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Introduction to IoT Patterns

The Internet of Things (IoT) has gained significant traction in the recent past and this field is poised for exponential growth in the coming years. This growth will span all the major domains/industry verticals, including consumer, home, manufacturing, health, travel, and transportation. This book will provide a novel perspective to those who want to understand the fundamental IoT patterns and how these patterns can be mixed and matched to implement unique and diverse IoT applications.

This introductory chapter details the architectural considerations that you must bear in mind while designing IoT solutions. Architecting IoT solutions is challenging as there are additional complexities due to the physical hardware selection, complex integrations, and connectivity requirements involved. This chapter also serves as a foundation for the patterns that will be introduced in the subsequent chapters.

In this chapter, we will cover the following topics:

• An overview of IoT
• IoT reference architecture
• Unique requirements of IoT use cases
• Recommended architecture principles and considerations

An overview of IoT

IoT has generated a lot of interest recently and has moved from a purely academic pursuit to the point where real use cases are being realized. IoT implementation is inherently complex due to multiple and diverse technologies (embedded, cloud, edge, big data, artificial intelligence (AI), machine learning (ML), and so on) being involved and due to the range of deployment options that are available (constraint devices in the field to the almost unlimited availablity of compute and other resources in the cloud). IoT enables diverse use cases and spans multiple domains (home automation, healthcare, track-and-trace, connected vehicles, autonomous driving, and more).

The relevance of IoT is only going to increase in the coming years because of the following reasons:

• IoT use cases encompass physical and virtual worlds and as a result, interesting and rich use cases can be developed (compared to purely virtual/software systems such as word processors, ERP systems, and more). It can be said that the scope and variety of IoT use cases is only limited by a person's imagination.
• The immense potential of IoT has been validated by both academics and implementors. This can be attributed to the following reasons:
  • The increased capability and efficiency of hardware components with a continous decrease in cost (and size) in line with Moore's law. Efficient battery utilization by the current generation of hardware components has also reduced the hassles of frequent battery replacement.
  • The rise of commercial cloud providers (hyperscalers), which enables unlimited scalability in terms of compute, storage, complex analytics, high-speed data ingestion, and more. These characteristics are very well suited to the needs of IoT applications. The following are some of the services that are provided by public cloud providers/hyperscalers and that can be leveraged while developing IoT use cases:
    • Device management
    • Firmware updates
    • Edge management/analytics
    • Device/data security
    • Digital twins
    • IoT analytics
    • Data ingestion
    • Data visualizations
    • Data storage
    • Video analytics
    • Notifications
  • Ubiquitous and low-cost connectivity in the form of traditional connectivity options (for example, Wi-Fi, 3G, and 4G) as well as connectivity options such as LoRaWAN and NB-IoT. Technologies such as NB-IoT are especially useful for IoT as they support long-range connectivity and provide long battery life. The advent of 5G has further enlarged the scope of IoT use cases by providing high bandwidth and minimal latency.
  • The increased maturity of related technologies such as blockchain, robotics, AI/ML, energy harvesting, AR/VR, drones, social media, and more. These technologies enable IoT practitioners to augment IoT capabilities with the capabilities provided by these technologies to push the boundaries of innovation further and envisage non-conventional ideas.
  • The increased adoption of mobile and wearable devices. These devices enable anytime access to IoT data and help control and monitor IoT devices in real time.
• Increased market competition, which forces enterprises to treat data as the fulcrum for decision-making as well as monetization opportunities. IoT also acts as the foundation for operationalizing additional revenue models such as service revenue over and above product sale revenue.

It is important to understand how IoT systems are different from non-IoT systems. A few of the key differentiators are as follows:

• Humans play a vital role in operating and managing most non-IoT (traditional IT/OT systems) systems, whereas IoT systems are designed to operate on their own or with minimal human intervention.
• IoT devices are constrained in terms of the compute, storage, or both, whereas most non-IoT applications are deployed on standard workstations where an ample amount of storage and compute is available.
• IoT applications, once deployed, are expected to last for years (10 to 15 years is the norm in the manufacturing industry) compared to non-IoT applications, where the shelf life is less (typical refresh cycles range between 3 to 5 years). Accordingly, IoT systems must be architected by balancing both current and long term needs.
• Considerable heterogeneity is observed in the selection of the hardware/software components as well as connectivity protocols. This is because there are different technologies to choose from, and for each technology choice, there are multiple implementations and offerings available from vendors. In comparison, there are fewer technology options possible in non-IoT systems.
• There are differences in the characteristics of the data that is generated in IoT and non-IoT systems. All the seven V's of big data (velocity, variety, variability, volume, veracity, visualization, and value) are high in IoT systems compared to non-IoT systems.
• Very few IoT systems operate in isolation and are generally integrated with other enterprise systems. In IoT systems, there is a need to integrate Information Technology (IT) and Operational Technology (OT), as well as hardware devices. This presents an entirely new level of integration complexity that is rarely seen in traditional systems.
• Security is important in any connected system, but it becomes much more important in IoT as the attacks can result in physical harm (industrial robots gone rogue) in addition to reputation/financial loss. Additionally, most IoT field devices are installed in vulnerable environments where they can easily be tampered with. Therefore, the attack surface in an IoT use case is much larger than that of a non-IoT use case.

These unique characteristics of IoT systems can be visualized in the following diagram:

Figure 1.1 - Unique characteristics of an IoT system

This complexity can be quite daunting to anyone who has just ventured into the IoT domain. Although a rich variety of IoT use cases (or solution domains) is possible, there is also a certain degree of commonality that is present in most of the IoT use cases and related architectures. We have mentioned these similarities so that anyone who is new to this domain can understand the existing architectures and use cases.

IoT reference architecture

The IoT reference architecture follows a layered model, as shown in the following diagram:

Figure 1.2 - Layered IoT reference architecture

Let's look at these layers in more detail:

• Perception/actuation layer: This layer indicates the physical layer where sensors (pressure, temperature, and so on) gather information about the environment. In turn, the environment is affected by the actuators (electric motor, thermostat control, and so on).
• Connectivity layer: This layer provides the connectivity required to send data (perception data from sensors and control commands to actuators, and so on) to/from the aggregation/processing layer. This layer is realized by leveraging connectivity options (5G, Wi-Fi, NB-IoT, LoRA, and so on). The decision to choose a specific connectivity option depends on various factors such as range and bandwidth.
• Processing layer: The processing layer ingests, analyzes, and stores data received from the connectivity layer. The processing can be performed either near the data source (edge computing) or in a...