Skip to main content
main-content
Top

About this book

This book gathers state-of-the-art research contributions written by academics and researchers, which address emerging trends in system design and implementation for the Internet of Things (IoT), and discuss how to promote IoT technologies and applications.

The book is chiefly intended for researchers and academics who want to get caught up with the latest trends in enabling technologies for IoT and related applications and services. However, it also includes chapters on the fundamentals of IoT, offering essential orientation for general readers.

Table of Contents

Frontmatter

Introduction

Abstract
The Internet of Things (IoT) is the rapid advancement of the current Internet that has been transformed from the tradition human interaction into a network of interconnected devices. Due to explosion in the IoT devices networked based on wireless transmission, spectral efficient solutions needs to be accommodated efficiently to enhance connectivity and operational efficiency among large number of heterogeneous devices intelligently and autonomously.
Mohammad Abdul Matin

Current Research Trends on Cognitive Radio Based Internet of Things (IoT)

Abstract
The attractive features of Internet of Things (IoT) and the concept of cognitive radio have raised the opportunity of creating a smart world. The advancement of cost-effective technologies and protocols empower us to make practical implementation of IoT which impact on human lifestyle, business and industries. Research interest has thus been dragged into the IoT domain to exploit its potential. However, the increased number of devices have caused the spectrum crisis issue. To mitigate this crisis, Cognitive Radio (CR) technology is integrated with IoT that can search for the available spectrum and reuse it for communication. By using cognitive capabilities, cognitive radio can avoid collision among the network elements to ensure better connectivity, accessibility, scalability and reliability of the IoT system. Currently, the research on CR-IoT is at its early stage. This chapter attempts to focus on the recent research efforts related to spectrum sensing, sharing and allocation, cost-effective architectures, transmission parameter adaptation, energy efficient proposals and security provisioning problems for CR-IoT. Some design issues in CR-IoT system are also being discussed in this chapter.
M. Rezwanul Mahmood, Mohammad Abdul Matin

Cognition Radio Enabled IoT

Abstract
Internet of things (IoT) has changed human lifestyle by introducing various smart applications. In recent years, sophisticated automation systems have become an essential outcome of the IoT paradigm. Due to the characteristics of IoT applications, devices need to communicate with each other seamlessly. New networking technologies and architectures have also been designed to support the communication requirements of current and future IoT devices. Wireless and radio communications are very desirable for achieving communication among the devices with various proximity. As there is a rapid growth in the number of IoT devices networked based on wireless transmission, radio frequency resource needs to be allocated efficiently to enhance radio spectrum utilization. In wireless connection, Cognitive Radio (CR) is an opportunistic radio access technology targeted to improve the spectrum usage and to mitigate the excessive contention of radio communication. In this chapter, we have identified the functional similarities between IoT and CR, and the challenges that are important to be addressed to integrate CR technology for IoT. We have also proposed a framework for cognitive radio enabled IoT that provides efficient radio spectrum utilization for IoT.
Md. Mahfuzur Rahman, Mohammad Abdul Matin

SDN-Enabled IoT: Ensuring Reliability in IoT Networks Through Software Defined Networks

Abstract
Ensuring reliability for IoT networks is very crucial for the use cases like autonomous self-driving car, tactile internet, healthcare devices, etc., which requires continued communication to facilitate un-interrupted services. Software defined networks (SDN) facilitates to program the network and enables efficient control over the complicated network infrastructure like IoT. For a continued and effective implementation of SDN in IoT networks, it must solve the network reliability challenges to provision the low-latency and ultra-reliable transmission scenarios even in the case of failures in the network. In comparison with the path-based recovery, the local rerouting is a preferred solution for rapid failure recovery. For achieving the rapid local recovery, backup paths must be pre-configured for every flow on the link, which results in memory consumption of the switch for maintaining flow rules of the backup paths. Also, the efforts required for rerouting of every flow can delay the failure recovery. The book chapter will focus on the issues associated with failure management in software defined IoT networks and proposes forwarding table configuration in network, which can autonomously recover an OpenFlow-based IoT network from a link or a node failure. We firstly present Local Immediate (LIm) and Immediate Controller Dependent (ICoD) failure management approaches to overcome the shortcomings of link failure management approaches in SDN. Our proposed approaches conserve the memory of switches by reducing the backup path rules by aggregating the flow rule on the common network component using VLAN-enabled flow labelling. The proposed approaches are expected to accomplish recovery in the range of 2–20 ms and will fulfill the stringent 50 ms recovery condition of Carrier Grade Networks (CGNs). Next, we extend our solution on single link recovery and present forwarding table configuration for the network to accomplish the switch recovery. To validate our proposed approaches for the link and switch recovery, we evaluated the performance in following points; (i) Number of flow entries can be saved to enable the protection against failures, and (ii) how quickly the recovery can be accomplished.
Pankaj Thorat, Sukhdeep Singh, Avinash Bhat, V. Lakshmi Narasimhan, Gaurav Jain

QoS Aware Spectrum Selection for IoT

Abstract
Selecting appropriate spectrum becomes one of the key challenges for IoT devices in satisfying the QoS aspects of the applications while integrated with Cognitive Radio (CR). Most of the existing research is focused on maximizing the spectrum utilization by merging CR with IoT paradigm whereas QoS aspects of IoT applications have largely been neglected. We propose a spectrum selection mechanism that can be employed by IoT devices to meet the QoS requirements of IoT applications. The approach includes identifying the QoS requirements of IoT applications, matching appropriate spectrum (in CR) satisfying the QoS requirements. This QoS aware spectrum matching strategy provides IoT applications a suitable solutions for satisfying the QoS requirements.
Md. Mahfuzur Rahman, Mohammad Abdul Matin

