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    ICN Research Group                                              J.K.Choi
    Internet-Draft                                                   N.K.Kim
    Intended status: Informational                                  J.S.Han
    Expires: January 8, 2020                                      M.K.Kim
                                                             July 7, 2019

      Requirements and Challenges for User-level Service Managements of IoT
                  Network by utilizing Artificial Intelligence


    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
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   Internet-Drafts are draft documents valid for a maximum of six months
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    Table of Contents

    1. Introduction
                ....................................................... 3
    2. Challenging Issues of IoT network ...................................... 5
          2.1. Untrusted and incorrect IoT devices ............................. 5
          2.2. Traffic burstiness of IoT network ................................ 6
          2.3. Management overheads of heterogeneous IoT sensors ............... 7
    3. Overview of AI/ML-based IoT services................................... 8
    4. Requirements for AI/ML-based IoT services.............................. 10
          4.1. Requirements for AI/ML-based IoT data collection and delivery........ 11
          4.2. Requirements for intelligent and context-aware IoT services ......... 11
          4.3. Requirements for applying AI/ML to IoT data ...................... 13
             4.3.1. Training AI/ML algorithm................................. 13
             4.3.2. AI/ML inference in IoT application.......................... 13
    5. State of arts of the artificial intelligence/machine learning technologies for IoT services14
          5.1. Machine learning and artificial intelligence technologies review ........ 14

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             5.1.1. Supervised learning for IoT............................... 14
             5.1.2. Unsupervised learning for IoT............................. 15
             5.1.3. Reinforcement learning for IoT ............................ 16
             5.1.4. Neural Network based algorithms for IoT..................... 17
          5.2. Technologies for lightweight and real-time intelligence .............. 19
    6. Use cases of AI/ML into IoT service.................................... 20
          6.1. Use case 1: Surveillance and Security in Smart Home ............... 20
          6.2. Use case 2: Smart Health in Smart Home ........................ 21
    7. IANA Considerations ............................................... 21
    8. Acknowledgements ................................................ 21
    9. Contributors ..................................................... 21
    10. Informative References ............................................ 21

    1. Introduction
    The document explains the effects of applying artificial intelligence/machine learning (AI/M
    L) algorithms in the Internet of Thing (IoT) service environments.

    IoT applications will be deployed in heterogeneous and different areas such as the energy,
    transportation, automation and manufacturing industries as well as the information and com
    munication technology (ICT) industry. Many IoT sensors and devices can connect to an IoT
    service 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 w
    idely deployed. Future IoT applications occur at multiple domains of heterogeneity with vari
    ous time scales.

    The IoT service requirements for common architectures and public APIs poses some challe
    nges to the underlying service environment and networking technologies. Some IoT applicat
    ions require signif
icant security and privacy as well as significant resource and time constra
    ints. These mission-critical applications can be separated from many common IoT applicati
  ons that current technology may not provide. It means that IoT service requirements are diff
    icult to classify common requirements and functional requirements depending on IoT servic
    e scenario.

    Recently, artificial intelligence technologies can help the context-aware IoT service scenari
    os apply rule-based knowledge accumulation. The IoT service assumes that many sensing

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    devices are connected to single or multiple IoT network domains. Each sensor sends small
    packets to the IoT servers periodically or non-periodically. Detection data contains periodic
     status information that monitors whether the system is in a normal state or not. In some ca
    ses, alert information is included for quick processing. Most IoT applications can operate in
     two modes. One is a simple monitoring mode and the other is an abnormal mode 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 th
    e operating mode to an abnormal phase and activate future probes. Alarm conditions shoul
    d be promptly notified to responsible persons. For mission-critical applications, reliable co
    mmunication with robust QoS requirements in terms of error and latency is required.

