Index
643
IoT data processing
bandwidth limitation, 436
energy efficiency, 435–436
system architecture, 434–435
IoT frameworks and platforms
AWS IoT, 22
FIWARE, 21
Microsoft Azure IoT (see Azure IoT)
SmartThings, 21–22
IoT medication, 531–532
IoT reference models
IoTWF, 19–20
simplified, 20–21
IoT stack, 39, 48, 415
IoT World Forum (IoTWF)
AWS, 22
FIWARE, 21
Microsoft Azure (see Azure IoT)
reference model, 19–21
SmartThings, 21–22
ISA100.11a, 139–140
ISO/OSI reference model
communication patterns, 103
comparison, 103, 104
IoT network standards, 110–112
layer
application, 106
data link, 107–108
network, 107
physical, 108
transports, 106
links and topologies, 100–101
network
nodes, 100
size, 102
PDU, 104–106
quality of service, 101–102
J
Joint Test Action Group (JTAG), 92–93, 489,
493
K
Kappa architecture, 189, 191
K-means clustering, 255, 310–311, 454, 465
K-nearest neighbor (KNN), 281–282
Knowledge symbiosis
AI, intelligent education, 618–619
development of ITS, 621–622
implicit knowledge
caregivers, 623–627
nurses, 622–623
ITS, 619–621
L
Lambda architecture, 189–190, 227, 234, 340
Lean model, 41
Linear algebra, 250
Load balancing, 220, 222, 225, 229–231, 350,
366
Logging, 182, 209, 210, 336, 490, 497, 501,
506
Logistic regression
cost function, 285, 286
decision boundary, 284–285
logit and sigmoid (logistic) functions,
282–283
target email, 302
LoRaWAN, 128–133
cellular networks, 112
and Sigfox, 163
6LoWPAN, 136–138
edge router, 137–138
energy-efficient wireless mesh network,
125
FAN, 525
IPv6, 136
ISA100.11a, 139
protocol stack, 137
ZigBee, 112
Low power modes, 90–91, 94, 439
LTE category M1 (LTE Cat M1/LTE-M), 143
M
Machine learning (ML)
accelerators, 439–440
advanced data analytics, 228–229
and AI, 18
algorithms, 14, 42
applications, 325
Azure, 25
big data, 42
characteristics, 244
data preparation, 257–260
fundamental terminologies, 245–247
healthcare, 541–543
in IoT, 254–256
linear algebra, 250–252
overall flow, 256–257
643
IoT data processing
bandwidth limitation, 436
energy efficiency, 435–436
system architecture, 434–435
IoT frameworks and platforms
AWS IoT, 22
FIWARE, 21
Microsoft Azure IoT (see Azure IoT)
SmartThings, 21–22
IoT medication, 531–532
IoT reference models
IoTWF, 19–20
simplified, 20–21
IoT stack, 39, 48, 415
IoT World Forum (IoTWF)
AWS, 22
FIWARE, 21
Microsoft Azure (see Azure IoT)
reference model, 19–21
SmartThings, 21–22
ISA100.11a, 139–140
ISO/OSI reference model
communication patterns, 103
comparison, 103, 104
IoT network standards, 110–112
layer
application, 106
data link, 107–108
network, 107
physical, 108
transports, 106
links and topologies, 100–101
network
nodes, 100
size, 102
PDU, 104–106
quality of service, 101–102
J
Joint Test Action Group (JTAG), 92–93, 489,
493
K
Kappa architecture, 189, 191
K-means clustering, 255, 310–311, 454, 465
K-nearest neighbor (KNN), 281–282
Knowledge symbiosis
AI, intelligent education, 618–619
development of ITS, 621–622
implicit knowledge
caregivers, 623–627
nurses, 622–623
ITS, 619–621
L
Lambda architecture, 189–190, 227, 234, 340
Lean model, 41
Linear algebra, 250
Load balancing, 220, 222, 225, 229–231, 350,
366
Logging, 182, 209, 210, 336, 490, 497, 501,
506
Logistic regression
cost function, 285, 286
decision boundary, 284–285
logit and sigmoid (logistic) functions,
282–283
target email, 302
LoRaWAN, 128–133
cellular networks, 112
and Sigfox, 163
6LoWPAN, 136–138
edge router, 137–138
energy-efficient wireless mesh network,
125
FAN, 525
IPv6, 136
ISA100.11a, 139
protocol stack, 137
ZigBee, 112
Low power modes, 90–91, 94, 439
LTE category M1 (LTE Cat M1/LTE-M), 143
M
Machine learning (ML)
accelerators, 439–440
advanced data analytics, 228–229
and AI, 18
algorithms, 14, 42
applications, 325
Azure, 25
big data, 42
characteristics, 244
data preparation, 257–260
fundamental terminologies, 245–247
healthcare, 541–543
in IoT, 254–256
linear algebra, 250–252
overall flow, 256–257