Requirements and Challenges for User-level Service Managements of IoT Network by utilizing Artificial Intelligence
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Junkyun Choi
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Jaeseob Han
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Gyeong Lee
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ICN Research Group J.K. Choi
Internet Draft J.S. Han
Intended status: Informational G.H. Lee
Expires: June 30, 2022 KAIST
Dec 30, 2021
Requirements and Challenges for User-level Service Managements of
IoT Network by utilizing Artificial Intelligence
draft-choi-icnrg-aiot-08
Abstract
This document describes the requirements and challenges to employ
artificial intelligence (AI) into the constraint Internet of Things
(IoT) service environment for embedding intelligence and increasing
efficiency.
The IoT service environment includes heterogeneous and multiple IoT
devices and systems that work together in a cooperative and
intelligent way to manage homes, buildings, and complex autonomous
systems. Therefore, it is becoming very essential to integrate IoT
and AI technologies to increase the synergy between them. However,
there are several limitations to achieve AI enabled IoT as the
availability of IoT devices is not always high, and IoT networks
cannot guarantee a certain level of performance in real-time
applications due to resource constraints.
This document intends to present a right direction to empower AI in
IoT for learning and analyzing the usage behaviors of IoT
devices/systems and human behaviors based on previous records and
experiences. With AI enabled IoT, the IoT service environment can be
intelligently managed in order to compensate for the unexpected
performance degradation often caused by abnormal situations.
Status of this Memo
This Internet-Draft is submitted in full conformance with the
provisions of BCP 78 and BCP 79.
Internet-Drafts are working documents of the Internet Engineering
Task Force (IETF), its areas, and its working groups. Note that
other groups may also distribute working documents as Internet-Drafts.
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Internet-Drafts are draft documents valid for a maximum of six months
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This Internet-Draft will expire on June 30, 2022.
Copyright Notice
Copyright (c) 2021 IETF Trust and the persons identified as the
document authors. All rights reserved.
This document is subject to BCP 78 and the IETF Trust's Legal
Provisions Relating to IETF Documents
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Legal Provisions and are provided without warranty as described in
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Table of Contents
1. Introduction ................................................ 3
2. Challenging Issues of IoT network
............................ 6
2.1. Untrusted and incorrect IoT devices ..................... 6
2.2. Traffic burstiness of IoT network ....................... 7
2.3. Management overheads of heterogeneous IoT sensors
........ 8
3. Overview of AI/ML-based IoT services ......................... 9
4. Requirements for AI/ML-based IoT services ................... 12
4.1. Requirements for AI/ML-based IoT data collection and delivery
........................................................... 12
4.2. Requirements for intelligent and context-aware IoT services13
4.3. Requirements for applying AI/ML to IoT data ............ 15
4.3.1. Training AI/ML algorithm
.......................... 15
4.3.2. AI/ML inference in IoT application ................ 15
4.3.3. AI/ML models update in IoT application ............ 16
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5. State of arts of the artificial intelligence/machine learning
technologies for IoT services
.................................. 16
5.1. Machine learning and artificial intelligence technologies
review ..................................................... 17
5.1.1. Supervised learning for IoT ....................... 17
5.1.2. Unsupervised learning for IoT ..................... 18
5.1.3. Reinforcement learning for IoT .................... 19
5.1.4. Neural Network based algorithms for IoT ........... 20
5.2. Technologies for lightweight and real-time intelligence
. 22
6. Use cases of AI/ML into IoT service ......................... 23
6.1. Surveillance and Security in Smart Home ................ 23
6.1.1. Characteristics of Smart Home for AI/ML processing
. 23
6.1.2. Use case ......................................... 24
6.2. Smart home for elderly welfare ......................... 24
6.2.1. Characteristics of Smart Home for elderly welfare
.. 24
6.3. Smart Building Management
.............................. 25
6.3.1. Characteristics of Smart Building for AI/ML processing25
6.3.2. Use case ......................................... 25
6.4. Manufacturing optimization in Smart factory ............ 26
6.4.1. Characteristics of Smart Building for AI/ML processing26
6.4.2. Use case ......................................... 26
6.5. Smart parking ......................................... 26
6.5.1. Characteristics of Smart Parking for AI/ML processing26
7. IANA Considerations ........................................ 27
8. Acknowledgements ........................................... 27
9. Contributors ............................................... 27
10. Informative References
..................................... 27
1. Introduction
The document explains the effects of applying artificial intelligence
/machine learning (AI/ML) algorithms in the Internet of Thing (IoT) s
ervice environments.
IoT applications will be deployed in heterogeneous and different area
s such as the energy, transportation, automation and manufacturing in
dustries as well as the information and communication technology (IC
T) industry. Many IoT sensors and devices can connect to an IoT servi
ce environment where IoT objects cannot interoperate with each other
and can interact with different applications. The IoT service may not
run in a single administrative domain. If market demand exists, the
cross-domain service scenarios for IoT applications could be widely d
eployed. Future IoT applications occur at multiple domains of heterog
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eneity with various time scales.
The IoT service requirements for common architectures and public APIs
poses some challenges to the underlying service environment and netw
orking technologies. Some IoT applications require significant securi
ty and privacy as well as significant resource and time constraints.
These mission-critical applications can be separated from many common
IoT applications that current technology may not provide. It means t
hat IoT service requirements are difficult to classify common require
ments and functional requirements depending on IoT service scenario.
