Machine Learning for Internet of Things

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

Big data and artificial intelligence based early risk warning system of fire hazard for smart cities

The Internet of Things (IoT) has revolutionized data collection and analysis from diverse sources. IoT-based monitoring systems are now widespread in manufacturing, healthcare, and smart cities. These systems gather vast amounts of data from sensors and devices, enabling the detection of anomalies and patterns. The IoT has become an integral part of our lives, transforming various industries by enabling seamless connectivity between devices and increasing automation and efficiency. However, the reliability of IoT systems is often compromised due to the complexity and scale of these networks, making them vulnerable to failures and security breaches. To mitigate this problem, anomaly detection using efficient learning in IoT is a promising solution to identify unusual patterns that could indicate system faults or threats. This paper implements an efficient learning-driven approach for anomaly detection classification in IoT-based monitoring systems. The study aims to improve the accuracy and speed of identifying unusual patterns or behaviours in IoT data streams. By using advanced machine learning techniques, the proposed method can effectively detect anomalies in real-time, enhancing the overall security and reliability of IoT monitoring systems. The proposed framework enhances IoT system reliability by reducing downtime, improving security, and ensuring consistent performance of connected devices. Experimental results demonstrate the efficiency of machine learning techniques and their capabilities in anomaly detection. Empirical findings show that Decision Trees (DT) and Random Forests (RF) outperform other competitive models like Logistic Regression (LR), Naïve Bayes (NB), K-Nearest Neighbour (KNN), Extreme Gradient Boosting (XGB), and Adaptive Boosting (AB) and Voting Classifier (VC) for network intrusion detection in the context of anomaly detection.

In this paper, IoT (Internet of Things) information technology has been widely applied to the supply chain risk management (SCRM). Firstly, the source of risks has been sorted out, the external and internal risks have been described in detail with the risk management of supply chain system. Secondly, the supply chain risk and case reasoning were mentioned. Finally, the work actively explored the supply chain risk management by the IoT information, such as 3G network, RFID and GPS. The research on SCRM based on IoT information contributes to the construction and improvement of supply chain informatisation.

The Internet of Things (IoT) will be a main data generation infrastructure for achieving better system intelligence. However, the extensive data collection and processing in IoT also engender various privacy concerns. This paper provides a taxonomy of the existing privacy-preserving machine learning approaches developed in the context of cloud computing and discusses the challenges of applying them in the context of IoT. Moreover, we present a privacy-preserving inference approach that runs a lightweight neural network at IoT objects to obfuscate the data before transmission and a deep neural network in the cloud to classify the obfuscated data. Evaluation based on the MNIST dataset shows satisfactory performance.

‘Machine Learning (ML) in Internet of Things (IoT)’ delves deeply into the complex and dynamic interplay between ML and IoT, revealing the game-changing effects of their association across a wide range of technological fields. To lay a solid beginning, the core ideas of IoT like data transfer and acquisition, and basic ML are reviewed. Readers will acquire a thorough understanding of the foundations of both ML and IoT by going over these key ideas. The potential and inherent issues that come with managing IoT data are explored. The main challenges include managing massive and quickly created data streams, guaranteeing data quality, and preserving data security. An investigation is done to exhibit how IoT data presents unseen prospects for industry-wide optimization, predictive analytics, and data-driven decision-making. The investigation of ML strategies adapted to the specifics of IoT become the core of this chapter. Supervised, unsupervised, and reinforcement learning techniques are analyzed to see whether they are applicable to specific IoT applications. Distributed ML and edge computing, which are crucial for making decisions in real-time in IoT settings with constrained computational resources are also dealt with. To reinforce the usefulness of the discussion, a few interesting case studies and actual implementations are included. These demonstrate how ML may be used to improve predictive maintenance procedures in industrial settings, reduce energy usage in smart homes, and transform healthcare through wearable IoT devices. ML being used in IoT ecosystems for anomaly detection, failure prediction, and environmental monitoring is also illustrated. This chapter is a technical reserve and a thorough guide for anybody looking to leverage the revolutionary power of IoT and ML in an increasingly linked world.

