Abstract
AIoT, or artificial intelligence of things, is a transformative combination of artificial intelligence and the internet of things (IoT) that has far-reaching ramifications across multiple domains. This chapter examines the theories and models underlying its development and implementation. Businesses can assess the costs, benefits, and competitive advantages of AIoT by using economic models and market dynamics. Understanding human behaviour and trust is crucial for user acceptance, while ethical considerations underpin the development of accountable AIoT applications. Data management, security, and interoperability are technical facets that architectural frameworks address. The alignment of AIoT with human needs is enhanced by cognitive models and user experience, thereby fostering well-being. Change management and organizational learning are essential for effective implementation, which fosters innovation. AIoT promotes innovation and efficiency in manufacturing, healthcare, and smart cities.
TopIntroduction
AIoT is a paradigm shift that signifies the fusion of two influential technologies, Artificial Intelligence (AI) and the Internet of Things (IoT). AIoT makes IoT devices sharper, more responsive, and capable of advanced data processing and decision-making through AI algorithms and machine learning techniques. IoT devices can analyze data, adapt to changing circumstances, and make intelligent, autonomous decisions without human intervention (Ye et al., 2023; Kuzlu et al., 2021). AIoT systems are designed to integrate seamlessly with IoT devices, sensors, and networks, allowing them to comprehend context, learn from data, and progress over time. This convergence of AI and IoT technologies is at the core of AIoT, and it can potentially revolutionize multiple domains, including smart cities, healthcare, manufacturing, and agriculture (Adli et al., 2023; Hung, 2021). The relevance of AIoT lies in its capacity to drive substantial progress and revolutionize diverse domains. In manufacturing, agriculture, and logistics industries, AIoT systems can optimize processes, predict and prevent faults, and automate duties, resulting in greater productivity. By enabling IoT devices to analyze and process data, AIoT allows organizations and individuals to make informed decisions in real-time, resulting in enhanced resource management and outcomes.
AIoT can substantially reduce operational costs and improve resource efficiency through predictive maintenance, energy management, and optimization. AIoT enables innovative and customized services (Kuzlu et al., 2021). For instance, it can facilitate remote patient monitoring and personalized treatment plans in healthcare. IoT addresses environmental challenges by optimizing energy consumption, reducing waste, and promoting sustainability in smart cities and industries. The integration of Artificial Intelligence of Things (AIoT) can enhance individuals' quality of life by providing cutting-edge and user-friendly solutions for everyday chores, such as smart homes and wearable gadgets. Integrating AIoT into various industries can stimulate economic growth by fostering innovation and generating new business opportunities. AIoT also raises significant concerns regarding data privacy, security, ethics, and the potential for job displacement, making it a subject of substantial societal and policy debates.
AIoT, or Artificial Intelligence of Things, is a profoundly impactful technology movement that can revolutionize industries, improve our lifestyles and productivity, and address intricate challenges. Its significance in the technological landscape is highlighted by its multifaceted effect on society and the economy. Artificial Intelligence of Things (AIoT) is a multidimensional field that encompasses a variety of domains and draws from numerous disciplines. AIoT's multidisciplinary nature reflects the diversity of its components and effects. AIoT is founded on the Internet of Things, which includes sensors, actuators, and communication networks. AI techniques such as machine learning, deep learning, and natural language processing are crucial for enhancing the intellect and decision-making capabilities of AIoT. AIoT generates enormous data, necessitating expertise in managing and analyzing large data sets. Decision-making in AIoT applications requires data-driven insights (Ye et al., 2023; Adli et al., 2023). The development of AI and IoT-integrated applications requires software engineering expertise. IoT systems must address security and privacy concerns, rendering cybersecurity expertise essential. AIoT's foundational aspect is designing and engineering IoT devices and sensors. Understanding the economic implications of AIoT applications and devising viable business models are imperative.
Key Terms in this Chapter
Psychology: Study of the human mind and behaviour, encompassing thoughts, feelings, perceptions, and actions, aiming to understand and explain individual and group dynamics.
Artificial Intelligence of Things (AIoT): Integrating artificial intelligence algorithms and techniques into IoT systems to enhance data analysis, decision-making, and automation processes.
Behavioral Science: Interdisciplinary field examining human behaviour, encompassing psychology, sociology, anthropology, and economics, to understand actions, decisions, and interactions.
Technology: Application of scientific knowledge, tools, and techniques to solve practical problems, improve processes, and enhance human capabilities, driving innovation and progress.
IoT (Internet of Things): Network of interconnected devices capable of collecting and exchanging data, enabling automation, monitoring, and control of physical objects or environments.
Organizational Studies: Examination of organizations and their functioning, focusing on structures, processes, behaviours, and strategies to enhance effectiveness, efficiency, and adaptability.
Models: Simplified representations of real-world systems or processes used for analysis, prediction, and problem-solving, aiding comprehension and decision-making.