Recently, artificial intelligence technologies can help the context-a
ware IoT service scenarios apply rule-based knowledge accumulation. T
he IoT service assumes that many sensing devices are connected to sin
gle or multiple IoT network domains. Each sensor sends small packets
to the IoT servers periodically or non-periodically. Detection data c
ontains periodic status information that monitors whether the system
is in a normal state or not. In some cases, alert information is incl
uded for quick processing. Most IoT applications can operate in two m
odes. One is a simple monitoring mode and the other is an abnormal mo
de for rapid processing. In a simple monitoring phase, the IoT device
periodically sends sensing data to the server. If the measured data
is outside the normal range, the IoT service can change the operating
mode to an abnormal phase and activate future probes. Alarm conditio
ns should be promptly notified to responsible persons. For mission-cr
itical applications, reliable communication with robust QoS requireme
nts in terms of error and latency is required.
Periodic data accumulation from IoT devices is cumbersome. Under norm
al conditions, the IoT data is simply accumulated without further act
ion. In an unusual situation, incoming IoT data can cause an urgent a
ction to notify the administrator of the problem. Streaming data traf
fic from thousands of IoT devices is annoying to store in the databas
e because it is not easy to extract unidentified or future incidents.
Only a significant portion of the incoming data stream can be stored
in a real-time database that is time-sensitive and capable of rapid
query processing. A combination of different IoT detection data, incl
uding location, time, and status, allows you to sort and categorize a
portion of streaming data when an additional inspection is required,
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and perform real-time processing. One of the missions of the IoT dat
abase is to be able to extract preliminary symptoms of unexpected acc
idents from a large amount of streaming data.
If some transmitted data is important to invoke the corresponding act
ion, there are some questions about whether the incoming data is corr
ect. If the incoming data contains accurate and time-critical events,
appropriate real-time control and management can be performed. Howev
er, if the incoming data is inaccurate or intentionally corrupted, ad
ditional accidents may occur. In these cases, incoming data can trigg
er to initiate additional inspections to protect against future unacc
eptable situations. But, if time-critical data is missed due to error
s in the sensing devices and the delivery protocol, there is no reaso
n to configure IoT networks and devices at a high cost.
It is not easy to analyze data collected through IoT devices installe
d to monitor complex IoT service environments. If the sensor malfunct
ions, the data of the sensor cannot be trusted. Additional investigat
ion should be done if abnormal status from specific sensors is collec
ted. The data of the redundant sensor installed in the same area shou
ld be received or combined with other sensor information adjacent to
the sensor to determine the abnormal state.
For sensors installed in a specific area, sensing records will remain
for a certain period of time. IoT service operators can look at the
operational history of the sensor for a period of time to determine w
hat problems were encountered when data was collected. When an abnorm
al situation occurs, IoT sensor should investigate whether it noticed
normal operations and notified the IoT service operator. If the abno
rmal situation is not properly detected, the operator should analyze
whether it was caused by malfunction of the IoT sensor or other reaso
ns.
In the IoT service environment, it is possible to analyze the situati
on accurately by applying recent artificial intelligence and machine
learning technologies. If there is an operational record of the past,
it is possible to determine when an abnormal situation arises. Most
problems are likely to be repeated, so if the past learning experienc
e is accumulated, the anomaly of IoT services can be easily and immed
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iately identified. In addition, when information gathered from variou
s sensors is synthesized, it is possible to accurately determine whet
her abnormal situations have occurred.
Various types of IoT sensors are installed with certain purposes. It
expects that all the IoT sensors intend to monitor the occurrence of
special abnormal situations in advance. Therefore, it should be set i
n advance what actions are required when a specific anomaly occurs. T
he appropriate work is performed on the abnormal situation according
to the procedure, predefined by the human. By using artificial intell
igence and machine learning algorithms, the appropriate actions are t
aken when an abnormal situation is detected from various IoT sensors.
2. Challenging Issues of IoT network
This section describes the challenging issues of data sensing,
collection, transfer, and intelligent decision from untrusted data
quality and unexpected situations of IoT service environments.
2.1. Untrusted and incorrect IoT devices
IoT traffic is similar to traditional Internet traffic with small
packet sizes. Mobile IoT traffic can cause some errors and delays
because wireless links are unstable and signal strength may be
degraded with device mobility. If the signal strength of the IoT
device with a power limit is not so strong, the reception quality of
the IoT server may not be sufficient to obtain the measurement data.
For mission-critical applications, such as smart-grid and factory-
automation, expensive IoT sensors with self-rechargeable batteries
and redundant hardware logic may be required. However, unexpected
abnormal situations may occur due to sensor malfunctions. There are
trade-offs between implementation cost and efficiency for cost-
effective IoT services. When smart-grid and factory-automation
applications are equipped with IoT devices, the acceptable quality
from IoT solutions can be required. Sometimes, expensive and
duplicated IoT solutions may be needed.
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2.2. Traffic burstiness of IoT network
IoT traffic includes two types of traffic characteristic: periodic
with small packet sizes and bursty with high bandwidth. Under normal
conditions, the IoT traffic periodically transmits status information
with a small bandwidth, several kilobits/sec. However, in an abnormal
state, IoT devices need a high bandwidth, up to several tens of
megabits/sec, in order to identify actual events and investigate
accurate status information. In addition, traffic volume can
explosively increase in response to emergencies. For example, in the
case of smart-grid application, the bandwidth of several kilobits/sec
is usually used, and when an urgent situation occurs, a broadband
channel is required up to several tens of megabits/sec.
The other traffic can be integrated at an IoT network to increase
bandwidth efficiency. If an emergency situation occurs in the IoT
service, IoT traffic volumes suddenly increase, in which case network
processing capacity may be not sufficient. If the IoT service is
integrated with voice and video applications, the problem can become
more complex. As time goes by, traffic congestion and bottlenecks are
frequent in some areas. In addition, if an existing service policy
changes (for example, prioritizing certain traffic or suddenly
changing the route), other unexpected problems may be encountered.
