Study on Using Signal Filtering Techniques for Machine Learning-based Indoor Positioning Systems(IPS)

The purpose of this paper is to explore the role of Machine Learning (ML) in fortifying the security of cloudbased Internet of Things (IoT) systems, using a comprehensive security management approach. The methodological approach involved comparing different ML techniques such as Decision Trees, Random Forest, Support Vector Machines, and Convolutional Neural Networks. Their effectiveness was evaluated based on the accuracy of threat detection in cloud-based IoT systems. The findings revealed that Convolutional Neural Networks demonstrated the highest accuracy rate (98%) in threat detection, thereby significantly enhancing the security of IoT systems. It also identified improvements in threat detection, prevention, response, and system recovery across all ML techniques. Research limitations were primarily the rapidly evolving nature of both ML and IoT technologies, necessitating continual reassessments. The scope was also limited to cloud-based IoT systems, leaving room for further research on other types of IoT systems. The practical implications included improved system security, which could lead to increased trust and wider adoption of IoT technology in various sectors, from healthcare to home security. The social implications entail a safer digital environment, contributing to data privacy and reducing the risk of cyber threats for individuals and communities. The originality of this paper lies in its comprehensive approach to IoT security management using ML, providing valuable insights into the effectiveness of different ML techniques in enhancing threat detection accuracy.

The growth of the Internet of Things (IoT) and smart systems has paved the way for the integration of machine learning (ML) and optimization techniques to enhance their efficiency, security, and scalability. This survey provides a comprehensive overview of recent advancements in applying machine learning algorithms and optimization strategies to IoT and smart systems. It highlights key areas where these technologies intersect, such as energy efficiency, intelligent decision-making, resource management, and anomaly detection. The paper discusses various machine learning approaches, including deep learning, reinforcement learning, and supervised/unsupervised models, that have been applied to optimize system performance in real-time applications. Furthermore, the survey examines optimization techniques like metaheuristics, evolutionary algorithms, and gradient-based methods that improve IoT systems' reliability and resource utilization. The integration of these techniques offers significant advantages, including reduced latency, enhanced security, and increased adaptability in smart systems. The findings emphasize the importance of adopting ML and optimization methods to address the challenges posed by the growing complexity of IoT environments, fostering future innovations in connected and autonomous systems. This survey serves as a resource for researchers and practitioners seeking to explore the synergies between IoT, ML, and optimization techniques.

A comparative analysis of various machine learning methods for anomaly detection in cyber attacks on IoT networks

Time series modeling and forecasting is an indispensable field of supervised machine learning (ML) because of its esteemed influences on several research works and real-life applications involving companies, industries, science, and engineering. Consequently, significant contributions were devoted to the development of proficient extrapolative models. On the other hand, the Internet of Things (IoT) has enhanced the surveillance of various environmental sensations, such as air pollution, through a wireless sensor network (WSN). This chapter presents an inclusive time-series predictive model that uses supervised ML techniques and the data gathered from IoT devices. The aim is to develop an artificial intelligence-IoT (AIoT) time series analytical using IoT and ML techniques in an automated and intelligent air quality-control system. A comprehensive framework of the predictive system displaying internal subsystems and modules is summarized to form a roadmap for AIoT time series model designers. This framework includes discussing alternatives and datasets for air quality data gases like the carbon monoxide (CO) collection for IoT sensors such as MQ-2. Experiments study will be conducted and reported to support the theoretical assumptions and presentation.

SummaryThe Internet of Things (IoT) continues to expand the current Internet, opening the door to a wide range of novel applications. The increasing volume of the IoT requires effective strategies to overcome its challenges. Machine Learning (ML) has led to a growing technology that enables computers to solve problems without the need for knowledge of their intricate details. Over the past years, various ML techniques have been used to efficiently manage IoT networks. Clustering is a technique that has proven its performance in the networking domain. Many works in the literature have studied ML‐based clustering methods for IoT networks, including their main properties, characteristics, underlying technologies, and open issues. In this paper, we focus on topology‐centered ML‐based clustering protocols for IoT networks. Specifically, we investigate the potential benefits of adopting the clustering approach to address several IoT challenges. Moreover, we provide a comprehensive taxonomy of ML‐based clustering algorithms for IoT networks. Finally, we statistically analyze the incorporation of ML techniques for clustering in various IoT systems and highlight the related open issues.

