4

Communications and Information Theory

There are a significant number of technologies and data paths to move data, and a bulk of the material in this book will investigate the aspects, constraints, and comparisons of communication choices for the architect. The previous chapter detailed sensor architecture and design, and now we must marshal the data to the Internet. This requires an understanding of communications and the limitations of moving data.

We begin the short-range and long-range communications discussion with a review of wireless RF signals and the contributing factors that impact signal quality, limitations, interference, models, bandwidth, and range. There are many PAN/WAN communication protocols to choose from in different bands, and the architect must understand the pros and cons of choosing one radio spectrum over the other.

By the end of this chapter, you should understand the limits of radio communication speed and bandwidth. Whether you are architecting a Bluetooth-enabled IoT device or an edge router with a 5G radio, the rules and physics of radios all apply equally.

The following graphic helps delineate the various range and data rates for wireless protocols we will cover in later chapters.

WPAN is often used with other near-range communication acronyms such as wireless field area network (FAN), wireless local area network (WLAN), wireless home area network (HAN), wireless neighborhood area network (NAN), and wireless body area network (WBAN):

Figure 1: Various wireless communication protocols and categories designed for different ranges, data rates, and use cases (power, vehicular, and so on)

This chapter will provide foundational models and theory on communication systems, frequency spaces, and information theory. Communication constraints and comparison models will be provided to allow the architect to understand how and why certain types of data communication work and where they won't work.

We start with communication theory as it plays a fundamental part in choosing the correct mix of wireless technologies to deploy an IoT solution.

Communication theory

The IoT is a conglomeration of many disparate devices autonomously producing and/or consuming data on the very far edge of layers of networks and protocols. It is important to understand the constraints in building communication systems for the IoT or any form of networking. The IoT will combine body area networks, personal area networks, local area networks, and long-range wide area networks into a web of communication channels.

Most of what makes the IoT possible is built around communication fabric; therefore, this chapter is dedicated to reviewing the fundamentals of network and communication systems. We will now focus on communication and signaling systems. We will explore the range, energy, and limitations of communication systems and how the architect will use these tools to develop a successful IoT solution.

RF energy and theoretical range

It is important when talking about wireless personal area networks, or any RF wireless protocol, to consider the range of transmission. Competing protocols use range, speed, and power as differentiators. As architects, we need to consider different protocols and design choices when implementing a complete solution. The range of transmission is based on the distance between transmitter and receiver antennas, the frequency of transmission, and the power of transmission.

The best form of RF transmission is an unobstructed line of sight in a radio signal-free area. In most situations, this ideal model will not exist. In the real world, there are obstructions, reflections of signals, multiple wireless RF signals, and noise.

When considering a particular WAN and a slower speed signal like 900 MHz versus a 2.4 GHz carrier signal, you can derive the attenuation of a function of wavelength for each frequency. This will provide guidance as to the signal strength at any range. The general form of the Friis transmission equation helps us here:

The decibel (dB) variant of the Friis equation is given as:

where GTx and GRx are the transmitter and receiver antenna gains, R is the distance between the transmitter and receiver, and PR and PT are the receiver and transmitter power respectively.

The following figure illustrates the equation:

Figure 2: Friis equation graphical representation

A 900 MHz signal at 10 meters will have a loss of 51.5 dB and a 2.4 GHz signal at 10 meters will have a 60.0 dB loss.

We can prove how power and range affect the quality of a signal by using a ratio referred to as the link budget. This is a comparison of the transmission power to sensitivity level and is measured in the logarithmic (dB) scale. One may simply want to increase the power level to meet range requirements, but in many instances, this violates regulatory compliance or affects battery life. The other option is to improve the receiver's sensitivity level, which is exactly what Bluetooth 5 did in the latest specification. The link budget is given by the ratio of transmitter power over the receiver sensitivity as shown:

The link budget is measured in the dB log scale; therefore, adding decibels is equivalent to multiplying the numeric ratios, which gives us the equation:

Assuming there is no factor contributing to any signal gains (for example, antenna gains), the only two ways of improving reception are increasing the transmission power or decreasing the loss.

