**1. Introduction**

In their 1973 article, "The Network City," Craven & Wellman conceptualized cities as a multitude of social networks comprising systems of interaction, systems of resource allocation, and systems of integration and coordination. While at roughly the same time, the first Ethernet was invented and the first VOIP phone call was made, and the information technology is notable by its absence in "The Network City." Within 5 years, numerous researchers envisioned a "network nation" and "wired society" driven by advances in communications technology [1, 2]. Over the last four decades, the opportunities and challenges of a society mediated by technology have been a major focus of academia, policymakers, and industry expanding

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chapter concludes.

**2. Intelligent systems**

*Toward the Intelligent Internet of Everything: Observations on Multidisciplinary Challenges…*

with each new generation of information and communication technology [3, 4]. More recently, the emergence of the so-called third ICT platform characterized by the ubiquity, convergence, and interdependence of social media, mobile, cloud, big data, and sensor technologies is transforming how society operates and interacts [5]. Today, the networked society is increasingly a society whose social structures and activities, to a greater or lesser extent, are organized around digital information networks that connect people, processes, things, data, and social networks. This convergence of the virtual (cyber), the physical, and human worlds is commonly

The focus by academia, industry, and policymakers on the Internet of Everything is not mere altruism. Estimates on the value of the Internet of

representing and making sense of a dynamic physical world.

The remainder of the chapter is organized as follows: Section II introduces intelligent systems, a conceptual framework, and design principles for general information system architecture. Section III provides an overview of intelligent methods and paradigms used for analyzing data and supporting decision-making in intelligent systems. Section IV discusses the Intelligent Internet of Everything and some of the opportunities and challenges for such a concept, after which the

In computer science, intelligent systems research has its roots in the natural sciences and the study of natural systems and specifically how intelligent behavior occurs. Since the 1950s, computer scientists have sought to understand the nature of intelligence by constructing artifacts that exhibited the same breadth and depth of cognition as humans and other biological entities, such as ant colonies, swarms, etc. [9]. The search for artificial intelligence (AI) is a search for systems that think like humans, think rationally, act like humans, and act rationally [10]. In this respect, AI and intelligent systems are often used synonymously. Both involve agents whose behavior is informed by inputs from their environment and take actions that

Everything to the public and private sector by 2022 exceed \$4.6 trillion and \$14.4 trillion, respectively [6, 7]. Improvements to asset utilization and employee productivity, supply chain and logistics, and customer experience, as well as accelerated innovation are just some of the cited contributions from connecting a relatively small fraction of the 1.4 trillion things and billions of people that we can connect today. Realizing the Internet of Everything requires overcoming numerous technical challenges, not least complexity. The Internet of Things, the next logical step in the evolution toward the Internet of Everything, alone comprises an extremely large number of entities, with different storage, computing, networking, reasoning capabilities and profiles [8]. These entities may operate and interact autonomously in vastly different, dynamic, and uncertain environmental conditions where time may or may not be of the essence. The heterogeneity and scale of the Internet of Everything, the uncertainty and dynamism of the environments in which people and things operate and interact, and the criticality of information and data requirements require novel approaches to manage complexity and not least deciding where decisions should be made—locally at the edge (by the thing), centrally or somewhere in between—if they can be made locally at all. Recently, intelligent systems have emerged that can perceive and respond to the physical and social world around them with a greater degree of autonomy; these systems make things smart. However, such intelligent systems and smart things present both interesting and significant multilevel computational and societal research challenges, not least

*DOI: http://dx.doi.org/10.5772/intechopen.83691*

referred to as the "Internet of Everything".

#### *Toward the Intelligent Internet of Everything: Observations on Multidisciplinary Challenges… DOI: http://dx.doi.org/10.5772/intechopen.83691*

with each new generation of information and communication technology [3, 4]. More recently, the emergence of the so-called third ICT platform characterized by the ubiquity, convergence, and interdependence of social media, mobile, cloud, big data, and sensor technologies is transforming how society operates and interacts [5]. Today, the networked society is increasingly a society whose social structures and activities, to a greater or lesser extent, are organized around digital information networks that connect people, processes, things, data, and social networks. This convergence of the virtual (cyber), the physical, and human worlds is commonly referred to as the "Internet of Everything".

The focus by academia, industry, and policymakers on the Internet of Everything is not mere altruism. Estimates on the value of the Internet of Everything to the public and private sector by 2022 exceed \$4.6 trillion and \$14.4 trillion, respectively [6, 7]. Improvements to asset utilization and employee productivity, supply chain and logistics, and customer experience, as well as accelerated innovation are just some of the cited contributions from connecting a relatively small fraction of the 1.4 trillion things and billions of people that we can connect today. Realizing the Internet of Everything requires overcoming numerous technical challenges, not least complexity. The Internet of Things, the next logical step in the evolution toward the Internet of Everything, alone comprises an extremely large number of entities, with different storage, computing, networking, reasoning capabilities and profiles [8]. These entities may operate and interact autonomously in vastly different, dynamic, and uncertain environmental conditions where time may or may not be of the essence. The heterogeneity and scale of the Internet of Everything, the uncertainty and dynamism of the environments in which people and things operate and interact, and the criticality of information and data requirements require novel approaches to manage complexity and not least deciding where decisions should be made—locally at the edge (by the thing), centrally or somewhere in between—if they can be made locally at all. Recently, intelligent systems have emerged that can perceive and respond to the physical and social world around them with a greater degree of autonomy; these systems make things smart. However, such intelligent systems and smart things present both interesting and significant multilevel computational and societal research challenges, not least representing and making sense of a dynamic physical world.

The remainder of the chapter is organized as follows: Section II introduces intelligent systems, a conceptual framework, and design principles for general information system architecture. Section III provides an overview of intelligent methods and paradigms used for analyzing data and supporting decision-making in intelligent systems. Section IV discusses the Intelligent Internet of Everything and some of the opportunities and challenges for such a concept, after which the chapter concludes.
