Learning Outcomes

After reading this chapter, the reader will be able to:

• Assess the various evolving aspects and paradigms of IoT

• Understand the most prominent challenges encountered during the design and development of IoT solutions

• Research upcoming and emerging domains, which find significant applicability in IoT

Introduction

Since the inception of IoT, the technology has passed through multiple stages of development and revolutionized the industrial as well as consumer sectors rapidly. However, developers face multiple challenges while developing their ideas into reality. The IoT devices are usually resource-constrained in terms of their computational capability, battery power, as well as, storage. These limitations mandate the developers to develop routines that execute complex operations on the devices with ease, making IoT applications dependent on external platforms such as cloud and fog computing. Another significant issue is the rising number of IoT devices. IoT has penetrated diverse domains and extends its scope to smart homes, vehicles, cities, utility meters, and others. Such an increase in the number of interacting devices increases the consumption of resources and causes congestion in the network. In addition to that, the devices, along with their interconnections, have a non-trivial topology, which leads to complex networks. The IoT industries, therefore, need to develop new algorithms to deal with the increasing lattices in the complex network as well as multiple access protocols to avoid delays and packet drops. The increasing number of devices also consumes a lot of power, which calls for the development of new low-power hardware and schemes that involve smart energy harvesting and its consumption.

IoT devices include both static as well as mobile nodes, depending on the user's applications. Although mobility in such environments may or may not be random, developers need to be prepared for any pattern while providing their services. Since the precise prediction of mobility is challenging, it can only be analyzed statistically. Additionally, IoT environments also have multiple access points in close proximity. Such a deployment of access points creates interference, causing a reduction in signalto- noise-plus-interference ratio (SINR) values, which causes a decline in signal quality and packet drops. IoT service providers need to impose smart channel selection as well as schemes to overcome these interferences. Developers also need to create new models to mimic the mobility of the users and propose schemes to facilitate smooth handoffs among the access points for uninterrupted service.

Learning Outcomes

After reading this chapter, the reader will be able to:

• Understand the basic principles of computer networking

• List the basic terminologies and technologies

• Relate new concepts of IoT with the basics of networking

• Discuss various network configurations and topologies

• Explain various OSI (open systems interconnections) and TCP/IP (transmission control protocol/Internet protocol) layers and their associated uses

• Describe basics of network addressing

Introduction

In the present era of data- and information-centric operations, everything—right from agriculture to military operations—relies heavily on information. The quality of any particular information is as good as the variety and strength of the data that generates this information. Additionally, the speed at which data is updated to all members of a team (which may be a group of individuals, an organization, or a country) dictates the advantage that the team has over others in generating useful information from the gathered data. Considering the present-day global scale of operations of various organizations or militaries of various countries, the speed and nature of germane information are crucial for maintaining an edge over others in the same area. To sum it up, today's world relies heavily on data and networking, which allows for the instant availability of information from anywhere on the earth at any moment.

Typically, networking refers to the linking of computers and communication network devices (also referred to as hosts), which interconnect through a network (Internet or Intranet) and are separated by unique device identifiers (Internet protocol, IP addresses and media access control, MAC addresses). These hosts may be connected by a single path or through multiple paths for sending and receiving data. The data transferred between the hosts may be text, images, or videos, which are typically in the form of binary bit streams [1].

Learning Outcomes

After reading this chapter, the reader will be able to:

• Relate to the applicability of IoT in real-life scenarios

• List the salient features of healthcare IoT

• Understand and examine the basic implementation aspects of healthcare IoT

• Understand the requirements, challenges, and advantages in implementing IoT in healthcare

• Relate to the appropriate use of various IoT technologies through a real-life use case of healthcare IoT system

Introduction

Internet of Things (IoT) has resulted in the development and emergence of a variety of technologies that has had a huge impact on the medical field, especially wearable healthcare. The salient features of IoT encourage researchers and industries to develop new IoT-based technologies for healthcare. These technologies have given rise to small, power-efficient, health monitoring and diagnostic systems. Consequently, the development of numerous healthcare technologies and systems has rapidly increased over the last few years. Currently, various IoT-enabled healthcare devices are in wide use around the globe for diagnosing human diseases, monitoring human health conditions, caring/monitoring for elders, children, and even infants. Moreover, IoT-based healthcare systems and services help to increase the quality of life for common human beings; in fact, it has a promising scope of revolutionizing healthcare in developing nations. IoT-based healthcare devices provide access and knowledge about human physiological conditions through hand held devices. With this development, users can be aware of the risks in acquiring various diseases and take necessary precautions to avoid preventable diseases. The basic skeleton of an IoT-based healthcare system is very similar to the conventional IoT architectures. However, for IoT-based healthcare services, the sensors are specifically designed to measure and quantify different physiological conditions of its users/patients. A typical architecture for healthcare IoT is shown in Figure 14.1. We divide the architecture into four layers. The detailed description of these layers are as follows:

• (i) Layer 1: We have already explained in previous chapters that sensors are one of the key enablers of IoT infrastructure. Layer 1 contains different physiological sensors that are placed on the human body. These sensors collect the values of various physiological parameters. The physiological data are analyzed to extract meaningful information.