When architects must model the maximum range of a particular protocol, they will use the free-space path loss (FSPL) formula. This is the amount of signal loss of an electromagnetic wave over a line of sight in free space (no obstacles). The contributing factors of FSPL formula are the frequency (f) of the signal, the distance (R) between transmitter and receiver, and the speed of light (c). In terms of calculating FSPL formula in decibels, the equation is:

The FSPL formula is a simple first-order calculation. A better approximation takes into account reflections and wave interference from the earth ground plane, such as the plane earth loss formula. Here, ht is the height of the transmitting antenna, and hr is the height of the receiving antenna. k represents the free-space wave number and is simplified as shown. We convert the equation to use dB notation:

What is notable of the plane earth loss is the fact that the distance affects the loss by 40 dB per decade. Increasing the antenna height helps. The types of interference that can naturally occur include:

This is an important concept since the architect must choose a WAN solution whose frequency balances the data bandwidth, the ultimate range of the signal, and the ability of the signal to penetrate objects. Increasing frequency naturally increases free-space loss (for example, a 2.4 GHz signal has 8.5 dB less coverage than a 900 MHz signal).

Generally speaking, 900 MHz signals will be reliable at twice the distance of 2.4 GHz signals. 900 MHz has a wavelength of 333 mm versus 125 mm for a 2.4 GHz signal. This allows a 900 MHz signal to have better penetration ability and not as much effect from scattering.

Scattering is a significant issue for WAN systems as many deployments do not have a free line of sight between antennas—instead the signal must penetrate walls and floors. Certain materials contribute differently to the attenuation of a signal.

A 6 dB loss equates to a 50% reduction in signal strength, while a 12 dB loss equates to a 75% reduction.

We see that 900 MHz has an advantage over 2.4 GHz in material penetration:

Material Loss (dB) 900 MHz Loss (dB) 2.4 GHz

0.25" glass

-0.8 dB

-3 dB

Brick or masonry block wall (8")

-13 dB

-15 dB

Sheet rock

-2 dB

-3 dB

Solid wood door

-2 dB

-3 dB

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Figure 3: Free space loss versus plane earth loss (in dB) using a 2.4 GHz signal with antennas at 1 meter high

As we will see, many protocols are commercially available and used globally in the 2.4 GHz spectrum. 2.4 GHz provides up to five times the data bandwidth as a 900 MHz signal and can have a much smaller antenna. Additionally, the 2.4 GHz spectrum is unlicensed and available for use in multiple countries.

900 MHz 2.4 GHz
Signal strength

Generally reliable

Crowded band subject to interference
Distance

2.67x further than 2.4 GHz

Shorter but can compensate with improved encoding (Bluetooth 5)
Penetration

Long wavelength can penetrate most materials and vegetation

Potential for interference with some building material. Potential interference with water vapor.
Data rate

Limited

About 2x to 3x faster than 900 MHz
Signal interference

Signal can be affected by tall objects and obstructions; better through foliage

Less chance of channel interference with certain objects
Channel interference

Interference with 900 MHz cordless phones, RFID scanners, cell signals, baby monitors

Interference with 802.11 Wi-Fi
Cost

Medium

Low

These equations provide a theoretical model; no analytical equations give an accurate prediction for certain real-world scenarios such as multipath losses.

RF interference

We will see throughout this chapter several novel schemes to reduce interference of signals. This is an issue for many forms of wireless technology as the spectrum is unlicensed and shared. (We will discuss this more in the next section). Because there may be several devices emanating RF energy in a shared space, interference will occur.

Take Bluetooth and 802.11 Wi-Fi; both operate in the shared 2.4 GHz spectrum but remain functional even in congested environments. Bluetooth Low Energy (BLE), as we will see, will randomly select one of 40-2 MHz channels as a form of frequency hopping.

We see in the following figure 11 free channels (three being advertising) on BLE that have a 15% chance of collision (especially since 802.11 does not hop between channels). The new Bluetooth 5 specification provides techniques such as slot availability masks to lock out Wi-Fi areas from the frequency hop list. Other techniques exist as well, which we will explore later. Here we show the ILM band for Zigbee and BLE. Also shown is a possible contention with three Wi-Fi channels in the 2.4 GHz spectrum.

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Figure 4: Comparison of BLE and Zigbee interference with 802.11 Wi-Fi signals in the 2.4 GHz band. BLE provides more slots and frequency hopping to communicate in the event of Wi-Fi collisions.

