Critical infrastructure awareness based on IoT context data

Due to the rapid growth of the Internet of Things (IoT), researchers have demonstrated various IoT solutions, which are used to interconnect a wide range of IoT devices through the Internet. However, IoT stumbled into vertical silos; the available solutions provide specific IoT infrastructure, devices, protocols, data formats and models. This diversity and heterogeneity lead to interoperability issues. Heterogeneity happens at all IoT layers, especially at the application layer; devices often adopt mutually incompatible application-layer communication protocols to connect devices to IoT services. Furthermore, in order to integrate semantics to raw data, each system uses its one domain-specific ontology to make data more understandable and interpretable by adding semantic annotations. Working in isolation reduces the interoperability among IoT devices and systems, things across domains need to internetwork and collaborate to provide high level IoT services. Therefore, to alleviate the problem of both communication protocol interoperability and semantic interoperability across vertical silos of systems at the application layer, this paper proposes a semantic gateway (SGIoT) that acts as a bridge between heterogeneous sink nodes at the physical level and IoT services. SGIoT enables interconnectivity between communication protocols such as CoAP and MQTT regardless of their communication model, meanwhile it enables semantics integration throu gh cross-domain ontology (CDOnto) for semantic annotation, in order to provide interpretation of messages among IoT applications across domains. Our approach focuses on modularity and extensibility.

The developers and users of Internet of Things (IoT) systems and devices bear a collective obligation to ensure that IoT systems and devices do not make the Internet and other users vulnerable to any potential harm. This chapter proposes a novel network secure framework that can be adapted to the future IoT and finally gives details for the security verification system design. This framework should focus on enhancing the efficacy, dependability, accountability, and regulation in the overall structure that checks the security issues. The chapter discusses the structuring of a security authentication system in the field of IoT. It presents the factors that pose a threat to the IoT security. These include Unprotected Network services, Cloud, mobile and an absence of security configuration. The chapter illustrates the essential components of IoT security and lists the potential challenges in terms of IoT security. It also discusses the concepts representing the risks that increase the probability of intrusions in the IoT devices.

The Internet of Things (IoT) has had a considerable influence on our daily lives by enabling enhanced connection of devices, systems, and services that extends beyond machine-to-machine interactions and encompasses a wide range of protocols, domains, and applications. However, despite privacy concerns shown by IoT users, little has been done to reduce and protect individual information exposure. It is extremely difficult to mitigate IoT devices from reidentification threats which is why it is still a major challenge for IoT users to securely protect their information. The trust controls how we regulate privacy in our IoT platforms in the same way that it governs personal relationships. As IoT devices become increasingly linked, more data is shared across individuals, businesses, governments, and ecosystems. Technologies, sensors, machines, data, and cloud connections all rely largely on trust relationships that have been formed. With the rapid growth of additional types of IoT devices that are being introduced, it, therefore, expands privacy concerns and is difficult to develop trust with an IoT system or device without the option to regulate information privacy settings. Privacy has always been a barrier for many devices as they race for the early adoption of IoT technologies. Several Internet of Things devices or systems will continue to pose privacy threats. As a result, the main objective of this study was to examine the individual understanding of privacy and to promote information privacy protection awareness not only to IoT users but also to organizations that use IoT devices or platforms to run their day-to-day business operations. Furthermore, the objective extends to compare user knowledge and concerns about IoT privacy, as well as to identify any common attitudes and variances. However, in terms of enhancing individuals’ knowledge, an artifact was developed to educate and enhance information privacy awareness among IoT users. A pre- and postquestionnaire was generated to test and validate user knowledge regarding information privacy protection in IoT. The study was conducted using a quantitative research method. Findings indicate that IoT users’ awareness of information privacy protection turned out to be average, suggesting a need for education and awareness. Several participants stated that information privacy protection awareness is required within the community to educate, raise awareness, eliminate human error, and enable individuals to be conscious of their privacy when surfing the Internet.