Various congestion control and load balancing algorithms with the
help of artificial intelligence can be applied to handle time-varying
traffic on a network.
Until now, much research has been done on traffic variability in an
integrated network service environment. All networks have their own
traffic characteristics, depending on geographical area, number of
subscribers, subscribers' preferences, and types of applications used.
In the case of IoT traffic, the normal bandwidth is very small. If
the IoT traffic volume increases abruptly in an abnormal situation,
the network may suffer unacceptable delay and loss. If emergency
situations detected by IoT networks occur in a smart grid or
intelligent transportation system, the processing power of the IoT
network alone cannot solve the problem and the help of existing
network resources is inevitable.
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2.3. Management overheads of heterogeneous IoT sensors
Traffic management in an integrated network environment is not easy.
In order to operate the network steadily, a network operator has its
own know-hows and experiences. If there are plenty of network
resources, it is easy to set up a bypass route even if network
failure or congestion occurs in a specific area. For operating
network steadily, network resources may be designed to be over-
provisioned in order to cope with various possible outages. A network
operator predicts the amount of traffic generated by the
corresponding equipment and grasps to what extent a transmission
bandwidth is required. If traffic fluctuation is very severe, the
network operator can allocate network resources in advance. In case
of frequent failures or severe traffic fluctuation, some network
resources are separated in order not to affect normal traffic.
More than a billion IoT devices are expected to connect to
smartphones, tablets, wearables, and vehicles. Therefore, IoT
services are targeted at mobile applications. In particular,
intelligent transportation systems need the help of IoT technology to
provide traffic monitoring and prevent public or private traffic
accidents. IoT technology can play an important role in reducing
traffic congestion, saving people's travel time and costs, and
providing a pleasant journey.
The IoT service has troublesome administrative problems to configure
an IoT network which consists of IoT servers, gateways, and many
sensing devices. The small-sized but large-numbered IoT devices may
incur administrative overhead since all the IoT devices should be
initialized and the bootstrapping information of IoT resources should
be loaded into the IoT service environments. Whenever some IoT
devices are newly added and some devices have to be removed, the
dynamic reconfiguration of IoT resources is essential. In addition,
the IoT device's preinstalled software should be regularly inspected
and upgraded according to its version. Frequent upgrades and changes
to some IoT devices may require autonomic management and
bootstrapping techniques.
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Network management generally assumes that all network resources
operate reliably with acceptable quality. In most failure situations,
the network operator decides to switch to a redundant backup device
or bypass the failed communication path. If some IoT devices are not
stable, duplicate IoT devices can be installed for the same purpose.
If IoT resources are not duplicated, various mechanisms are needed to
reduce the damage. Therefore, it is necessary to prioritize the
management tasks to be performed first when an abnormality occurs in
the IoT service environment. However, managing duplicate networks can
cause another problem. If two IoT devices are running at the same
time, the recipient can get redundant information. If two or more
unusual situations occur at the same time, it is difficult to solve
the problem since tasks for urgent processing should be distinguished
from tasks that can be performed over time.
In addition, the operations manager's mistakes or misunderstanding of
problem situations can lead to other unexpected complications.
Therefore, artificial intelligence technologies can help what kind of
network management work is required when an unexpected complicated
situation occurs even though a procedure for an abnormal situation is
already prepared.
3. Overview of AI/ML-based IoT services
In this section, successful applications of artificial intelligence i
n IoT domains are provided. The common property of IoT applications a
nd services is that they require fast analytics rather than later ana
lytics with piled data. Recently, neural-network-based artificial int
elligence technologies are widely used across many IoT applications.
Simple IoT applications include dynamic contexts that share common fe
atures among social relations at the same administration domain. IoT
devices in the same domain can provide their service contexts to the
IoT server. When a dynamic change occurs in an IoT service context, t
he IoT device needs real-time processing to activate urgent events, a
lert notifications, update, and reconnect contexts. The IoT service m
ust support real-time interactions between the IoT device and the sys
tem in the same domain. The IoT service contexts must be shared betwe
en physical objects and social members in the same domain as well.
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Artificial intelligence technologies have been shown promising in man
y areas, including IoT. For example, contextual information for a car
-sharing business must interact with customers, car owners, and car s
haring providers. All entities in the value chain of a car sharing bu
siness must share the corresponding situation to pick up, board, and
return shared cars. Communication networks and interactive informatio
n, including registration and payment, can be shared tightly among th
e entities. Home IoT service environment can be equipped with sensors
for theft detection, door lock, temperature, fire detection, gas det
ection, short circuit, air condition to name a few. Office IoT servic
e environments, including buildings such as shopping centers and bus/
airport terminals, have their own sensors, including alarm sensors. W
hen an alarm signal is detected by the sensor, the physical position
and occurrence time of the sensor is determined in advance. All signa
ls from various sensors are analyzed comprehensively to make the righ
t decision. If some sensors frequently malfunction, the situation can
be grasped more accurately by analyzing the information of the adjac
ent sensor. In particular, when installing multiple sensors in a part
icular building (e.g., surveillance camera, location monitoring, temp
erature, etc.), a much wider range of sensors can be used when utiliz
ing artificial intelligence and machine learning technologies.
(Smart home) Smart home concept span over multiple IoT applications,
health, energy, entertainment, education, etc. It involves voice reco
gnition, natural language processing, image-based object recognition,
appliance management, and many more artificial intelligence technolo
gies integrated with IoT. Smart connected-devices monitor the house t
o provide better control over home supplies and expenses. The energy
consumption and efficiency of home appliances are monitored and analy
zed with deep learning based technologies, such as artificial neural
network, long-short-term-memory, etc.
