IoT and Low-Power Wireless.pdf

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IoT and Low-Power
Wireless
Circuits, Architectures, and
Techniques
Edited by
Christopher Siu
Managing Editor
Krzysztof Iniewski

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Title: IoT and low-power wireless : circuits, architectures, and techniques /
Christopher Siu and Krzysztof Iniewski.
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Table of Contents
List of Figures vii
List of Tables xix
Preface xxi
Series Editor xxv
Editor xxvii
List of Contributors xxix
1 The Internet of Things—Physical and Link
Layers Overview 1
Christopher Siu and Kris Iniewski
2 Low-Power Wearable and Wireless Sensors for
Advanced Healthcare Monitoring 13
Ifana Mahbub, Salvatore A. Pullano, Samira Shamsir, and
Syed Kamrul Islam
3 Biomedical Algorithms for Wearable Monitoring 33
Su-Shin Ang and Miguel Hernandez-Silveira
4 Approaches and Techniques for Maintenance and
Operation of Multisink Wireless Sensor Networks 89
Miriam Carlos-Mancilla, Ernesto López-Mellado, and
Mario Siller
5 Energy-Efficient Communication Solutions Based on
Wake-Up Receivers 119
Heikki Karvonen and Juha Petäjäjärvi
6 All-Digital Noise-Shaping Time-to-Digital Converters
for Mixed-Mode Signal Processing 153
Fei Yuan
v

vi Table of Contents
7 Power-Efficient CMOS Power Amplifiers for
Wireless Applications 183
Haoyu Qian, Suraj Prakash, and Jose Silva-Martinez
8 Injection-Locking Techniques in Low-Power
Wireless Systems 207
Yushi Zhou and Fei Yuan
9 Low-Power RF Digital PLLs with Direct
Carrier Modulation 247
Salvatore Levantino and Carlo Samori
10 Frequency Synthesis Technique for 60 GHz
Multi-Gbps Wireless 285
Teerachot Siriburanon, Hanli Liu, Kenichi Okada,
Akira Matsuzawa, Wei Deng, Satoshi Kondo,
Makihiko Katsuragi, and Kento Kimura
11 60 GHz Multiuser Gigabit/s Wireless Systems
Based on IEEE 802.11ad/WiGig 319
Koji Takinami, Naganori Shirakata, Masashi Kobayashi,
Tomoya Urushihara, Hiroshi Takahashi, Hiroyuki Motozuka,
Masataka Irie, and Kazuaki Takahashi
12 Adaptive and Efficient Integrated Power Management
Structures for Inductive Power Delivery 345
Hesam Sadeghi Gougheri and Mehdi Kiani
Index 375

List of Figures
1.1 Simplified IoT system block diagram. . . . . . . . . . . . . . 2
1.2 The 5-layer model in relation to WiFi. . . . . . . . . . . . . 3
1.3 Wake-up radio concept. . . . . . . . . . . . . . . . . . . . . . 4
1.4 Superregenerative receiver: (a) block diagram and (b) internal
waveforms. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5
1.5 Wake-up radio MAC layer requirement. . . . . . . . . . . . . 6
1.6 Thread specification in the 5-layer model. . . . . . . . . . . . 7
2.1 Publications of papers on wearable devices indexed by Scopus
in the last 20 years. . . . . . . . . . . . . . . . . . . . . . . . 15
2.2 Classification of crystal symmetry and flexible polymer sub-
strate. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 17
2.3 Readout circuits for current mode (left) and voltage mode
(right). . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18
2.4 System-level block diagram of an IR-UWB transmitter. . . . 20
2.5 Schematic of the impulse generator block. . . . . . . . . . . . 21
2.6 Signals in different stages of the delay block. . . . . . . . . . 21
2.7 BER simulation using MATLABr
for OOK modulation. . . 22
3.1 An example of a feature space and the corresponding hyper-
plane, derived from the support vector machine [6]. F1 and
F2 are two different features. . . . . . . . . . . . . . . . . . . 37
3.2 A back-propagated artificial neural network, with input x and
output y. All of these nodes contain adjustable weights, to
minimise the errors between the y and the expected
outcomes. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39
3.3 A binary decision tree for arrhythmia classification, using
features RMSSD and mean Normalised R–R interval (NN)
(corrected beat-to-beat intervals). T1–T8 represent thresholds
derived from the C4.5 algorithm [10]. . . . . . . . . . . . . . 40
3.4 State space diagram for the hidden Markov model. . . . . . 43
3.5 Examples of ECGs from a healthy patient (a) [14] and a
patient suffering from atrial fibrillation (b) [15]. . . . . . . . 46
3.6 The chart at the top shows a signal segment containing an
ECG QRS complex, while the chart at the bottom shows the
compacted spectrum of the DCT. . . . . . . . . . . . . . . . 49
vii

viii List of Figures
3.7 (a) DCT-based encoder. (b) DCT-based decoder. . . . . . . 50
3.8 Illustration of the Lagrangian trade-off curve. . . . . . . . . 52
3.9 An indirect calorimeter. . . . . . . . . . . . . . . . . . . . . . 54
3.10 The branch equation model for calorie energy expenditure
estimation. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 55
3.11 (a) HR and corresponding E. (b) AAC and corresponding
E. The dataset comprises of data collected from an indirect
calorimeter, corresponding with HR and AAC values, from
eight subjects. . . . . . . . . . . . . . . . . . . . . . . . . . . 57
3.12 Bar chart of overall accuracy for a floating-point, fixed-point
versions of the calibrated branch equation model in compari-
son with indirect calorimetry. . . . . . . . . . . . . . . . . . . 59
3.13 MATLAB graphical user interface (GUI) used for data selec-
tion and the specification of prior distribution. . . . . . . . 61
3.14 (a) MATLAB graphical user interface (GUI) used for feature
space visualisation. (b) Probe used to investigate the nature
of the data point and trace it back to its point of origin . . . 62
3.15 IP signals simultaneously recorded with SensiumVitals R

and
a reference bedside monitor. The top figure corresponds to
a good quality respiration signal. Respiration events (inspira-
tion and exhalation) can be seen in the waveforms due to their
quasi-periodic cyclical nature so that valid and accurate RRs
can be obtained. In contrast, the bottom figure shows poor
quality IP signals for both devices, which were severely cor-
rupted by motion artefacts. It is evident that the periodicity
of the signals is lost, and RRs are inaccurate and invalid. . . 67
3.16 Process for the development and evaluation of probabilistic
machine learning models for inspection of respiration signals
acquired with the SensiumVitals R

patch. . . . . . . . . . . . 68
3.17 Two-dimensional logistic regression model. The top graph
shows a rectilinear decision boundary with its 95% confidence
intervals that separates vectors corresponding to ‘good quality’
from ‘bad quality’ signals. Note that both classes are not 100%
linearly separable, as some A vectors overlap the ‘good region’
and some B vectors overlap the ‘bad region’. The bottom-left
plot corresponds to the ROC analysis for all the models created
from all possible combinations of the eight features contained
in the training dataset. . . . . . . . . . . . . . . . . . . . . . 71
3.18 (Right) separation hyperplane for 3D model fitted with a lin-
ear function. Note that the hyperplane separates very well
valid inputs (B) from invalids. (Left) A 3D model fitted with
a quadratic function performing almost as good as its linear
counterpart. Note that the separation hyperplane is now a
parabolic surface. . . . . . . . . . . . . . . . . . . . . . . . . 72

