Book

Abstract

This book gathers recent research work on emerging Artificial Intelligence (AI) methods for processing and storing data generated by cloud-based Internet of Things (IoT) infrastructures. Major topics covered include the analysis and development of AI-powered mechanisms in future IoT applications and architectures. Further, the book addresses new technological developments, current research trends, and industry needs. Presenting case studies, experience and evaluation reports, and best practices in utilizing AI applications in IoT networks, it strikes a good balance between theoretical and practical issues. It also provides technical/scientific information on various aspects of AI technologies, ranging from basic concepts to research grade material, including future directions. The book is intended for researchers, practitioners, engineers and scientists involved in the design and development of protocols and AI applications for IoT-related devices. As the book covers a wide range of mobile applications and scenarios where IoT technologies can be applied, it also offers an essential introduction to the field.
... The fusion of the Internet of Things (IoT) and artificial intelligence (AI) [130][131][132] has brought significant advancements to environmental sciences [133], enabling more efficient and data-driven decision-making processes. By leveraging the complementary capabilities of these technologies, integrated systems facilitate improved data collection, real-time analysis, and the generation of actionable insights. ...
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The integration of artificial intelligence (AI) agents with the Internet of Things (IoT) has marked a transformative shift in environmental monitoring and management, enabling advanced data gathering, in-depth analysis, and more effective decision making. This comprehensive literature review explores the integration of AI and IoT technologies within environmental sciences, with a particular focus on applications related to water quality and climate data. The methodology involves a systematic search and selection of relevant studies, followed by thematic, meta-, and comparative analyses to synthesize current research trends, benefits, challenges, and gaps. The review highlights how AI enhances IoT’s data collection capabilities through advanced predictive modeling, real-time analytics, and automated decision making, thereby improving the accuracy, timeliness, and efficiency of environmental monitoring systems. Key benefits identified include enhanced data precision, cost efficiency, scalability, and the facilitation of proactive environmental management. Nevertheless, this integration encounters substantial obstacles, including issues related to data quality, interoperability, security, technical constraints, and ethical concerns. Future developments point toward enhancements in AI and IoT technologies, the incorporation of innovations like blockchain and edge computing, the potential formation of global environmental monitoring systems, and greater public involvement through citizen science initiatives. Overcoming these challenges and embracing new technological trends could enable AI and IoT to play a pivotal role in strengthening environmental sustainability and resilience.
... Within the swiftly changing terrain of technology, the amalgamation of Artificial Intelligence (AI), Big Data (BD), and Blockchain (BC) has surfaced as a revolutionary power with vast possibilities. This integration holds promise for enhancing efficiency, innovation, and societal development across various sectors (Kalenzi, 2022;Kumble, 2020;Mastorakis et al., 2020). However, this convergence also introduces a myriad of legal and governance challenges that require meticulous exploration. ...
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In the swiftly changing realm of education, technology serves as a key instrument in transforming the methods of teaching, learning experiences, and educational outcomes. Legal and governance issues, integral to maintaining order and justice in societies, are equally pertinent in the realm of education. The digital age introduces concerns like cybersecurity, data protection, and the need for adaptive cybercrime legislation, all of which intersect with the integration of technology in education. As technology advances, legal frameworks must adapt to regulate emerging technologies within educational settings. A collaborative effort is essential to address these issues, requiring continuous review and adaptation of legal frameworks to meet evolving challenges. This research explores the complex aspects of the legal and governance challenges emerging from the integration of Artificial Intelligence (AI), Big Data (BD), and Blockchain (BC) technologies and their influence on education. Employing a quantitative research approach, data were gathered from a diverse pool of 347 professionals, including legal experts, cybersecurity specialists, and AI researchers in China. The research methodology integrated SPSS and Smart PLS3 for robust statistical analysis. The findings illuminate legal challenges, regulatory gaps, ethical concerns, and emphasize the pivotal roles of cross-border collaboration and adaptive regulatory approaches in navigating the complexities of converging technologies. The novelty of this study is rooted in its thorough investigation of the complex legal and governance aspects related to the merging of AI, BD, and BC. This research contributes to both advancing academic understanding and offering practical applications for policymakers, industry professionals, and researchers engaged in shaping the regulatory landscape of these transformative technologies.
