Chapter 3. Smart Objects: The “Things” in IoT
Imagine the IoT-enabled connected vehicle and roadway highlighted in Chapter 1, “What Is IoT?” That car has an impressive ecosystem of sensors that provides an immense amount of data that can be intelligently consumed by a variety of systems and services on the car itself as well as shared externally with other vehicles, the connected roadway infrastructure, or even a whole host of other cloud-based diagnostic and consumer services. From behind the steering wheel, almost everything in the car can be checked (sensed) and controlled. The car is filled with sensors of all types (for example, temperature, location [GPS], pressure, velocity) that are meant to provide a wealth of rich and relevant data to, among many other things, improve safety, simplify vehicle maintenance, and enhance the driver experience.
Such sensors are fundamental building blocks of IoT networks. In fact, they are the foundational elements found in smart objects—the “things” in the Internet of Things. Smart objects are any physical objects that contain embedded technology to sense and/or interact with their environment in a meaningful way by being interconnected and enabling communication among themselves or an external agent.
This chapter provides a detailed analysis of smart objects and their architecture. It also provides an understanding of their design limitations and role within IoT networks. Specifically, the following sections are included:
Sensors, Actuators, and Smart Objects: This section defines sensors, actuators, and smart objects and describes how they are the fundamental building blocks of IoT networks.
Sensor Networks: This section covers the design, drivers for adoption, and deployment challenges of sensor networks.
Sensors, Actuators, and Smart Objects
The following sections describe the capabilities, characteristics, and functionality of sensors and actuators. They also detail how the economic and technical conditions are finally right for IoT to flourish. Finally, you will see how to bring these foundational elements together to form smart objects, which are connected to form the sensor and actuator networks that make most IoT use cases possible.
Sensors
A sensor does exactly as its name indicates: It senses. More specifically, a sensor measures some physical quantity and converts that measurement reading into a digital representation. That digital representation is typically passed to another device for transformation into useful data that can be consumed by intelligent devices or humans.
Naturally, a parallel can be drawn with humans and the use of their five senses to learn about their surroundings. Human senses do not operate independently in silos. Instead, they complement each other and compute together, empowering the human brain to make intelligent decisions. The brain is the ultimate decision maker, and it often uses several sources of sensory input to validate an event and compensate for “incomplete” information.
Sensors are not limited to human-like sensory data. They can measure anything worth measuring. In fact, they are able to provide an extremely wide spectrum of rich and diverse measurement data with far greater precision than human senses; sensors provide superhuman sensory capabilities. This additional dimension of data makes the physical world an incredibly valuable source of information. Sensors can be readily embedded in any physical objects that are easily connected to the Internet by wired or wireless networks. Because these connected host physical objects with multidimensional sensing capabilities communicate with each other and external systems, they can interpret their environment and make intelligent decisions. Connecting sensing devices in this way has ushered in the world of IoT and a whole new paradigm of business intelligence.
There are myriad different sensors available to measure virtually everything in the physical world. There are a number of ways to group and cluster sensors into different categories, including the following:
Active or passive: Sensors can be categorized based on whether they produce an energy output and typically require an external power supply (active) or whether they simply receive energy and typically require no external power supply (passive).
Invasive or non-invasive: Sensors can be categorized based on whether a sensor is part of the environment it is measuring (invasive) or external to it (non-invasive).
Contact or no-contact: Sensors can be categorized based on whether they require physical contact with what they are measuring (contact) or not (no-contact).
Absolute or relative: Sensors can be categorized based on whether they measure on an absolute scale (absolute) or based on a difference with a fixed or variable reference value (relative).
Area of application: Sensors can be categorized based on the specific industry or vertical where they are being used.
How sensors measure: Sensors can be categorized based on the physical mechanism used to measure sensory input (for example, thermoelectric, electrochemical, piezoresistive, optic, electric, fluid mechanic, photoelastic).