List of Figures ix
3.19 The chart in the middle shows the RSSs derived from a
tri-axial accelerometer, and illustrates the signal variation cor-
responding to the biomechanical movements of the stick fig-
ure at the top; the figure at the bottom shows a sub-segment
of the signal showing the pre-fall and fall phases, extracted
from [51]. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 74
3.20 Position of a hip mounted accelerometer in the initial reference
standing position (a), sitting (b) and kneeling position (c),
extracted from [51]. . . . . . . . . . . . . . . . . . . . . . . . 75
3.21 Top-level diagram of the fall detection algorithm: (a) Control
flow diagram (b) Structural data flow diagram, involving data
from a tri-axial accelerometer - Ax, Ay and Az [51]. . . . . . 76
3.22 ROC analysis of the five different impact classification tech-
niques, by [53] is licensed under CC by 2.0. . . . . . . . . . . 77
3.23 Data-flow diagram of the adaptive fall prediction algorithm. 78
3.24 Design and build flowchart for biomedical algorithms. . . . . 81
4.1 WSN classification. . . . . . . . . . . . . . . . . . . . . . . . 92
4.2 Centralized strategy. . . . . . . . . . . . . . . . . . . . . . . 94
4.3 Distributed strategy. . . . . . . . . . . . . . . . . . . . . . . 98
4.4 Routing protocol generalization. . . . . . . . . . . . . . . . . 100
4.5 Data aggregation and collection through the network. . . . . 102
4.6 Topology formed from an event detection. . . . . . . . . . . 103
4.7 Cluster-based formation. . . . . . . . . . . . . . . . . . . . . 105
4.8 Cluster-tree based formation. . . . . . . . . . . . . . . . . . . 107
4.9 Tree-based formation. . . . . . . . . . . . . . . . . . . . . . . 107
4.10 Ad hoc formation. . . . . . . . . . . . . . . . . . . . . . . . . 110
5.1 High-level architecture for a hierarchical network with hetero-
geneous devices. . . . . . . . . . . . . . . . . . . . . . . . . . 122
5.2 Distributed heterogeneous network example. . . . . . . . . . 124
5.3 Principle for (a) synchronous duty-cycling, (b) asynchronous
duty-cycling, and (c) wake-up radio-based MAC. . . . . . . . 126
5.4 Sensor node architecture for dual-radio approach. . . . . . . 127
5.5 Source-initiated mode of the GWR-MAC protocol. . . . . . . 128
5.6 Sink-initiated mode of the GWR-MAC protocol. . . . . . . . 129
5.7 Typical wake-up receiver architectures: (a) RF envelope detec-
tion, (b) uncertain-IF, (c) matched filter, (d) injection-locking,
(e) superregenerative oscillator, and (f) subsampling. . . . . 131
5.8 Comparison of wake-up receivers and their architectures. . . 133
5.9 Main differences between the WSN and the LPWAN. . . . . 134
5.10 Examples of hierarchical WSN architecture application areas
and techniques. . . . . . . . . . . . . . . . . . . . . . . . . . 136
5.11 Network energy consumption comparison as a function of
event per hour and duty cycle. . . . . . . . . . . . . . . . . . 140

x List of Figures
5.12 Energy efficiency comparison for WUR-based and DCM-based
hierarchical network. . . . . . . . . . . . . . . . . . . . . . . 141
5.13 Energy consumption comparison of UWB-WUR based
approach and duty-cycling based approach for WBAN. . . . 142
5.14 Energy consumption comparison for a Tx-Rx link when using
different parameter setting for WUR. . . . . . . . . . . . . . 143
6.1 Gated ring oscillator TDCs with counter-based phase
readout. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 155
6.2 Vernier-gated ring oscillator TDCs. . . . . . . . . . . . . . . 157
6.3 Gated ring oscillator TDCs with frequency readout. . . . . . 158
6.4 Gated relaxation oscillator TDCs with phase readout. . . . . 158
6.5 Switched ring oscillator TDC with frequency readout. . . . . 159
6.6 All-digital ∆Σ time-to-digital converters. . . . . . . . . . . . 160
6.7 Time register using gated delay cells. . . . . . . . . . . . . . 161
6.8 Time register using switched delay units. . . . . . . . . . . . 162
6.9 Time register using a unidirectional gated delay line. When
RST=1 is asserted, v1,...,N = 0V. . . . . . . . . . . . . . . . . 162
6.10 Time register using a gated discharge path. . . . . . . . . . . 163
6.11 Time adder using gated delay cells. . . . . . . . . . . . . . . 164
6.12 Time adder using a switched delay unit. . . . . . . . . . . . 165
6.13 Time adder using gated discharge paths. . . . . . . . . . . . 165
6.14 Time adder using a unidirectional gated delay line. When
RST=1 is asserted, D1,2,...,N = 0. . . . . . . . . . . . . . . . 166
6.15 Bidirectional gated delay line. . . . . . . . . . . . . . . . . . 167
6.16 Bidirectional gated delay line time adder. (a) Tin1, Tin2 > 0.
(b) Tin1 > 0, Tin2 < 0, and |Tin1| > |Tin2|, (c) Tin1 > 0,
Tin2 < 0, and |Tin1| < |Tin2|. . . . . . . . . . . . . . . . . . . 168
6.17 Time integrator utilizing GDC time adders. . . . . . . . . . 169
6.18 Time integrator using an SDU and a pair of SDU-embedded
ring oscillators. . . . . . . . . . . . . . . . . . . . . . . . . . . 170
6.19 Time integrator using a gated discharge path time adder and
a gated discharge path time register. . . . . . . . . . . . . . 171
6.20 Bidirectional gated delay line time integrator. . . . . . . . . 172
6.21 1-1 MASH time-mode ∆Σ TDC utilizing differential GDP time
integrators. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 174
6.22 Spectrum of 1-1 MASH time-mode ∆Σ TDC with GDP time
integrators and registers. 1024 samples with Hanning window
(Copyright c

IEEE). . . . . . . . . . . . . . . . . . . . . . . 175
6.23 First-order ∆Σ TDC with differential BiGDL time
integrators. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 176
6.24 Spectrum of first-order ∆Σ TDC with differential BiGDL time
integrators. 2048 samples with Hanning window (Copyright
c

IEEE). . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 177

List of Figures xi
7.1 Simplified schematic of a typical direct conversion
transmitter. . . . . . . . . . . . . . . . . . . . . . . . . . . . 184
7.2 Simulated and predicted multitone adjacent channel leakage
ratio (ACLR) as a function of number of in-band tones. . . . 189
7.3 Multitone output spectrum: theoretical, simulated, and exper-
imental results. . . . . . . . . . . . . . . . . . . . . . . . . . 189
7.4 Correlation between control phases and baseband signal ampli-
tude: (a) input signal and (b) digitally segmented signal. . . 190
7.5 Simplified schematic of the proposed architecture employing
three binary weighted switchable arrays. . . . . . . . . . . . 191
7.6 Simplified model for timing mismatch analysis. . . . . . . . . 192
7.7 PA output waveforms (RF component is not shown for sim-
plicity): (a) prewarped signal with and without timing delay
and (b) error waveform due to timing mismatch between φ3
and Si(t − τ). . . . . . . . . . . . . . . . . . . . . . . . . . . 193
7.8 Schematic of the PA output stage; the core consists of 1536
replicas. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 195
7.9 Conceptual schematic of the driver stage. . . . . . . . . . . . 195
7.10 Two-section impedance matching network. . . . . . . . . . . 196
7.11 Insertion loss simulation with process variations. . . . . . . . 197
7.12 Transient simulation results: (a) input signal before and after
digital prewarping (top trace), (b) output signal at drain volt-
age (middle trace), and (c) output signal after impedance
matching network. . . . . . . . . . . . . . . . . . . . . . . . . 198
7.13 Microphotograph of the chip. . . . . . . . . . . . . . . . . . . 199
7.14 Measured gain, output power, and PAE as a function of input
at 1.9 GHz. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 199
7.15 ACLR measured at maximum output power of 31 dBm. . . . 200
7.16 ACLR as a function of maximum output power. . . . . . . . 201
7.17 EVM as a function of maximum output power. . . . . . . . . 201
8.1 Injection-locked procedure represented in the simplified spec-
trum diagram. (a) Free-running, (b) ωinj deviates far from the
locking range ωL, (c) under perturbation, and (d) locked. . . 208
8.2 A negative feedback system. . . . . . . . . . . . . . . . . . . 210
8.3 (a) Injection-locked oscillators and (b) block diagram of
injection-locked oscillators. . . . . . . . . . . . . . . . . . . . 211
8.4 Waveform and spectrum of harmonic and nonharmonic oscil-
lators. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 212
8.5 Spectrum of free-running frequency and injection locked fre-
quency for the 1st-harmonic. . . . . . . . . . . . . . . . . . . 213
quency for the 3rd-harmonic. . . . . . . . . . . . . . . . . . . 214
quency for the 5th-harmonic. . . . . . . . . . . . . . . . . . . 214