... They have stopped being just a constellation of devices for gathering and transmitting information. Instead, have become systems which can adjust their behavior in real time, based on the collected data [4]. ...
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Nowadays, with the ongoing, wide scale digitization and development of AI in pursuit of automation, the IoT industry becomes one of the very important parts in this process. The development of IoT devices computational capabilities, as well as the massive amounts of data which is being generated by them, create a need for methods to load balance workloads efficiently. Since the IoT devices are receiving more processing power, it becomes important to leverage that power for executing curtain tasks inside an IoT ecosystem itself, rather than delegating to the Cloud. One of the main goals of the research is to understand, to what extent an IoT ecosystem can be self reliant in managing various tasks. In particular, what mechanism would allow to load balance tasks among IoT devices and Cloud servers. The paper focuses on exploration of the existing solutions and offers an alternative concept of such mechanism, which includes application of Machine Learning methods to load balance the workloads. It proposes tasks distribution based on runtime complexity estimated by Machine Learning and historical data from the previous tasks.
... The need for continuous data transmission between edge devices and central processing units may also pose latency issues. Balancing the trade-off between accuracy and resource efficiency becomes crucial in this context [4]. ...
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This paper explores the dynamic intersection of Neural Networks, Internet of Things (IoT), and strategic Information Technology (IT) supply chain execution to foster intelligent convergence. The integration of these technologies has become imperative for organizations seeking enhanced efficiency and competitiveness. We delve into the profound impact of this convergence on building intelligent systems, particularly in the context of supply chain management. The study also investigates the potential of this synergy in fostering strategic execution, with a focus on mergers and acquisitions in the IT supply chain, effective sales strategies, and the unique challenges posed by the sales of medical devices in the SAP supply chain.
... The authors discuss AIoT's architecture across cloud, fog, and edge computing, along with applications and challenges. Mastorakis et al. [35] adopt a broader perspective on the convergence of AI and IoT, covering various AI methods in IoT, research trends, industry needs, and practical implementation. Their work offers a balance between theoretical concepts and real-world applications, serving as a comprehensive resource for researchers and practitioners alike. ...
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The Metaverse represents an always-on 3D network of virtual spaces, designed to facilitate social interaction, learning, collaboration, and a wide range of activities. This emerging computing platform originates from the dynamic convergence of Extended Reality (XR), Artificial Intelligence of Things (AIoT), and platform-mediated everyday life experiences in smart cities. However, the research community faces a pressing challenge in addressing the limitations posed by the resource constraints associated with XR-enabled IoT applications within the Internet of City Things (IoCT). Additionally, there is a limited understanding of the synergies between XR and AIoT technologies in the Metaverse and their implications for IoT applications within this framework. Therefore, this study provides a detailed overview of the literature on the potential applications, opportunities, and challenges pertaining to the deployment of XR technologies in IoT applications within the broader framework of IoCT. The primary focus is on navigating the challenges pertaining to the IoT applications powered by VR and AR as key components of MR in the Metaverse. This study also explores the emerging computing paradigm of AIoT and its synergistic interplay with XR technologies in the Metaverse and in relation to future IoT applications in the realm of IoCT. This study’s contributions encompass a comprehensive literature overview of XR technologies in IoT and IoCT, providing a valuable resource for researchers and practitioners. It identifies challenges and resource constraints, identifying areas that require further investigation. It fosters interdisciplinary insights into XR, IoT, AIoT, smart cities, and IoCT, bridging the gap between them. Lastly, it offers innovation pathways for effective XR deployment in future IoT/AIoT applications within IoCT. These contributions collectively advance our understanding of synergistic opportunities and complementary strengths of cutting-edge technologies for advancing the emerging paradigms of urban development.
... However, the precedence has been set regarding adaptation of GT. The internet and, in this instance, the virtual learning environment are still in their infancy in terms of social science research and they open up new possibilities for online research (Blank 2008) which will require adaptation from traditional methodologies and methods for concepts to emerge (Brent 2008). ...
