SCADA systems, widely used in critical infrastructure such as energy, water management, and industrial processes, are undergoing a transformative shift with the integration of IoT. IoT-enabled sensors and devices provide real-time data streams from diverse operational environments, enabling SCADA systems to achieve enhanced situational awareness and remote monitoring capabilities. Machine Learning further augments SCADA systems by introducing advanced analytics for predictive maintenance and anomaly detection. ML algorithms analyse vast datasets collected through IoT devices to forecast system failures, optimize resource utilization, and ensure uninterrupted operations. This predictive approach minimizes downtime, reduces maintenance costs, and improves overall efficiency. Cloud-driven security frameworks are pivotal in addressing the growing cybersecurity challenges associated with SCADA and IoT integration. These frameworks provide scalable and resilient solutions for protecting data integrity, preventing unauthorized access, and mitigating cyber threats. The deployment of ML-driven cybersecurity solutions enhances the ability to detect and respond to sophisticated attacks targeting SCADA environments. This paper examines the evolution of SCADA systems in the era of IoT, focusing on the transformative role of ML and cloud-driven security. It explores innovative applications, highlights the challenges of integrating these technologies, and provides insights into future advancements for creating robust and secure automated systems.

Background: The rapidly expanding concept, the Internet of Things (IoT), connects a large number of devices to the internet for effective real-time data sharing and communication. But as IoT technology advances, new security risks also arise, and for this reason, machine learning (ML)-based intrusion detection systems (IDS), particularly anomaly-based ones, have emerged as a crucial defence against these new dangers to IoT networks. Objective: The primary goal of this study is to develop and assess anomaly-based IDS based on ML techniques for use in Internet of Things environments. The goal is to improve the models' performance in terms of accuracy and usefulness. Methods: The first phase of the study is a thorough review of the recent studies in the field to support the model design. The steps of the experimental stage include data preprocessing, encoding, and normalization. Data balancing was achieved using the SMOTEENN technique. The experiments and validation studies were performed on the UNSW-NB15 dataset. The employed ML algorithms for the study include DT, DNN, RF, XGBoost, and KNN, and the performance was in terms of precision, recall, accuracy, and F1-score. Results: XGBoost recorded the best detection accuracy (96.37%), followed by RF and DNN. The models were evaluated on both normal and attack traffic, and the suggested model outperformed many existing approaches in recent literature. Conclusion: The experimental findings confirm the suitability of hybrid and ensemble ML models in improving intrusion detection performance in IoT systems. Future research should consider integrating real-time datasets and combining deep learning with ensemble methods to develop more robust and adaptive models.

The term Internet of Things (IoT) refers to a technological revolution that allows nearly all environmental devices to connect to the internet and share data to create services and applications that improve our standard of living. As IoT systems generate huge amounts of data, new IoT applications and facilities arise. Machine learning has made significant progress in several fields and is now being used to solve a variety of IoT network problems. Machine learning has recently become more popular as a means of improving IoT applications. Users can find deep analytics and develop efficient intelligent IoT applications using machine learning for IoT. This chapter focuses on offering an overview of machine learning techniques in the IoT platform. The chapter begins with an overview of the IoT, including its history, architecture, and its applications. Then, many machine learning algorithms, various types, and their applications, with examples, are discussed.

The technology boom in Industry 4.0 has resulted in the generation of large volume of medical data. Smart healthcare systems have burgeoned with the convergence of blockchain, machine learning, and internet of things (IoT). The amalgamation of privacy-preserving blockchain technology and predictive diagnosis by machine learning models produces an enhanced healthcare system. This chapter aims to shed light on the machine learning algorithms and blockchain technologies currently used in the healthcare industry. The advantages of opting for blockchain for preserving data integrity and privacy are highlighted. A few applications and steps involved in implementing blockchain and machine learning in healthcare, as well as the challenges faced while doing so, are discussed. Finally, a theoretical blockchain-machine learning internet of medical things(IoMT) framework is proposed, drawing from the recent research advancements.