Learning Outcomes

After reading this chapter, the reader will be able to:

• Relate to the applicability of IoT in real scenarios

• List the salient features of agricultural IoT

• Understand the requirements, challenges, and advantages in implementing IoT in agriculture

• Relate to the appropriate use of various IoT technologies through real-life use cases on IoT-based leaf area index assessment and an IoT-based irrigation system

Introduction

Currently, IoT-enabled technologies are widely used for increasing crop productivity, generating significant revenue, and efficient farming. The development of the IoT paradigm helps in precision farming. Agricultural loT systems perform crop health monitoring, water management, crop security, farming vehicle tracking, automatic seeding, and automatic pesticide spraying over the agricultural fields. In an IoTbased agricultural system, different sensors necessarily have to be deployed over agricultural fields, and the sensed data from these sensors need to be transmitted to a centralized entity such as a server, cloud, or fog devices. Further, these data have to be processed and analyzed to provide various agricultural services. Finally, a user should be able to access these services from handheld devices or computers. Figure 12.1 depicts a basic architecture of an agricultural IoT.

Components of an agricultural IoT

The development of an agricultural IoT has helped farmers enhance crop productivity and reduce the overhead of manual operations of the agricultural equipment in the fields. Different components such as analytics, drone, cloud computing, sensors, hand-held devices, and wireless connectivity enable agricultural IoT as depicted in Figure 12.2.

The different components of an agricultural IoT are discussed as follows:

• Cloud computing: Sensors such as the camera, devices to measure soil moisture, soil humidity, and soil pH-level are used for serving different agricultural applications. These sensors produce a huge amount of agricultural data that need to be analyzed. Sometimes, based on the data analysis, action needs to be taken, such as switching on the water pump for irrigation. Further, the data from the deployed sensors are required to be stored on a long-term basis since it may be useful for serving future applications. Thus, for agricultural data analysis and storage, the cloud plays a crucial role.

The Internet of Things (IoT) paradigm has grown by leaps and bounds in the past decade. Nowadays, IoT is a common presence in households, transportation, markets, retail, banking, industries, education, and logistics. Yet regular innovative developments in IoT continue to flood the market. IoT has given rise to many interesting applications and resulted in the development of new networking and communication technologies that are designed specifically for IoT-oriented tasks. Manually intensive, yet crucial, domains of healthcare, agriculture, and transportation now rely heavily on IoT applications. The inclusion of IoT applications in these domains has resulted in facilitating their automation, enhanced safety, and precision of operations and allowed the inclusion of scientifically optimized practices. It is popularly considered that the rapid rise of IoT resulted from the inclusion of the beneficial features from the paradigms and technologies of Internet computing, cloud computing, wireless sensor networks (WSN), cyber-physical systems (CPS), and machine-to-machine (M2M) communications. All through its development, IoT has been supported by the popular networking paradigms in distributed systems, namely of cloud computing and edge and fog computing. These have allowed the massive, yet affordable, deployment of IoT across various domains. The emergence of the recent paradigm of edge computing can be directly attributed to IoT. IoT has also motivated the development of numerous connectivity and communication protocols and technologies such as IPv6, MQTT, 6LoWPAN, and LoRA, amongst others.

Unlike other books on IoT, already available on the market, this book provides detailed and interlinked coverage of topics related to IoT networks. The authors have designed this book carefully so that it acts as a guide and a single point of reference to IoT networks for beginners, as well as those already familiar with the technologies connected to IoT. With applications in the domains of agriculture, healthcare, electronics, power sector, industries, households, consumer electronics, computing, analytics, environment, transportation, logistics, security, military, surveillance, and many others, it is no wonder that the demand for deeper insights into IoT technologies is increasing day by day. The involvement of people from diverse backgrounds makes it necessary to create a concise repository of information on this new technology. The Internet hosts much information on IoT (theory, tutorials, courses, and implementations). However, they are so scattered that even professionals in this field have trouble obtaining connected and concise information.

Cloud-assisted wireless networks are emerging solutions that unite wireless networks and cloud computing to deliver cloud services directly from the network edges to support the foreseen massive demands from data- and computation-hungry mobile users. In this chapter, we first provide an overview of the two emerging cloud-assisted wireless network paradigms – namely, the cloud radio access network (C-RAN), which aims at centralization of base station (BS) functionalities, and mobile-edge computing (MEC), which aims at providing the RAN with computing and storage resources. We then leverage the C-RAN and MEC paradigms to design novel cooperative caching frameworks that explore the synergies of the in-network computing and storage resources. Specifically, a novel cooperative hierarchical caching framework is designed in C-RAN, where caching is performed both at the distributed BSs and at the cloud processing unit (CPU), which bridges the gap between the traditional edge-based and core-based caching schemes. Furthermore, a joint cooperative caching and processing framework is designed in a MEC network, where the MEC servers perform both cache storage and video transcoding to support adaptive bitrate (ABR) video streaming.