Information theory

There are preliminary theories that need to be understood before detailing WAN specifics. One thing that is germane to communication is how bitrate affects transmission power, which in turn affects range. There are limits to the integrity of data and bitrates, as we will learn. Additionally, we need to classify narrowband versus wideband communication.

Bitrate limits and the Shannon-Hartley theorem

In long-range communication and short-range communication, the goal is to maximize bitrate and distance within the constraints of spectrum and noise. The Shannon-Hartley theorem is composed of work from Claude Shannon of MIT in the 1940s (C. E. Shannon (1949/1998). The Mathematical Theory of Communication. Urbana, IL: University of Illinois Press) and Ralph Hartley from Bell Labs in the 1920s (R. V. L. Hartley (July 1928). "Transmission of Information" (PDF). Bell System Technical Journal). Foundational work was developed by Harry Nyquist, also of Bell Labs, who determined the maximum number of pulses (or bits) that could travel in a telegraph in a unit of time (H. Nyquist, Certain Topics in Telegraph Transmission Theory, in Transactions of the American Institute of Electrical Engineers, vol. 47, no. 2, pp. 617-644, April 1928).

Essentially, Nyquist developed a sampling limit that determines how much theoretical bandwidth one has at a given sample rate. This is called the Nyquist rate and is shown in the following equation:

Here, fp is the pulse frequency and B is the bandwidth in hertz. This states that the maximum bitrate is limited to twice the sampling rate. Looking at it another way, the equation identifies the minimum bitrate at which a finite bandwidth signal needs to be sampled to retain all information. Undersampling leads to an aliasing effect and distortion.

Hartley then devised a way to quantify the information in what is called the line rate. The line rate can be thought of as bits per second (for example, Mbps). This is known as Hartley's law and is the precursor to Shannon's theorem. Hartley's law simply states the maximum number of distinguishable pulse amplitudes that can be transmitted reliably is limited by the dynamic range of the signal and the precision with which a receiver can accurately interpret each individual signal. Shown here is Hartley's law in terms of M (number of unique pulse amplitude shapes), which is equivalent to the ratio of the number of voltage:

Converting the equation to a base-2 log gives us the line rate R:

If we combine this with the preceding Nyquist's rate, we get the maximum number of pulses that can be transmitted over a single channel of bandwidth B. Hartley, however, did not work out with precision; the value of M (number of distinct pulses) could be affected by noise.

Shannon reinforced Hartley's equation by considering the effects of Gaussian noise and completed Hartley's equation with a signal-to-noise ratio. Shannon also introduced the concept of error correction coding instead of using individually distinguishable pulse amplitudes. This equation is affectionately known as the Shannon-Hartley theorem:

Here, C is the channel capacity in bits per second, B is the channel bandwidth in hertz, S is the average received signal measured in watts, and N is the average noise on the channel measured in watts. The effect of this equation is subtle but important. For every decibel level increase in noise to a signal, capacity drops precipitously. Likewise, improving the signal-to-noise ratio will increase capacity. Without any noise, the capacity would be infinite.

It is also possible to improve the Shannon-Hartley theorem by adding a multiplier n to the equation. Here, n represents additional antennas or pipes. We have reviewed this previously as multiple input, multiple output (MIMO) technology.

To understand how Shannon's rule applies to the limits of wireless systems mentioned in this book, we need to express the equation in terms of energy per bit rather than the signal-to-noise ratio (SNR). A useful example in practice is to determine the minimum SNR needed to achieve a certain bitrate. For example, if we want to transmit C=200 kbps over a channel with a bandwidth capacity of B=5000 kbps, then the minimum SNR required is given as:

This shows that it is possible to transmit data using a signal that is weaker than the background noise.

There is a limit to data rate, however. To show the effect, let Eb represent the energy of a single bit of data in joules. Let N0 represent the noise spectral density in watts/hertz. Eb/N0 is a dimensionless unit (however, usually expressed in dB) that represents the SNR per bit, or commonly known as the power efficiency. Power efficiency expressions remove biases of modulation techniques, error coding, and signal bandwidth from the equation. We assume that the system is perfect and ideal such that RB= C where R is the throughput. The Shannon-Hartley theorem can be rewritten as:

This is known as the Shannon limit for an additive white Gaussian noise (AWGN). An AWGN is a channel and is simply a basic form of noise used commonly in information theory to express the effects of random processes in nature. These noise sources are always present in nature and include things such as thermal vibrations, black body radiation, and the residual effects of the Big Bang. The "white" aspect of noise implies equal amounts of noise are added to each frequency.