    Periodic data accumulation from IoT devices is cumbersome. Under normal conditions, the
    IoT data is simply accumulated without further action. In an unusual situation, incoming IoT
    data can cause an urgent action to notify the administrator of the problem. Streaming data t
    raffic from thousands of IoT devices is annoying to store in the database because it is not
    easy to extract unidentified or future incidents. Only a significant portion of the incoming da
    ta 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, including location, time, a
    nd status, allows you to sort and categorize a portion of streaming data when an additional
    inspection is required, and perform real-time processing. One of the missions of the IoT da
    tabase is to be able to extract preliminary symptoms of unexpected accidents from a large
    amount of streaming data.

    If some transmitted data is important to invoke the corresponding action, there are some qu
    estions about whether the incoming data is correct. If the incoming data contains accurate
    and time-critical events, appropriate real-time control and management can be performed.
     However, if the incoming data is inaccurate or intentionally corrupted, additional accidents
    may occur. In these cases, incoming data can trigger to initiate additional inspections to pr
    otect against future unacceptable situations. But, if time-critical data is missed due to error
    s in the sensing devices and the delivery protocol, there is no reason to configure IoT netw
    orks and devices at a high cost.

    It is not easy to analyze data collected through IoT devices installed to monitor complex IoT
     service environments. If the sensor malfunctions, the data of the sensor cannot be trusted.
     Additional investigation should be done if abnormal status from specific sensors is collecte

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    d. The data of the redundant sensor installed in the same area should be received or combi
    ned 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 ti
    me. IoT service operators can look at the operational history of the sensor for a period of ti
    me to determine what problems were encountered when data was collected. When an abno
    rmal situation occurs, IoT sensor should investigate whether it noticed normal operations an
    d notified the IoT service operator. If the abnormal situation is not properly detected, the op
    erator should analyze whether it was caused by malfunction of the IoT sensor or other reas

    In the IoT service environment, it is possible to analyze the situation accurately by applying
    recent artificial intelligence and machine learning technologies. If there is an operational rec
    ord of the past, it is possible to determine when an abnormal situation arises. Most problem
    s are likely to be repeated, so if the past learning experience is accumulated, the anomaly o
    f IoT services can be easily and immediately identified. In addition, when information gather
    ed from various sensors is synthesized, it is possible to accurately determine whether abnor
    mal situations have occurred.

    Various types of IoT sensors are installed with certain purposes. It expects that all the IoT s
    ensors intend to monitor the occurrence of special abnormal situations in advance. Therefo
    re, it should be set in advance what actions are required when a specific anomaly occurs. T
    he appropriate work is performed on the abnormal situation according to the procedure, pre
    defined by the human. By using artificial intelligence and machine learning algorithms, the a
    ppropriate actions are taken when an abnormal situation is detected from various IoT senso

    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

    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

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       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.

    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 additi
       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 intell
     igence can be applied to handle time-varying traffic on a network.

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       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

    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

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       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

       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 in IoT domains are provided.
  The common property of IoT applications and services is that they require fast analytics rat
    her than later analytics with piled data. Recently, neural-network-based artificial intelligence
    technologies are widely used across many IoT applications.

    Simple IoT applications include dynamic contexts that share common features among socia
    l relations at the same administration domain. IoT devices in the same domain can provide t
    heir service contexts to the IoT server. When a dynamic change occurs in an IoT service co
    ntext, the IoT device needs real-time processing to activate urgent events, alert notification

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    s, update, and reconnect contexts. The IoT service must support real-time interactions bet
    ween the IoT device and the system in the same domain. The IoT service contexts must be
    shared between physical objects and social members in the same domain as well.