(Smart city) Smart city, as well, contains multiple IoT domains, tran
sportation, infrastructure, energy, agriculture, etc. Since heterogen
eous data from different domains are gathered in smart cities, variou
s artificial intelligence approaches are studied in smart-city applic
ation. Public transportation behaviors and crowd movements patterns a
re important issues, and they are often dealt with neural network bas
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ed methods, long-short-term-memory and convolutional neural network.
(Smart energy) As two-way communication energy infrastructure is depl
oyed, smart grid has become a big IoT application, which requires int
elligent data processing. The traditional energy providers are highly
interested in recognizing local energy consumption patterns and fore
casting the needs in order to make appropriate decisions on real-time.
Moreover, the energy consumers, as well, want analyzed information o
n their own energy consumption behaviors. Recently, many works on ene
rgy consumption prediction, energy flexibility analysis, etc. are act
ively ongoing. Most works are based on the latest deep learning techn
ologies, such as multi-layered-perceptron, recurrent neural network,
long-short-term-memory, autoencoder, etc.
(Smart transportation) The intelligent transportation system is anoth
er source of big data in IoT domains. Many use cases, such as traffic
flow and congestion prediction, traffic sign recognition, vehicle in
trusion detection, etc., have been studied. Moreover, a lot of advanc
ed artificial intelligence technologies are required in autonomous an
d smart vehicles, which require many intelligent sub-tasks, such as p
edestrian's detection, obstacle avoidance, etc.
(Smart healthcare) IoT and artificial intelligence are integrated int
o the healthcare and wellbeing domain as well. By analyzing food imag
es with convolutional neural network on mobile devices, dietary intak
es can be measured. With voice signal captured from sensor devices, v
oice pathologies can be detected. Moreover, recurrent neural network
and long-short-term-memory technologies are actively being studied fo
r early diagnosis and prediction of diseases with time series medical
data.
(Smart agriculture) To manage a vast area of land, IoT and artificial
intelligence technologies are recently used in agriculture domains.
Deep neural network and convolutional neural network are utilized for
crop detection or classification and disease recognition in the plan
ts. Moreover, for automatic farming with autonomous machine operation,
obstacle avoidance, fruit location, and many more sub-tasks are hand
led with advanced artificial intelligence technologies.
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4. Requirements for AI/ML-based IoT services
In this section, the requirements for AI/ML-based IoT data collection
and delivery, intelligent and context-aware IoT services, and applyi
ng AI/ML to IoT data will be described.
4.1. Requirements for AI/ML-based IoT data collection and delivery
IoT services store a vast amount of data that IoT devices
periodically generate, and the refining and analyzing are costly.
Effective analysis of IoT data has been considered to be the most
important factor in data processing, but the analysis of efficient
data collection and delivery methods are becoming other significant
factors as the amount of the data collected is explosively increasing.
In particular, as a number of IoT devices have been deployed within
the IoT network, controlling data collection and delivery for each of
them has become impossible. The introduction of AI/ML techniques for
simultaneous and efficient management of the IoT devices should be
considered as a countermeasure. For IoT data collection and delivery,
the following two factors will need to be considered, IoT devices
energy and data quality.
(IoT Device Energy) As many IoT devices have begun to be deployed
within the IoT network, it is impossible to deliver energy to many
IoT devices simultaneously. Consequently, the efficient battery use
has become an important issue.
If IoT data collection and delivery periods are too short, a lifetime
of the IoT device will be shortened through the reckless use of IoT
device energy. Thereby, it increases the cost required to provide IoT
service. On the other hand, if IoT data collection and delivery
period are too long, the quality of the IoT services provided will be
reduced due to the lack of details in the data for situation
recognition and real-time processing. Therefore, taking into account
the energy consumption of the IoT devices, research on proper IoT
data collection and delivery period is necessary.
(Data Quality) Since the data collected from the majority of IoT
devices usually contain redundant information, it causes additional
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costs for the data collection and refinement processes. Therefore, it
will be necessary to select and deliver meaningful information from
redundant IoT data to reduce unnecessary cost on the IoT network. To
do so, it will need the research to identify the relationships among
the data collected various devices and interpret the information that
the data contains.
(Optimal IoT Device Operation) Each IoT service may have its own
defined information requirements and it is imperative that the data
with certain quality level should be collected to extract the core
information for each situation. As scheme of data collection
considerably affects the data quality, the operation of IoT devices
should be effectively adjusted to ensure the data quality. In general,
high resolution of data collected from IoT devices as well as data
preprocessing are required for high quality data. However, these
methods leading tremendous operation cost of IoT devices causes a
lifetime of IoT networks, degrading the QoS of IoT services.
Eventually, an optimal IoT device operation technique for each IoT
service must be considered.
4.2. Requirements for intelligent and context-aware IoT services
In a context-aware IoT service environment, it is important to
establish a context to be aware of in advance since IoT devices will
be deployed according to a pre-designed architecture and to check how
characteristics of IoT data and data-to-data characteristics are
expressed under these circumstances. For the data produced by IoT
devices, since it contains the device's relative location information,
sensing value over time, event information, it should be reviewed to
provide the target context-aware service using this information. Some
of the necessary technologies will be described in the following.
(Physical Clustering) To increase the accuracy of context-awareness,
the provision of context-aware services should be considered in a
situation where the relationship between IoT devices with respect to
physical layout or physical environment is taken into account.
Setting a rule using the service provider's domain knowledge may be
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possible, but introducing the physical clustering into a diverse IoT
environment (e.g., in bedroom, kitchen, balcony, or a space connected
through an open door) will require identifying the physical
relationship between the devices using data generated from IoT
devices.