The limit can be drawn on a graph showing spectral efficiency versus SNR per bit:

Figure 5: Spectral efficiency to SNR (power efficiency) curve. The dotted line represents the Shannon limit, which converges at ln(2)=-1.6. Various modulation schemes are shown under the Shannon limit with the typical range of 4G-LTE signals.

Regions of interest in the figure include the R>B Impossible Region. This region is above the Shannon limit of the curve. It states that no reliable form of information exchange can be above the limit line. The region below the Shannon limit is called the Realizable Region and is where R<B. Every protocol and modulation technique in any form of communication attempts to get as close as possible to the Shannon limit. We can see where typical 4G-LTE using various modulation forms exist.

There are two other regions of interest. A Bandwidth Limited region toward the upper right allows for high spectral efficiency and good Eb/N0 SNR values. The only constraint in this space is trading off a fixed or mandated spectral efficiency against the unconstrained power of transmission P, meaning the capacity has grown significantly over the available bandwidth. The opposite effect is called the Power Limited region toward the bottom left of the chart. The Power Limited region is where the Eb/N0 SNR is very low; therefore, the Shannon limit forces us down to low values of spectral efficiency. Here, we sacrifice spectral efficiency to get a given transmission quality P.

An example of a power limited use case is a space flight vehicle, such as the Cassini probe to Saturn. In that case, the free space path loss of signal is extremely large and the only way to get reliable data is to drop the data rate to very low values. We also see this in Bluetooth 5 using the new BLE Coded PHY. In that case, Bluetooth drops from 1 or 2 Mbps to as low as 125 kbps to improve range and data integrity.

The chart also shows some typical modulation schemes used today such as phase shifting, QAM, and others. The Shannon limit also shows that arbitrarily improving a modulation technique such as quadrature amplitude modulation for 4-QAM to 64-QAM doesn't scale linearly. The benefit of higher orders of modulation (for example, 4-QAM versus 64-QAM) is the fact that you can transmit more bits per symbol (two versus six). The main disadvantages with higher orders of modulation are:

The Shannon theorem states that there is a maximum rate of information that can be transmitted over a communication channel in the presence of additive Gaussian white noise. As the noise decreases, the rate of information will increase but has an ultimate bound that cannot be breached. In any situation, if the transmission rate R is less than the channel capacity C, then there should be a method or technology to transmit the data without error.

Bit error rate

Another important characteristic of data transmission is the bit error rate (BER), which refers to the number of bit errors received over a communication channel. BER is a unitless measurement expressed as a ratio or percentage. For example:

If an original transmission sequence is 1 0 1 0 1 1 0 1 0 0 and the received sequence is 0 0 1 0 1 0 1 0 1 0 (differences in bold) then the BER is 5 errors / 10 bits transferred = 50%

BER is affected by channel noise, interference, multipath fading, and attenuation. Techniques to improve BER include increasing transmission power, improving receiver sensitivity, using less dense/lower order modulation techniques, or adding more redundant data. The last technique is typically referred to as forward error correction (FEC). FEC simply adds extra information to a transmission. In the most basic sense, one would add triple redundancy and a majority vote algorithm; however, this would reduce bandwidth by 3x. Modern FEC techniques include Hamming codes and Reed-Solomon error correct codes. The BER can be expressed as a function of Eb/N0 SNR.

The following diagram shows a variety of modulation techniques and their respective BERs for various SNRs:

Figure 6: Bit error rates (Pb) versus power efficiency (Eb/N0) SNR for various modulation schemes. As the SNR increases toward the right the BER naturally decreases.

What should be understood at this point is the following:

For 4G-LTE cellular signals, which are covered later in this book, it operates in the 700 MHz to 5 GHz spectrum with dozens of segregated bands within that range. A cell phone (or battery-based IoT device) has substantially less power than a cell tower, but it is often the case that an IoT device will be transmitting sensor data to the cloud. The uplink from the IoT device is what we examine here. The limit to uplink power is 200 mW, which is 23 dBm. This limits the overall range of the transmission; that limit, however, is dynamic and will vary based on the bandwidth of the channel and the data rate. 4G systems, like several WPAN and WLAN devices, use orthogonal frequency-division multiplexing. Each channel has many subcarriers to address the multipath fading problems. If you sum all the data transmitting over subcarriers, you achieve high data rates.