This chapter focuses on the performance enhancement brought by the addition of caching capabilities to full-duplex (FD) radios in the context of ultra-dense networks (UDNs). In particular we aim at showing that the interference footprint of such networks, i.e., the major bottleneck to overcome to observe the theoretical FD throughput doubling at the network level, can be significantly reduced thanks to edge caching. A caching model is designed to mimic a geographical caching policy based on the popularity of local files and to compute their associated cache-hit probability. Subsequently, we calculate the probability of successful transmission of a file requested by a use equipment, either directly by its serving small cell base station (SCBS) or by the corresponding backhaul node (BN): this quantity is then used to lower-bound the throughput of the considered network. Our approach makes use of tools from stochastic geometryto guarantee the generality of our results and analytical tractability of the problem.

We study the delivery of 360°-navigable videos to 5G virtual reality (VR) wireless clients in future cooperative multi-cellular systems. A collection of small cell base stations interconnected via backhaul links are sharing their caching and computing resources to maximize the aggregate reward they earn by serving 360° videos requested by the wireless clients. We design an efficient representation method to construct the 360° videos such that they deliver only the remote scene viewpoint content genuinely needed by the VR users, thereby overcoming the present highly inefficient approach of sending a bulky 360° video, whose major part is made up of scene information never navigated by a user. Moreover, we design an optimization framework that allows the base stations to select cooperative caching/rendering/streaming strategies that maximize the aggregate reward they earn when serving the users for the given caching/computational resources at each base station.

In this chapter, we study the technoeconomic challenges for one of the most promising new caching paradigms, the elastic wireless edge caching solution, by which third parties dynamically lease storage resources in a wireless cloud. The main idea is the following: a mobile network operator (MNO) advertises storage prices for servers placed in proximity to the end users, and various content providers lease on-demand capacity to improve the quality of their services. We describe the main concepts and existing business models for the elastic CDN solution, provide an overview of the related work, and discuss the key differences between in-network and edge caching. We then present a detailed model for this system where the caches reside in cellular base stations. We formulate a problem where cache dimensioning, content caching, and request routing decisions are jointly optimized by a central processor (CP) to reduce content delivery delay, subject to a given leasing budget. We design a suite of dynamic solution algorithms, based on the Lyapunov drift-minus-benefit technique and present numerical experiments that quantify the benefits of elastic over typical static cache deployments

Wireless edge caching for mobile social networks (MSNs) has emerged as one of the prospective solutions to provide reliable and low-latency communication services for mobile users on social networking. In this chapter, we first give an overview of MSNs, including their development and challenges. We then discuss mobile edge caching (MEC) paradigms to address emerging issues for MSNs, e.g., service delay, users’ experience, and economic efficiency. In addition to the advantages, the development of MEC networks also places some key challenges’ such as hierarchical architecture of MEC networks, proactive caching, privacy, and security issues. The framework can authenticate MSN users based on public-key cryptography and predict their content demands utilizing a matrix factorization method. Based on the prediction, an optimal content caching policy for an MEC node is presented to minimize the average latency of all MSN users under the MEC nodes’ storage capacity constraints. Furthermore, this framework provides an optimal business model to maximize the revenue for MSN service providers based on the demands of the MSN users and the obtained optimal caching policy.

Large-scale data analysis is becoming an important source of information for mobile network operators (MNOs). MNOs can now investigate the feasibility of possible new technological advances such as storage/memory utilization, context awareness, and edge/cloud computing using analytic platforms designed for big data processing. Within this context, studying caching from a mobile data traffic analytical perspective can offer rich insights on evaluating the potential benefits and gains of proactive caching at base stations. In this chapter, we study how data collected from MNOs can be leveraged using machine learning tools in order to infer insights into the benefits of caching. Through our practical architecture, vast amount of data can be harnessed for content popularity estimations and placing strategic contents at base stations (BSs).Ourresults demonstrate several gains in terms of both content demand satisfaction and backhaul offloading rates while utilizing real-world data sets collected from a major MNO.

This chapter presents a content-centric framework for transmission optimization in cloud radio access networks (RANs) by leveraging wireless edge caching and physical-layer multicasting. We consider a cache-enabled cloud RAN, where each base station (BS) is equipped with a local cache and connected to a central processor (CP) via a backhaul link. The BSs acquire the requested contents either from their local caches or from the core network via the backhaul links. We first study the caching effects on multicast-enabled access downlink, where users requesting the same content are grouped together and served by the same BS or BS cluster using multicasting. We study the cache-aware joint design of the content-centric BS clustering and multicast beam-forming to minimize the system total power cost and backhaul cost subject to the quality-of-service (QoS) constraints for each multicast group.