(Extra Data Processing) In order to prevent degradation of service
quality from errors in data values or device malfunctions, extra
sensors should be placed in the majority of IoT environments. In a
context-aware service, they contain the same information, so the
technologies filtering the data that contains only essential part
among the same information while preventing data errors would be
required.
(Unreported data handling) If an event is detected on a particular
IoT device, it will transmit data regardless of the device's sensing
and delivery interval. At this time, the data of IoT devices which
are physically clustered are needed to accurately detect events that
occurred, and it is difficult to expect that these devices will
provide data at the same time. A gateway can request data from
clustered devices, but it has a problem for real-time processing for
emergency situations. Therefore, handling unreported data will be
required based on previously collected data.
(Abnormal data in AI/ML) In the case of context-aware services that
operates based on the predetermined rule, the flexibility to cope
with emergency situations that have not been considered is low, and
thus AI/ML algorithms are required to intelligently cope with a
myriad of situations. However, many abnormal data are generated
depending on environmental conditions such as device status, so AI/ML
algorithms that can operate in that imperfect environment should be
considered.
(Edge computing in IoT) There are two necessary prerequisites
required in context-aware IoT services: IoT devices real-time
management and IoT network architecture supporting the high-volume
data transmission. When an abnormal situation is discovered, high-
volume data should be utilized to adequately to monitor the situation
through the IoT device's real-time management. As contrasted with
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conventional cloud computing structures, an edge computing structure,
where IoT data processing servers are located in closer proximity to
IoT devices, provides higher energy efficiency and lesser
communication delay.
4.3. Requirements for applying AI/ML to IoT data
In this subsection, the requirements for applying AI/ML to IoT data
are described.
4.3.1. Training AI/ML algorithm
To use AI/ML algorithm, two elements are required, AI/ML model and
training data. The presence of training dataset in good quality is an
important factor of the AI/ML model performance since the model is
iteratively trained with the training data. However, for anomaly
detection, there is not enough training data since not only the
probability of anomaly occurrence is very low but also it is almost
impossible to retrieve the ground truth value even when the situation
has occurred. Therefore, using domain knowledge, AI/ML learning based
on abnormal situation data generation or simulation should be
considered. For example, for an external intrusion detection
application within a smart home, when a camera and a motion sensor
detect an intruder, a light sensor checks the measuring value. If the
light does not turn on, then the IoT application recognizes it as an
abnormal situation. In this way, by using the domain knowledge, the
rule regarding the operational scenario of the IoT application is
generated as the training data, and the generated training data can
be used for model learning. This will not only enable learning the
anomaly detection algorithm in IoT application but also improving the
accuracy. Therefore, IoT application, in which it is difficult to
acquire dataset in good quality, will require data generation based
on domain knowledge for AI/ML.
4.3.2. AI/ML inference in IoT application
In order for AI ML technology to be applied to IoT applications, the
training data and the input data for model testing and inferencing
must have the same characteristics such as dimension, time interval,
types of features, etc. However, due to the volatile IoT data
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characteristics that vary from situations in many IoT applications,
it is difficult to directly apply the AI/ML algorithms. For example,
in a simple monitoring phase, the IoT devices periodically send
sensing data, and AI/ML have no difficulty in operating. However, in
an abnormal mode, the IoT applications require a fast response, and
IoT devices transmit data at shorter intervals than normal, which
changes the characteristics of the data being input to the AI/ML
algorithm. Therefore, data preprocessing technology handling the
abnormal data will be required in advance, such as data imputation,
correcting data anomalies, and Interpolation of unreported data.
4.3.3. AI/ML models update in IoT application
While IoT devices deployed in an IoT environment continuously collect
and transmit the necessary data to an IoT server, the IoT server
delivers intelligent IoT services using AI/ML models, based on these
collected data. As IoT devices monitor a wide variety of IoT
environmental information such as time-relevant and place-relevant
information, the data gathered from IoT devices changes significantly
depending on the events occurred in an IoT environment. For this
reason, complete information is no longer reflected during AI/ML
training and this, sooner or later, affects the quality of IoT
services provided. Therefore, AI/ML models must be updated
periodically upon the data subsequently collected. However, if the
models are updated in a too short period, not only the high
management (or labor) cost for updating is required, but also it may
detrimentally affect the overall performance of the models. Therefore,
models should be updated by fairly considering between the cost
required for updating AI/ML models and the merits gained from the
usage of updated AI/ML models.
5. State of arts of the artificial intelligence/machine learning
technologies for IoT services
In this section, well-known machine learning and artificial
intelligence technologies applicable to IoT applications are reviewed.
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5.1. Machine learning and artificial intelligence technologies review
The classical machine learning models can be divided into three types,
supervised, unsupervised, and reinforcement learnings. Therefore, in
this subsection, machine learning and artificial intelligence
technology reviews are done in four different categories: supervised,
unsupervised, reinforcement, and neural-network-based.
5.1.1. Supervised learning for IoT
Supervised learning is a task-based type of machine learning, which
approximates function describing the relationship and causality
between input and output data. Therefore, the input data needs to be
clearly defined with proper output data since supervised learning
models learn explicitly from direct feedback.
(K-Nearest Neighbor) Given a new data point in K-Nearest Neighbor
(KNN) classifier, it is classified according to its K number of the
closest data points in the training set. To find the K nearest
neighbors of the new data point, it needs to use a distance metric
which can affect classifier performance, such as Euclidean,
Mahalanobis or Hamming. One limitation of KNN in applying for IoT
network is that it is unscalable to large datasets because it
requires the entire training dataset to classify a newly incoming
data. However, KNN required less processing power capability compared
to other complex learning methods.