A survey of trust computation models for service management in internet of things systems

Deep learning-enabled anomaly detection for IoT systems

Usually, IoT devices are designed to perform specific tasks, while robots have to adapt to unpredictable situations. Artificial intelligence and machine learning help these robots cope with the emerging unexpected conditions. The Internet of Robotic Things is an evolving concept that brings together all-encompassing sensors and devices with robotic and autonomous systems. Both IoT devices and robots rely on sensors to understand the surrounding environment, to process data quickly, and to decide how to respond. Nevertheless, while most IoT systems can handle only well-defined tasks, robots can handle expected situations as well. The Internet of Robotic Things is a better Internet of Things (IoT) solution due to its ability to bridge the gap between IT and real operations. The IoRT may face several challenges in power consumption and network bandwidth limitation due to the growth in the information dissemination over the Robotic network. The main objective of this paper is to introduce an overview of the concepts and challenges in the Internet of Robotic Things based on Fog Computing technique. Further, a framework of Fog based IoRT is introduced.

The apparent growth of the internet of things (IoT) has allowed its deployment in many domains. The IoT devices sense their surroundings and transmit the data via the Web. According to statistics, due to the proliferation of smart devices, the number of active IoT devices is expected to exceed 25.4 billion by 2030.1 A large number of IoT objects gather an enormous amount of raw data. The data generated by various IoT objects and sensors are heterogeneous, with varying types and formats. Therefore, it is difficult for IoT systems to share and reuse raw IoT data, which causes the problem of lack of interoperability. The lack of interoperability in IoT systems creates a problematic issue that prevents IoT systems from performing well. To address this issue, data modeling and knowledge representation using semantic web technologies may be an appropriate solution to give meaning to raw IoT data and convert it to an enriched data format. The primary goal of this research section is to highlight the best outcomes for semantic interoperability among IoT systems, which can serve as a guideline for future studies via the presentation of a literature review on semantic interoperability for Internet of Things systems, including challenges, prospects, and recent work. The paper also provides an overview of the application of semantic web technologies in IoT systems, such as specific ontologies, frameworks, and application domains that use semantic technologies in the IoT areas to solve interoperability and heterogeneity problems.

The Internet of Things (IoT) has revolutionised the way people live and work, and its increasing popularity has led to the widespread adoption of smart homes. However, the lack of interoperability among IoT devices and systems used in smart homes limits their capacity to enhance the user experience and improve energy efficiency. Therefore, this study focuses on improving interoperability between various IoT systems and devices. It was anticipated that the research would contribute to the creation of a more cohesive and integrated smart home environment by helping to develop a data analytics framework that enables the integration and analysis of data from various devices. The study then produces an intended user design strategy for smart homes that takes into account users' wants, preferences, and behaviours during the design phase. The goal of this project was to create smart houses that are more intelligent, environmentally green, and user-friendly and that will benefit both individuals and the whole society.

The rapid growth of Internet of Things (IoT) devices, including smartwatches, home assistants, and connected appliances, has brought significant convenience to daily life, but it has also introduced serious security challenges. These devices often transmit sensitive data, making them vulnerable to theft, misuse, and unauthorized access. Current security measures are insufficient to address the complex and evolving nature of IoT systems, leaving many of them exposed to potential breaches and cyberattacks. This review explores recent developments in IoT security, focusing on how advanced technologies, such as machine learning, can be utilized to enhance the protection of IoT systems. The main objective of this paper is to examine potential solutions to the security problems that arise in IoT environments. It includes a thorough analysis of recent research and technological innovations in the field, with a particular emphasis on how different security methods are applied across IoT systems. By identifying the most common security vulnerabilities and outlining their impact on IoT networks, the review suggests improved methods to safeguard IoT data and ensure privacy. The findings aim to support researchers, developers, and businesses in designing more secure IoT solutions, and contribute to the establishment of stronger data protection policies. Ultimately, the review serves as a resource for those seeking to enhance the security of IoT devices and systems in an increasingly interconnected world.