The Internet of Things (IoT) is becoming a buzz word due to the extensive application of it. It can be found anywhere nowadays, Healthcare, Smart cities, Smart energy, smart buildings and so on. The internet of thing has been a subject of improvement in terms of the price, size and persistence, but one of the main challenges in understanding IoT is in dealing with the device and data heterogeneities and establishing the interoperability between devices, information, and services built on top of it. Therefore, Ontologies can bring the change we are aiming in establishing a standard inter-devices communication and enable interoperability in m2m communication that allows data and data semantics to be described in application-independent ways.

The fusion of Internet of Intelligent Things (IoIT) in remote diagnosis of obstructive Sleep Apnea: A survey and a new model

to identify works involving IoT that deal with the development, architecture, application, implementation, use of technological equipment in the area of patient rehabilitation. Technology or Method: A systematic review based on Kitchenham's suggestions combined to the PRISMA protocol. The search strategy was carried out comprehensively in the IEEE Xplore Digital Library, Web of Science and Scopus databases with the data extraction method for assessment and analysis consist only of primary studies articles related to the IoT and Rehabilitation of patients. We found 29 studies that addressed the research question, and all were classified based on scientific evidence. This systematic review presents the current state of the art on the IoT in health rehabilitation and identifies findings in interdisciplinary researches in different clinical cases with technological systems including wearable devices and cloud computing. The gaps in IoT for rehabilitation include the need for more clinical randomized controlled trials and longitudinal studies. Clinical Impact: This paper has an interdisciplinary feature and includes areas such as Internet of Things Information and Communication Technology with their application to the medical and rehabilitation domains.

Internet of Things (IoT) have opened the door to a world of unlimited possibilities for implementations in varied sectors in society, but it also has many challenges. One of those challenges is security and privacy. IoT devices are more susceptible to security threats and attacks. Due to constraints of the IoT devices such as area, power, memory, etc., there is a lack of security solutions that are compatible with IoT devices and applications, which is leading this world of securely connected things to the “internet of insecure things.” A promising solution to this problem is going beyond the standard or classical techniques to implementing the security solutions in the hardware of the IoT device. The integration of emerging technologies in IoT networks, such as machine learning, blockchain, fog/edge/cloud computing, and quantum computing have added more vulnerable points in the network. This paper introduces a comprehensive study on IoT security threats and solutions. Additionally, this survey outlines how emerging technologies such as machine learning and blockchain are integrated in IoT, challenges resulted from this integration, and potential solutions to these challenges. The paper utilizes the 4-layer IoT architecture as a reference to identify security issues with corresponding solutions.

Although the Internet of Things (IoT) will play a key role in the development of next generation information, network, and communication technologies, many are still unclear about what makes IoT different from similar concepts.Answering fundamental questions about IoT architectures and models, Unit and Ubiquitous Internet of Things introduces essential IoT concepts from the perspectives of mapping and interaction between the physical world and the cyber world. It addresses key issues such as strategy and education, particularly around unit and ubiquitous IoT technologies. Supplying a new perspective on IoT, the book covers emerging trends and presents the latest progress in the field. It also: Outlines a fundamental architecture for future IoT together with the IoT layered model Describes various topological structures, existence forms, and corresponding logical relationships Establishes an IoT technology system based on the knowledge of IoT scientific problems Provides an overview of the core technologies, including basic connotation, development status, and open challenges The book examines ubiquitous sensing, networking, and communications, as well as information management involved in unit IoT. It describes global IoT applications and includes coverage of ubiquitous, local, industrial, national, and transnational IoT. Presenting detailed case studies that illustrate various application scenarios, the text considers the main IoT supporting technologies including resource management, loop control in actuation, session management, space-time consistency, security and privacy, energy management, spectrum management, nanotechnology, quantum technology, and big data.

The Raspberry Pi is being increasingly adopted as a suitable platform in both research and applications of the Internet of Things (IoT). This study presents a novel project-based teaching and learning approach devised in an Internet of Things course for undergraduate students in the computer science major, where the Raspberry Pi platform is used as an effective vehicle to greatly enhance students’ learning performance and experience. The devised course begins with learning simple hardware and moves to building a whole prototype system. This paper illustrates the outcome of the proposed approach by demonstrating the prototype IoT systems designed and developed by students at the end of one such IoT course. Furthermore, this study provides insights and lessons regarding how to facilitate the use of the Raspberry Pi platform to successfully achieve the goals of project-based teaching and learning in IoT.