xii List of Figures
8.8 Representation of injection-locked nonharmonic oscillators
with a single-tone injection. . . . . . . . . . . . . . . . . . . 215
8.9 Divide-by-2 wireless receiver. . . . . . . . . . . . . . . . . . . 216
8.10 A phase-locked loop. . . . . . . . . . . . . . . . . . . . . . . 217
8.11 (a) D flip-flop and (b) timing diagram. . . . . . . . . . . . . 217
8.12 (a) CML latch and (b) divide-by-2 circuit. . . . . . . . . . . 218
8.13 TSPC flip-flop. . . . . . . . . . . . . . . . . . . . . . . . . . . 218
8.14 (a) Miller divider and (b) model. . . . . . . . . . . . . . . . . 219
8.15 A differential LC-based frequency divider: (a) schematic and
(b) signal at Vout and VP . . . . . . . . . . . . . . . . . . . . . 221
8.16 A tuned amplifier. . . . . . . . . . . . . . . . . . . . . . . . . 221
8.17 Enhanced Locking range topology. Top right: [32], bottom
right: [51]. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 222
8.18 Direct injection-locked frequency divider. . . . . . . . . . . . 223
8.19 Direct injection-locked frequency divider with boosted 3rd-
order harmonic. . . . . . . . . . . . . . . . . . . . . . . . . . 224
8.20 Dual-direct injection-locked frequency divider. . . . . . . . . 224
8.21 Direct injection for odd division: (a) [54] (b) [40] and [39]. . 225
8.22 Superregenerative receiver. . . . . . . . . . . . . . . . . . . . 229
8.23 One-port mode of the oscillator. . . . . . . . . . . . . . . . . 229
8.24 Response of the oscillator according to the quench signal and
OOK signal. . . . . . . . . . . . . . . . . . . . . . . . . . . . 230
8.25 (a) Simplified architecture of Q enhancement superregenera-
tive receiver and (b) timing diagram of the quench signal. . . 231
8.26 (a) Simplified architecture of the superregenerative receiver for
BFSK modulation and (b) timing diagram of demodulation
(t1 < t2: bit “0”, t4 < t3: bit “1”). . . . . . . . . . . . . . . . 233
8.27 Injection-locked receiver. . . . . . . . . . . . . . . . . . . . . 234
8.28 Frequency-to-amplitude conversion. . . . . . . . . . . . . . . 235
8.29 (a) Diagram of [56]’s transceiver, and (b) envelope of
frequency-to-amplitude. . . . . . . . . . . . . . . . . . . . . . 236
8.30 BFSK demodulation in ref. [58]. Note, f1 is injection pulling
and f2 is injection locked. . . . . . . . . . . . . . . . . . . . . 237
8.31 (a) The simplified BPSK receiver and (b) the output signal
after the combiner. . . . . . . . . . . . . . . . . . . . . . . . 238
9.1 Simplified diagram of (a) Cartesian, (b) direct polar (DP) and
(c) out-phasing (OP) radio transmitter. . . . . . . . . . . . . 249
9.2 Phase modulator architecture: (a) direct and (b) indirect phase
modulation. . . . . . . . . . . . . . . . . . . . . . . . . . . . 251
9.3 Waveforms of phase- and frequency-modulation signal. . . . 252
9.4 PLL architectures: (a) analog and (b) digital. . . . . . . . . 253
9.5 Block schematic of a DPLL. . . . . . . . . . . . . . . . . . . 255
9.6 Block schematic of a DTC-based DPLL. . . . . . . . . . . . 256

List of Figures xiii
9.7 Input/output characteristic of a mid-rise TDC, probability of
input phase difference and average characteristic of
the TDC. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 257
9.8 Equivalent model of the DTC-based DPLL in Figure 9.6. . . 259
9.9 DPLL with the two-point injection scheme for direct FM. . . 261
9.10 Equivalent model of two-point injection scheme in
Figure 9.9. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 261
9.11 DPLL with pre-emphasis scheme for direct FM. . . . . . . . 263
9.12 Equivalent model of the DPLL with pre-emphasis scheme in
Figure 9.11. . . . . . . . . . . . . . . . . . . . . . . . . . . . 264
9.13 Model for the description of the loop gain adaptation block. 265
9.14 Frequency responses for (a) two-point injection and (b) pre-
emphasis schemes. . . . . . . . . . . . . . . . . . . . . . . . . 267
9.15 Segmented DCO topology and resulting non-linear tuning
characteristic. . . . . . . . . . . . . . . . . . . . . . . . . . . 270
9.16 DPLL with two-point injection scheme and multi-gain DCO
predistortion. . . . . . . . . . . . . . . . . . . . . . . . . . . . 271
9.17 Model of DPLL with two-point injection scheme and multi-
gain DCO predistortion. . . . . . . . . . . . . . . . . . . . . 272
9.18 Block schematic of practical DPLL with two-point injection
scheme and automatic DCO predistortion. . . . . . . . . . . 274
9.19 Circuit schematic of the DTC block. . . . . . . . . . . . . . . 275
9.20 Die photo of the phase modulator fabricated in a 65 nm CMOS
process. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 276
9.21 Measured spectrum of the implemented DPLL for a near-
integer-N synthesised channel. . . . . . . . . . . . . . . . . . 277
9.22 Measured performance of the phase modulator: (a) 20 Mb/s
QPSK modulation and (b) 10 Mb/s GMSK. . . . . . . . . . 278
10.1 Bandwidth allocation for different spectrum bands [1]. . . . 286
10.2 Simplified block diagram of (a) 60 GHz receiver and
(b) 60 GHz transmitter with amplitude and phase calibration
using 20 GHz PLL and 60 GHz QILO as phase shifter. . . . 287
10.3 Simplified diagram of TX and RX with analog and digital
baseband. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 290
10.4 (a) Optimum tracking bandwidth and integrated phase noise
for IEEE802.11ac and IEEE802.11ad and (b) target phase
noise performance for mm-wave PLL to satisfy 16QAM and
64QAM without and with carrier recovery circuit,
respectively. . . . . . . . . . . . . . . . . . . . . . . . . . . . 291
10.5 (a) Simplified block diagram of quadrature injection-locked
oscillator and (b) its phasor diagram. . . . . . . . . . . . . . 293
10.6 Simplified block diagram of the 20 GHz PLL and 60 GHz QILO
with single-sided injection. . . . . . . . . . . . . . . . . . . . 293

xiv List of Figures
10.7 Simplified architecture of (a) SS-PLL, (b) mm-wave direct
SS-PLL, and (c) mm-wave SS-PLL in subharmonic injection
architecture. . . . . . . . . . . . . . . . . . . . . . . . . . . . 295
10.8 Detailed block diagram of the proposed 60 GHz subsampling
frequency synthesizer. . . . . . . . . . . . . . . . . . . . . . . 296
10.9 (a) Simplified diagram of conventional direct injection induc-
torless ILFD and (b) Simplified diagram of even-harmonic-
enhanced direct injection inductorless ILFD. . . . . . . . . . 297
10.10 Timing injection of (a) conventional ILFD for divide-by-2 and
divide-by-4 operation and (b) dual-step-mixing ILFD for a
divide-by-4 operation with differential injections. . . . . . . . 298
10.11 Equivalent circuit model for (a) conventional direct-mixing
ILFD and (b) proposed dual-step-mixing ILFD with an
assumption of a single injection point. . . . . . . . . . . . . . 299
10.12 Equivalent circuit model for the proposed dual-step mixing
ILFD with differential injection. . . . . . . . . . . . . . . . . 301
10.13 Detailed schematic of the dual-step-mixing ILFD. . . . . . . 302
10.14 Theoretical and simulated locking range of conventional single-
step mixing and proposed dual-step mixing divide-by-4 ILFD
using differential injection. . . . . . . . . . . . . . . . . . . . 302
10.15 Detailed schematic of 20 GHz class-B VCO with tunable tail
filtering. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 304
10.16 Simplified circuit schematic of (a) conventional oscillator with
conventional cross-coupled pair and (b) its small-signal
circuit. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 305
10.17 Simplified circuit schematic of (a) proposed tail-cross-coupling
oscillator for gm-enhancement and (b) its small-signal
circuit. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 306
10.18 Comparisons of its (a) negative transconductance of an oscil-
lator and (b) parasitic capacitance (CPAR) seen from the tank
using conventional cross-coupled pair and tail cross-coupling
pair. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 307
10.19 Tank optimization of (a) inductance and (b) tank resistance
(Rp) versus switched ratio (αSW) for power reduction of the
60 GHz QILO. . . . . . . . . . . . . . . . . . . . . . . . . . . 307
10.20 Detailed schematic of the proposed QILO. . . . . . . . . . . 308
10.21 Chip microphotographs of (a) 20 GHz SS-PLL and (b) gm-
enhanced QILO. . . . . . . . . . . . . . . . . . . . . . . . . . 309
10.22 Measured locking range of the 20 GHz dual-step mixing
ILFD. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 309
10.23 Measured locking range of the proposed 60 GHz QILO over
the frequency range with 3-bit switch and tuning voltage. . . 310
10.24 Simulation and measured phase noise of the 20 GHz subsam-
pling PLL and 60 GHz QILO at a carrier of 20.16 GHz and
60.48 GHz, respectively. . . . . . . . . . . . . . . . . . . . . . 310