(Naive Bayes) Given a new data point in Naive Bayes classifiers,
it is classified based on Bayes' theorem with the "naive" assumption
of independence between the features. Since Naive Bayes classifiers
don't need a large number of data points to be trained, they can deal
with high-dimensional data points. Therefore, they are fast and
highly scalable. However, since its "naive" assumptions are somewhat
strong, a certain level of prior knowledge on the dataset is required.
(Support Vector Machine) Support Vector Machine (SVM) is a binary and
non-probabilistic classifier which finds the hyperplane maximizing
the margin between the classes of the training dataset. SVM has been
the most pervasive machine learning technology until the study on
neural network technologies are advanced recently. However, SVM still
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has advantages over neural network based and probabilistic approaches
in terms of memory usage and capability to deal with high-dimensional
data. In this manner, SVM can be used for IoT applications with
severe data storage constraint.
(Regression) Regression is a method for approximating the
relationships of the dependent variable, which is being estimated,
with the independent variables, which are used for the estimation.
Therefore, this method is widely used for forecasting and inferring
causal relationships between input data and output data in time-
sensitive IoT application.
Random Forests) In random forests, instead of training a single
decision tree, a group of trees is trained. Each tree is trained on a
subset of the training set using a randomly chosen subset of M input
variables. Random forests considering various tree structures have
very high accuracy, so it can be utilized in the accuracy-critical
IoT applications.
5.1.2. Unsupervised learning for IoT
Unsupervised learning is a data-driven type of machine learning which
finds hidden structure in unlabeled dataset without feedback during
the learning process. Unlike supervised learning, unsupervised
learning focuses on discovering patterns in the data distributions
and gaining insights from them.
(K-means clustering) K-means clustering aims to assign observations
into K number of clusters in which each observation belongs to the
cluster having the most similarities. The measure of similarity is
the distance between K cluster centers and each observation. K-means
is a very fast and highly scalable clustering algorithm, so it can be
used for IoT applications with real-time processing requirements such
as smart transportation.
(Density-based spatial clustering of applications with noise)
Density-Based approach to Spatial Clustering of Applications with
Noise (DBSCAN) is a method that clusters dataset based on the density
of its data samples. In this model, dense regions which include data
samples with many close neighbors are considered as clusters, and
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data samples in low-density regions are classified as outliers
[Kriegal]. Since this method is robust to outliers, DBSCAN is
efficient data clustering method for IoT network environments with
untrusted big datasets in practice.
5.1.3. Reinforcement learning for IoT
Reinforcement learning is a reactive type of machine learning that
learn a series of actions in a given set of possible states, actions,
and rewards or penalties. It can be seen as the exploring decision-
making process and choosing the action series with the most reward or
the least penalty which can be cost, priority, time to name a few.
Reinforcement learning can be helpful for selecting action of IoT
device by providing a guideline.
(Q-learning) Q-Learning is a model-free, off-policy reinforcement
learning algorithm based on the well-known Bellman Equation. The goal
is to learn an action-selection policy maximizing the Q-value, which
tells an agent what action to take. It can be used for IoT device to
determine which action it should take according to conditions.
(State-Action-Reward-State-Action) Though State-Action-Reward-State-
Action (SARSA) is a much similar algorithm to Q-learning, the main
difference is that it is an on-policy algorithm in which agent
interacts with the environment and updates the policy based on
actions taken. It means that the Q-value is updated by an action
performed by the current policy instead of the greed policy that
maximizes Q-value. In this perspective, it is relevant when an action
of one IoT device will greatly influence the condition of the
environment.
(Deep Q Network) Deep Q network (DQN) is developed to solve the
exploration problem for unseen states. In the case of Q-learning, the
agent is not capable of estimating value for unseen states. To handle
this generality problem, DQN leverages neural network technology. As
a variation of the classic Q-Learning algorithm, DQN utilizes a deep
convolutional neural net architecture for Q-function approximation.
In real environments not all possible states and conditions are not
able to be observed. Therefore, DQN is more relevant than Q-learning
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or SARSA in real applications such as IoT. Since DQN could be used
within only discrete action space, it can be utilized for traffic
routing in the IoT network.
(Deep Deterministic Policy Gradient) DQN has solved generality and
exploration problem of the unseen or rare states. Deep Deterministic
Policy Gradient (DDPG) takes DQN into the continuous action domain.
DDPG is a deterministic policy gradient based actor-critic, model-
free algorithm. The actor decides the best action for each state and
critic is used to evaluate the policy, the chosen action set. In IoT
applications, DDPG can be utilized for the tasks that require
controlled in continuous action spaces, such as energy-efficient
temperature control, computation offloading, network traffic
scheduling, etc.
5.1.4. Neural Network based algorithms for IoT
(Recurrent Neural Network) Recurrent Neural Network (RNN) is a
discriminative type of supervised learning model that takes serial or
time-series input data. RNN is specifically developed to address
issue of time dependency of sequential time-series input data. It
processes sequences of data through internal memory, and it is useful
in IoT applications with time-dependent data, such as identifying
time-dependent patterns of sensor data, estimating consumption
behavior over time, etc.
(Long Short Term Memory) As an extension of RNN, Long Short Term
Memory (LSTM) is a discriminative type of supervised learning model
that is specialized for serial or time-series input data as well
[Hochreiter]. The main difference of LSTM from RNN is that it
utilizes the concept of gates. It actively controls forget gates to
prevent the long term time dependency from waning. Therefore,
compared to RNN, it is more suitable for data with long time
relationship and IoT applications requiring analysis on the long lag
of dependency, such as activity recognition, disaster prediction, to
name a few [Chung].
