Leveraging Edge Computing ML Model Implementation and IoT Paradigm towards Reliable Postoperative Rehabilitation Monitoring

With the continuous development of the Internet of Things (IoT) and the needs of the times, the number of IoT edge devices is rapidly increasing, and to be able to provide users with more reliable information services and decision-making, this paper constructs an IoT system based on cloud-edge collaboration. Based on cloud-edge collaboration, data mining technology is used to categorize data, and regression models and branch neural networks are combined to further improve the IoT system based on cloud-edge collaboration. Finally, the particle swarm algorithm is used to optimize reinforcement learning, and the framework of an IoT system built on cloud-edge partnership is established. Through the data processing experiments on IoT edge computing, the data mining performance of the IoT edge computing-based data mining method is improved by 0.0295, 0.1870, and 0.2059 on the large datasets of Dataset4 and Dataset5, respectively. the average turnaround time is lower than 6.5 for different edge computing power levels. The system rewards are gradually improved from 5.3 to 8.2, and the number of packet loss of the algorithm is less than 5. The feasibility and effectiveness of the IoT data mining method and system based on cloud-edge collaboration are confirmed by this study, and a feasible method for data mining can be applied to IoT systems under cloud-edge collaboration.

The proliferation of Internet of Things (IoT) devices in personal and household environments has led to a significant increase in security vulnerabilities. These devices, due to their limited computational resources, often struggle to support conventional security solutions, making them prime targets for cyberattacks. This paper explores the potential of lightweight machine learning (ML) models to enhance the security of personal IoT devices. By leveraging the power of AI, lightweight models can offer real-time threat detection and anomaly identification without compromising the device's performance or requiring extensive computational resources. The paper investigates various ML techniques that are well-suited for IoT environments, such as decision trees, k-nearest neighbours (KNN), and support vector machines (SVM), focusing on their ability to detect intrusions, unauthorized access, and other malicious activities while maintaining efficiency. Additionally, the study highlights the trade-offs between model complexity, accuracy, and resource consumption, offering practical insights for deploying ML solutions in resource-constrained IoT systems. Key challenges, including data privacy, model generalization, and the adaptability of models to diverse IoT ecosystems, are addressed. Finally, the paper discusses future directions for the integration of more advanced lightweight models, such as federated learning and edge computing, which could further enhance security capabilities while ensuring minimal impact on IoT device performance. Through this review, the paper advocates for the adoption of lightweight ML models as a feasible and scalable solution to securing personal IoT devices in an increasingly connected world.

Commonly, an Internet of Things (IoT) system follows the architecture of the Cloud‐centric Internet of Things (CIoT) in which the physical objects are represented in the form of Web resources that are managed by the servers in the global Internet. In general, the common IoT system involves three major technologies: embedded systems, middleware, and cloud services. Although the CIoT model is a common approach to implement IoT systems, it is facing the growing challenges in IoT. This chapter discusses foundations of computing paradigms for realizing emerging IoT applications, especially fog and edge computing, their background, characteristics, architectures and open challenges. It presents related technologies to fog and edge computing. The chapter describes how fog and edge can improve CIoT and explains the hierarchy of fog and edge computing environments. It illustrates the business models of fog and edge computing, and provides the information regarding the opportunities and challenges in fog and edge computing.

IoT data analytics in dynamic environments: From an automated machine learning perspective

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.

Federated Learning (FL, or Collaborative Learning (CL)) has surely gained a reputation for not only building Machine Learning (ML) models that rely on distributed datasets, but also for starting to play a key role in security and privacy solutions to protect sensitive data and information from a variety of ML-related attacks. This made it an ideal choice for emerging networks such as Internet of Things (IoT) systems, especially with its state-of-the-art algorithms that focus on their practical use over IoT networks, despite the presence of resource-constrained devices. However, the heterogeneous nature of the current devices and models in complex IoT networks has seriously hindered the FL training process's ability to perform well. Thus, rendering it almost unsuitable for direct deployment over IoT networks despite ongoing efforts to tackle this issue and overcome this challenging obstacle. As a result, the main characteristics of FL in the IoT from both security and privacy aspects are presented in this study. We broaden our research to investigate and analyze cutting-edge FL algorithms, models, and protocols, with a focus on their efficacy and practical application across IoT networks and systems alike. This is followed by a comparative analysis of the recently available protection solutions for FL that can be based on cryptographic and non-cryptographic solutions over heterogeneous, dynamic IoT networks. Moreover, the proposed work provides a list of suggestions and recommendations that can be applied to enhance the effectiveness of the adoption of FL and to achieve higher robustness against attacks, especially in heterogeneous dynamic IoT networks and in the presence of resource-constrained devices.