List of Figures xv
10.25 Measured spectrum of 20 GHz SS-PLL at a carrier frequency
of 19.44 GHz. . . . . . . . . . . . . . . . . . . . . . . . . . . 311
10.26 Performance comparison with state-of-the-art mm-wave fre-
quency synthesizers. . . . . . . . . . . . . . . . . . . . . . . . 313
11.1 60 GHz mobile use case examples. (a) Peer-to-peer connection
and (b) multiuser access in a dense environment. . . . . . . . 321
11.2 Frequency allocations by region and channel in IEEE
802.11ad/WiGig [14]. . . . . . . . . . . . . . . . . . . . . . . 322
11.3 Operation of beamforming protocol. . . . . . . . . . . . . . . 324
11.4 (a) Sliding IF and (b) direct conversion architectures. . . . . 325
11.5 Heterodyne architecture using coaxial cable. . . . . . . . . . 326
11.6 Block diagram of a transceiver chipset. (From [29], copyright
c

2017 IEICE.) . . . . . . . . . . . . . . . . . . . . . . . . . 327
11.7 Phase shifter circuit. (a) Schematic and (b) variable output.
(From [26], copyright c

2016 IEICE.) . . . . . . . . . . . . . 328
11.8 (a) RF signal distribution and (b) Wilkinson divider. (From
[26], copyright c

2016 IEICE.) . . . . . . . . . . . . . . . . 328
11.9 (a) Die photo of RFIC and (b) miniaturized antenna module.

2017 IEICE.) . . . . . . . . . . . . . 329
11.10 Measured phase shifter performance. (a) All measured points
and (b) unit circle for phase shift. (From [26], copyright c

2016 IEICE.) . . . . . . . . . . . . . . . . . . . . . . . . . . . 329
11.11 Measured analog beamforming performance and picture of
antenna module. (From [26], copyright c

2016 IEICE.) . . . 330
11.12 Comparison of area coverage. (a) Without beamforming
and (b) with beamforming. (From [29], copyright c

2017
IEICE.) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 330
11.13 System architecture example. (From [29], copyright c

2017
IEICE.) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 331
11.14 USB dongle prototype. (a) Internal unit and (b) exterior.

2017 IEICE.) . . . . . . . . . . . . . 332
11.15 Block diagram of AP prototype. (From [29], copyright c

2017
IEICE.) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 333
11.16 Operation principle of spatial sharing and time sharing. . . . 333
11.17 RF module for AP. (a) Top view and (b) bottom view.

2017 IEICE.) . . . . . . . . . . . . . 334
11.18 Measured output power (at 58.32 GHz). (a) Azimuth direc-
tion and (b) elevation direction. (From [29], copyright c

2017
IEICE.) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 335
11.19 AP prototype. (From [29], copyright c

2017 IEICE.) . . . . 336
11.20 Experimental demonstration at Narita International Airport.

2017 IEICE.) . . . . . . . . . . . . . 337
11.21 4K tablet with USB dongle prototype. (From [29], copyright
c

2017 IEICE.) . . . . . . . . . . . . . . . . . . . . . . . . . 337

xvi List of Figures
11.22 Prototype network system. (From [29], copyright c

2017
IEICE.) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 338
11.23 Measured content server throughput in nominal operation.

2017 IEICE.) . . . . . . . . . . . . . 338
11.24 System architecture example composed of mmWave access and
mobile edge cloud. . . . . . . . . . . . . . . . . . . . . . . . . 339
12.1 Some inductive coupling applications. (a) Biomedical
applications, (b) charging mobile electronics, (c) RFID, and
(d) charging electric cars [10–14]. . . . . . . . . . . . . . . . 346
12.2 The principle of inductive coupling between two wire loops.
The time-variant voltage across the primary coil, V1(t), gener-
ates a time-variant magnetic field, leading to induced current
of i2(t) in the secondary loop. . . . . . . . . . . . . . . . . . 347
12.3 Schematic diagram of an inductive power transmission link
with series and parallel resonance within the Tx and Rx sides,
respectively . . . . . . . . . . . . . . . . . . . . . . . . . . . . 349
12.4 (a) Equivalent circuit diagram of the inductive link in Figure
12.3 with the effect of Rx shown on the Tx side. (b) Cref res-
onates out with k2
12L1 at the power carrier frequency, leaving
behind Rref as the only effect of Rx on the Tx side. . . . . . 350
12.5 Generic block diagram of a conventional inductive WPT
system. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 351
12.6 Schematic diagram of a full-wave active rectifier [38]. . . . . 353
12.7 Simplified waveforms for (a) conventional two-step voltage
rectification and regulation and (b) combined
rectifier-regulator. . . . . . . . . . . . . . . . . . . . . . . . . 353
12.8 Schematic diagram of an active voltage doubler to achieve high
VCE [48]. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 354
12.9 Schematic diagram of a reconfigurable active voltage dou-
bler/rectifier [55]. . . . . . . . . . . . . . . . . . . . . . . . . 355
12.10 Schematic diagram of a current-mode full-wave active rectifier
with series Rx LC-tank [57]. . . . . . . . . . . . . . . . . . . 355
12.11 (a) A Q-modulation technique for dynamic transformation of
RL during operation. (b) Key switching waveforms to control
Q2L,eq by the adjustment of D = 2Ton/Tp [60]. . . . . . . . . 356
12.12 (a) Simplified circuit schematic of CM-resonant power deliver
(CRPD) technique to achieve VCE > 1 and load matching
for large RL with only adding a single switch (SW ). (b) Key
operational waveform of the CRPD technique [64]. . . . . . . 358
12.13 Measured VL and PTE of the CRPD-based inductive link vs.
fsw for RL = 100 kΩ [64]. . . . . . . . . . . . . . . . . . . . . 359
12.14 Measured PTE and optimal fsw of the CRPD-based inductive
link vs. RL, compared with a conventional half-wave rectifier
[64]. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 359