Summary form only given, as follows. The complete presentation was not made available for publication as part of the conference proceedings. The Internet of Things (IoT) represents a great opportunity to connect people, information, and things, which will in turn cause a paradigm shift in the way we work, interact, and think. The IoT is envisioned as the enabling technology for smart cities, power grids, health care, and control systems for critical installments and public infrastructure. This diversity, increased control and interaction of devices, and the fact that IoT systems use public networks to transfer large amounts of data make them a prime target for cyber attacks. In addition, IoT devices are usually small, low cost and have limited resources. Therefore, any protocol designed for IoT systems should not only be secure but also efficient in terms of usage of chip area, energy, storage, and processing. This presentation will start by highlighting the unique security requirements of IoT devices and the inadequacy of existing security protocols and techniques of the Internet in the context to IoT systems. Next, we will focus on security solutions for the IoT, with special focus on protection against physical and side channel attacks. In particular, we will focus on mutual authentication protocols for IoT devices based on security primitives that exploit hardware level characteristics of IoT devices.

The emergence of various smart services delivered by heterogeneous Internet of Things (IoT) devices has made daily human-life easy and comfortable. IoT devices have brought enormous convenience to various applications, no matter the IoT systems include homogeneous devices like in most sensor networks or heterogeneous devices like in smart homes or smart business applications. However, several known communication infrastructures of IoT systems are at risk to various security attacks and threats. The practice of discovering uncommon occurrences of conventional behaviors is known as anomaly detection. It is an essential tool for detecting fraud as well as network intrusion. In this work, we provide an anomaly-based model on the Extended Isolation Forest method. In our work, the available dataset ’UNSW_2018_IoT_Botnet_Final_10_best_Testing’ has been used for the experiment. Performance indicators, including accuracy, precision, recall, and F1-Score, are used to validate the performance of our suggested system. We get an Accuracy Score of 93% and F1-Score of 96% through the experiment. In addition, the most important top 12 features have a more substantial impact on correct prediction for anomaly identification and have also been identified in this study.KeywordsAnomaly detectionIsolation forestExtended isolation forestIot securityFeature set

Privacy preserving Internet of Things: From privacy techniques to a blueprint architecture and efficient implementation

Relevance. The growing integration of Internet of Things (IoT) technologies into all areas of human life – from intelligent households to smart city infrastructure – is accompanied by an exponential increase in the volume of data being collected, transmitted, and processed in real time. When combined with artificial intelligence technologies, this data becomes the foundation for making autonomous decisions, predicting user behavior, and adapting environments to the needs of specific individuals. However, it is precisely in this context that the critically important issue of personal data protection arises. Many IoT devices operate in uncontrolled environments, have limited resources for cryptographic protection, and are vulnerable to cyberattacks and unauthorized data collection. Meanwhile, artificial intelligence algorithms used to analyze this data often exhibit the “black box” problem, where it is impossible to fully explain how and why a particular decision was made based on personalized data. The lack of transparency, combined with broad access to sensitive information, threatens fundamental human rights to privacy. The relevance of this topic is driven by the need to find balanced technical solutions that enable both effective analysis of large-scale data in IoT environments and a high level of data security. In this regard, the study of modern methods for processing, analyzing, and protecting data in IoT systems, adapted to the requirements of ethical artificial intelligence and digital privacy standards, represents one of the key challenges of contemporary digital science. The object of research: the processes of data collection, processing, analysis, and protection in IoT systems, particularly those components related to the use of users' personal information and its processing through artificial intelligence methods. Purpose of the article: research of modern methods for data processing and analysis in IoT systems. The objective of the work is to identify the most effective approaches to secure data handling, characterize existing privacy threats, and assess the potential for integrating protected analytical algorithms that meet both the technical and ethical requirements of the digital environment. Research results. A comprehensive analysis of modern approaches to data collection, processing, analysis, and protection in IoT systems has been conducted, particularly in the context of the growing role of artificial intelligence. The technological foundations of IoT functionality were examined, key architectural components identified, and their role in creating digital ecosystems for monitoring, management, and decision-making across various sectors – from household systems to critical infrastructure – was investigated. Special attention was paid to data preprocessing methods, which help reduce information load, improve the quality of analysis, and adapt data flows to the requirements of intelligent algorithms. It was demonstrated that the use of edge processing and local-level aggregation enhances both system performance and security. The main types of databases for IoT – especially those optimized for time series – were analyzed, along with tools for handling large volumes of data in cloud and hybrid environments. Conclusions. Data collection methods in IoT are multilayered and closely linked to the requirements for energy efficiency, security, latency, and system scalability. The quality and reliability of the collected information form the foundation for subsequent processing, analysis, and decision-making; therefore, the selection of sensors, communication protocols, and architectural models is of strategic importance for any IoT system. Preprocessing and efficient data storage in IoT are critical stages that ensure the quality, security, and usability of information for further analysis. They determine not only the accuracy of analytics but also the stability, scalability, and compliance with regulatory standards. This creates a demand for the development of adaptive, intelligent data processing and storage systems capable of dynamically responding to changes in device operation context and user requirements. The successful implementation of secure IoT solutions requires an integrated approach that combines technical expertise, legal knowledge, and ethical responsibility.