Cognitive M2M Communications: Enablers for IoT

Abstract
In recent years, there has been a significant development of Internet of Things (IoT). The IoT is seen as an important part of the Future Internet. The number of machines which will be connected to the Internet is expected to reach billions in near future. This enormous number of heterogeneous devices, both physical and virtual, are operated using various different communication protocols to form extended global networks. This puts a massive burden and challenges on present communication technologies in terms of performance and connectivity due to spectrum crisis and inefficient spectrum utilization. Incorporating cognitive radio technologies in such scenarios can help to overcome such challenges. This chapter provides an overview of cognitive machine-to-machine (M2M) technologies along with some potential applications.
Rezwana Ahmed, Mohammad Abdul Matin

Cognitive Radio Engine Design for IoT Using Monarch Butterfly Optimization and Fuzzy Decision Making

Abstract
The Internet of Things (IoT) paradigm expands the current Internet and enables communication through machine to machine (M2M), while posing new challenges. Cognitive Radio (CR) Systems have received much attention over the last decade, because of their ability to flexibly adapt their transmission parameters to their changing environment. Current technology trends are shifting to the adaptability of Cognitive Radio Networks (CRNs) into IoT. The determination of the appropriate transmission parameters for a given wireless channel environment is the main feature of a cognitive radio engine. For wireless multicarrier transceivers, the problem becomes high dimensional due to the large number of decision variables required. Evolutionary Algorithms (EAs) are suitable techniques to solve the above-mentioned problem. In this chapter, we propose a new approach for designing a CR engine for wireless multicarrier transceivers using monarch butterfly optimization (MBO). Moreover, we also apply a modified MBO version that includes a Greedy strategy and a self-adaptive Crossover operator, called Greedy Crossover MBO (GCMBO). Additionally, the CR engine also uses a fuzzy decision maker for obtaining the best compromised solution. The simulation results show that the GCMBO driven CR engine can obtain better results than the original MBO and outperform other popular algorithms. Moreover, GCMBO is more efficient when applied to high-dimensional problems in cases of multicarrier system.
Sotirios K. Goudos

Physical Layer Security of Cognitive IoT Networks

Abstract
Internet of Things (IoT) networks have the potential to drastically change our daily lives with a diverse set of applications. Healthcare, manufacturing, transportation and various other industries may encounter their impacts. As the IoT nodes evolve, it is only natural to expect an increase in their capabilities. Cognitive IoT (CIoT) networks contain IoT nodes with cognitive capabilities that can sense, and analyze the environment. These nodes can also act based on their analysis results. With such a potential, CIoT networks may also become a target for malicious users or attackers. In this chapter, we provide an overview of the main security requirements. Main attack types are summarized. Possible attacks on the CIoT nodes, including primary user emulation attacks, sensing data falsification attacks, objective function attacks and eavesdropping attacks are detailed. A case study targeting primary user emulation and sensing data falsification attacks based on a trust metric is detailed. Open issues are also highlighted.
Güneş Karabulut Kurt, Özge Cepheli

Internet of Energy Harvesting Cognitive Radios

Abstract
The Internet of Things (IoT) offers enhanced connectivity so that any system, being, or process can be reached from anywhere at any time by perpetual surveillance, which results in very large and complex data sets, i.e., Big Data. Despite numerous advantages, IoT technology comes with some unavoidable drawbacks. Considering the number of devices to be added to the current electromagnetic spectrum, it is a fact that wireless communications will severely suffer and eventually become inoperable. Furthermore, as wireless devices are equipped with limited capacity batteries, frequent replenishments and/or maintenance will be needed. However, this is neither practical nor achievable due to the excessive number of devices envisioned by the IoT paradigm. Here, the unification of Energy Harvesting (EH) and Cognitive Radio (CR) stands highly promising to alleviate the current drawbacks, enabling more efficient data generation, acquisition, and analysis. This chapter outlines a new vision, namely Internet of Energy Harvesting Cognitive Radios (IoEH-CRs), to take the IoT-enabled Big Data paradigm a step further. It discusses the basics of the EH-assisted spectrum-aware communications and their implications for the IoT, as well as the challenges posed by the unification of these techniques. An operational framework together with node and network architectures is also presented.
O. Cetinkaya, M. Ozger, O. B. Akan

Cultural IoT Framework Focusing on Interactive and Personalized Museum Sightseeing

Abstract
Museum visitors are very focused and demanding. Immersive technologies as virtual and augmented reality, interactive haptics, 3D scanning and plotting, content digitization, and personalized automatic navigation must be exploited by museums in order to stimulate museum visitors and extract their attention. The authors of this work propose an open source IoT InteRactive Museum Experience (IRME) framework. IRME offers information classified in thematic sections. The visitors have the opportunity to explore specific thematic sections of interest. Navigation instructions and artwork guidelines are obtained with the help of a smart phone application. Data-mining, artificial intelligence and cognitive services offer the ability to learn from visitor’s preferences and respond more accurately to future requests and in this way enhance visitor’s experience in the museum. IRME provides a real-time, responsive and personalized navigation to museum visitors. It includes indoor positioning technology, IoT sensors and actuators, haptic devices orchestrated over cloud services. Wherever possible, IRME uses low power technology such as Bluetooth Low Energy devices, led plates-spots-cubes and 3D printing modeling capabilities, in order to promote museum artifacts and to enhance the visitors’ knowledge acquisitions and entertainment. Moreover, the reflection of such recreational improvements to the visitors is also measured using IoT sensors and the results are used as feedback for future thematic land planning, and IoT illustration techniques.
Sotirios Kontogiannis, George Kokkonis, Ioannis Kazanidis, Michael Dossis, Stavros Valsamidis
Additional information