1. Introduction

In the coming fourth industrial revolution, several technological fields are impacting all disciplines, economies, and industries, such as artificial intelligence (AI) and the Internet of Things (IoT). Artificial intelligence permits machines to learn and adapt to different situations and problems. Due to its great versatility, AI can work in conjunction with the Internet of Things, allowing smart home devices or microcontroller units (MCUs) to create many Internet of Things networks (IoTNs) to be able to collect, process, and share data of different nature that flows through the network. It is expected that by 2025 26–30 million MCUs in homes and offices will be connected as networks to the Internet and equipped with sensors, processors, and embedded software.

As a network, IoTN behaves as a collective entity which does not depend on a central control or hub. Therefore, it is necessary to design IoTN topologies that are not subject to the performance of a single MCU or node for guarantying the quality of service (QoS) requirements due to an efficient connectivity based on a bandwidth that links each of MCUs with their immediate neighbors at 300 Mbps, in order to ensure that all of them really share their parameters when they are operating. This implies to design networks with shared parameters between their nodes and where the locality is preserved and limited the maximum average distance between the MCUs by using alternative tools, such as space-filling curves. The Hilbert fractal is a continuous space-filling curve whose locality-preserving behavior is better than that of Z-order curves, because the distance between each node in a Hilbert curve does not fluctuate, whereas that distance in a Z-order curve does fluctuate.

In this sense, we can say that the smart home is the result of the intersection between architecture, interior design, and the most advanced technology, which allows the automation in an unattended way of certain domestic tasks, but also with the condition that there can be supervision or fixed objectives on the part of the

Optimizing a Centralized Control Topology of an IoT Network Based on Hilbert Space

• Increase our security cameras or motion sensors which are elements that warn us of intrusions and suspicious movements even when we are absent.

• Provide us more time. By automating certain tasks, we stop thinking about them. If we wake up every morning at a certain time, the same home (through its artificial intelligence) can raise the blinds and start to heat the coffee, so we can save this time to get to work a little earlier or simply to sleep 5 more minutes.

• Allow us remote control. Turning on the air-conditioning unit 10 min before

• The energy saving with multiple sensors scattered around the home are more reliable than the human sensation, where exactly the energy needed to heat or

The latter statement guides us to consider as one of the most important issues the way to save energy among sensors, namely, how efficient is the energy consumption. In this way we identify energy efficiency as the most critical challenges in IoTNs because the nodes or smart home devices in such networks have limited resources. In this way, software-defined networks (SDNs) are a way to approach the creation of networks in which control is detached from the hardware and given to a software application called a controller. So, SDNs, also known as programmable and automated networks, are presented as a proposal that provides greater speed, agile infrastructure, and better costs in cloud IT platforms; it is urgent to respond to

When a packet arrives at a switch in a conventional home network, the rules built into the proprietary firmware of the switch tell the switch where to transfer the packet. The switch or router sends each packet to the same destination on the same path and treats all packets in the exact same way. In this proposal, intelligent routers designed with specific MCUs are sophisticated enough to recognize different types of packages and treat them differently, but these router can be only the seed or the begging of the home network. Furthermore, in an SDN, a home network administrator can shape traffic from a centralized control console without having to touch individual smart devices, in this case sensors. The administrator can modify any rule of network switches when necessary—giving or removing priority or even

blocking specific types of packets with a very detailed level of control.

This is especially useful in a multi-tenant architecture for cloud computing because it allows the administrator to handle traffic loads flexibly and more efficiently. Essentially, this allows the administrator to use fewer and more intelligent routers and have more control than ever over the flow of all the smart devices of the network traffic. Nowadays, the most popular specification for creating a softwaredefined network is an open standard called OpenFlow. OpenFlow allows network

In addition, home networks have not changed about 20 years ago; unlike programs, we are facing a paradigm shift to which we must adapt. The development of

residents.

Thus, a smart home seeks to:

DOI: http://dx.doi.org/10.5772/intechopen.87206

we get home so that when we enter it.

the dynamism of the applications required by the user.

administrators to control routing tables remotely.

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cool the house is used.

Accordingly, in this chapter an effective and a reliable optimized fractal algorithm (OFA) for extending the range of transmission of a given IoTN in an intelligent, adaptive, and dynamic way is proposed. The OFA finds a path from a source to a target to optimize the links by considering the quality of service constraints such as end-to-end reliability and delay. In this way, with OFA we pretend to achieve the best two MCUs where each smart home of the IoTN can transfer or share its parameters. Finally, through laboratory tests and computer simulations, we demonstrate the effectiveness of our approach by means of a fractal routing in IoTN, using from 16 to 64 smart home devices for real-time monitoring of different parameters, in order to make more efficient the Smart Home Automation by reducing the IoTN energy consumption.

The further sections of this chapter are organized as follows. First, in Section 2 we explain the relation between smart home devices distributed in networks and the energy efficiency. In Section 3 an explanation of the theoretical definition of the Hilbert space-filling fractal is given. OFA scheme is defined in Section 4, while in Section 5, the simulation and the comparison of the performances are announced, in order to verify the correction and feasibility of the proposal. Finally, the arguments and discussion of this chapter are analyzed in Section 6.
