Wireless Sensors and Instruments

Networks, Design, and Applications

Halit Eren

• Publisher: CRC Press
• Year: 2018
• Print ISBN: 9780849336744
• eBook ISBN: 9781351836401
• Edition: 1
• Topic: Technology & Engineering
• Subtopic: Electrical Engineering & Telecommunications
• Index: Technology & Engineering

Advances such as 3-G mobile communications networks demonstrate the increasing capability of high-quality data transmission over wireless media. Adapting wireless functionality into instrument and sensor systems endows them with unmatched flexibility, robustness, and intelligence. Wireless Sensors and Instruments: Networks, Design, and Applications explains the principles, state-of-the-art technologies, and modern applications of this burgeoning field.From underlying concepts to practical applications, this book outlines all the necessary information to plan, design, and implement wireless instrumentation and sensor networks effectively and efficiently. The author covers the basics of instruments, measurement, sensor technology, communication systems, and networks along with the theory, methods, and components involved in digital and wireless instruments. Placing these technologies in context, the book also examines the principles, components, and techniques of modern communication systems followed by network standards, protocols, topologies, and security.Building on these discussions, the book uses examples to illustrate the practical aspects of constructing sensors and instruments. Finally, the author devotes the closing chapter to applications in a broad array of fields, including commercial, human health, and consumer products applications.Filled with up-to-date information and thorough coverage of fundamentals, Wireless Sensors and Instruments: Networks, Design, and Applications supplies critical, hands-on tools for efficiently, effectively, and immediately implementing advanced wireless systems.

1

Instruments and Instrumentation

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Instruments are developed for sensing and measuring physical variables that are essential in industrial operations, environmental applications, research and development (R&D), transportation, military equipment, and in our daily lives. Instrumentation systems are collections of instruments networked to communicate with each other directly or via some intermediate device such as computers or microprocessors. The majority of instrument communication systems have been based on wired media, but today wireless communication is developing rapidly and becoming common in industrial and many other applications. In this chapter, brief but essential background information is provided on measurements, instruments, instrumentation, sensor technology, communication systems, and networks.

If the behavior of the physical variable is known, its performance can be monitored and assessed. Applications of instruments range from laboratory conditions to arduous environments, such as inside nuclear reactors, to remote locations, such as satellite systems or spaceships. Manufacturers produce a wide range of instruments in order to meet diverse requirements. The majority of modern instruments have a great degree flexibility in their range of uses, types of displays, and methods of communication with other devices.

In instrument communication, information generated by a source is passed to a sink. The source converts the measured or sensed variable into electrical signals. Electrical signals are then processed and modified into communication signals that are passed through a communication channel in the form of useful information or a message. The received signals are then converted back into signals at the sink. Information can be transmitted through wired or wireless media by a variety of techniques.

In recent years, considerable progress has been made in measurements, instruments, and instrumentation systems because of the progress in integrated circuit (IC) technology, the availability of low cost analog and digital components, and efficient microprocessors. Consequently the performance of measuring and monitoring instruments has improved significantly because of the availability of on-line and off-line analysis, advanced signal processing techniques, and local and international standards. Today’s wireless communication technology is able to address the needs of effective and efficient communication in all types of instruments and instrumentation systems.

In this chapter, measurement issues are introduced and instrument architecture explained. Digital instruments and their associated theory, methods, and components are highlighted. Sensor technology is the backbone of all types of instruments, therefore sensors will be explained in detail. A general introduction is provided on instrument communication as applied to common instruments as well as those used in industry. Instrument networks and their associated standards and protocols are discussed. Overall, this chapter concentrates on the source and associated issues such as noise, distortion, and interference during communication.

1.1 Measurements

Measurement is a process of gathering information from the physical world within agreed upon national and international standards and procedures. Measurements are carried out using manmade instruments that are designed and manufactured to perform specific functions. The functionality of an instrument is to maintain a prescribed relationship between the measured numerical values and the physical variable, or measurand, under investigation.

A typical instrument has many components, the sensors and transducers being the primary elements that respond to the physical variable and generate useful signals. A sensor is a physical entity that converts a physical variable into a processable electrical signal. Sensor signals in the majority of modern instruments are in electrical form or they are ultimately converted to an electrical form. This is because electrical signals are easy to process, display, store, and transmit. Similar to sensors, transducers also convert energy from one form to another between two physical systems.

Transducers are used in a wide variety of industries, including aerospace, automotive, biomedical, industrial control, manufacturing, and process control. As the demand for automation systems grows, the need for transducers increases. This demand is partially met by the development of smart sensors and transducers and by the integration of wireless technologies, making today’s instruments and instrumentation systems much more flexible, cheaper, and easer to use.

Once converted to electrical form, the relation between the sensor signals and physical variations can be expressed in a transfer function, a mathematical model between the sensor signal and the physical variable. In a continuous system, the transfer function may be linear or nonlinear. For example, a linear relationship is expressed by the following equation:

where y is the electric signal from the sensor, x is the physical stimulus, a is the intercept on the y-axis, which gives the output signal for a zero input, and b is the slope, also known as the sensitivity.