4G-LTE generally uses 20 MHz channels, and LTE-A can use 100 MHz channels. These wide channels are limited by the overall spectrum of availability and are in contention with multiple carriers (ATT, Verizon, and so on) and other technologies sharing the spectrum.

An additional complexity of cellular communication is that a carrier may have portions of the spectrum divided up and disjointed from each other.

Cat-3 LTE can use 5, 10, or 20 MHz channels. The smallest channel granularity is 1.4 MHz. LTE-A is allowed to aggregate up to five 20-MHz channels for an aggregate bandwidth of 100 MHz.

A method to measure the distance a wireless device is functional within is the maximum coupling loss (MCL). The MCL is the maximum distance at which total channel loss occurs between a transmitter and a receiving antenna but data service can still be delivered. MCL is a very common way to measure the coverage of a system. MCL will include antenna gains, path loss, shadowing, and other radio effects. Generally, a 4G-LTE system will have an MCL of about 142 dB. We will revisit MCL when examining the cellular IoT technologies such as Cat-M1.

What we should grasp at this point is that if we increase the listening time per bit, the noise level will go down. If we reduce the bitrate by 2x, then the following is true: (Bit_Rate / 2) = (Bit_Duration * 2). Additionally, energy per bit increases by 2x and noise energy increases by sqrt(2). For example, if we reduce the Bit_Rate from 1 Mbps to 100 kbps, then Bit_Duration = increases by 10x. The range improves by the sqrt(10) = 3.162x.

Narrowband versus wideband communication

Many of the wireless protocols that we will cover are known as wideband. We will see that the converse (narrowband) also has a place, especially for LPWAN. The differences between narrowband and wideband are as follows:

Coherence time is a measure of the minimum time required for an amplitude or phase change to become uncorrelated from the previous value.

We have covered some forms of fading effects; however, there are many more. Path loss is a typical case where the loss is proportional to distance. Shadowing is where terrain, buildings, and hills create signal obstructions versus free space, and multipath fading occurs with the recombined scattering and wave interference of a radio signal on objects (due to diffraction and reflections). Other loss includes doppler shifts if the RF signal is in a moving vehicle. There are two categories of the fading phenomenon:

The following figure illustrates the difference between fast and slow fading paths:

Figure 7: Different RF signal fading effects. From left to right: General path loss across a line of sight. Middle: Effects of slow fading due to large structures or terrains. Right: Combined effects of distance, slow fading, and fast fading.

We will see that technologies employing narrowband signals use what is known as time diversity to overcome issues with fast fading. Time diversity simply means the signal and payload are transmitted multiple times in the hope that one of the messages gets through.

In a multipath scenario, the delay spread is the time between pulses from various multipath signals. Specifically, it is the delay between the first arrival of a signal and the earliest arrival of a multipath component of the signal.

The coherence bandwidth is defined as the statistical range of frequencies in which a channel is considered flat. This is a period of time when two frequencies are likely to have comparable fading. The coherence bandwidth Bc is inversely proportional to the delay spread D:

The time at which a symbol can be transmitted without intersymbol interference is 1/D. The following figure illustrates coherence bandwidth for narrowband and wideband communication. Since wideband is larger than the coherence bandwidth Bc, it is more likely to have independent fading attributes. That implies different frequency components will experience uncorrelated fading, whereas narrowband frequency components all fit within Bc and will experience uniform fading.

Figure 8: Coherence bandwidth and effects on narrowband and wideband: Frequencies f1 and f2 will fade independently if |f1 - f2| Bc. Here narrowband is clearly shown to reside within Bc, and wideband clearly exceeds the range of Bc by some margin.

One must ensure that the time between sending multiple signals from a multipath scenario is spread far enough as not to interfere with symbols. This is called intersymbol interference (ISI). The following figure illustrates a delay spread being too short and causing ISI. Given that the overall bandwidth B>> 1/T (where T is the pulse width time) and B1/D is implied, we can then generally state that bandwidth must be much larger than the coherence bandwidth: B>>Bc.

Figure 9: Delay spread example: two signals from a multipath event. If the delay spread D is less than the pulse width T, then the signals may not be spread far enough to override another multipath component, whereas if the delay spread is large enough, there may be no multipath conflict.