Internet of Things (IoT) systems are rightly receiving considerable interest for many real-world applications, from in-body networks to satellite networks. Such a massive-scale system generates a considerable amount of traffic data, making IoT systems a distributed data source generator. For many reasons, such as the functionality of IoT applications and Quality of Service (QoS) provisioning, classifying these traffic data is of high importance. In the last few years, widespread interest has been expressed in applying Machine Learning (ML)-based techniques for Network Traffic Classification (NTC) tasks. However, the traditional centralized learning-based traffic classifiers pose serious challenges, especially in IoT networks. The centralized ML techniques call for collecting a large amount of data from various IoT devices, which in turn introduces data governance and privacy challenges. Furthermore, in the centralized ML, training data need to be transferred to the Cloud, which increases communication cost and latency. To address these problems, we propose Federated Learning (FL) Internet of Things (IoT) Traffic Classifier (FLITC)-a Federated Learning (FL)-based IoT traffic classification method which is based on the Multi-Layer Perception (MLP) neural network and holds the local data unimpaired on IoT devices by sending only the learned parameters to the aggregation server. Our experimental results show that the FLITC beats centralized learning in preserving the privacy of sensitive data and offers a better degree of accuracy at the cost of a longer training time.

Since GPS (Global Positioning System) cannot meet accuracy requirements indoors, indoor Location-Based Services (LBSs) have become increasingly important. BLE (Bluetooth Low Energy) offers cost and accuracy advantages. Typically, the position fingerprinting method is used for indoor positioning. However, due to irregular reflection and absorption, the indoor environment introduces various offsets in Bluetooth RSSI (Received Signal Strength Indicator). This study analyzed the RSSI space and proposed a pre-processing workflow to improve position estimation accuracy by correcting offsets in RSSI space for BLE fingerprinting methods using machine learning. Experiments performed using different position estimation methods showed that the corrected data achieved a 6% improvement over the filter-only result. This study also evaluated the effects of different pre-processing and post-processing filters on positioning accuracy. Experiments were also conducted using a published dataset and showed similar results.

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.

With the rapid development of Internet of Things (IoT) and the use of smart devices and social networks in our daily lives, applications-based on IoT are growing exponentially in many fields such industries, business and daily life activities. The IoT technology brings a lot of promise for humanity by improving life quality and comfort and by strengthening human bonds, among others. In the next few years, billions of connected devices will be spread across smart homes, vehicles, cities, and industries. Such connected devices, with restricted resources, will interchange with users and the surrounding environment. In this context, Machine Learning (ML), Which is able to provide embedded intelligence in the IoT devices and networks, can be leveraged to decode the meaning and behavior behind the device’s data, implement accurate predictions, and make decisions for several tasks. In this chapter, we present an overview of research works about ML-base IoT systems in different areas of applications. First, we present a deep overview of IoT’s technology. Then, we highlight the most fundamental concepts of ML categories and algorithms. After that, we shed light on the ML-based IoT critical challenges and provide some potential future research directions. Eventually, we present an IoT-based ML technique scenario for smart irrigation in Agriculture 4.0.

Indoor localization has gained significant attention recently due to the growing demand for location-aware applications within indoor environments. Among various techniques, Wi-Fi fingerprinting combined with the Internet of Things (IoT) has emerged as a promising solution for accurate and cost-effective indoor localization. This paper surveys the existing research related to indoor localization using Wi-Fi fingerprinting with the IoT, aiming to provide a comprehensive understanding of the advancements, challenges, and potential applications in this field. The paper begins by introducing the fundamental concepts of indoor localization and the role of Wi-Fi fingerprinting in achieving accurate position estimation. In addition, this paper focuses on the contributions of Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning (DL) in improving localization accuracy, robustness, and scalability. The reviewed papers have been examined from various aspects, including system architecture, deployment strategies, fingerprint creation techniques, and localization algorithms, with a discussion about the advantages and limitations of each approach. In addition to discussing the state-of-the-art techniques, this paper identifies research gaps and open challenges in indoor localization using Wi-Fi fingerprinting with the IoT. The findings presented in this paper can guide future research efforts, leading to the development of intelligent and context-aware IoT applications within indoor environments.