    Artificial intelligence technologies have been shown promising in many areas, including IoT.
     For example, contextual information for a car-sharing business must interact with custome
    rs, car owners, and car sharing providers. All entities in the value chain of a car sharing bus
    iness must share the corresponding situation to pick up, board, and return shared cars. Co
    mmunication networks and interactive information, including registration and payment, can
    be shared tightly among the entities. Home IoT service environment can be equipped with s
    ensors for theft detection, door lock, temperature, fire detection, gas detection, short circui
    t, air condition to name a few. Office IoT service environments,
including buildings such as
    shopping centers and bus/airport terminals, have their own sensors, including alarm sensor
    s. When an alarm signal is detected by the sensor, the physical position and occurrence ti
    me of the sensor is determined in advance. All signals from various sensors are analyzed c
    omprehensively to make the right decision. If some sensors frequently malfunction, the situ
    ation can be grasped more accurately by analyzing the information of the adjacent sensor. I
    n particular, when installing multiple sensors in a particular building (e.g., surveillance came
    ra, location monitoring, temperature, etc.), a much wider range of sensors can be used wh
    en utilizing artificial intelligence and machine learning technologies.

    (Smart home) Smart home concept span over multiple IoT applications, health, energy, ente
    rtainment, education, etc. It involves voice recognition, natural language processing, image
    -based object recognition, appliance management, and many more artif
icial intelligence te
   chnologies integrated with IoT. Smart connected-devices monitor the house to provide bett
    er control over home supplies and expenses. The energy consumption and efficiency of ho
    me appliances are monitored and analyzed 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, transportation, infrastructure,
     energy, agriculture, etc. Since heterogeneous data from different domains are gathered in
    smart cities, various artificial intelligence approaches are studied in smart-city application.
    Public transportation behaviors and crowd movements patterns are important issues, and th
    ey are often dealt with neural network based methods, long-short-term-memory and convo
    lutional neural network.

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    (Smart energy) As two-way communication energy infrastructure is deployed, smart grid ha
    s become a big IoT application, which requires intelligent data processing. The traditional e
    nergy providers are highly interested in recognizing local energy consumption patterns and
    forecasting the needs in order to make appropriate decisions on real-time. Moreover, the e
    nergy consumers, as well, want analyzed information on their own energy consumption beh
    aviors. Recently, many works on energy consumption prediction, energy flexibility analysis,
    etc. are actively ongoing. Most works are based on the latest deep learning technologies, s
    uch as multi-layered-perceptron, recurrent neural network, long-short-term-memory, auto
    encoder, etc.

    (Smart transportation) The intelligent transportation system is another source of big data in
    IoT domains. Many use cases, such as traffic flow and congestion prediction, traffic sign re
    cognition, vehicle intrusion detection, etc., have been studied. Moreover, a lot of advanced
     artificial intelligence technologies are required in autonomous and smart vehicles, which re
    quire many intelligent sub-tasks, such as pedestrian's detection, obstacle avoidance, etc.

    (Smart healthcare) IoT and artificial intelligence are integrated into the healthcare and wellb
    eing domain as well. By analyzing food images with convolutional neural network on mobile
     devices, dietary intakes can be measured. With voice signal captured from sensor devices,
     voice pathologies can be detected. Moreover, recurrent neural network and long-short-ter
    m-memory technologies are actively being studied for early diagnosis and prediction of dis
    eases with time series medical data.

    (Smart agriculture) To manage a vast area of land, IoT and artificial intel ligence technologie
   s are recently used in agriculture domains. Deep neural network and convolutional neural ne
    twork are utilized for crop detection or classification and disease recognition in the plants.
    Moreover, for automatic farming with autonomous machine operation, obstacle avoidance,
    fruit location, and many more sub-tasks are handled with advanced artificial intelligence te

    4. Requirements for AI/ML-based IoT services
    In this section, the requirements for AI/ML-based IoT data collection and delivery, intelligen
    t and context-aware IoT services, and applying AI/ML to IoT data willbe described.

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    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 additionalcosts 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.

    4.2. Requirements for intelligent and context-aware IoT services

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       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
       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

       (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

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       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.

    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, willrequire 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
       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

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       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.

    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.

    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.

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       (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 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

       (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

       (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.

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       (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

       (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 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

       (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

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       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 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

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       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 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

       (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 are
       reduced [Mohammadi]. Based on the optimal trade-off between accuracy and run-
       time or energy consumption, the network can be adjustably approximated. The network
       approximate technology can be well-used in such situations when the response time is

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       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.