With the increasing popularity of the Internet of Things (IoT) platforms, the cyber security of these platforms is a highly active area of research. One key technology underpinning smart IoT systems is machine learning, which classifies and predicts events from large-scale data in IoT networks. Machine learning is susceptible to cyber attacks, particularly data poisoning attacks that inject false data when training machine learning models. Data poisoning attacks degrade the performances of machine learning models. It is an ongoing research challenge to develop trustworthy machine learning models resilient and sustainable against data poisoning attacks in IoT networks. We studied the effects of data poisoning attacks on machine learning models, including the gradient boosting machine, random forest, naive Bayes, and feed-forward deep learning, to determine the levels to which the models should be trusted and said to be reliable in real-world IoT settings. In the training phase, a label modification function is developed to manipulate legitimate input classes. The function is employed at data poisoning rates of 5%, 10%, 20%, and 30% that allow the comparison of the poisoned models and display their performance degradations. The machine learning models have been evaluated using the ToN_IoT and UNSW NB-15 datasets, as they include a wide variety of recent legitimate and attack vectors. The experimental results revealed that the models’ performances will be degraded, in terms of accuracy and detection rates, if the number of the trained normal observations is not significantly larger than the poisoned data. At the rate of data poisoning of 30% or greater on input data, machine learning performances are significantly degraded.

Anomaly detection is an important security mechanism for the Internet of Things (IoT). Existing works have been focused on developing accurate anomaly detection models. However, due to the resource-constrained nature of IoT networks and the requirement of real-time security operation, cost efficient (regarding computational efficiency and memory-consumption efficiency) approaches for anomaly detection are highly desirable in IoT applications. In this paper, we investigated machine learning (ML) enabled anomaly detection models for the IoT with regard to multi-objective optimization (Pareto optimization) that minimizes the detection error, execution time, and memory consumption simultaneously. Making use of well-known datasets consisting of network traffic traces captured in an IoT environment, we studied a variety of machine learning algorithms through the world-class H2O AI platform. Our experimental results show that the Gradient Boosting Machine, Random Forest, and Deep Learning models are the most accurate and fastest anomaly detection models; the Gradient Boosting Machine and Random Forest are the most accurate and memory-efficient models. These ML models form the Pareto-optimal set of anomaly detection models. Our results can be used by the industry to facilitate their selection of ML models for anomaly detection on various IoT networks based on their security requirements and system constraints.

Internet of Things (IoT) paradigm is gaining a mountainous attention because of its noteworthy impact on everyday life of the people. Billions of things are expected to be a part of IoT paradigm in the near future, providing enormous applications ranging from home automation, smart environment, health care, etc. One of the major hurdles for IoT to be effective with existing network architectures is a prolonged and seamless connectivity between the things and with the end user. Due to heterogeneity in the characteristics of Wireless Sensor Network (WSN), it has become a most prominent technology to bind the things with the Internet and with the end users. Wireless Sensor Networks are self healing and active in nature, that is, they can sense, instruct, request, respond and can easily be adopted to Wireless Sensor and Actuator Networks (WSAN). Addressing scheme in Wireless Sensor networks has to be foreseen if it has to be deployed in Internet of Things paradigm. This paper proposes a discrete cluster based addressing scheme on IEEE 802.15.4 standard for all the things included in a Wireless Sensor Network based IoT applications. Results have been evaluated with existing IPv6 based addressing and it is found that cluster based addressing scheme on the traditionally used IEEE 802.15.4 communication channel suits well over the end to end IPv6 communication channel.

