Chapter loT Sensing and Actuation Learni After reading this chapter, the reader will be able to: * List the salient features of transducers * Differentiate between sensors and actuators * Characterize sensors and distinguish between types of sensors * List the multi-faceted considerations associated with sensing * Characterize actuators and distinguish between types of actuators * List the multi-faceted considerations associated with actuation 5.1 Introduction ‘A major chunk of IoT applications involves sensing in one form or the other. Almost all the applications in IoT—be it a consumer IoT, an industrial IoT, or just plain hobby-based deployments of IoT solutions—sensing forms the first step. Incidentally, actuation forms the final step in the whole operation of IoT application deployment in a majority of scenarios. The basic science of sensing and actuation is based on the process of transduction. Transduction is the process of energy conversion from one form to another. A transducer is a physical means of enabling transduction. Transducers take energy in any form (for which it is designed)—electrical, mechanical, chemical, light, sound, and others—and convert it into another, which may be electrical, mechanical, chemical, light, sound, and others. Sensors and actuators are deemed as transducers. For example, in a public announcement (PA) system, a microphone (input device) converts sound waves into electrical signals, which is amplified by an amplifier system (a process). Finally, a loudspeaker (output device) outputs this into audible sounds by converting the amplified electrical signals backIntroduction to Internet of Things into sound waves. Table 5.1 outlines the basic terminological differences between transducers, sensors, and actuators. Table 5.1 Basic outline of the differences between transducers, sensors, and actuators Parameters Transducers ‘Sensors. Actuators Definition Converts Converts various forms of ‘Converts electrical energy from. energy into electrical signals. signals into one form to various forms of another. energy, typically mechanical energy: Domain Canbeused _It is an input transducer. Itis an output to represent a transducer. sensor as well as an actuator. Function Can workas Used for quantifying Used for asensororan environmental stimuli into converting signals actuator but not _ signals. into proportional simultaneously. mechanical or electrical outputs. Examples Any sensor or Humidity sensors, Temperature Motors (convert actuator sensors, Anemometers electrical energy (measures flow velocity), torotary motion), Manometers (measures fluid Force heads pressure), Accelerometers (which impose (measures the acceleration of a a force), Pumps body), Gas sensors (measures (which convert concentration of specific gas or rotary motion of gases), and others shafts into either a pressure or a fluid velocity). 5.2 Sensors Sensors are devices that can measure, or quantify, or respond to the ambient changes in their environment or within the intended zone of their deployment. They generate responses to external stimuli or physical phenomenon through characterization of the input functions (which are these external stimuli) and their conversion into typically electrical signals. For example, heat is converted to electrical signals in a temperature sensor, or atmospheric pressure is converted to electrical signals in a barometer. AIoT Sensing and Actuation 9 sensor is only sensitive to the measured property (e.g., a temperature sensor only senses the ambient temperature of a room). It is insensitive to any other property besides whatit is designed to detect (e.g. a temperature sensor does not bother about light or pressure while sensing the temperature). Finally, a sensor does not influence the measured property (e.g., measuring the temperature does not reduce or increase the temperature). Figure 5.1 shows the simple outline of a sensing task. Here, a temperature sensor keeps on checking an environment for changes. In the event of a fire, the temperature of the environment goes up. The temperature sensor notices this change in the temperature of the room and promptly communicates this information to a remote monitor via the processor. Figure 5.1 The outline of a simple sensing operation The various sensors can be classified based on: 1) power requirements, 2) sensor output, and 3) property to be measured. + Power Requirements: The way sensors operate decides the power requirements that must be provided for an loT implementation. Some sensors need to be provided with separate power sources for them to function, whereas some sensors do not require any power sources. Depending on the requirements of power, sensors can be of two types. (i) Active: Active sensors do not require an external circuitry or mechanism to provide it with power. It directly responds to the external stimuli from its ambient environment and converts it into an output signal. For example, a photodiode converts light into electrical impulses. (ii) Passive: Passive sensors require an external mechanism to power them up. The sensed properties are modulated with the sensor’s inherent characteristics to generate patterns in the output of the sensor. For example, a thermistor’s resistance can be detected by applying voltage difference across it or passing a current through it. Output: The output of a sensor helps in deciding the additional components to be integrated with an IoT node or system. Typically, almost all modern-day processors are digital; digital sensors can be directly integrated to the processors.100 Introduction to Internet of Things However, the integration of analog sensors to these digital processors or IoT nodes requires additional interfacing mechanisms such as analog to digital converters (ADC), voltage level converters, and others. Sensors are broadly divided into two types, depending on the type of output generated from these sensors, as follows. () Analog: Analog sensors generate an output signal or voltage, which is proportional (linearly or non-linearly) to the quantity being measured and is continuous in time and amplitude. Physical quantities such as temperature, speed, pressure, displacement, strain, and others are all continuous and categorized as analog quantities. For example, a thermometer or a thermocouple can be used for measuring the temperature of a liquid (e.g., in household water heaters). These sensors continuously respond to changes in the temperature of the liquid. (ii) Digital: These sensors generate the output of discrete time digital representation (time, or amplitude, or both) of a quantity being measured, in the form of output signals or voltages. Typically, binary output signals in the form of a logic 1 or a logic 0 for ON or OFF, respectively are associated with digital sensors. The generated discrete (non-continuous) values may be output as a single “bit” (serial transmission), eight of which combine to produce a single “byte” output (parallel transmission) in digital sensors. Measured Property: The property of the environment being measured by the sensors can be crucial in deciding the number of sensors in an IoT implementation. Some properties to be measured do not show high spatial variations and can be quantified only based on temporal variations in the measured property, such as ambient temperature, atmospheric pressure, and others. Whereas some properties to be measured show high spatial as well as temporal variations such as sound, image, and others. Depending on the properties to be measured, sensors can be of two types. (@ Scalar: Scalar sensors produce an output proportional to the magnitude of the quantity being measured. The output is in the form of a signal or voltage. Scalar physical quantities are those where only the magnitude of the signal is sufficient for describing or characterizing the phenomenon and information generation. Examples of such measurable physical quantities include color, pressure, temperature, strain, and others. A thermometer or thermocouple is an example of a scalar sensor that has the ability to detect changes in ambient or object temperatures (depending on the sensor's configuration). Factors such as changes in sensor orientation or direction do not affect these sensors (typically). (i) Vector: Vector sensors are affected by the magnitude as well as the direction and/or orientation of the property they are measuring. Physical quantities such as velocity and images that require additional information besidesIoT Sensing and Actuation 101 their magnitude for completely categorizing a physical phenomenon are categorized as vector quantities. Measuring such quantities are undertaken using vector sensors. For example, an electronic gyroscope, which is commonly found in all modem aircraft, is used for detecting the changes in orientation of the gyroscope with respect to the Earth’s orientation along all three axes. tt A sensor node is made up of a combination of sensor/sensors, a processor unit, a radio unit, and a power unit. The nodes are capable of sensing the environment they are set to measure and communicate the information to other sensor nodes or a remote server. Typically, a sensor node should have low-power requirements and be wireless. This enables them to be deployed in a vast range of scenarios and environments without the constant need for changing their power sources or managing wires. The wireless nature of sensor nodes would also allow them to be freely relocatable and deployed in large numbers without bothering about managing wires. The functional outline of a typical loT sensor node is shown in Figure 5.2. Light Temp. Force Position ‘Speed ‘Pressure ‘Chemical Figure 5.2 The functional blocks of a typical sensor node in loT102 Introduction to Internet of Things Figure 5.3 shows some commercially available sensors used for sensing applications. am § (a) Camera sensor (@) Colorsensor (¢) Compass and (4) Current sensor (e) Digital temperature barometer and humidity sensor “2 | he (O Flame sensor (g) Gassensor _(h) Infrared sensor (i) Rainfall sensor (j) Ultrasonic distance measurement Figure 5.3 Some common commercially available sensors used for loT-based sensing applications 5.3 Sensor Characteristics Allsensors can be defined by their ability to measure or capture a certain phenomenon and report them as output signals to various other systems. However, even within the same sensor type and class, sensors can be characterized by their ability to sense the phenomenon based on the following three fundamental properties. + Sensor Resolution: The smallest change in the measurable quantity that a sensor can detect is referred to as the resolution of a sensor. For digital sensors, the smallest change in the digital output that the sensor is capable of quantifying is its sensor resolution. The more the resolution of a sensor, the more accurate is the precision. A sensor's accuracy does not depend uponits resolution. For example, a temperature sensor A can detect up to 0.5" C changes in temperature; whereas another sensor B can detect up to 0.25° C changes in temperature. Therefore, the resolution of sensor Bis higher than the resolution of sensor A. + Sensor Accuracy: The accuracy of a sensor is the ability of that sensor to measure the environment of a system as close to its true measure as possible. For example, aweight sensor detects the weight of a 100 kg mass as 99.98 kg. We can say that this sensor is 99.98% accurate, with an error rate of 40.02%. * Sensor Precision: The principle of repeatability governs the precision of a sensor. Only if, upon multiple repetitions, the sensor is found to have the same errorIoT Sensing and Actuation 103 rate, can it be deemed as highly precise. For example, consider if the same weight sensor described earlier reports ts of 98.28 kg, 100.34 kg, and 101.11 kg, upon three repeat measurements for a mass of actual weight of 100 kg. Here, the sensor precision is not deemed high because of significant variations in the temporal measurements for the same object under the same conditions. er The more the resolution of a sensor, the more accurate is the precision. A sensor's accuracy does not depend upon its resolution. 