List of Figures xvii
12.15 Block diagram of the self-regulated reconfigurable voltage/
current-mode integrated power management (VCIPM) chip
that can operate in either VM or CM based on the VR
amplitude [66]. . . . . . . . . . . . . . . . . . . . . . . . . . . 360
12.16 Key waveforms for self-regulation and OVP in VCIPM chip
during VM and CM operation [66]. . . . . . . . . . . . . . . 362
12.17 Schematic circuit diagrams and key waveforms of (a) VMC
and (b) CMC blocks in VCIPM to generate proper SW2 and
SW1 signals, respectively [66]. . . . . . . . . . . . . . . . . . 363
12.18 (a) Measured VL and VR waveforms in VM when the Tx volt-
age (Vs in Figure 12.15) has been increased from 11 to 15
Vp−p at RL = 100 kΩ. (b) Zoomed waveforms for VL and VR,
demonstrating how reverse current has regulated VL at 3.2 V
despite Vs variations [66]. . . . . . . . . . . . . . . . . . . . . 364
12.19 (a) Measured VL and VR waveforms in CM when Vs is
increased from 4 Vp−p to 9 Vp−p at RL = 100kΩ. (b) Zoomed
waveforms for VL and VR demonstrating how changes in fsw
has regulated VL at 3.2 V despite Vs variations [66]. . . . . . 365
12.20 Measured VL, VR, and Vs waveforms when Vs is manually
increased from 4 Vp−p to 10 Vp−p, resulting in the automatic
reconfiguration of the VCIPM chip from CM to VM based on
the VR amplitude (1.2 V vs. 3.35 V) to regulate VL at 3.2 V
[66]. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 365
12.21 Measured VL vs. (a) coupling distance, d, (b) Rx coil orien-
tation, φ, and (c) Rx coil misalignment for conventional VM
only and VCIPM chip at RL = 100 kΩ and fixed input power
of 145 mW. The VCIPM chip can extend robustness against d,
φ, and misalignment for 125%, 150%, and 500%, respectively
[66]. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 366

List of Tables
2.1 IR-UWB Transmitter Design Specifications . . . . . . . . . . 22
3.1 Dataset Partitions for Training and Evaluating the AF Clas-
sification Algorithm . . . . . . . . . . . . . . . . . . . . . . . 63
3.2 Results for the AF Classifier Using the Hold-Out Method . . 64
3.3 Useful features for fall impact detection. Features are extracted
from a window of N samples, where si is the ith
sample, by [53]
is licensed under CC by 2.0 . . . . . . . . . . . . . . . . . . . 77
5.1 Parameters Used for Different Radios in the Energy Efficiency
Comparison . . . . . . . . . . . . . . . . . . . . . . . . . . . 140
7.1 Impedance Matching Network Component Values . . . . . . 196
7.2 Comparison with Recent Publications . . . . . . . . . . . . . 202
8.1 Comparison of injection-locked frequency dividers. . . . . . . 220
8.2 The state-of-the-art ultra-low-power receiver. (SR: Superre-
generative; IL: Injection locking) . . . . . . . . . . . . . . . . 228
10.1 Required TX EVM for Different Modulation Schemes in
IEEE802.11ad [2] . . . . . . . . . . . . . . . . . . . . . . . . 289
10.2 Comparison of Theoretical Locking Range of a Divide-by-4
ILFD in a Four-Stage Ring Topology . . . . . . . . . . . . . 300
10.3 Comparison of High-Speed Divider Chain in mm-Wave Fre-
quency Synthesizers . . . . . . . . . . . . . . . . . . . . . . . 303
10.4 Performance Comparison with the State-of-the-Art 60 GHz
Frequency Synthesizers . . . . . . . . . . . . . . . . . . . . . 312
11.1 Features of the IEEE 802.11ad/WiGig . . . . . . . . . . . . 323
11.2 MCS Examples (Not All Listed) in the
IEEE 802.11ad/WiGig . . . . . . . . . . . . . . . . . . . . . 323
11.3 USB Dongle Specifications . . . . . . . . . . . . . . . . . . . 332
11.4 AP Specifications . . . . . . . . . . . . . . . . . . . . . . . . 334
11.5 IEEE 802.11ay Use Cases [30] . . . . . . . . . . . . . . . . . 339
xix

Preface
Sometime in the future, we may look back and reflect that we are living
in times during which a unique confluence of technologies is creating a new
paradigm for networked devices, commonly referred to as the internet of things
(IoT). The idea behind IoT dates back to the 1990s, when Kevin Ashton
was a brand manager at Proctor & Gamble (P&G). In 1997, Ashton and his
team were tasked with promoting Oil of Olay lipsticks. When Ashton noticed
that some retail stores were not stocked with the product, he realized that
human data entry for restocking the lipstick is unreliable. He thus came up
with the idea of taking the Radio Frequency Identification (RFID) chip out
of a contactless smart card and attaching one to each lipstick to track store
inventory. Ashton then extended this idea, and pitched a solution to solve
P&G’s supply chain problem to the executives. Although the price of RFID
tags was still prohibitive at that time, Ashton was convinced that one day
the price will drop enough for this idea to be economically feasible. P&G
executives funded the research project, and Ashton eventually became the
executive director of Massachusetts Institute of Technology (MIT)’s Auto-ID
Center, where he was able to further his vision.
Today, roughly 20 years after Ashton’s idea, we are able to see his IoT
concept coming to fruition. The convergence and advancement of several tech-
nologies have made this possible, including
• Sensor and actuator technology
• Wireless technology
• Computational power and network protocol
• Miniaturization of devices, with integrated circuit technology riding
Moore’s law to the limit
The chapters in this book cover some of the wireless research that will enable
the implementation of IoT. The book also looks ahead at advanced wireless
techniques that will continue the evolution in ubiquitous wireless communica-
tion.
Chapter 1: This chapter provides an overview of IoT, focusing on the
technologies deployed for the physical and link layers. Emerging standards for
IoT are also outlined.
Chapter 2: Low-power wearables have entered into the mainstream con-
sumer market, with fitness devices that monitor exercise and heart rate being
xxi

xxii Preface
the most prevalent. This chapter explores the usage of wearables in the medical
market, and the challenges that come with designing sensors and electronics
for such devices.
Chapter 3: The challenge of wearable medical monitoring is further
explored in the context of algorithms and firmware. Algorithms that can reli-
ably interpret the physiological and biomechanical signals, derive metrics from
them, and predict clinically significant events are one of the keys to success
in this market.
Chapter 4: Connecting numerous devices into a wireless sensor network
is the focus of this chapter. Distributed versus centralized architectures are
discussed, including techniques that can improve the efficiency and robustness
of the network.
Chapter 5: A key technique for IoT devices to run years on a single battery
is to put the receiver to sleep for as long and as often as possible. This chapter
addresses this important issue with the wake-up receiver method to achieve
energy-efficient communication.
Chapter 6: As Complementary Metal Oxide Semiconductor (CMOS) pro-
cess scaling continues and the supply voltage continues to shrink, voltage
resolution and dynamic range in analog circuits also deteriorate. Over the
past decade, engineers have adjusted their design strategy by taking advan-
tage of the time resolution of CMOS, resulting in the time-to-digital converter
(TDC). Various innovations have been developed for TDCs, and this chapter
presents an all-digital TDC architecture with delta-sigma noise shaping.
Chapter 7: The power amplifier is one of the power hungry blocks within a
Radio Frequency (RF) transmitter. The aim of achieving high efficiency and
high linearity is a continual design challenge. In this chapter, a systematic
design technique is presented, along with the analysis of a current mode digital
RF power amplifier incorporating predistortion.
Chapter 8: Frequency synthesis using a phase locked loop (PLL) is another
power hungry function within an RF transceiver. Within the PLL, the voltage-
controlled oscillator and frequency divider consumes much of the power. As a
result, injection locking has been studied to reduce power consumption, and
this chapter provides an analysis of various injection-locked techniques.
Chapter 9: The Cartesian In-Phase and Quadrature (I/Q) modulator
driven by a PLL has been a conventional architecture used in RF trans-
mitters, but the need for RF mixers and filters has presented challenges in
deep-submicron CMOS. Over the past decade, efficient digital transmitter
architectures that avoid the use of mixers and filters have gained traction.
In this chapter, the use of powerful digital calibration techniques in a direct
modulation PLL has enabled further performance gains.
Chapter 10: As the spectra at 2.4 and 5 GHz have become very crowded,
engineers are looking at higher frequencies for future deployment. WiGig is one
example of moving WiFi to the 60 GHz band for enabling multi-Gbps wireless
communication. Techniques for frequency synthesis at 60 GHz are discussed
in this chapter. An injection-locked 60 GHz oscillator is used in conjunction