Today, our built environment is not only producing large amounts of data, but –driven by the Internet of Things (IoT) paradigm– it is also starting to talk back and communicate with its inhabitants and the surrounding systems and processes. In order to unleash the power of IoT enabled environments, they need to be trained and configured for space-specific properties and semantics. This paper investigates the potential of communication and transfer learning between smart environments for a seamless and automatic transfer of personalized services and machine learning models. To this end, we explore different knowledge types in context of smart built environments and propose a collaborative framework based on Knowledge Graph principles and IoT paradigm for supporting transfer learning between spaces.

As deployment of Internet of Things (IoT) devices gain momentum, there is an increased interest in implementing machine learning (ML) algorithms on IoT devices. Most of the existing ML solutions, however, rely on a central server to execute data-intensive ML models, because most devices in IoT systems do not have sufficient storage and computing resources. This paper presents a distributed Artificial Neural Networks (ANN) architecture, called PCANN, which allows execution of a complex image recognition task on a collection of resource-constrained IoT devices. Our solution separates a single ML model into multiple small modules that are executed by the distributed IoT devices. The solution effectively reduces storage and computing requirements for individual devices to store and process ML model. We design multiple PCANN models and utilize the models for human posture recognition as the case study. The experimental results show that the distributed PCANN architecture achieves comparable accuracy as the classical ANN model, while the average size of each PCANN module is largely reduced.

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.

With the emergence of fog and edge computing, new possibilities arise regarding the data-driven management of citizens’ mobility in smart cities. Internet of Things (IoT) analytics refers to the use of these technologies, data, and analytical models to describe the current status of the city traffic, to predict its evolution over the coming hours, and to make decisions that increase the efficiency of the transportation system. It involves many challenges such as how to deal and manage real and huge amounts of data, and improving security, privacy, scalability, reliability, and quality of services in the cloud and vehicular network. In this paper, we review the state of the art of IoT in intelligent transportation systems (ITS), identify challenges posed by cloud, fog, and edge computing in ITS, and develop a methodology based on agile optimization algorithms for solving a dynamic ride-sharing problem (DRSP) in the context of edge/fog computing. These algorithms allow us to process, in real time, the data gathered from IoT systems in order to optimize automatic decisions in the city transportation system, including: optimizing the vehicle routing, recommending customized transportation modes to the citizens, generating efficient ride-sharing and car-sharing strategies, create optimal charging station for electric vehicles and different services within urban and interurban areas. A numerical example considering a DRSP is provided, in which the potential of employing edge/fog computing, open data, and agile algorithms is illustrated.

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.

Machine learning (ML) within the edge internet of things (IoT) is instrumental in making significant shifts in various industrial domains, including smart farming. To increase the efficiency of farming operations and ensure ML accessibility for both small and large-scale farming, the need for a low-cost ML-enabled framework is more pressing. In this paper, we present an end-to-end solution that utilizes tiny ML (TinyML) for the low-cost adoption of ML in classification tasks with a focus on the post-harvest process of olive fruits. We performed dataset collection to build a dataset that consists of several varieties of olive fruits, with the aim of automating the classification and sorting of these fruits. We employed simple image segmentation techniques by means of morphological segmentation to create a dataset that consists of more than 16,500 individually labeled fruits. Then, a convolutional neural network (CNN) was trained on this dataset to classify the quality and category of the fruits, thereby enhancing the efficiency of the olive post-harvesting process. The goal of this study is to show the feasibility of compressing ML models into low-cost edge devices with computationally constrained settings for tasks like olive fruit classification. The trained CNN was efficiently compressed to fit into a low-cost edge controller, maintaining a small model size suitable for edge computing. The performance of this CNN model on the edge device, focusing on metrics like inference time and memory requirements, demonstrated its feasibility with an accuracy of classification of more than 97.0% and minimal edge inference delays ranging from 6 to 55 inferences per second. In summary, the results of this study present a framework that is feasible and efficient for compressing CNN models on edge devices, which can be utilized and expanded in many agricultural applications and also show the practical insights for implementing the used CNN architectures into edge IoT devices and show the trade-offs for employing them using TinyML.