(Convolutional Neural Network) Convolutional neural network (CNN) is
a discriminative type of supervised learning model. It is developed
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specifically for processing 2-dimensional image data by considering
local connectivity, but now generally used for multidimensional data
such as multi-channel sound signals, IoT sensor values, etc. As in
CNN neurons are connected only to a small subset of the input and
share weight parameters, CNN is much more sparse compared to fully
connected network. However, it needs a large training dataset,
especially for visual tasks. In CNN, a new activation function for
neural network, Rectified Linear Unit (ReLU), was proposed, which
accelerates training time without affecting the generalization of the
network [Krizhevsky]. In IoT domains, it is often used for detection
tasks that require some visual analysis.
(Variational Autoencoder) Autoencoder (AE) is a generative type
of unsupervised learning model. AE is trained to generate output to
reconstruct input data, thus it has the same number of input and
output units. It is suitable for feature extraction and
dimensionality reduction. Because of its behavior to reconstructing
the input data at the output layer, it is often used for machinery
fault diagnosis in IoT applications. The most popular type of AE,
Variational Autoencoder (VAE) is a generative type of semi-supervised
learning model. Its assumptions on the structure of the data are weak
enough for real applications and its training process through
backpropagation is fast [Doersch]. Therefore, VAE is suitable in IoT
applications where data tends to be diverse and scarce.
(Generative Adversarial Network) Generative Adversarial Network (GAN)
is a hybrid type of semi-supervised learning model which contain two
neural networks, namely the generative and discriminative networks
[Goodfellow]. The generator is trained to learn the data distribution
from a training dataset in order to generate new data which can
deceive the latter network, so-called the discriminator. Then, the
discriminator learns to discriminate the generated data from the real
data. In IoT applications, GAN can be used in situations when
something needs to be generated from the available data, such as
localization, way-finding, and data type conversion.
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5.2. Technologies for lightweight and real-time intelligence
As the era of IoT has come, some sort of light-weight intelligence is
needed to support smart objects. Prior to the era of IoT, most of the
works on learning did not consider resource-constrained environments.
Especially, deep learning models require many resources such as
processing power, memory, stable power source, etc. However, it has
been recently shown that the parameters of the deep learning models
contain redundant information, so that some parts of them can be
delicately removed to reduce complexity without much degradation of
performance [Ba], [Denil]. In this section, the technologies to
achieve real-time and serverless learning in IoT environments are
introduced.
(network compression) Network compression is a method to convert a
dense network into a sparse one. With this technology the network can
be reduced in its size and complexity. By pruning irrelevant parts or
sharing redundant parameters, the storage and computational
requirements can be decreased [Han]. After pruning, the performance
of the network is examined and the pruning process is repeated until
the performance reaches the minimum requirements for the specific
applications and use cases. As many parameters are removed or shared,
the memory required is reduced, as well as computational burden and
energy. Especially as most energy in neural network is used to access
memory, the consumed energy dramatically drops. Although its main
limitation is that there is not a general solution to compress all
kinds of network, but it rather depends on the characteristics of
each network. However, network compression is still the most
widespread method to make deep learning technologies to be
lightweight and IoT-friendly.
(approximate computing) Approximate computing is an approach to
support deep learning in smart devices [Venkataramani], [Moons]. It
is based on the facts that the results of deep learning do not need
to be exact in many IoT applications but still valid if the results
are in an acceptable range. By integrating approximate computing into
deep learning, not only the execution time but also the energy
consumption is reduced [Mohammadi]. Based on the optimal trade-off
between accuracy and run-time or energy consumption, the network can
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be adjustably approximated. The network approximate technology can be
well-used in such situations when the response time is more important
than sophisticatedly analyzed results. Although it is a technology to
facilitate real-time and lightweight intelligence, the process of
training models and converting it to approximate network require some
amount of resource. Therefore, the approximated model can be deployed
on smart devices but the learning and approximation processes still
need to take places on resource rich platforms.
6. Use cases of AI/ML into IoT service
Many IoT service environments are equipped with camera, door lock,
temperature sensor, fire detector, gas detector, alarm, and so on.
Each sensor is deployed with particular purposes of each own to
provide a specific service. However, there is a problem that the
sensor utilization is not high enough due to the provision of the
service using only a single sensor rather than multiple sensors and
their mutual relations. Therefore, the quality of the service
provided is not high as well. To enhance the sensor utilization and
the service quality, all signals from various sensors should be
analyzed comprehensively to make the right decision. This section
describes the use cases for introducing AI / ML techniques in actual
IoT service, utilizing multiple sensors. In advance of each use case
description of various IoT service domains, characteristics of each
domain to adopt AI/ML techniques are investigated.
6.1. Surveillance and Security in Smart Home
To minimize users' inconvenience and ensure their safety,
surveillance and safety IoT applications provided within smart homes
require fast notification with good level of precision IoT service
quality for abnormal conditions detection. To do this, both data
preprocessing techniques and AI/ML technologies for analysis of
anomalies with high accuracy will be required.
6.1.1. Characteristics of Smart Home for AI/ML processing
(Training Data Generation) For Surveillance and Security, the
processed data is necessary because there is little data for
anomalies and the ground truth values are hardly available. Therefore,
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first, the steps to detect and calibrate the abnormal data are
essential before the anomaly data should be generated using domain
knowledge. First, constructing simulators about targeted smart home
and generating events against external intrusions and then collecting
the anomaly data can be considered. Furthermore, based on the data
collected in the actual environment, anomaly data generation can
proceed by breaking the relationship between sensors considering
possible links between them within any intrusive environment.
(AI/ML Algorithm) One of the characteristics of the IoT environment
for surveillance and safety is that a massive amount of data is
collected and real-time responses are required. For the kNN algorithm,
since the more data sets, the stronger against the noise and the
higher the accuracy. If the appropriate dataset is used, the fast
response can be expected. It makes suitable for the service
environment to be considered. In addition, considering real-time data
forecasting and analysis via LSTM, it is believed that improved
accuracy for real-time anomalies detection can be expected.