Preface xxiii
with a subsampling PLL to achieve low-power and low-phase noise. An imple-
mentation of these techniques in 65-nm CMOS is presented along with the
measured results.
Chapter 11: Fifth generation wireless is presently under definition and
development, and one consideration is the integration of IoT into the network.
Heterogeneous architectures have been proposed, where Wireless Local Area
Network (WLAN) is used in dense small cells. The latest status of IEEE
802.11ad/WiGig in the 60 GHz band is presented in this chapter, including a
low-power CMOS transceiver with beamforming capability.
Chapter 12: Battery life has always been a key issue in portable devices,
and it has become crucial for IoT as it is impractical to replace the battery
in billions of devices regularly. While the earlier chapters focused on circuit
techniques and protocol innovations to extend the battery life, this chapter
looks at ways that we can recharge the battery without user intervention.
While energy scavenging has been considered for IoT nodes, wireless charging
has also made its way into the consumer market. This chapter presents an
efficient power management structure for inductive power delivery and its
applications in markets such as implantable medical devices.
Christopher Siu
Kris Iniewski
Editors
Vancouver, Canada
MATLABr
is a registered trademark of The MathWorks, Inc. For product
information, please contact:
The MathWorks, Inc.
3 Apple Hill Drive
Natick, MA 01760-2098 USA
Tel: 508-647-7000
Fax: 508-647-7001
E-mail: info@mathworks.com
Web: www.mathworks.com

Series Editor
Krzysztof (Kris) Iniewski is managing
R&D at Redlen Technologies Inc., a start-
up company in Vancouver, Canada. Redlen’s
revolutionary production process for advanced
semiconductor materials enables a new gener-
ation of more accurate, all-digital, radiation-
based imaging solutions. Kris is also a
President of CMOS Emerging Technolo-
gies (www.cmoset.com), an organization of
high-tech events covering Communications,
Microsystems, Optoelectronics, and Sensors. In
his career, Dr. Iniewski held numerous faculty and management positions at
University of Toronto, University of Alberta, SFU, and PMC-Sierra Inc. He
has published over 100 research papers in international journals and confer-
ences. He holds 18 international patents granted in USA, Canada, France,
Germany, and Japan. He is a frequent invited speaker and has consulted
for multiple organizations internationally. He has written and edited several
books for IEEE Press, Wiley, CRC Press, McGraw Hill, Artech House, and
Springer. His personal goal is to contribute to healthy living and sustainabil-
ity through innovative engineering solutions. In his leisurely time, Kris can
be found hiking, sailing, skiing, or biking in beautiful British Columbia. He
can be reached at kris.iniewski@gmail.com.
xxv

Editor
Christopher Siu is a faculty member at
the Department of Electrical and Computer
Engineering Technology, British Columbia
Institute of Technology (BCIT), located in
Burnaby, British Columbia, Canada. Chris is
also a founder of Wavelink Electronics Ltd. and
Tyche Technologies Inc., consulting companies
specializing in the design of analog and radio
frequency electronics. He obtained a master’s
degree from Stanford University, California and
a bachelor’s degree from Simon Fraser Univer-
sity, British Columbia, both in electrical engi-
neering. Chris is also a licensed professional
engineer in the province of British Columbia.
During his career, Chris has worked in Sili-
con Valley and in Canada, for companies such
as Hewlett Packard, Philips Semiconductor, and PMC-Sierra. He has designed
analog and RF integrated circuits that have been released to production as
well as managed engineering teams across multiple sites. When not teaching
or practicing engineering, he likes to spend his time skiing, playing tennis,
and traveling.
xxvii

List of Contributors
Su-Shin Ang
Inova Design Solutions Ltd.
London, United Kingdom
Miriam Carlos-Mancilla
Universidad del Valle de Mexico
Tlaquepaque, Jalisco, Mexico
Wei Deng
Apple Inc.
Cupertino, California
Hesam S. Gougheri
Department of Electrical Engineering
School of Electrical Engineering and
Computer Science
Pennsylvania State University
University Park, Pennsylvania
Miguel Hernandez-Silveira
Sensiumr
Healthcare Ltd.
Abingdon, United Kingdom
Kris Iniewski
ET CMOS Inc.
Port Moody, British Columbia,
Canada and
Redlen Technologies Inc.
Saanichton, British Columbia,
Canada
Masataka Irie
Wireless Technology Department
Platform Development Center
Automotive & Industrial Systems
Company Panasonic Corporation
Yokohama, Japan
Syed K. Islam
Department of Health Sciences
University of Tennessee
Knoxville, Tennessee
Heikki Karvonen
Centre for Wireless
Communication
University of Oulu
Oulu, Finland
Makihiko Katsuragi
Semiconductor Research and
Development
Toshiba Corporation
Kawasaki, Japan
Mehdi Kiani
Department of Electrical
Engineering
School of Electrical Engineering and
Computer Science
Pennsylvania State University
University Park, Pennsylvania
Kento Kimura
Fujitsu Ltd.
Kawasaki, Japan
Masashi Kobayashi
Yokohama, Japan
xxix

xxx List of Contributors
Satoshi Kondo
Corporate Research and
Development Center
Toshiba Corporation
Kawasaki, Japan
Salvatore Levantino
Dipartimento di elettronica,
informazione e bioingegneria
(DEIB)
Politecnico di Milano
Milan, Italy
Hanli Liu
Department of Physical Electronics
Tokyo Institute of Technology
Tokyo, Japan
Ernesto López-Mellado
CINVESTAV Unidad Guadalajara
Zapopan, Jalisco, Mexico
Ifana Mahbub
University of North Texas
Denton, Texas
Akira Matsuzawa
Tokyo, Japan
Hiroyuki Motozuka
Company
Panasonic Corporation
Yokohama, Japan
Kenichi Okada
Tokyo, Japan
Juha Petäjäjärvi
Centre for Wireless Communication
University of Oulu
Oulu, Finland
Suraj Prakash
Department of Electrical and
Computer Engineering
Texas A&M University
College Station, Texas
Salvatore Pullano
and Computer Science
and
Department of Health Sciences
University Magna Graecia of
Catanzaro Catanzaro, Italy
Haoyu Qian
Qualcom Technologies Inc.
San Diego, California
Carlo Samori
Dipartimento di elettronica,
informazione e bioingegneria
(DEIB)
Politecnico di Milano
Milan, Italy
Samira Shamsir
and Computer Science
Naganori Shirakata
Yokohama, Japan

List of Contributors xxxi
Mario Siller
CINVESTAV Unidad Guadalajara
Zapopan, Jalisco, Mexico
Jose Silva-Martinez
Texas A&M University
College Station, Texas
Teerachot Siriburanon
School of Electrical, Electronic &
Communications Engineering
University College Dublin
Dublin, Ireland
Christopher Siu
Computer Engineering Technology
British Columbia Institute of
Technology
Burnaby, British Columbia,
Canada
Hiroshi Takahashi
Yokohama, Japan
Kazuaki Takahashi
Yokohama, Japan
Koji Takinami
Yokohama, Japan
Tomoya Urushihara
Yokohama, Japan
Fei Yuan
Ryerson University
Toronto, Ontario, Canada
Yushi Zhou
Lakehead University
Thunder Bay, Ontario, Canada

1
The Internet of Things—Physical and
Link Layers Overview
Christopher Siu
British Columbia Institute of Technology (BCIT)
Kris Iniewski
Redlen Technologies Inc.
CONTENTS
1.1 Introduction ...................................................... 1
1.2 Radio and MAC Technologies for IoT ........................... 2
1.2.1 Physical layer with existing radio frequency (RF)
standards ................................................ 3
1.2.2 Physical layer with emerging radio frequency (RF)
standards ................................................ 5
1.2.3 Link layer considerations for WUR ..................... 6
1.2.4 Link layer example—6LoWPAN ........................ 7
1.2.5 Application layer protocols .............................. 8
1.2.6 Future directions ........................................ 9
1.3 Conclusions ....................................................... 10
Bibliography ...................................................... 10
1.1 Introduction
The internet of things (IoT) has sometimes been referred to as the digitization
of the physical world. It is a confluence of different technologies at low-enough
costs that makes this possible. While different definitions of IoT exist, we will
use the following description for this book:
A device embedded with a sensor and/or actuator, connected to the
internet, that shares its information with other devices and hosts,
with the potential to act on this information based upon some rules
and intelligence.
1