Internet of Things (IoT) and Big Data have played a significant role in the development and sustainability of Smart Cities. They are the backbones of Smart Cities' efforts to connect human resources, social capital, and ICT infrastructure to address public challenges, achieve sustainable development and improve people's standard of life. It helps Smart Cities improve public services, such as water management, healthcare, education, security issues, transportation, energy usage, and other community to achieve a better life. Big Data helps to analyze, store and back-ups a massive amount of data generated in smart cities in seconds day by day. For this matter, different analytical tools are employed, such as data analytics, machine learning, statistics, and data mining. Even though there are many benefits offered to smart cities; the Smart Cities encounters challenges that need to be addressed. Many of these challenges focus on security and privacy, infrastructures and operational and handling massive data. This paper describes the overview of IoT, Big Data, and Smart Cities. The Applications of IoT and Smart Cities were also discussed; the features of Smart Cities and Big Data; Different challenges of IoT, Big Data, and Smart Cities are described. Also, the proposed research work has presented the relationship between IoT and Big Data to Smart Cities; the role of IoT and Big Data in smart cities was identified; finally, the future directions for IoT and Big Data regarding Smart Cities are discussed.

Many researchers have recently considered patients’ health and provided an optimal and appropriate solution. With the advent of technologies such as cloud computing, Internet of Things and 5G, information can be exchanged faster and more securely. The Internet of things (IoT) offers many opportunities in the field of e-health. This technology can improve health services and lead to various innovations in this regard. Using cloud computing and IoT in this process can significantly improve the monitoring of patients. Therefore, it is important to provide a useful method in the medical industry and computer science to monitor the status of patients using connected sensors. Thus, due to its optimal efficiency, speed, and accuracy of data processing and classification, the use of cloud computing to process the data collected from remote patient sensors and IoT platform has been suggested. In this paper, a prioritization system is used to prioritize sensitive information in IoT, and in cloud computing, LSTM deep neural network is applied to classify and monitor patients’ condition remotely, which can be considered as an important innovative aspect of this paper. Sensor data in the IoT platform is sent to the cloud with the help of the 5th generation Internet. The core of cloud computing uses the LSTM (long short-term memory) deep neural network algorithm. By simulating the proposed method and comparing the obtained results with other methods, it is observed that the accuracy of the proposed method is 97.13%, which has been improved by 10.41% in average over the other methods.

Integrating Internet of Things (IoTs) devices with secure smart home networks assisted by the cloud signifies a cutting-edge and potent tool for contemporary home automation. This allows various appliances and devices in a home remotely controlled by the internet to communicate and share data. The typical smart home system depends on the cloud service or centralized server, which makes them further vulnerable to potential security breaches and single points of failure. As a decentralized nature, Blockchain (BC) distributes the control and storage of data across the network, preventing unauthorized attacks. Integrating BC technology into the protected smart home network boosts the system's dependability, safety, and privacy. In addition, machine learning (ML) and analytics offer behaviour analysis and predictive maintenance for optimized energy consumption. Finally, combining IoT with cloud-assisted security transforms homes into smart, connected ecosystems, offering convenience without integrating confidentiality or dependability. Accordingly, this study presents a BC-based Deep Learning in the Secure Smart Home Network (BPDL-SSHN) methodology in the IoT-cloud platform. In the BPDL-SSHN methodology, BC technology permits secret proficient data from the smart home network. Furthermore, the BPDL-SSHN method follows a series of processes to detect malicious activities such as Binary Fox Optimization Algorithm (BFOA) based feature selection, Attention-based Long Short-Term Memory (ALSTM)-based classification, and Harbor Seal Whiskers Optimization (HSWO)-based hyperparameter tuning. The HSWO method's design helps better the hyperparameter choice of the ALSTM method, significantly enhancing the recognition performance. The comparative outcome of the BPDL-SSHN methodology reported the proficient solution of the smart home network to detect and monitor malicious or harmful activities. The experimental outcome implied that the BPDL-SSHN methodology accomplishes a maximum accuracy performance of 98.91% over other approaches.