In recent years, the Internet of Things (IoT) has attracted attention according to the development of sensing technology and wireless communication technology. IoT devices are interconnected to realize a specific service with sensors and actuation functions over several communication methods. Internet Protocol (IP) exists as a basic communication technology for realizing an IoT system. Since IPv4 is a mainstream protocol on the Internet, the lack of compatibility between IPv4 and IPv6 should be great issues to realize IoT services. Additionally, recent networks employ the Network Address Port Translation (NAPT) technology to protect a network and to reduce the number of required IPv4 global addresses. The introduction of NAPT causes an accessibility issue due to the blocking of incoming packets. As a new issue, the mobility of IoT devices also becomes a concern about seamless communication because a change of IP address causes a disconnection of transport layer sessions. As a technique for solving these problems, the authors have developed CYber PHysical Overlay Network over Internet Communication (CYPHONIC) that supports accessibility and seamless mobility over the Internet. Since high-performance IoT devices typically implement the full IP stack, both IPv4 and IPv6 can be supported. Therefore, CYPHONIC can provide inter-connectivity among IoT devices. On the contrary, resource-constrained IoT devices with a low power operation support only the limited IP stack such as IPv6 over Low power Wireless Personal Area (6LowPAN). This paper proposes a new interconnectivity mechanism between IP networks and 6LowPAN by extending CYPHONIC. Therefore, the proposed mechanism can provide interconnectivity among 6LowPAN devices and IP devices to collaborate on a service. The proposed system introduces a gateway device for connecting between IP networks and 6LowPAN. Additionally, it also extends the signaling process of CYPHONIC to support 6LowPAN based IoT devices. Since the object of this paper is confirming the feasibility of the proposed mechanism, we have designed the implementation with Thread protocol that support 6LowPAN.

As location-aware IoT (Internet of Things) devices intend to spread across almost every application, IoT devices are going to require different GNSS (Global Navigation Satellite System) receiver design requirements. To deliver on IoT opportunities, system architects and designers need to look carefully at every aspect of system design and architecture to be able to enable the full potential of the IoT era. IoT devices are equipped with different connectivity sensors that can provide a reliable integrated location-awareness solution. IoT devices are mainly relying on GNSS chip to deliver a service when no other RF signal is available. Hence, GNSS chip is expected to maintain a robust and reliable performance even in harsh environment. The harsh environment includes multipath environment, NLOS (Non-Line of Sight) GNSS signal reception, weak GNSS signal power reception, superimposed motion (e.g., arm swing, floating platform fluctuation …etc.), operation under freezing temperature, and interference. The targeted low-cost system design along with the expected harsh operational environment derived us to face exceptional GNSS signal reception condition. This paper discusses lessons learned, and challenges faced signal acquisition, reacquisition, and tracking for several IoT applications. Different operational modes are also explored to accommodate various use cases scenarios for several IoT applications. These different power consumption operational modes allow the GNSS chip to achieve an Ultra-low power consumption which indeed required for IoT applications. This paper provides an overview about the addressed GNSS chip fabricated in a 28nm FD-SOI process. We demonstrate a system performance and power consumption comparison of this chip along with other state-of-the-art GNSS chip. We compare the positioning performance for this device alongside another state-of-the-art GNSS modules and Novatel’s tightly coupled GNSS/INS (Inertial Navigation System) reference solution at numerous scenarios ranging from walking, running, and driving at several sites under different environment.