2 IoT and Low-Power Wireless
Senors µC
Media access
control
Media access
control
Gateway &
data filtering
End user
Data
analysis &
reporting
Internet
link
Cloud/data storage
Wireless link
FIGURE 1.1
Simplified IoT system block diagram.
In the simplified block diagram given later, sensors in an end node collect data
at specified intervals. The data are framed into packets by the microcontroller,
which also contains parts of the protocol stack to perform media access control
(MAC). The packets are modulated and transmitted over the wireless link,
which is received by a gateway connected to the internet. The gateway may
have a rules engine to reduce the amount of data before it is stored. The sensor
data may then be transferred to an end user for further analysis and report
generation (Figure 1.1).
Note that while the gateway may be mains powered, the sensor nodes will
be powered by battery and/or energy scavenging. Since it is not feasible to
change the battery regularly on a large number of sensor nodes, there is great
motivation to reduce the power consumption of end nodes as much as possible.
1.2 Radio and MAC Technologies for IoT
In conceptualizing computer networks, many of us have seen the 7-layer
open systems interconnection (OSI) model. The 7 layers, from the lowest
to the highest, are the physical, link, network, transport, session, presenta-
tion, and application layers. Over the past two decades, with the exponential
growth of the internet running transmission control protocol/internet protocol
(TCP/IP), the OSI model has been eclipsed by a 5-layer model, sometimes
referred to as the TCP model or the IP stack [1]. Shown later is the TCP
model with the corresponding standards and protocols for WiFi (Figure 1.2).
The physical layer defines the hardware aspects of the communication link,
such as the modulation method, voltage levels, and physical medium (e.g.,
copper wire, over-the-air). The link layer provides several services, typically
implemented with a combination of hardware and software. If the physical
medium is shared by multiple users, such as wireless communication on a
certain frequency band, then orderly access to the medium must be controlled

The Internet of Things 3
Link
Physical
Network
Transport
Application
802.11 MAC
802.11 PHY
Internet
Protocol (IP)
TCP
HTTP
(a) (b)
FIGURE 1.2
The 5-layer model in relation to WiFi.
so that users don’t interfere with each. The mechanism for this is aptly named
MAC, and it is a key function of the link layer. Other services provided by the
link layer include framing of higher layer data and delivering the data reliably.
The focus of this book is on the physical and link layer technologies that
are in development for IoT. As such, this chapter provides an overview of
these technologies, but the higher layers will also be mentioned where it is
appropriate.
1.2.1 Physical layer with existing radio frequency
(RF) standards
One of the main energy consumers in mobile systems is the wireless
transceiver. Hence, research into low-power circuit techniques is ongoing, but
there are limits on using this approach alone. Additional innovations in MAC
and network architecture have also been necessary to drastically reduce the
transceiver power consumption. At the present time, there is no de facto RF
standard for IoT; existing standards are repositioning themselves, and new
standards are being introduced to support this new market. In the following,
we will briefly survey some of these RF technologies and standards.
Bluetooth (IEEE 802.15.1) was conceived to be a wire replacement for com-
puter peripherals, for example, the connection between a PC and a mouse.
As such, it falls within the classification of a wireless personal area network
(WPAN), for short-range point-to-point connections. Today, a large number
of mobile devices like smartphones include Bluetooth capability, and the stan-
dard is constantly evolving to create Bluetooth low energy with mesh network-
ing to support new market needs.
IEEE 802.15.4 was created as a lower power, lower data rate alternative to
Bluetooth. In 802.15.4–2006, a 2.4 GHz physical layer using spread spectrum
is specified at 250 kbps. Over the years, it has been used as the platform
for Zigbee, Thread, and other proprietary solutions. It is also part of IPv6

over Low-Power Wireless Personal Area Network (6LoWPAN), which supports
IPv6 addressing for network nodes. Although there is no native support for
mesh networking in 802.15.4, it has been implemented in the higher layers
for various applications. Similar to Bluetooth, however, it is a short-range
standard.
WiFi (IEEE 802.11) has become one of the most ubiquitous wireless stan-
dards on the planet. The standard is designed to support an ethernet-based
wireless local area network (WLAN), and so the range and power consumption
are necessarily higher than those of Bluetooth and 802.15.4. Although WiFi
radio transceivers are not a popular choice for low-power wireless systems, a
new task group called 802.11ba has been formed to address this. In particu-
lar, this task group is creating a new standard for low-power wake-up radio
(LP-WUR) in WiFi, intended to make WiFi an attractive technology for IoT.
One of the key ideas for LP-WUR is to use an ultra-low power auxil-
iary receiver to detect a wake-up packet, while keeping the main WiFi radio
transceiver in sleep mode most of the time. In fact, the auxiliary receiver
itself may be duty-cycled between sleep and wake to further reduce power
consumption (Figure 1.3).
To make an ultra-low power receiver, some obvious tradeoffs such as per-
formance, data rate, and modulation scheme need to be considered. For
the IEEE 802.11ba initiative, on–off keying is used to allow for a simple
demodulation. Furthermore, low-power radio circuits can be used, including
techniques such as
• superregenerative receiver (Figure 1.4)
• envelope detection
• injection locking
• subsampling architectures
Main radio
Low power
wake-up
radio
Wake up signal
FIGURE 1.3
Wake-up radio concept.

Periodic
quenching
signal
LNA output
Quench
Oscillator
output
t1 t1 t2
LNA
(a) (b)
Demodulator
FIGURE 1.4
Superregenerative receiver: (a) block diagram and (b) internal waveforms.
Superregeneration is an idea developed by Edwin Armstrong in the early
1920s, and in its modern implementation, it removes the phase locked loop
from a typical radio receiver. In conjunction with on–off keying, the oscillator
start-up time depends on whether a signal is received by the low noise amplifier
(LNA) or not. By detecting this time difference, the receiver decides whether
a logic 0 or 1 was transmitted.
1.2.2 Physical layer with emerging radio frequency
(RF) standards
The existing RF standards competing for market share in IoT have tended
to be WPAN and WLAN standards, since the strict need for low power con-
sumption favors these short-range applications. The architecture implied here
is a large number of sensor nodes connected to gateway(s) either directly or
via a mesh network. The short range of these standards also creates potential
problems if one node is not in range of any other nodes and/or gateways.
Many of us are accustomed to a wide area cellular coverage; we never think
about being near a gateway or base station before communicating on our
mobile phones. A wireless wide area network (WWAN) is thus very attractive
in terms of network access, but devices connected to a WWAN also have high
power consumption. Just as the IEEE 802.x standards are evolving to meet
IoT needs, so are the cellular standards. We will survey the following WWAN
for IoT:
• Narrowband IoT (NB-IoT)
• Sigfox
• LoRaWAN
The 3rd Generation Partnership Project has been defining cellular standards
since the third generation, and this now includes the 4th generation long term
evolution (LTE) standard that is in use. LTE has undergone a number of

revisions, and one of the latest releases (Rel 13) defines NB-IoT, which is a
low-power, low-data rate service at 250 kbps.
Sigfox is a proprietary standard operated by a company of the same name.
Sigfox uses a scheme called ultra narrow band modulation, which requires only
100 Hz of bandwidth per message, with a correspondingly low rate of 100–
600 bps. At the present time, the coverage and deployment are much more
extensive in Western Europe than in the United States.
LoRaWAN and LoRa are open standards for low-power WWAN;
LoRaWAN specifies the MAC, and LoRa specifies the physical layer. LoRa
uses spread spectrum modulation, and hence has built-in resistance to inter-
ference and multipath fading. LoRa also has a low-data rate in the tens of
kbps, allowing the present integrated circuit implementations (SEMTECH
SX127n series) to receive sensitivity in the −140 to −150 dBm range.
1.2.3 Link layer considerations for WUR
If radio duty cycling is fundamental to low-power wireless, then the MAC layer
must be designed to support this need. For example, the 802.11ba LP-WUR
uses a new wake-up packet to inform the wake-up receiver that the main radio
needs to be taken out of sleep and prepare for data exchange (Figure 1.5).
Since the WUR itself is duty-cycled, this scheme inevitably introduces
latency into system. Optimization between power consumption and latency
is a topic of research, extending into the format of the wake-up packet. For
example, Yoon [2] implemented a WUR with two modes of operation. The
WUR starts in monitoring mode used to detect the packet start bits at a very
low data rate; this mode is duty-cycled to further reduce the WUR power
consumption. After detecting the start bits, the WUR enters identification
mode (ID) to receive device addresses at a higher data rate.
Main radio
Low power
wake-up
radio
Wake up signal
Wake-up
packet
Transmitter
FIGURE 1.5
Wake-up radio MAC layer requirement.

