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    ICN Research Group                                              J.K.Choi
    Internet-Draft                                                   N.K.Kim
    Intended status: Informational                                  J.S.Han
    Expires: September 12, 2019                                      M.K.Kim
                                                                      KAIST
                                                                    G.M.Lee
                                           Liverpool John Moores University
                                                             March 11, 2019
    
    
    
    
       Requirements and Challenges for User-level Service Managements of IoT
                   Network by utilizing Artificial Intelligence
    
                             draft-choi-icnrg-aiot-00
    
    
    
    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
    
    
    
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      Task Force (IETF). Note that other groups may also distribute
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       This Internet-Draft will expire on September 12, 2019
    
    
    
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      Copyright (c) 2019 IETF Trust and the persons identified as the
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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 ....................... 6
          2.3. Management overheads of heterogeneous IoT sensors
                                                               ........ 7
       3. Overview of AI/ML-based IoT services ......................... 9
       4. Requirements for AI/ML-based IoT services ................... 11
          4.1. Requirements for AI/ML-based IoT data collection and delivery
    
           ........................................................... 11
          4.2. Requirements for intelligent and context-aware IoT services12
       5. State of arts of the artificial intelligence/machine learning
       technologies for IoT services
                                    .................................. 12
          5.1. Machine learning and artificial intelligence technologies
          review ..................................................... 12
    
    
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             5.1.1. Supervised learning for IoT ....................... 12
             5.1.2. Unsupervised learning for IoT ..................... 13
             5.1.3. Reinforcement learning for IoT .................... 14
             5.1.4. Neural Network based algorithms for IoT ........... 15
          5.2. Technologies for lightweight and real-time intelligence
                                                                     . 17
       6. IANA Considerations ........................................ 18
       7. Acknowledgements ........................................... 18
       8. Contributors ............................................... 18
       9. Informative References
                                ...................................... 19
    
    1. Introduction
    
       The document explains the effects of applying artificial
       intelligence/machine learning (AI/ML) 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 communication
       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 widely deployed. Future IoT applications occur
       at multiple domains of heterogeneity with various time scales.
    
       The IoT service requirements for common architectures and public APIs
       poses some challenges to the underlying service environment and
       networking technologies. Some IoT applications require significant
       security 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 that IoT service requirements are difficult to
       classify common requirements and functional requirements depending on
       IoT service scenario.
    
       Recently, artificial intelligence technologies can help the context-
       aware IoT service scenarios apply rule-based knowledge accumulation.
       The IoT service assumes that many sensing devices are connected to
       single or multiple IoT network domains. Each sensor sends small
       packets to the IoT servers periodically or non-periodically.
    
    
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       Detection data contains periodic status information that monitors
       whether the system is in a normal state or not. In some cases, 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 the operating mode to an abnormal phase and
       activate future probes. Alarm conditions should be promptly notified
       to responsible persons. For mission-critical applications, reliable
       communication 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 traffic 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 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, including location, time, and 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 database 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 questions 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 protect
       against future unacceptable situations. But, if time-critical data is
       missed due to errors in the sensing devices and the delivery protocol,
       there is no reason to configure IoT networks and devices at a high
       cost.
    
    
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       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 collected. The data of the redundant sensor installed in the same
       area should 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
       what problems were encountered when data was collected. When an
       abnormal situation occurs, IoT sensor should investigate whether it
       noticed normal operations and notified the IoT service operator. If
       the abnormal situation is not properly detected, the operator should
       analyze whether it was caused by malfunction of the IoT sensor or
       other reasons.
    
       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 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 experience is accumulated, the anomaly of IoT services can
       be easily and immediately identified. In addition, when information
       gathered from various sensors is synthesized, it is possible to
       accurately determine whether 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
       in advance what actions are required when a specific anomaly occurs.
       The appropriate work is performed on the abnormal situation according
       to the procedure, predefined by the human. By using artificial
       intelligence and machine learning algorithms, the appropriate actions
       are taken when an abnormal situation is detected from various IoT
       sensors.
    
    
    
    
    
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    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.
    
    
    
    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.
    
    
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       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.
    
    
    
    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
    
    
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       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.
    
       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.
    