6.1.2. Use case
(To be continued)
6.2. Smart home for elderly welfare
In the currently existing smart home IoT system for elders, it is
difficult to detect exact physical and mental health status from IoT
data because it is a very complicated problem mixed with various
factors such as depression levels, heart rates, surroundings, etc. If
health status can be detected correctly, it could be used for various
single living-elderly care services. To do this, various techniques
will be required such as protecting user's privacy and recognizing
complex situation.
6.2.1. Characteristics of Smart Home for elderly welfare
IoT devices for in-home healthcare operations need to be small and
unobtrusive. This form factor limitation means that these devices are
also energy constrained. At the same time, the devices need to be
ultra-reliable and provide timely and accurate data, coupled with
processing capabilities to extract vital information. However, it is
not an easy task for single-living elders to manage the status of all
IoT devices in the house. To support reliable smart healthcare
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services for elders, the trade-offs between the energy-efficiency of
the devices and the reliability of the collected data should be
properly considered. Additionally, to manage and handle various
emergency situations, AI/ML-based real-time data monitoring and
automatic behavior recognition, sophisticated situation aware
technology should be required.
6.3. Smart Building Management
Smart buildings often consist of heterogeneous IoT devices. These
devices cooperate and their data is integrated for efficient
autonomous building management. Many of the events in a large
building may not require deep, complicated learning or processing.
Some of them may require a fast response than an accurate analysis.
Above all, a lot of events simultaneously occur and can arise heavy
loads on the main server. The edge-computing techniques can be used
to offload the main server's tasks.
6.3.1. Characteristics of Smart Building for AI/ML processing
(Training Data Generation) In smart buildings, heterogeneous IoT
devices are deployed. They are diverse in their types, functions,
performances, etc. To utilize the data from diverse devices, data
needs to be able to well-integrated. Therefore, it is better for data
to be in a common data format, or it needs to be able to transform
into one another. The other characteristic is that the IoT devices
may interact in local and global environments of the building.
Therefore, the scope of the dataset used in training can play a
critical role in developing AL/ML model for building management.
(AI/ML Algorithm) To offload and reduce the burden of the main server
and to provide fast, efficient decision makings, the IoT and the
other network-related devices can use their computing resources.
Various edge-computing techniques can be applied to do so, such as
developing light-weighted AI/ML models that can be easily deployed in
the edge devices or balancing the learning and processing computation
load from the server to the edge devices.
6.3.2. Use case
(To be continued)
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6.4. Manufacturing optimization in Smart factory
When IoT meets smart factory Industry, Industrial Internet of Things
(IIoT) technology integrating various technologies such as AI/ML, big
data analytics, massive sensor connection, Machine to Machine
communication (M2M) and automation are generally utilized. Its IIoT
applications has initiated a profound change within companies making
them increasingly connected and allowing them to exploit data to
optimize their production processes, since IIoT is able to support
real-time monitoring of each process line, thanks to different types
of sensors positioned in critical points.
However, to anticipate and prevent possible failures, the health
state of assets is always monitored, which generating tremendous
volume of data, and instantons and intelligent responses to
unexpected behavior are required.
6.4.1. Characteristics of Smart Building for AI/ML processing
(To be continued)
6.4.2. Use case
(To be continued)
6.5. Smart parking
Parking operators are nowadays in the process of transforming their
business model to satisfy a more demanding end-user, by looking for
new technologies that allow the enhancement of drivers'user-
experience. These drivers are currently facing mobility issues in
urban areas, where the correlation of traffic congestion in cities is
highlighted with drivers looking for parking spaces. As a consequence,
current drivers are spending more time searching for a free place,
contributing to higher fuel consumption and increased environmental
pollution. Therefore, intelligent smart parking system is required to
collect and process real-time parking occupancy information.
6.5.1. Characteristics of Smart Parking for AI/ML processing
In smart parking system, any end-user has access to real-time parking
occupancy information both through urban furniture (information
displays) and mobile/automobile apps, reducing the time each driver
spends looking for a parking space. Moreover, parking operators can
enhance their ongoing operations by knowing in every instant what is
happening and by analyzing reports on the behavioural patterns
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detected by the platform, improving their operational and enforcement
efficiency. To achieve this, the usage of a video camera will be
required to monitor in real time the available slots in a parking
park. Additionally, the media acquired from the deployed cameras will
be processed using image recognition AI/ML model allowing to perceive
in real time the status of each parking spot. A lightweight AI/ML
model will be initially used to support real-time processing, however,
if any potential change is detected (a parking slot was potentially
filled up for instance) more powerful AI/ML models can be utilized to
confirm/dismiss the potential change. It can be also confirmed which
entity is occupying the slot (car, bike, truck person, etc.)
7. IANA Considerations
This document requests no action by IANA.
8. Acknowledgements
9. Contributors
10. Informative References
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Authors' Addresses
Jun Kyun Choi (editor)
Korea Advanced Institute of Science and Technology (KAIST)
193 Munji Ro, Yuseong-gu, Daejeon, Korea
Email: jkchoi59@kaist.ac.kr
Jae Seob Han
Korea Advanced Institute of Science and Technology (KAIST)
193 Munji Ro, Yuseong-gu, Daejeon, Korea
Email: j89449@kaist.ac.kr
Gyeong Ho Lee
Korea Advanced Institute of Science and Technology (KAIST)
193 Munji Ro, Yuseong-gu, Daejeon, Korea
Email: gyeongho@kaist.ac.kr
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