What sensors measure: Sensors can be categorized based on their applications or what physical variables they measure.
Note that this is by no means an exhaustive list, and there are many other classification and taxonomic schemes for sensors, including those based on material, cost, design, and other factors. The most useful classification scheme for the pragmatic application of sensors in an IoT network, as described in this book, is to simply classify based on what physical phenomenon a sensor is measuring. This type of categorization is shown in Table 3-1.
Sensors come in all shapes and sizes and, as shown in Table 3-1, can measure all types of physical conditions. A fascinating use case to highlight the power of sensors and IoT is in the area of precision agriculture (sometimes referred to as smart farming), which uses a variety of technical advances to improve the efficiency, sustainability, and profitability of traditional farming practices. This includes the use of GPS and satellite aerial imagery for determining field viability; robots for high-precision planting, harvesting, irrigation, and so on; and real-time analytics and artificial intelligence to predict optimal crop yield, weather impacts, and soil quality.
Among the most significant impacts of precision agriculture are those dealing with sensor measurement of a variety of soil characteristics. These include real-time measurement of soil quality, pH levels, salinity, toxicity levels, moisture levels for irrigation planning, nutrient levels for fertilization planning, and so on. All this detailed sensor data can be analyzed to provide highly valuable and actionable insight to boost productivity and crop yield. Figure 3-1 shows biodegradable, passive microsensors to measure soil and crop and conditions. These sensors, developed at North Dakota State University (NDSU), can be planted directly in the soil and left in the ground to biodegrade without any harm to soil quality.
Figure 3-1 Biodegradable Sensors Developed by NDSU for Smart Farming (Reprinted with permission from NDSU.)
IoT and, by extension, networked sensors have been repeatedly named among a small number of emerging revolutionary technologies that will change the global economy and shape the future. The staggering proliferation of sensors is the principal driver of this phenomenon. The astounding volume of sensors is in large part due to their smaller size, their form factor, and their decreasing cost. These factors make possible the economic and technical feasibility of having an increased density of sensors in objects of all types. Perhaps the most significant accelerator for sensor deployments is mobile phones. More than a billion smart phones are sold each year, and each one has well over a dozen sensors inside it (see Figure 3-2), and that number continues to grow each year. Imagine the exponential effect of extending sensors to practically every technology, industry, and vertical. For example, there are smart homes with potentially hundreds of sensors, intelligent vehicles with 100+ sensors each, connected cities with thousands upon thousands of connected sensors, and the list goes on and on.
It’s fascinating to think that that a trillion-sensor economy is around the corner. Figure 3-3 shows the explosive year-over-year increase over the past several years and some bold predictions for sensor numbers in the upcoming years. There is a strong belief in the sensor industry that this number will eclipse a trillion in the next few years. In fact, many large players in the sensor industry have come together to form industry consortia, such as the TSensors Summits (www.tsensorssummit.org), to create a strategy and roadmap for a trillion-sensor economy. The trillion-sensor economy will be of such an unprecedented and unimaginable scale that it will change the world forever. This is the power of IoT.
Actuators
Actuators are natural complements to sensors. Figure 3-4 demonstrates the symmetry and complementary nature of these two types of devices. As discussed in the previous section, sensors are designed to sense and measure practically any measurable variable in the physical world. They convert their measurements (typically analog) into electric signals or digital representations that can be consumed by an intelligent agent (a device or a human). Actuators, on the others hand, receive some type of control signal (commonly an electric signal or digital command) that triggers a physical effect, usually some type of motion, force, and so on.