This paper examines the enhancement of security measures for the Internet of Things (IoT) systems through the application of Machine Learning (ML) techniques. As the number of IoT devices continues to rise, ensuring their security has become increasingly critical, given that conventional methods frequently struggle to identify advanced threats. This study explores the implementation of several ML algorithms, including Random Forest (RF), Decision Trees (DT), Support Vector Machines (SVM), and Convolutional Neural Networks (CNN), to identify anomalies and intrusions within IoT networks. By conducting a comprehensive review of existing research and experiments, it highlights the effectiveness of ML in enhancing IoT security, with high detection rates for various threats, including botnet attacks, Denial-of-Service (DoS) and Distributed Denial-of-Service (DDoS) incidents, and intrusion attempts. DoS/DDoS attacks and many types of botnets are the most devastating attacks that have been spreading for a long time, and they are still branching out in new ways against IoT networks. They can damage IoT services and prevent these services from being used by legitimate users. Therefore, securing IoT networks becomes a significant concern. The proposed model is used to increasingly monitor network traffic for any deviations from standard patterns IoT networks. This paper also stresses the necessity of utilising suitable datasets and feature selection techniques to enhance the efficacy of ML models. To train our model, we have utilized a dataset called the IoT23 dataset, which is one of the most recent datasets that has many IoT scenarios and anomalous activities. Further-more, we utilised two types of feature selection algorithms, the Correlation-based Feature Selection (CFS) algorithm and the Genetic Algorithm (GA), and then we compared the results of these algorithms when training our model. The best performances were obtained with DT and RF classifiers when they were trained with features selected by CFS However, for training and testing times metrics, DT performance was superior across both feature selection methods.

Key challenges for 5G and Beyond networks relate with the requirements for exceptionally low latency, high reliability, and extremely high data rates. The Ultra-Reliable Low Latency Communication (URLLC) use case is the trickiest to support and current research is focused on physical or MAC layer solutions, while proposals focused on the network layer using Machine Learning (ML) and Artificial Intelligence (AI) algorithms running on base stations and User Equipment (UE) or Internet of Things (IoT) devices are in early stages. In this paper, we describe the operation rationale of the most recent relevant ML algorithms and techniques, and we propose and validate ML algorithms running on both cells (base stations/gNBs) and UEs or IoT devices to handle URLLC service control. One ML algorithm runs on base stations to evaluate latency demands and offload traffic in case of need, while another lightweight algorithm runs on UEs and IoT devices to rank cells with the best URLLC service in real-time to indicate the best one cell for a UE or IoT device to camp. We show that the interplay of these algorithms leads to good service control and eventually optimal load allocation, under slow load mobility.

The Internet of Things (IoT) has transformed various sectors by enabling connectivity and automation through smart devices. However, the expansion of IoT networks has introduced new security challenges, particularly concerning the detection and prevention of cyber threats. This study explores the application of machine learning (ML) models for enhancing network security in IoT-enabled smart systems. The primary objective was to evaluate the performance of different ML algorithms, including Convolutional Neural Networks (CNN), Random Forest (RF), Support Vector Machine (SVM), Decision Tree (DT), K-Means, and Autoencoder, in detecting and mitigating security threats in IoT environments. The study also aimed to identify the most effective model in terms of accuracy, precision, recall, and F1-score. The research methodology involved training and testing these models on publicly available IoT security datasets, followed by performance evaluation based on the key metrics. Statistical analysis, including One-Way ANOVA and post-hoc testing, was used to assess the significance of the performance differences across the models. The results revealed that CNN outperformed all other models, achieving the highest accuracy (96.7%), precision (95.8%), recall (97.2%), and F1-score (96.5%). Random Forest also demonstrated strong performance with an accuracy of 95.6% and a balanced evaluation across all metrics. In contrast, traditional models like SVM and DT showed comparatively lower performance, while Autoencoder struggled with classification tasks, particularly in real-time threat detection. Statistical analysis confirmed the significant differences in model performance. In conclusion, the study demonstrates the effectiveness of deep learning and ensemble models, particularly CNN and Random Forest, in enhancing IoT network security. The research highlights the importance of selecting the appropriate ML model based on the specific requirements of the IoT environment and computational constraints, and suggests that hybrid models and model optimization strategies may offer promising solutions for resource-constrained IoT systems.