Generally, lower frequencies have a greater penetrating ability and less interference, but they require larger antennas and have less available bandwidth for transmission. Higher frequencies have greater path loss but smaller antennas and more bandwidth.

The overall bitrate will be impacted by the delay spread. For example, say we use QPSK modulation and the BER is 10-4; then for various delay spreads (D) we have:

The radio spectrum

Wireless communication is based on radio waves and bands of frequencies within the overall radio spectrum. We will cover long-range communication in the next chapter for cellular and other long-range mediums. Here we focus on the 1000-meter range or less. We will look at the spectrum allocation process as well as the typical frequency uses for WAN devices.

Governing structure

The spectrum ranges from 3 Hz to 3 THz, and allocation within the spectrum is governed by the International Telecommunication Union (ITU). A band is considered a portion of the spectrum that can be allocated, licensed, sold, or freely used depending on the frequency. From an ITU perspective, the bands are categorized as follows:

Figure 10: Frequency and band identification matrix for IEEE, EU, and ITU

Within the United States, the Federal Communications Commission (FCC) and the National Telecommunications and Information Administration (NTIA) control the frequency spectrum usage rights. The FCC administrates the non-federal spectrum usage, while the NTIA administers the federal usage (US Army, FAA, FBI, and so on).

The overall spectrum managed by the FCC ranges from the KHz spectrum through and including the GHz frequencies. The overall frequency distribution and allocation is shown in the following graphic. Highlighted are the interesting frequencies that will be discussed in this book.

Figure 11: Full frequency allocation spectrum of the FCC with highlights of ranges covered in this book

The following figure shows a small portion of the allocation of frequencies within the 900 MHz to 2.7 GHz range (common for WPAN signals) and how the frequencies are currently allocated and distributed. If you'd like to see this image in greater detail, please refer to: https://www.fcc.gov/engineering-technology/policy-and-rules-division/general/radio-spectrum-allocation. In many areas, the usage is multipurpose and shared:

Figure 12: FCC and NTIA frequency allocation chart between 300 MHz and 3 GHz. The chart represents a small portion of the overall frequency allocation. Source: FCC, "United States Frequency Allocations: The Radio Spectrum", October 2003

The FCC also assigns frequencies in licensed and unlicensed spectrums. In the "unlicensed" or "licensed exempt" areas, users can operate without an FCC license but must use certified radio equipment and comply with technical requirements such as power limits and duty cycles. These are detailed in the FCC Part 15 Rules document. Users can operate within these spectrums but are subject to radio interference.

The licensed areas of the spectrum allow for exclusive usage for particular areas/locations. The allocation may be granted on a national basis or in discrete segments site by site. Since 1994, exclusivity and rights to these areas of the spectrum have been granted by auction for particular areas/segments/markets (for example, cellular market areas, economic areas, and so on). Some bands can be a hybrid of the two models, where bands may have been licensed on a site-by-site basis and later bands surrounding those licenses were auctioned for larger geographic or national areas. The FCC also allows for a secondary market and has established policies and procedures through spectrum leasing and transfer of control.

Within Europe, frequency allocation governance is controlled by the European Commission (EC). The fellow members of the European Union attempt to create a fair and balanced spectrum allocation within the region. As European countries are small and share several borders, a central authority is necessary to achieve harmony. The EC also controls the trade and sale of frequencies within the region.

IoT deployments will typically use the licensed regions for long-range communication, which is covered in the next chapter. The unlicensed spectrum is typically used for industrial, scientific, and medical (ISM) devices. For IoT, IEEE 802.11 Wi-Fi, Bluetooth, and IEEE 802.15.4 protocols all reside in the 2.4 GHz unlicensed spectrum.

Summary

This chapter has provided foundation material to understand the theory and limitations of wireless communication. More information and deeper study are encouraged to understand the second and third order constraints of data transport. The architect should understand different models and constraints of wireless signals, RF energy dispersion, range, and the fundamental limits of information theory provided by Shannon-Hartley theorem. The architect should also understand the frequency space governance and allocation strategy. This chapter also provides a dialogue and vernacular that will be reused in the upcoming chapters on WPAN, WLAN, and WAN.

The next chapter will start the journey of IoT data from the sensor to the cloud on its first hop across a near-range personal area network. We build up from there to WLAN and WAN systems.