    
    
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       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 rather 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 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, the IoT device needs real-time processing to activate urgent
       events, alert notifications, update, and reconnect contexts. The IoT
       service must support real-time interactions between 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 customers, car owners, and
       car sharing providers. All entities in the value chain of a car
       sharing business must share the corresponding situation to pick up,
       board, and return shared cars. Communication networks and interactive
       information, including registration and payment, can be shared
       tightly among the entities. Home IoT service environment can be
       equipped with sensors for theft detection, door lock, temperature,
       fire detection, gas detection, short circuit, air condition to name a
       few. Office IoT service environments, including buildings such as
    
    
    
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       shopping centers and bus/airport terminals, have their own sensors,
       including alarm sensors. When an alarm signal is detected by the
       sensor, the physical position and occurrence time of the sensor is
       determined in advance. All signals from various sensors are analyzed
       comprehensively to make the right decision. If some sensors
       frequently malfunction, the situation can be grasped more accurately
       by analyzing the information of the adjacent sensor. In particular,
       when installing multiple sensors in a particular building (e.g.,
       surveillance camera, location monitoring, temperature, etc.), a much
       wider range of sensors can be used when utilizing artificial
       intelligence and machine learning technologies.
    
       (Smart home) Smart home concept span over multiple IoT applications,
       health, energy, entertainment, education, etc. It involves voice
       recognition, natural language processing, image-based object
       recognition, appliance management, and many more artificial
       intelligence technologies integrated with IoT. Smart connected-
       devices monitor the house to provide better control over home
       supplies and expenses. The energy consumption and efficiency of home
       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 they are often dealt
       with neural network based methods, long-short-term-memory and
       convolutional neural network.
    
       (Smart energy) As two-way communication energy infrastructure is
       deployed, smart grid has become a big IoT application, which requires
       intelligent data processing. The traditional energy 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 energy consumers, as well, want analyzed
       information on their own energy consumption behaviors. Recently, many
    
    
    
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       works on energy consumption prediction, energy flexibility analysis,
       etc. are actively ongoing. Most works are based on the latest deep
       learning technologies, such as multi-layered-perceptron, recurrent
       neural network, long-short-term-memory, autoencoder, 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 recognition,
       vehicle intrusion detection, etc., have been studied. Moreover, a lot
       of advanced artificial intelligence technologies are required in
       autonomous and smart vehicles, which require many intelligent sub-
       tasks, such as pedestrian's detection, obstacle avoidance, etc.
    
       (Smart healthcare) IoT and artificial intelligence are integrated
       into the healthcare and wellbeing 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-term-memory technologies are actively
       being studied for 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
       plants. Moreover, for automatic farming with autonomous machine
       operation, obstacle avoidance, fruit location, and many more sub-
       tasks are handled with advanced artificial intelligence technologies.
    
    
    
    4. Requirements for AI/ML-based IoT services
    
       (to be included)
    
    
    
    4.1. Requirements for AI/ML-based IoT data collection and delivery
    
       (to be included)
    
    
    
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    4.2. Requirements for intelligent and context-aware IoT services
    
       (to be included)
    
    
    
    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
    
    
    
    
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       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
       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
    
    
    
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       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
       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.
    
    
    
    
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       (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
       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
    
    
    
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       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
       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
    
    
    
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       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.
    
    
    
    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,
    
    
    
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       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 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. IANA Considerations
    
     This document requests no action by IANA.
    
    
    
    7. Acknowledgements
    
    
    
    8. Contributors
    
    
    
    
    
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    9. 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], 2014.
    
       [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. 1097-1105.
    
       [Doersch] C. Doersch, "Tutorial on variational autoencoders," arXiv
       preprint 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 &
    
    
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       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
        Korea
    
        Email: jkchoi59@kaist.ac.kr
    
        Na Kyoung Kim
        Korea Advanced Institute of Science and Technology (KAIST)
        193 Munji Ro, Yuseong-gu, Daejeon
        Korea
    
        Email: nkim71@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
    
        Min Kyung Kim
        Korea Advanced Institute of Science and Technology (KAIST)
        193 Munji Ro, Yuseong-gu, Daejeon
        Korea
    
        Email: mkkim1778@kaist.ac.kr
    
        Gyu Myoung Lee
        Liverpool John Moores University
        Barkhill Rd, Merseyside, Liverpool L17 6BD
        United Kingdom
    
        Email: G.M.Lee@ljmu.ac.uk
    
    
    
    
    
    
    
    
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