Now that we have a clear understanding of what is IoT and the similar terms around it, lets understand the ecosystem better. For convenience, IoE will be referred as IoT while exploring the four logical components of the stack in brief.
IoTs logical stack
When we deconstruct the IoT ecosystem into logical units, we have People, Processes, Data, and Things. Lets explore each of these components in brief.
People or we interact with devices and other people on a daily basis. The communication could be either People to People, People to Device, or Device to People. Considering People as a separate dimension in the IoT ecosystem is an essential move as the complexity in understanding this is really challenging. When any form of communication occurs where People play a role on either end of the interaction, it embeds a unique pattern that is intrinsic to the People dimension. Lets understand this better with an example. Most of us use social networking sites such as Facebook, Twitter, LinkedIn, and so on, where we are connected to multiple people/friends. Here, the communication paths are mainly People to People. Considering the previous example, we had people to device and device to people communication paths (communication between the smartphone and microwave). Considering People as a dimension, everyone would differ in the way they interact with the system. I might find the new interface of Facebook very difficult to use but a friend may find it extremely easy. The real problem here is everyone is skilled, but the skillsets differ from person to person. The characteristics of the interaction identified by a person may be a representative for a very small community.
We have a population of six billion plus, and over 1/6th of them have already been connected. With such a huge population consisting of a plethora of communities representing people of different geographical areas, culture, thinking, and behavior, defining one generic set of rules or characteristics to define people interaction is very challenging. Instead, if we understand the People dimension in a more constructive way, we can tap the opportunity to capture the behavior more accurately and help them benefit from the ecosystem in the best way.
With the advent of IoT, we have sensors capturing information and characteristics at more granular levels than ever before. Here, if we can accurately define People as a complete dimension, personalized experience will be a complete game changer. The smart watch industry is investing humongous efforts to get its offering more personalized; if it succeeds, it will be a pivotal player in the coming revolution.
The most lucid definition for Processes would be everything required to deliver the right information to the right person/system at the right time. A wide variety of things fall in the Processes dimension that includes technology, protocols, business logic, communication infrastructure, and so on. Broadly, they can be classified into two components-Technology and Business Processes. Lets explore these two components in brief in order to understand the Processes dimension in more detail.
The technology required in the Processes dimension of IoT comprises of the software, protocol, and infrastructure. We will explore Technology by understanding its three broad divisions for Processes.
Software consists mainly of the operating system. Devices in IoT require a special kind of an operating device. Smart devices such as the smart refrigerator, smart microwave, and many others require an operating system running on them that can then enable it to be an active component in the network. Tasks executed can vary from sending, processing, and receiving data or executing instructions and sending signals to respective controllers within the device for action. Now, the question is, why do these devices require a special operating system? Why cant the existing rich flavors of Unix/Linux, Windows, Mac, or even Android be used? The answer is the same as the reason that we used Android for smartphones and not the existing OS back then. The devices that connect to the network in IoT are small or sometimes tiny. Ideally, these devices would be equipped with less powerful computing capabilities, lower memory, and lower battery life. It is almost impossible to run a fully-fledged operating system on them. We need a specially designed OS that can take care of the limited memory, processing power and battery life of the device and yet provide maximum functionality to tag the device as a smart device. Google recently launched an operating system for IoT devices called Brillo. Brillo is an Android-based embedded operating system specifically designed for low power and memory-constrained IoT devices. It provides the core platform services required for IoT devices along with a developer kit freely available for developers/hardware vendors to get the OS running and build additional services on their devices. Some similar examples would be Apple's Watch OS for Apple Watch, Android Wear from Google for smartwatches, and others. Soon, we can expect a vast community of devices running Brillo and a plethora of apps that can be installed additionally for even better functionality (something very similar to the Google Play store).
Once the devices are software-enabled, we need to get a protocol in place that can help them communicate with other heterogeneous devices in the network. To understand this better, recollect the first example where we could defrost the refrigerator using our smartphone. The smartphone needs to talk to the refrigerator that also needs to understand what exactly is being communicated. With a huge variety of heterogonous devices, this communication path just gets more and more complicated. Hence, we need to have a simplified protocol in place where complicated process can be abstracted and the devices can communicate with each other effectively. Google recently launched an open source protocol called Weave. Weave is basically an IoT protocol that is a communications platform for IoT devices that enables device setup, phone-to-device-to-cloud communication, and user interaction from mobile devices and the web. It has ushered productivity in the developers efforts by easing up device interoperability regardless of the brand or manufacturer.
Infrastructure can simply be defined as the integration of the operating system, communication protocol, and all other necessary components to harmonize the environment for an IoT use case. All major cloud infrastructure providers are now focusing on providing an IoT-specialized environment. Google launched IoT Cloud Solutions, Amazon launched AWS IoT, Microsoft launched Azure IoT Suite, and so on. All of these solutions integrate the disparate systems together to make the ecosystem scalable and agile. Digging deeper into these suites will be beyond the scope of this book.
The second part of the Processes dimension is Business Processes. It basically covers the set of rules and processes to govern the communication and operation of the devices connected in the IoT ecosystem. There isn't a concrete definition till now that can be used here and the discussion of the topic will be beyond the scope of this book. However, we will take a look at this closely while solving an IoT use case in Chapter 3, The What And Why - Using Exploratory Decision Science for IoT and Chapter 4, Experimenting Predictive Analytics for IoT.
Things form the crux of the IoT ecosystem. They include any form of sensors, actuators, or other type of devices that can be integrated into machines and devices to help them connect to the Internet and communicate with other devices and machines. These things will be always active during their lifetime and will sense events, capture important information, and communicate them with other devices.
A typical example would be the refrigerator, TV, or microwave that we considered in the previous use case. The sensors installed in these devices capture data and send information/signals to other devices that can then be used to take action.
Data is by all means the most value-adding dimension in the IoT ecosystem. Today, the devices that are connected to the Internet are capturing tons and tons of data that can represent the most granular-level details for the devices they are connected to. The magnitude of this data is colossal. Storing and processing such vast and varied amounts of data questions the fact whether the data is really valuable. In a true sense, most of the data is transient in nature and loses its value within minutes of generation. With ever-improving technology and computing capabilities, the amount of data processing and storage that the devices are capable of today is great, but we can leverage this power to deliver better value than just delivering raw data. Tons of algorithms can be executed and business rules can be applied where a lot of value can be extracted from the data before sending it over to the server. This requires the combination of multiple disciplines together to solve the problem and deliver value.
To understand this better, consider the example of a pedometer installed in our smart watch. Rather than just reporting the number of steps that we have walked, it can calculate the amount of calories we have burned, average time taken for the activity, metrics like deviation from the previous days activity, deviation from milestones, and other social information such as how do we compare with our friends, and so on. To capture and process all of this information locally and send the final results to the server that can be directly stored for future actions requires the combination of multiple disciplines to make the task efficient. Math, business, technology, design thinking, behavioral science, and a few others would need to be used together to solve the problem. In reality, it would be futile to send across all the raw data captured from devices to the servers assuming that it can be leveraged for future use. A variety of new algorithms have been designed to ingest this data locally and deliver only rich, condensed, and actionable insights in real time. We will explore this in more detail with fog computing in Chapter 8, Disruptions in IoT. Smart watches such as the Microsoft Band and self-driving cars such as Tesla Model S are the best examples to understand the true scenarios where we can study the challenges of processing data in real time for insights and actions. In all true sense, data is what essentially helps in delivering the last mile value in the IoT fraternity. Hence, we need talent to deal with the data as a separate dimension in the IoT stack.