Non line of sight (NLOS) conditions significantly degrade the accuracy of Wi-Fi-Based indoor positioning systems (IPS). Although round-trip time (RTT) is generally more reliable than the received signal strength indicator (RSSI), it remains vulnerable to attenuation and multipath; moreover, dense RTT-compatible AP deployments may threaten scalability. This paper proposes a fingerprinting-based IPS that achieves high accuracy in NLOS scenarios, while remaining scalable and lightweight for Internet of Things (IoT) devices. By combining RTT, its standard deviation, and RSSI, accurate position estimates are obtained with only two RTT requests, reaching 93% accuracy and a 62 cm root mean squared error (RMSE) despite harsh conditions.

This study aimed to model and predict iron concentrations in groundwater within Yenagoa, Bayelsa State, Nigeria, using machine learning techniques. It focused on evaluating spatial variability and determining the most influential predictors to support groundwater quality management. A total of 50 groundwater samples were collected from spatially distributed boreholes across multiple towns in Yenagoa. Geolocation data and iron concentrations were recorded. Two supervised machine learning models Multiple Linear Regression (MLR) and Random Forest Regression (RFR) were implemented. One-hot encoding was applied to categorical town data, and models were evaluated using R², MAE, and Root Mean Square Error (RMSE) metrics. Feature importance was assessed to identify key predictors. A geospatial heatmap was developed using Inverse Distance Weighting (IDW) to visualize spatial trends. The MLR model slightly outperformed the RFR, achieving an Coefficient of Determination (R²) of 0.92, Mean Absolute Error (MAE) of 0.13 mg/L, and RMSE of 0.15 mg/L. Longitude and specific towns (notably Beta and Opolo) emerged as dominant predictors, confirming spatial clustering of high iron concentrations in the eastern region of the study area. Cross-validation confirmed the models’ robustness. The findings support the use of machine learning (ML) techniques for cost-effective water quality prediction and spatial monitoring. This study introduces a hybrid geo-categorical modeling approach, integrating both spatial coordinates and administrative town identifiers into ML frameworks. It demonstrates the feasibility of lightweight, interpretable models like MLR for real-time deployment in low-resource settings, offering a replicable solution for groundwater quality assessment in data-scarce regions. Future research should expand datasets and explore additional hydrogeological variables to enhance model robustness.

Experimental investigation and AI prediction modelling of ceramic waste powder concrete – An approach towards sustainable construction

A suboptimal management or system malfunction can often lead to abnormal energy consumptions in buildings, which result in a significant waste of energy. For this reason, the adoption of advanced monitoring systems, based on Machine Learning (ML) and visualization techniques, is crucial to avoid possible deviations from the baseline energy consumption. However, the historical data on which analyses are based generally do not report the occurrence of anomalies. Therefore, the application of supervised ML techniques is limited and unsupervised approaches are favored. Moreover, domain experts find most Machine Learning (ML) techniques hard to interpret, and thus find it difficult to contextualize anomalies. To overcome these issues, this work proposes a machine learning-based Anomaly Detection Framework (ADF) that involves the use of two complementary semi-supervised ML applications to obtain a highly interpretable and accurate detection of anomalies. Both techniques use Symbolic Aggregate approXimation (SAX) encoding to extract the most relevant information from load profiles. The aim of the first approach is to maximize the interpretability of the definition and distinction between anomalous and normal behavior. This is achieved using a Classification And Regression Tree (CART), albeit at the expense of a coarser output granularity. The second approach exploits an Multi-Layer Perceptron (MLP) algorithm to obtain a higher and more accurate output resolution, although it leads to a less interpretable definition of any anomalous behavior. The ADF has been applied to a real case study using electricity consumption data provided by a large telecommunications service provider. The results show that combining both ML models enhances the accuracy and interpretability of the detected anomalies.

Artificial Intelligence (AI) and Machine Learning (ML) have experienced rapid growth in both industry and academia. However, the current ML and AI models demand significant computing and processing power to achieve desired accuracy and results, often restricting their use to high-capability devices. With advancements in embedded system technology and the substantial development in the Internet of Things (IoT) industry, there is a growing desire to integrate ML techniques into resource-constrained embedded systems for ubiquitous intelligence. This aspiration has led to the emergence of TinyML, a specialized approach that enables the deployment of ML models on resource-constrained, power-efficient, and low-cost devices. Despite its potential, the implementation of ML on such devices presents challenges, including optimization, processing capacity, reliability, and maintenance. This article delves into the TinyML model, exploring its background, the tools that support it, and its applications in advanced technologies. By understanding these aspects, we can better appreciate how TinyML is transforming the landscape of AI and ML in embedded and IoT systems.