5.4 Sensorial Deviations In this section, we will discuss the various sensorial deviations that are considered as errors in sensors. Most of the sensing in IoT is non-critical, where minor deviations in sensorial outputs seldom change the nature of the undertaken tasks. However, some critical applications of IoT, such as healthcare, industrial process monitoring, and others, do require sensors with high-quality measurement capabilities. As the quality of the measurement obtained from a sensor is dependent on a large number of factors, there are a few primary considerations that must be incorporated during the sensing of critical systems. In the event of a sensor's output signal going beyond its designed maximum and minimum capacity for measurement, the sensor output is truncated to its maximum or minimum value, which is also the sensor’s limits. The measurement range between a sensor’s characterized minimum and maximum values is also referred to as the full- scale range of that sensor. Under real conditions, the sensitivity of a sensor may differ from the value specified for that sensor leading to sensitivity error. This deviation is mostly attributed to sensor fabrication errors and its calibration. If the output of a sensor differs from the actual value to be measured by a constant, the sensor is said to have an offset error or bias. For example, while measuring an actual temperature of 0° C, a temperature sensor outputs 1.1° C every time. In this case, the sensors said to have an offset error or bias of 1.1° C. Similarly, some sensors have a non-linear behavior. If a sensor's transfer function (TF) deviates from a straight line transfer function, it is referred to as its non-linearity. The amount a sensor's actual output differs from the ideal TF behavior over the full range of the sensor quantifies its behavior. It is denoted as the percentage of the sensor's full range. Most sensors have linear behavior. If the output signal of a sensor changes slowly and independently of the measured property, this behavior of the sensor's output is termed as drift. Physical changes in the sensor or its material may result in long-term drift, which can span over months or years. Noise is a temporally varying random deviation of signals.104 Introduction to Internet of Things In contrast, if a sensor’s output varies/deviates due to deviations in the sensor's previous input values, it is referred to as hysteresis error. The present output of the sensor depends on the past input values provided to the sensor. Typically, the phenomenon of hysteresis can be observed in analog sensors, magnetic sensors, and during heating of metal strips. One way to check for hysteresis error is to check how the sensor's output changes when we first increase, then decrease the input values to the sensor over its full range. It is generally denoted as a positive and negative percentage variation of the full-range of that sensor. Focusing on digital sensors, if the digital output of a sensor is an approximation of the measured property, it induces quantization error. This error can be defined as the difference between the actual analog signal and its closest digital approximation during the sampling stage of the analog to digital conversion. Similarly, dynamic errors caused due to mishandling of sampling frequencies can give tise to aliasing errors. Aliasing leads to different signals of varying frequencies to be represented as a single signal in case the sampling frequency is not correctly chosen, resulting in the input signal becoming a multiple of the sampling rate. Finally, the environment itself plays a crucial role in inducing sensorial deviations, Some sensors may be prone to external influences, which may not be directly linked to the property being measured by the sensor. This sensitivity of the sensor may lead to deviations in its output values. For example, as most sensors are semiconductor based, they are influenced by the temperature of their environment. 5.5 Sensing Types Sensing can be broadly divided into four different categories based on the nature of the environment being sensed and the physical sensors being used to do so (Figure 5.4): 1) scalar sensing, 2) multimedia sensing, 3) hybrid sensing, and 4) virtual sensing—(2]. 5.5.1 Scalar sensing Scalar sensing encompasses the sensing of features that can be quantified simply by measuring changes in the amplitude of the measured values with respect to time [3]. Quantities such as ambient temperature, current, atmospheric pressure, rainfall, light, humidity, flux, and others are considered as scalar values as they normally do nothave a directional or spatial property assigned with them. Simply measuring the changes in their values with passing time provides enough information about these quantities. The sensors used for measuring these scalar quantities are referred to as scalar sensors, and the act is knownas scalar sensing, Figures 5.3(b), 5:3(d), 5:3(e), 53(f), 5:3(), 5.3(h), 5.3(i), and 5.3) show scalar sensors. A simple scalar temperature sensing of a fire detection event is shown in Figure 5.4(a).