The previous section draws a parallel between sensors and the human senses. This parallel can be extended to include actuators, as shown in Figure 3-5. Humans use their five senses to sense and measure their environment. The sensory organs convert this sensory information into electrical impulses that the nervous system sends to the brain for processing. Likewise, IoT sensors are devices that sense and measure the physical world and (typically) signal their measurements as electric signals sent to some type of microprocessor or microcontroller for additional processing. The human brain signals motor function and movement, and the nervous system carries that information to the appropriate part of the muscular system. Correspondingly, a processor can send an electric signal to an actuator that translates the signal into some type of movement (linear, rotational, and so on) or useful work that changes or has a measurable impact on the physical world. This interaction between sensors, actuators, and processors and the similar functionality in biological systems is the basis for various technical fields, including robotics and biometrics.
Much like sensors, actuators also vary greatly in function, size, design, and so on. Some common ways that they can be classified include the following:
Type of motion: Actuators can be classified based on the type of motion they produce (for example, linear, rotary, one/two/three-axes).
Power: Actuators can be classified based on their power output (for example, high power, low power, micro power)
Binary or continuous: Actuators can be classified based on the number of stable-state outputs.
Area of application: Actuators can be classified based on the specific industry or vertical where they are used.
Type of energy: Actuators can be classified based on their energy type.
Categorizing actuators is quite complex, given their variety, so this is by no means an exhaustive list of classification schemes. The most commonly used classification is based on energy type. Table 3-2 shows actuators classified by energy type and some examples for each type. Again, this is not a complete list, but it does provide a reasonably comprehensive overview that highlights the diversity of function and design of actuators.
Whereas sensors provide the information, actuators provide the action. The most interesting use cases for IoT are those where sensors and actuators work together in an intelligent, strategic, and complementary fashion. This powerful combination can be used to solve everyday problems by simply elevating the data that sensors provide to actionable insight that can be acted on by work-producing actuators.
We can build on the precision agriculture example from the previous section to demonstrate how actuators can complement and enhance a sensor-only solution. For example, the smart sensors used to evaluate soil quality (by measuring a variety of soil, temperature, and plant characteristics) can be connected with electrically or pneumatically controlled valve actuators that control water, pesticides, fertilizers, herbicides, and so on. Intelligently triggering a high-precision actuator based on well-defined sensor readings of temperature, pH, soil/air humidity, nutrient levels, and so on to deliver a highly optimized and custom environment-specific solution is truly smart farming.
Micro-Electro-Mechanical Systems (MEMS)
One of the most interesting advances in sensor and actuator technologies is in how they are packaged and deployed. Micro-electro-mechanical systems (MEMS), sometimes simply referred to as micro-machines, can integrate and combine electric and mechanical elements, such as sensors and actuators, on a very small (millimeter or less) scale. One of the keys to this technology is a microfabrication technique that is similar to what is used for microelectronic integrated circuits. This approach allows mass production at very low costs. The combination of tiny size, low cost, and the ability to mass produce makes MEMS an attractive option for a huge number of IoT applications.
MEMS devices have already been widely used in a variety of different applications and can be found in very familiar everyday devices. For example, inkjet printers use micropump MEMS. Smart phones also use MEMS technologies for things like accelerometers and gyroscopes. In fact, automobiles were among the first to commercially introduce MEMS into the mass market, with airbag accelerometers.
Figure 3-6 shows a torsional ratcheting actuator (TRA) that was developed by Sandia National Laboratory as a low-voltage alternative to a micro-engine.
Figure 3-6 Torsional Ratcheting Actuator (TRA) MEMS (Courtesy Sandia National Laboratories, SUMMiT™ Technologies, www.sandia.gov/mstc.)
As Figure 3-6 shows, this MEMS is only a few hundred micrometers across; a scanning electron microscope is needed to show the level of detail visible in the figure. Micro-scale sensors and actuators are immensely embeddable in everyday objects, which is a defining characteristic of IoT. For this reason, it is expected that IoT will trigger significant advances in MEMS technology, and manufacturing and will make them pervasive across all industries and verticals as they become broadly commercialized.