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Chapter
Due to the advancement of emerging technologies such as the Internet of Everything, augmented and virtual reality, holographic communication, and smart cities, 5G is unable to meet the network requirements of these innovative applications. To address this issue, researchers are already working toward the development of 6G, which will revolutionize the wireless communication network by incorporating artificial intelligence (AI), machine learning (ML), and edge computing. AI will play a pivotal role in the design and development of 6G architecture, creating a new type of Internet, which would make the network efficient, reliable, and intelligent. In this book chapter, we discuss different AI methods that will play a significant role in establishing and improving the efficiency of 6G networks. In addition, in the context of 6G, as countless devices connect to the Internet, we investigate the impact of integrating AI in making them smarter, leading to the emergence of IoT. Furthermore, we discuss the security issues posed by 6G networks and the role of AI-driven security in mitigating them. We also underscore the importance of AI in 6G for healthcare.
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Buildings consume a significant amount of energy throughout their lifecycle; Thus, sustainable energy management is crucial for all buildings, and controlling energy consumption has become increasingly important for achieving sustainable construction. Digital twin (DT) technology, which lies at the core of Industry 4.0, has gained widespread adoption in various fields, including building energy analysis. With the ability to monitor, optimize, and predict building energy consumption in real time. DT technology has enabled sustainable building energy management and cost reduction. This paper provides a comprehensive review of the development and application of DT technology in building energy. Specifically, it discusses the background of building information modeling (BIM) and DT technology and their application in energy optimization in buildings. Additionally, this article reviews the application of DT technology in building energy management, indoor environmental monitoring, and building energy efficiency evaluation. It also examines the benefits and challenges of implementing DT technology in building energy analysis and highlights recent case studies. Furthermore, this review emphasizes emerging trends and opportunities for future research, including integrating machine learning techniques with DT technology. The use of DT technology in the energy sector is gaining momentum as efforts to optimize energy efficiency and reduce carbon emissions continue. The advancement of building energy analysis and machine learning technologies is expected to enhance prediction accuracy, optimize energy efficiency, and improve management processes. These advancements have become the focal point of current literature and have the potential to facilitate the transition to clean energy, ultimately achieving sustainable development goals.
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LoRaWAN is a widespread protocol by which Internet of things end nodes (ENs) can exchange information over long distances via their gateways. To deploy the ENs, it is mandatory to perform a link budget analysis, which allows for determining adequate radio parameters like path loss (PL). Thus, designers use PL models developed based on theoretical approaches or empirical data. Some previous measurement campaigns have been performed to characterize this phenomenon, primarily based on distance and frequency. However, previous works have shown that weather variations also impact PL, so using the conventional approaches and available datasets without capturing important environmental effects can lead to inaccurate predictions. Therefore, this paper delivers a data descriptor that includes a set of LoRaWAN measurements performed in Medellín, Colombia, including PL, distance, frequency, temperature, relative humidity, barometric pressure, particulate matter, and energy, among other things. This dataset can be used by designers who need to fit highly accurate PL models. As an example of the dataset usage, we provide some model fittings including log-distance, and multiple linear regression models with environmental effects. This analysis shows that including such variables improves path loss predictions with an RMSE of 1.84 dB and an R2 of 0.917.
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The advances and convergence in sensor, information processing, and communication technologies have shaped the Internet of Things of today. The rapid increase of data and service requirements brings new challenges for Internet of Thing. Emerging technologies and intelligent techniques can play a compelling role in prompting the development of intelligent architectures and services in Internet of Things to form the artificial intelligence Internet of Things. In this article, we give an introduction and review recent developments of artificial intelligence Internet of Things, the various artificial intelligence Internet of Things computational frameworks and highlight the challenges and opportunities for effective deployment of artificial intelligence Internet of Things technology to address complex problems for various applications. This article surveys the recent developments and discusses the convergence of artificial intelligence and Internet of Things from four aspects: (1) architectures, techniques, and hardware platforms for artificial intelligence Internet of Things; (2) sensors, devices, and energy approaches for artificial intelligence Internet of Things; (3) communication and networking for artificial intelligence Internet of Things; and (4) applications for artificial intelligence Internet of Things. The article also discusses the combination of smart sensors, edge computing, and software-defined networks as enabling technologies for the artificial intelligence Internet of Things.
