**4. Toward an intelligent Internet of Everything**

The architecture for a future Internet of Everything has yet to be determined. Indeed, the very concept of the Internet of Everything lacks definition. Yet, despite this, there is what one might term an "irrational exuberance" about the benefits and value it might generate. We propose an extended definition of the Internet of Everything that includes intelligence at its core. Thus, we define an Intelligent Internet of Everything as a system of systems that connect people, processes, things, data, and social networks and through intelligent systems proactively create new value for individuals, organizations, and society as a whole. It assumes a vision of ambient intelligence (see **Figure 1**) where we live, work and interact in, and with a digitally infused environment that "proactively, but sensibly, supports people in their daily lives" ([34], p. 15). Achieving this vision presents significant opportunities and challenges, not least advances in ubiquitous sensing, cognitive architectures, adaptive infrastructure, and privacy by design.

#### **4.1 Ubiquitous sensing**

An Intelligent Internet of Everything built on intelligent systems assumes not only ubiquitous computing but a ubiquitous sensing network. Ubiquitous sensing provides rich multimodal sensing abilities to the ubiquitous computing infrastructure without increasing the burden on the users [35]. The last two decades have seen a rapid advance in the sophistication of sensors including intelligent sensors, *in vivo* sensors, and sensors that can increasingly mimic the biological senses. Like intelligent systems, intelligent sensors can take actions based on data from the environment in which they operate rather than merely measuring a signal [36].

The development of the next generation of sensors is closely linked to innovation in nanomaterials and nanotechnology methods which both enable embedded

#### **Figure 1.**

*The ambient-intelligence vision from an artificial intelligence perspective [34].*

advanced functionalities into sensors but also drive down costs, thereby increasing adoption and proliferation of sensors [36]. Similarly, nanotechnology is contributing to the development of electrochemical, optical, and magnetic resonance biosensors that are transforming our understanding of biology and treatment of disease including novel drug discovery for this purpose [37, 38]. As well as *in vivo* sensors, increasingly sophisticated sensors are providing multimodal sensing to not only allow machines to see and hear but also touch [39], taste [40], and smell [41] like humans. Advances in computer processing, networking, and miniaturization are introducing a new generation of bio-inspired mobile, wearable, and embedded sensors. These bio-inspired sensors are enabling machines to detect, alert, and prevent harmful events such as chemical threats in an unobtrusive fashion previously the domain of science fiction [42].

Despite the progress in developing increasingly novel and sophisticated sensors, Paulovich et al. [36] highlight the need for greater research into new materials and detection methods for wearable and implantable devices and specifically device engineering for both producing and commercializing low-cost, robust sensing units. Like all interdisciplinary research areas, the success of sensors and biosensing requires not only greater understanding of the technologies involved but also the underlying biophysical mechanisms, activities, and events inspiring sensors and specifically biosensor development. Furthermore, before intelligent systems can be relied upon particularly in high criticality situations or *in vivo*, concerns relating to predictability, reproducibility, and sensor drift and calibration, amongst others must be addressed over a sustained period [36].

**61**

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

Ambient intelligence and the wider Intelligent Internet of Everything assume that once our sensing network captures data, we will have the systems in place to analyze the various sensing inputs to interpret, analyze, plan, act, and otherwise interact with other systems and humans [34]. Despite significant advancements, the state-of-the-art in cognitive architectures remains at a relatively early stage relative to match the 3000+ cognitive abilities of humans [43]. Both Ramos et al. [34] and Kotseruba and Tsotsos [14] emphasize the need for advancements in approaches to model human cognition and increase the range of supported cognitive abilities. To this end, Kotseruba and Tsotsos [14] identify eight areas for further research based

• Adequate experimental validation—thorough testing in more diverse, challenging, and realistic environments, real-world situations, more elaborate

• Realistic perception—advancements in active vision, localization and tracking, performance under noise and uncertainty, and the use of context information

knowledge transfer, and accumulation of knowledge without affecting prior

• Natural communications—advancements in verbal communications including knowledge bases for generating dialogs, robustness, detection of emotional response and intentions, and personalized responses as well as performing and

• Computational performance—computation efficiency in embodied and nonembodied architectures, time, and space complexity. See discussion in C below.

• Comparative evaluation of cognitive architectures—combination of (i) objective and extensive evaluation procedures and (ii) theoretical analysis, software testing techniques, benchmarking, subjective evaluation, and challenges, to every aspect of the cognitive architecture and probing multiple abilities.

• Reproducibility of results—fuller technical detail and greater access to soft-

Conventional cloud architectures allocate functionality against a very different set of design principles than the emerging Intelligent Internet of Everything. Decisions are made within the boundaries of a cloud and its infrastructure. The emergence of the Internet of Everything requires the allocation and/or optimization of resources and activities along a Cloud-to-Things (C2T) continuum and to accommodate computation at the edge but also using intermediary data management and processing capabilities or fog computing as it is commonly known. Latency requirements, network bandwidth constraints, device profiles, uninterrupted service

• Human-like learning—more robust and flexible learning mechanisms,

• Autobiographic memory—episodic memory and lifelong memory.

on their survey of extant research on cognitive architectures:

detecting other nonverbal human communications.

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

scenarios, and diverse tasks.

learning.

ware and data.

**4.3 Infrastructure**

to improve detection and localization.

**4.2 Cognitive architectures**

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