Smart Objects
Smart objects are, quite simply, the building blocks of IoT. They are what transform everyday objects into a network of intelligent objects that are able to learn from and interact with their environment in a meaningful way. It can’t be stressed enough that the real power of smart objects in IoT comes from being networked together rather than being isolated as standalone objects. This ability to communicate over a network has a multiplicative effect and allows for very sophisticated correlation and interaction between disparate smart objects. For instance, recall the smart farming sensors described previously. If a sensor is a standalone device that simply measures the humidity of the soil, it is interesting and useful, but it isn’t revolutionary. If that same sensor is connected as part of an intelligent network that is able to coordinate intelligently with actuators to trigger irrigation systems as needed based on those sensor readings, we have something far more powerful. Extending that even further, imagine that the coordinated sensor/actuator set is intelligently interconnected with other sensor/actuator sets to further coordinate fertilization, pest control, and so on—and even communicate with an intelligent backend to calculate crop yield potential. This now starts to look like a complete system that begins to unlock the power of IoT and provides the intelligent automation we have come to expect from such a revolutionary technology.
Smart Objects: A Definition
Historically, the definition of a smart object has been a bit nebulous because of the different interpretations of the term by varying sources. To add to the overall confusion, the term smart object, despite some semantic differences, is often used interchangeably with terms such as smart sensor, smart device, IoT device, intelligent device, thing, smart thing, intelligent node, intelligent thing, ubiquitous thing, and intelligent product. In order to clarify some of this confusion, we provide here the definition of smart object as we use it in this book. A smart object, as described throughout this book, is a device that has, at a minimum, the following four defining characteristics (see Figure 3-7):
Processing unit: A smart object has some type of processing unit for acquiring data, processing and analyzing sensing information received by the sensor(s), coordinating control signals to any actuators, and controlling a variety of functions on the smart object, including the communication and power systems. The specific type of processing unit that is used can vary greatly, depending on the specific processing needs of different applications. The most common is a microcontroller because of its small form factor, flexibility, programming simplicity, ubiquity, low power consumption, and low cost.
Sensor(s) and/or actuator(s): A smart object is capable of interacting with the physical world through sensors and actuators. As described in the previous sections, a sensor learns and measures its environment, whereas an actuator is able to produce some change in the physical world. A smart object does not need to contain both sensors and actuators. In fact, a smart object can contain one or multiple sensors and/or actuators, depending upon the application.
Communication device: The communication unit is responsible for connecting a smart object with other smart objects and the outside world (via the network). Communication devices for smart objects can be either wired or wireless. Overwhelmingly, in IoT networks smart objects are wirelessly interconnected for a number of reasons, including cost, limited infrastructure availability, and ease of deployment. There are myriad different communication protocols for smart objects. In fact, much of this book is dedicated to how smart objects communicate within an IoT network, especially Chapter 4, “Connecting Smart Objects,” Chapter 5, “IP as the IoT Network Layer,” and Chapter 6, “Application Protocols for IoT.” Thus, this chapter provides only a high-level overview and refers to those other chapters for a more detailed treatment of the subject matter.
Power source: Smart objects have components that need to be powered. Interestingly, the most significant power consumption usually comes from the communication unit of a smart object. As with the other three smart object building blocks, the power requirements also vary greatly from application to application. Typically, smart objects are limited in power, are deployed for a very long time, and are not easily accessible. This combination, especially when the smart object relies on battery power, implies that power efficiency, judicious power management, sleep modes, ultra-low power consumption hardware, and so on are critical design elements. For long-term deployments where smart objects are, for all practical purposes, inaccessible, power is commonly obtained from scavenger sources (solar, piezoelectric, and so on) or is obtained in a hybridized manner, also tapping into infrastructure power.