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This research analyses new approaches to security enforcement in fifth generation (5G) architecture from end to end perspective. With the aim of finding a suitable and effective unified schema across the different network domains, it shows that policy control framework may become the cornerstone for the definition and enforcement of security policies in new 5G networks. The 5G core network architecture reference model is defined as a Service Based Architecture (SBA). The Policy Control Function (PCF) is a Network Function (NF) that constitutes, within the SBA architecture, a unique framework for defining any type of policies in the network and delivering those to other control plane NFs. In previous generations the policy control approach has been restricted to Quality of Service (QoS) and charging aspects. In contrast, the 5G system is now based on a unified policy control scheme that allows to build consistent policies covering the entire network. By utilizing the unified 5G policy framework we have found an effective security enforcement schema flexible to create new security policies, and agile to react to the constantly changing environment, across the end to end architecture. Within this schema we have defined mechanisms to apply the QoS principles to security use cases. We have also set up the user plane security enforcement within the session management and established security policies. Finally we have made proposals to extend the network analytics to security analytics. Our overall vision is to consider security as a quality element of the network.
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This paper proposes a two-phase algorithm for multi-criteria selection of packet forwarding in unmanned aerial vehicles (UAV), which communicate with the control station through commercial mobile network. The selection of proper data forwarding in the two radio link: From UAV to the antenna and from the antenna to the control station, are independent but subject to constrains. The proposed approach is independent of the intra-domain forwarding, so it may be useful for a number of different scenarios of Unmanned Aerial Vehicles connectivity (e.g., a swarm of drones). In the implementation developed in this paper, the connection is served by three different mobile network operators in order to ensure reliable connectivity. The proposed algorithm makes use of Machine Learning tools that are properly trained for predicting the behavior of the link connectivity during the flight duration. The results presented in the last section validate the algorithm and the training process of the machines.
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This position paper summarizes the main visions, opinions, and arguments of four experienced and well known researchers in the area of Internet of Things (IoT) and its relation to Data Science and Machine Learning (ML) as IoT permeates the globe and becomes "very large". These visions were raised in an enthusiastic discussion panel held during the Third International Workshop on Very Large Internet of Things Systems (VLIoT 2019), in conjunction with VLDB 2019, in Los Angeles, USA. Each panelist delivered a vision statement before the floor was opened for questions and comments from the audience. Instead of reproducing ipsis literis each of the speeches, questions and replies, we decided to structure a two-part paper summarizing in-depth the panel opinions and discussions. In this first installment, we present the panelists' opening statements and views on issues related to IoT infrastructure and how it can support the growing demands for integrated intelligence, including communication, coordination and distribution challenges and how such challenges can be faced in the new generation of IoT systems.
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Named Data Networking (NDN) architectural features, including multicast data delivery, stateful forwarding, and in-network data caching, have shown promise for applications such as video streaming and file sharing. However, collaborative applications, requiring multi-producer participation, introduce new NDN design challenges. In this article, we highlight these challenges in the context of the Network Time Protocol (NTP) and one of its most widely-used deployments for NTP server discovery, the NTP pool project. We discuss the design requirements for the support of NTP and NTP pool and present general directions for the design of a time synchronization protocol over NDN, called Named Data Networking Time Protocol (NDNTP).
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Delay-sensitive applications have been driving the move away from cloud computing, which cannot meet their low-latency requirements. Edge computing and programmable switches have been among the first steps toward pushing computation closer to end-users in order to reduce cost, latency, and overall resource utilization. This article presents the "compute-less" paradigm, which builds on top of the well known edge computing paradigm through a set of communication and computation optimization mechanisms (e.g.,, in-network computing, task clustering and aggregation, computation reuse). The main objective of the compute-less paradigm is to reduce the migration of computation and the usage of network and computing resources, while maintaining high Quality of Experience for end-users. We discuss the new perspectives, challenges, limitations, and opportunities of this compute-less paradigm.
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