The output signal y represents the physical variable in amplitudes, frequencies, phases, or other properties of electrical signals, depending on the design and construction of a particular sensor and the nature of the variable. The nonlinear transfer functions can be logarithmic, exponential, or in other forms of mathematical functions. In many applications, a nonlinear sensor signal may be linearized over some limited ranges.

For a specific measurement, a wide range of sensors and transducers may be available. Selection of the correct sensors, transducers, and components to retrieve the required information and employment of representative signal processing techniques is essential.

1.2 Instrument Architecture and Instrumentation

Instruments are manmade devices that are designed and constructed using the existing knowledge about a physical process and the available technology. Appropriate hardware and software are engineered that can perform well within the expected specifications and standards.

The functionality of a typical instrument can be broken into smaller components, as illustrated in Figure 1.1. All measuring instruments have some or all of these components. Instruments differ from each other in the way they handle, transmit, and display information.

Generally signals from sensors are not suitable for displaying, recording, or transmitting in their raw form. The amplitudes, power levels, or band-widths of sensor signals may be very small or may carry excessive noise and superimposed interference that masks the desired components. Signal conditioners adapt sensor signals to acceptable levels and shapes for processing and display.

1.2.1 Signals and Signal Conditioning

Measurement of physical variables generally leads to the generation electrical signals. A signal can be defined as “any physical quantity that varies with time, space, or any other independent variable or variables.” Naturally, different types of signals require different processing techniques, hence different types of analog and digital methods must be used and associated components must be selected.

The analysis and processing of signals requires some kind of mathematical description of the signal. A signal can be described as a function of one or more independent variables. Therefore signals generated by sensors can be classified in a number of mathematical ways, such as multichannel and multidimensional signals, continuous time or discrete signals, deterministic or random (stationary, nonstationary) signals, transient signals, etc.

FIGURE 1.1
Essential components of an instrument.

In some applications where multiple signal sources are present, multiple sensors are used to generate signals. These signals can be represented in vectors. Generally if the signal is a function of a single independent variable, it is called a one-dimensional signal. Similarly a signal is called m-dimensional if its value is a function of m independent variables (e.g., earthquake signals picked by various accelerometers). Vectors in matrix form can represent such signals, and appropriate techniques are applied for processing.

Any signal that can be expressed by an explicit mathematical expression or by well-defined rules is called a deterministic signal. That is, the past, present, and future value of the signal is known precisely without uncertainty. In this respect, the signal may be classified according to the characteristics of an independent variable (e.g., time) and the value it takes. Deterministic signals can be continuous or discrete.

Continuous signals, also known as analog signals, are defined for every value of time from −∞ to +∞. Continuous signals can be periodic or nonperiodic. In periodic signals, the signal repeats itself in an oscillatory manner, which can be represented as a sinusoidal waveform:

where x(t) is the time-dependent signal, ω is the angular frequency (2π ft), and X_m is the maximum value.

Continuous signals can be periodic, but not necessarily sinusoidal, such as triangular, sawtooth, rectangular, or other regular or irregular shapes. If the signals are periodic but nonsinusoidal, they can be expressed in Fourier series as a combination of a number of pure sinusoidal waveforms as

where ω₁, ω₂, …, ω_n are the frequencies (rad/s), X₀, X₁, …, X_n are the maximum amplitudes of respective frequencies, and ϕ₁, ϕ₂, …, ϕ_n are the phase angles.

In Equation 1.3, the number of terms may be infinite, and the greater the number of elements the better the approximation. These elements constitute the frequency spectrum. The signals can be represented in the time domain or frequency domain, both of which are extremely useful in the analysis.

Discrete signals are defined only at discrete intervals of time. These time intervals may not be equal, but in practice, for computational convenience, they are assumed to be equally spaced. Equation 1.2 can be represented in discrete form as

where

Digital signals are represented as 1s and 0s. These signals can be generated digitally or obtained from analog signals by the application of appropriate analog-to-digital (A/D) signal converters. Digital signals have many advantages over analog signals. Since they are only 1s and 0s, they are easy to generate, process, store, multiplex, and transmit. They are relatively immune to noise, error corrections can be carried out easily, and encryption and other security issues can be easily addressed. However, they require greater band-widths in communication, particularly if wireless techniques are used.

Random signals vary randomly in time. Random signals are often seen in nature, where they constitute irregular cycles that never repeat themselves exactly. Random signals cannot be deterministically described to any reasonable degree of accuracy or a description is too complicated for practical use. However, statistical methods and probability theory can be used for analysis by taking representative samples. Theoretically an infinitely long time period is necessary to obtain a complete description of these signals. Mathematical tools such as probability distributions, probability densities, frequency spectra, cross correlations, autocorrelations, digital Fourier transforms (DFTs), fast Fourier transforms (FFTs), and autospectral analysis, root mean square values, and digital filter analysis are some of the techniques that can be employed.

If the statistical properties of signals do not vary over ti...

Table of contents

• Cover Page
• Half Title
• Title Page
• Copyright Page
• dedication
• Preface
• Acknowledgments
• Author
• Introduction
• Table of Contents
• 1 Instruments and Instrumentation
• 2 Wireless Communication
• 3 Data Transfer, Networks, Protocols, and Standards
• 4 Wireless Instrument and Sensor Networks
• 5 Wireless Sensor and Instrument Applications
• Bibliography
• Index