1.2.4 Link layer example—6LoWPAN
A common IoT usage scenario is a network of sensors designed to run for
years on battery and/or energy scavenging. We have already illustrated how
low-power radio transceivers and duty cycling play a huge role in making this
possible. If we look at this usage scenario more closely, typical sensors need
to transfer small volumes of data, in contrast to the requirements of PCs,
smartphones, etc. Since short messages conserve power and bandwidth, the
design of the link layer must compromise between the overhead versus the
payload of a frame.
Over the past two decades, the network infrastructure using the TCP/IP
suite of protocols has grown exponentially. As a result, efforts to use IP
addressing for IoT has resulted in the Thread specification [3,4], which uses
IEEE 802.15.4 as the physical and MAC layers, and 6LoWPAN as a bridge
between the 802.15.4 MAC and the IP (Figure 1.6).
6LoWPAN is an open IoT networking protocol that is specified by the
Internet Engineering Task Force (IETF). It creates an adaptation mechanism
between IPv6 in layer 3 and the 802.15.4 MAC in layer 2. Since a full unmod-
ified TCP/IP stack may be incompatible with the limited hardware in IoT
devices, 6LoWPAN creates a streamlined routing protocol that reduces net-
work overhead and latency.
In using TCP/IP with 802.15.4, the following issues are addressed by
6LoWPAN:
• Adaptation needed for maximum transmission unit (MTU) size
• Reduction of overhead
• User datagram protocol (UDP) instead of TCP to reduce latency
Adaptation is needed to accommodate the different MTU sizes between IPv6
and IEEE 802.15.4. MTU is the size of the largest network layer protocol data
Link
Physical
Network
Transport
Application
802.15.4 MAC
802.15.4 PHY
IPv6
UDP
COAP, MQTT, etc
(a) (b)
6LoWPAN
FIGURE 1.6
Thread specification in the 5-layer model.

unit that can be communicated in a single network transaction [5]. IPv6 has
a packet size of 1280 bytes, while IEEE 802.15.4 allows for an MTU of only
127 bytes. 6LoWPAN introduces a fragmentation scheme to allow IPv6 to
operate over an 802.15.4 network, using a 11-bit fragmentation header that
allows for 2048 bytes packet size [6]. However, fragmentation can still lead to
bad performance over a lossy network, so it is best to avoid big packet sizes.
Of the 127 bytes allowed by the 802.15.4 MTU, the upper layers like the
IPv6 and UDP headers can consume significant amount of the MTU, leaving
only 33 bytes for the actual payload [7]. To reduce this overhead, header
compression is used to create more room for the payload [8].
IEEE 802.15.4 does not include mesh routing in the MAC specification; it
uses simple addressing that supports star and peer-to-peer topologies. Mesh
networks are thus outside of the 802.15.4 standard, and mesh support is imple-
mented between the MAC and network layers. 6LoWPAN has a field for mesh
headers and allows fast forwarding of packets in a mesh without traveling
through the IP stack; this is referred to as mesh-under (layer 2) forwarding
and route-over (layer 3) forwarding.
1.2.5 Application layer protocols
Given all the potential data collected by sensors and other “things,” certain
questions arise. How we should store all this data, if we should store it at all?
Are there any time constraints on analyzing this data and acting on it? The
answers to these questions depend on the application, and it may be useful to
put IoT devices into some of these frameworks:
• D2D—Device-to-device communication: intelligent machines collect data
and coordinate some action together. Also referred to machine-to-machine
communication.
• D2S—Device-to-server communication: device data are collected and sent
to a server and the IT infrastructure.
• S2S—Server-to-server communication: the servers share data to command
some action back to the devices, to analyze the data, and/or to generate
a report for humans to view.
In all these frameworks, the ability to connect thousands of devices and inter-
act with them in real time is crucial. Real time, however, varies depending on
the application; the tolerable latency is different between car accident avoid-
ance and farmland irrigation. As a result, different IoT protocols have emerged
that have strengths in different frameworks. Some of the protocols available
today include
• MQTT
• XMPP

• AMQP
• CoAP
• DDS
Message queue telemetry transport (MQTT) is targeted for D2S, collecting
data from large number of devices and transferring that to the server infras-
tructure. The telemetry in its name stems from using this data for remote
monitoring and control. Many sensors may connect to a data concentrator
such as IBM’s MessageSight appliance. Because the latency of several seconds
is tolerable and the data transfer must be reliable, TCP instead of UDP is
used in the transport layer. By design, MQTT is meant for data transfer from
the end node to the IT infrastructure, and not useful for D2D communica-
tion. MQTT has been defined as a standard under the Organization for the
Advancement of Structured Information Standards [9].
Extensible messaging and presence protocol (XMPP) is designed for con-
necting devices to humans; it uses XML text and name@domain.com address-
ing. As a result, it is useful in consumer IoT applications, such as connecting
home appliances to a web server that a person can access using a smartphone.
Advanced message queuing protocol (AMQP) is a queuing system designed
to connect servers together (S2S). Transactional messages are exchanged
between servers, buffered using a queue. Since reliability is of great impor-
tance here, TCP is used in this protocol. AMQP has been defined as an Orga-
nization for the Advancement of Structured Information Standards standard
[10] since 2012.
Constrained application protocol (CoAP) uses HTTP commands like GET
and PUT for D2D communication. It is a web transfer protocol designed for
constrained nodes and constrained networks that are low power but lossy. The
end nodes are typically constrained in computing power and memory, while
IP networks using 6LoWPAN can experience high error rates. CoAP has been
defined under IETF RFC 7252 [11].
Finally, the main purpose of data distribution service (DDS) is for D2D
communication. DDS can deliver millions of messages per second to many
devices and offers ways to filter data and select multiple destinations for this
data, in effect, implementing a multicast operation. D2D requires low latency
that can vary depending on the device, and hence instead of TCP, DDS uses a
quality-of-service (QoS) control scheme. The application of DDS includes the
hospital environment and military systems.
1.2.6 Future directions
The allocation of the industrial, scientific, and medical (ISM) frequency bands
for unlicensed use has been a resounding success. The 2.4 GHz band, available
worldwide, is already overcrowded by the proliferation of wireless devices such
as WiFi, Bluetooth, and so forth. Efforts have been made to migrate devices to

other bands, with one notable example being the definition of 802.11a WLAN
for operation in the 5.7 GHz band.
Although IoT deployment is still in its infancy, it is not too early to think
how this exponential growth in wireless nodes will coexist with other devices in
the ISM band. By design, wireless standards for the ISM bands are resistant
to some amount of interference, using techniques such as spread spectrum.
However, one can also look at the other ISM bands, such as 24 and 60 GHz,
to accommodate new sensors and “things.” Circuit technique at 60 GHz is an
ongoing area of research, and while the state of the art is still too power hun-
gry for IoT, performance will continue to improve. This improvement will
accelerate once 60 GHz ICs are used in the consumer space, such as the
IEEE 802.11ad standard to provide multi-Gbps wireless capability in this
band. Although there are fundamental limits such as increased path loss at
60 GHz, the small wavelengths in the millimeter range create opportunities
for advanced antennas and integration.
1.3 Conclusions
Traditional business models are based on a static information architecture,
and the IoT is poised to change that. When a customer’s buying preferences
are sensed in real time at a specific location, dynamic pricing may increase
the odds of a purchase. Knowing how often or intensively a product is used
can create additional options—usage fees rather than outright sale, for exam-
ple [12]. To realize the IoT vision, a great number of technical challenges are
yet to be defined and solved. This is indeed an exciting time for engineers and
researchers putting this ecosystem together.
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