Trends in Smart Objects
As this definition reveals, it is perhaps variability that is the key characteristic of smart objects. They vary wildly in function, technical requirements, form factor, deployment conditions, and so on. Nevertheless, there are certain important macro trends that we can infer from recent and planned future smart object deployments. Of course, these do not apply to all smart objects because there will always be application-dependent variability, but these are broad generalizations and trends impacting IoT:
Size is decreasing: As discussed earlier, in reference to MEMS, there is a clear trend of ever-decreasing size. Some smart objects are so small they are not even visible to the naked eye. This reduced size makes smart objects easier to embed in everyday objects.
Power consumption is decreasing: The different hardware components of a smart object continually consume less power. This is especially true for sensors, many of which are completely passive. Some battery-powered sensors last 10 or more years without battery replacement.
Processing power is increasing: Processors are continually getting more powerful and smaller. This is a key advancement for smart objects, as they become increasingly complex and connected.
Communication capabilities are improving: It’s no big surprise that wireless speeds are continually increasing, but they are also increasing in range. IoT is driving the development of more and more specialized communication protocols covering a greater diversity of use cases and environments.
Communication is being increasingly standardized: There is a strong push in the industry to develop open standards for IoT communication protocols. In addition, there are more and more open source efforts to advance IoT.
These trends in smart objects begin to paint a picture of increasingly sophisticated devices that are able to perform increasingly complex tasks with greater efficiency. A key enabler of this paradigm is improved communication between interconnected smart objects within a system and between that system and external entities (for example, edge compute, cloud). The power of IoT is truly unlocked when smart objects are networked together in sensor/actuator networks.
Sensor Networks
A sensor/actuator network (SANET), as the name suggests, is a network of sensors that sense and measure their environment and/or actuators that act on their environment. The sensors and/or actuators in a SANET are capable of communicating and cooperating in a productive manner. Effective and well-coordinated communication and cooperation is a prominent challenge, primarily because the sensors and actuators in SANETs are diverse, heterogeneous, and resource-constrained.
SANETs offer highly coordinated sensing and actuation capabilities. Smart homes are a type of SANET that display this coordination between distributed sensors and actuators. For example, smart homes can have temperature sensors that are strategically networked with heating, ventilation, and air-conditioning (HVAC) actuators. When a sensor detects a specified temperature, this can trigger an actuator to take action and heat or cool the home as needed.
While such networks can theoretically be connected in a wired or wireless fashion, the fact that SANETs are typically found in the “real world” means that they need an extreme level of deployment flexibility. For example, smart home temperature sensors need to be expertly located in strategic locations throughout the home, including at HVAC entry and exit points.
The following are some advantages and disadvantages that a wireless-based solution offers:
Advantages:
Greater deployment flexibility (especially in extreme environments or hard-to-reach places)
Simpler scaling to a large number of nodes
Lower implementation costs
Easier long-term maintenance
Effortless introduction of new sensor/actuator nodes
Better equipped to handle dynamic/rapid topology changes
Disadvantages:
Potentially less secure (for example, hijacked access points)
Typically lower transmission speeds
Greater level of impact/influence by environment
Not only does wireless allow much greater flexibility, but it is also an increasingly inexpensive and reliable technology across a very wide spectrum of conditions—even extremely harsh ones. These characteristics are the key reason that wireless SANETs are the ubiquitous networking technology for IoT.
Note
From a terminology perspective, wireless SANETs are typically referred to as wireless sensor and actuator networks (WSANs). Because many IoT deployments are overwhelmingly sensors, WSANs are also often interchangeably referred to as wireless sensor networks (WSNs). In this book, we commonly refer to WSANs as WSNs, with the understanding that actuators are often part of the wireless network.
Wireless Sensor Networks (WSNs)
Wireless sensor networks are made up of wirelessly connected smart objects, which are sometimes referred to as motes. The fact that there is no infrastructure to consider with WSNs is surely a powerful advantage for flexible deployments, but there are a variety of design constraints to consider with these wirelessly connected smart objects. Figure 3-8 illustrates some of these assumptions and constraints usually involved in WSNs.