    6.1. Use case 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.

       (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, 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.

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       (AI/ML Algorithm) Characteristics of the IoT environment for surveillance and safety are
       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.

    6.2. Use case 2: Smart Health in Smart Home

       (To be continued)

    7. IANA Considerations
     This document requests no action by IANA.

    8. Acknowledgements

    9. Contributors

    10. Informative References
    [Hochreiter] S. Hochreiter and J. Schmidhuber, "Long short-term memory," Neural
    Comput., vol. 9, no. 8, pp. 1735-1780, Nov. 1997.

    [Chung] J. Chung, C. Gulcehre, K. Cho, and Y. Bengio, "Empirical evaluation of gated
    recurrent neural networks on sequence modeling," arXiv preprint arXiv:1412.3555v1 [cs.NE],

    [Krizhevsky] A. Krizhevsky, I. Sutskever, and G. E. Hinton, "ImageNet classification with
    deep convolutional neural networks," in Proc. Adv. Neural Inf. Process. Syst., 2012, pp.

    [Doersch] C. Doersch, "Tutorial on variational autoencoders," arXiv preprint

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    arXiv:1606.05908v2 [stat.ML], 2016.

    [Goodfellow]. I. Goodfellow et al., "Generative adversarial nets," in Proc. Adv. Neural Inf.
    Process. Syst., 2014, pp. 2672-2680.

    [Ba] J. Ba and R. Caruana, "Do deep nets really need to be deep?" in Proc. Adv. Neural Inf.
    Process. Syst., Montreal, QC, Canada, 2014, pp. 2654-2662.

    [Denil] M. Denil, B. Shakibi, L. Dinh, N. de Freitas, and M. Ranzato, "Predicting parameters
    in deep learning," in Proc. Adv. Neural Inf. Process. Syst., 2013, pp. 2148-2156.

    [Han] S. Han, J. Pool, J. Tran, and W. Dally, "Learning both weights and connections for
    efficient neural network," in Proc. Adv. Neural Inf. Process. Syst., Montreal, QC, Canada,
    2015, pp. 1135-1143.

    [Venkataramani] S. Venkataramani, A. Ranjan, K. Roy, and A. Raghunathan, "AxNN:
    Energy-efficient neuromorphic systems using approximate computing," in Proc. Int. Symp.
    Low Power Electron. Design, ACM, 2014, pp. 27-32.

    [Moons] B. Moons, B. De Brabandere, L. Van Gool, and M. Verhelst, "Energy- efficient
    ConvNets through approximate computing," in Proc. IEEE Winter Conf. Appl. Comput. Vis.
    (WACV), Lake Placid, NY, USA: IEEE, 2016, pp. 1-8.

    [Mohammadi] Mohammadi, Mehdi, et al. "Deep learning for IoT big data and streaming
    analytics: A survey," IEEE Communications Surveys & Tutorials, 2018, pp. 2923-2960.

    [Kriegel] Kriegel, HansPeter, et al. "Densitybased clustering," Wiley Interdisciplinary
    Reviews: Data Mining and Knowledge Discovery, 2011, pp. 231-240.

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    Authors' Addresses

        Jun Kyun Choi (editor)
        Korea Advanced Institute of Science and Technology (KAIST)
        193 Munji Ro, Yuseong-gu, Daejeon

        Email: jkchoi59@kaist.ac.kr

        Na Kyoung Kim
        Korea Advanced Institute of Science and Technology (KAIST)
        193 Munji Ro, Yuseong-gu, Daejeon

        Email: nkim71@kaist.ac.kr

        Jae Seob Han
        Korea Advanced Institute of Science and Technology (KAIST)
        193 Munji Ro, Yuseong-gu, Daejeon

        Email: j89449@kaist.ac.kr

        Min Kyung Kim
        Korea Advanced Institute of Science and Technology (KAIST)
        193 Munji Ro, Yuseong-gu, Daejeon

        Email: mkkim1778@kaist.ac.kr

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