IoT and Low-Power Wireless : Circuits, Architectures, and Techniques

427 Pages·2018·19.97 MB·English

by  Siu

IoT and Low-Power
Wireless
Devices, Circuits, and Systems
Series Editor
Krzysztof Iniewski
Wireless Technologies
Circuits, Systems, and Devices
Krzysztof Iniewski
Circuits at the Nanoscale
Communications, Imaging, and Sensing
Krzysztof Iniewski
Internet Networks
Wired, Wireless, and Optical Technologies
Krzysztof Iniewski
Semiconductor Radiation Detection Systems
Krzysztof Iniewski
Electronics for Radiation Detection
Krzysztof Iniewski
Radiation Effects in Semiconductors
Krzysztof Iniewski
Electrical Solitons
Theory, Design, and Applications
David Ricketts and Donhee Ham
Semiconductors
Integrated Circuit Design for Manufacturability
Artur Balasinski
Integrated Microsystems
Electronics, Photonics, and Biotechnology
Krzysztof Iniewski
Nano-Semiconductors
Devices and Technology
Krzysztof Iniewski
Atomic Nanoscale Technology in the Nuclear Industry
Taeho Woo
Telecommunication Networks
Eugenio Iannone
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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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Library of Congress Cataloging-in-Publication Data
Names:Siu,Christopher,author.|Iniewski,Krzysztof,1960-author.
Title:IoTandlow-powerwireless:circuits,architectures,andtechniques/
ChristopherSiuandKrzysztofIniewski.
Description:BocaRaton,FL:CRCPress/Taylor&FrancisGroup,2018.|
“ACRCtitle,partoftheTaylor&Francisimprint,amemberoftheTaylor&
FrancisGroup,theacademicdivisionofT&FInformaplc.”|Includes
bibliograpicalreferencesandindex.
Identifiers:LCCN2018010550|ISBN9780815369714(hardback:acid-freepaper)|
ISBN9781351251662(ebook)
Subjects:LCSH:Internetofthings–Equipmentandsupplies.|Near-field
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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
IfanaMahbub,SalvatoreA.Pullano,SamiraShamsir,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 L´opez-Mellado, and
Mario Siller
5 Energy-Efficient Communication Solutions Based on
Wake-Up Receivers 119
Heikki Karvonen and Juha Pet¨aj¨aj¨arvi
6 All-Digital Noise-Shaping Time-to-Digital Converters
for Mixed-Mode Signal Processing 153
Fei Yuan
v
vi Table of Contents
7 Power-EfficientCMOSPowerAmplifiersfor
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-PowerRFDigitalPLLswithDirect
Carrier Modulation 247
Salvatore Levantino and Carlo Samori
10 Frequency Synthesis Technique for 60 GHz
Multi-Gbps Wireless 285
TeerachotSiriburanon,HanliLiu,KenichiOkada,
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 AdaptiveandEfficientIntegratedPowerManagement
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 Superregenerativereceiver:(a)blockdiagramand(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 MATLAB(cid:114) 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
minimisetheerrorsbetweentheyandtheexpected
outcomes.. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39
3.3 A binary decision tree for arrhythmia classification, using
features RMSSD and mean Normalised R–R interval (NN)
(correctedbeat-to-beatintervals).T1–T8representthresholds
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 Thebranchequationmodelforcalorieenergyexpenditure
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(cid:13)R and
a reference bedside monitor. The top figure corresponds to
a good quality respiration signal. Respiration events (inspira-
tionandexhalation)canbeseeninthewaveformsduetotheir
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(cid:13)R patch. . . . . . . . . . . . 68
3.17 Two-dimensional logistic regression model. The top graph
shows a rectilinear decision boundary with its 95% confidence
intervalsthatseparatesvectorscorrespondingto‘goodquality’
from‘badquality’signals.Notethatbothclassesarenot100%
linearlyseparable,assomeAvectorsoverlapthe‘goodregion’
and some B vectors overlap the ‘bad region’. The bottom-left
plotcorrespondstotheROCanalysisforallthemodelscreated
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-axialaccelerometer,andillustratesthesignalvariationcor-
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 Positionofahipmountedaccelerometerintheinitialreference
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
flowdiagram(b)Structuraldataflowdiagram,involvingdata
from a tri-axial accelerometer - A , A and A [51]. . . . . . 76
x y z
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 Typicalwake-upreceiverarchitectures:(a)RFenvelopedetec-
tion,(b)uncertain-IF,(c)matchedfilter,(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