The following are some of the most significant limitations of the smart objects in WSNs:
Limited processing power
Limited memory
Lossy communication
Limited transmission speeds
Limited power
These limitations greatly influence how WSNs are designed, deployed, and utilized. The fact that individual sensor nodes are typically so limited is a reason that they are often deployed in very large numbers. As the cost of sensor nodes continues to decline, the ability to deploy highly redundant sensors becomes increasingly feasible. Because many sensors are very inexpensive and correspondingly inaccurate, the ability to deploy smart objects redundantly allows for increased accuracy.
Note
Smart objects with limited processing, memory, power, and so on are often referred to as constrained nodes. Constrained nodes are discussed in more detail in Chapter 5.
Such large numbers of sensors permit the introduction of hierarchies of smart objects. Such a hierarchy provides, among other organizational advantages, the ability to aggregate similar sensor readings from sensor nodes that are in close proximity to each other. Figure 3-9 shows an example of such a data aggregation function in a WSN where temperature readings from a logical grouping of temperature sensors are aggregated as an average temperature reading.
These data aggregation techniques are helpful in reducing the amount of overall traffic (and energy) in WSNs with very large numbers of deployed smart objects. This data aggregation at the network edges is where fog and mist computing, discussed in Chapter 2, “IoT Network Architecture and Design,” are critical IoT architectural elements needed to deliver the scale and performance required by so many IoT use cases. While there are certain instances in which sensors continuously stream their measurement data, this is typically not the case. Wirelessly connected smart objects generally have one of the following two communication patterns:
Event-driven: Transmission of sensory information is triggered only when a smart object detects a particular event or predetermined threshold.
Periodic: Transmission of sensory information occurs only at periodic intervals.
The decision of which of these communication schemes is used depends greatly on the specific application. For example, in some medical use cases, sensors periodically send postoperative vitals, such as temperature or blood pressure readings. In other medical use cases, the same blood pressure or temperature readings are triggered to be sent only when certain critically low or high readings are measured.
As WSNs grow to very large numbers of smart objects, there is a trend toward ever-increasing levels of autonomy. For example, manual configuration of potentially thousands of smart objects is impractical and unwieldy, so smart objects in a WSN are typically self-configuring or automated by an IoT management platform in the background. Likewise, additional levels of autonomous functions are required to establish cohesive communication among the multitudinous nodes of large-scale WSNs that are often ad hoc deployments with no regard for uniform node distribution and/or density. For example, there is an increasing trend toward “smart dust” applications, in which very small sensor nodes (that is, MEMS) are scattered over a geographic area to detect vibrations, temperature, humidity, and so on. This technology has practically limitless capabilities, such as military (for example, detecting enemy troop movement), environmental (for example, detecting earthquakes or forest fires), and industrial (for example, detecting manufacturing anomalies, asset tracking). Some level of self-organization is required for networking the scads of wireless smart objects such that these nodes autonomously come together to form a true network with a common purpose. This capability to self-organize is able to adapt and evolve the logical topology of a WSN to optimize communication (among nodes as well as to centralized wireless controllers), simplify the introduction of new smart objects, and improve reliability and access to services.
Additional advantages of being able to deploy large numbers of wireless low-cost smart objects are the inherent ability to provide fault tolerance, reliability, and the capability to extend the life of a WSN, especially in scenarios where the smart objects have limited battery life. Autonomous techniques, such as self-healing, self-protection, and self-optimization, are often employed to perform these functions on behalf of an overall WSN system. IoT applications are often mission critical, and in large-scale WSNs, the overall system can’t fail if the environment suddenly changes, wireless communication is temporarily lost, or a limited number of nodes run out of battery power or function improperly.
Communication Protocols for Wireless Sensor Networks
There are literally thousands of different types of sensors and actuators. To further complicate matters, WSNs are becoming increasingly heterogeneous, with more sophisticated interactions. This heterogeneity is manifested in a variety of ways. For instance, WSNs are seeing transitions from homogenous wireless networks made up of mostly a single type of sensor to networks made up of multiple types of sensors that can even be a hybridized mix of many cheap sensors with a few expensive ones used for very specific high-precision functions. WSNs are also evolving from single-purpose networks to more flexible multipurpose networks that can use specific sensor types for multiple different applications at any given time. Imagine a WSN that has multiple types of sensors, and one of those types is a temperature sensor that can be flexibly used concurrently for environmental applications, weather applications, and smart farming applications.
Coordinated communication with sophisticated interactions by constrained devices within such a heterogeneous environment is quite a challenge. The protocols governing the communication for WSNs must deal with the inherent defining characteristics of WSNs and the constrained devices within them. For instance, any communication protocol must be able to scale to a large number of nodes. Likewise, when selecting a communication protocol, you must carefully take into account the requirements of the specific application and consider any trade-offs the communication protocol offers between power consumption, maximum transmission speed, range, tolerance for packet loss, topology optimization, security, and so on. The fact that WSNs are often deployed outdoors in harsh and unpredictable environments adds yet another variable to consider because obviously not all communication protocols are designed to be equally rugged. In addition to the aforementioned technical capabilities, they must also enable, as needed, the overlay of autonomous techniques (for example, self-organization, self-healing, self-configuration) mentioned in the previous section.
Wireless sensor networks interact with their environment. Sensors often produce large amounts of sensing and measurement data that needs to be processed. This data can be processed locally by the nodes of a WSN or across zero or more hierarchical levels in IoT networks. (These hierarchical levels are discussed in detail in Chapter 2.) Communication protocols need to facilitate routing and message handling for this data flow between sensor nodes as well as from sensor nodes to optional gateways, edge compute, or centralized cloud compute. IoT communication protocols for WSNs thus straddle the entire protocol stack. Ultimately, they are used to provide a platform for a variety of IoT smart services.
As with any other networking application, in order to interoperate in multivendor environments, these communication protocols must be standardized. This is a critical dependency for IoT and one of the most significant success factors. IoT is one of those rare technologies that impacts all verticals and industries, which means standardization of communication protocols is a complicated task, requiring protocol definition across multiple layers of the stack, as well as a great deal of coordination across multiple standards development organizations.
Recently there have been focused efforts to standardize communication protocols for IoT, but, as with the adoption of any significant technology movement, there has been some market fragmentation. While there isn’t a single protocol solution, there is beginning to be some clear market convergence around several key communication protocols. We do not spend time here discussing these specific protocols and their detailed operation because large chunks of this book are specifically dedicated to such discussion, including Chapters 4, 5, and 6.
Summary
Wireless sensor and actuator networks are a unique computing platform that can be highly distributed and deployed in unique environments where traditional computing platforms are not typically found. This offers unique advantages and opportunities to interact with and influence those environments. This is the basis of IoT, and it opens up a world of possibility, embedding sensors and/or actuators in everyday objects and networking them to enable sophisticated and well-coordinated automations that improves and simplifies our lives.
This chapter introduces the “things” that are the building blocks of IoT. It includes descriptions and practical examples of sensors and how they are able to measure their environment. It provides the same sort of discussion for actuators, which use environmental sensing information in a complementary way to act on their surroundings. This chapter also highlights recent manufacturing trends (such as MEMS) toward making sensors and actuators ever smaller and more embeddable in everyday objects. This chapter also covers smart objects, which are typically highly constrained devices with sensor(s) and/or actuator(s) along with very limited power, transmission, and compute capabilities.
As discussed in this chapter, we unlock the power of IoT by networking smart objects. Sensor and actuator networks (SANETs) are discussed, with particular attention and detail given to the overwhelmingly ubiquitous use case of wireless sensor networks (WSNs). The last topic discussed in this chapter is communication protocols for WSANs, which sets you up for the next chapter, on connecting smart objects.