Internet DRAFT - draft-kim-ml-iot

draft-kim-ml-iot







6Lo Working Group                                         Seo-Hyang. Kim
Internet-Draft                                           Duc-Lam. Nguyen
Intended status: Informational                           Chong-Kwon. Kim
Expires: July 30, 2018                         Seoul National University
                                                        January 26, 2018


 Building Resilient and Autonomous Systems for IoT Network Management -
  Advantages and Difficulties in adopting Machine Learning Techniques
                          draft-kim-ml-iot-00

Abstract

   This document shares knowledge and insights regarding applying
   machine learning techniques on wireless sensor networks.  It firstly
   introduces advantages and difficulties in adopting machine learning
   techniques on wireless sensor networks.  Though dynamicity and
   unpredictability of wireless networks make it difficult to train the
   model with various possible scenarios, it has strong ability in terms
   of flexibility.  This document also overviews several works that
   applied machine learning techniques on diverse research areas
   including networking, communications and lossy environment.  The
   ultimate purpose of this document is to discuss a proper research
   direction aiming the realization of a system that detects, predicts
   and recovers from abnormal situations on wireless sensor networks.

Status of This Memo

   This Internet-Draft is submitted in full conformance with the
   provisions of BCP 78 and BCP 79.

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   This Internet-Draft will expire on July 30, 2018.

Copyright Notice

   Copyright (c) 2018 IETF Trust and the persons identified as the
   document authors.  All rights reserved.




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   This document is subject to BCP 78 and the IETF Trust's Legal
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Table of Contents

   1.  Introduction  . . . . . . . . . . . . . . . . . . . . . . . .   2
   2.  Requirements Language . . . . . . . . . . . . . . . . . . . .   3
   3.  Terminology . . . . . . . . . . . . . . . . . . . . . . . . .   3
   4.  Advantages and Difficulties in adopting ML on Wireless
       Networks  . . . . . . . . . . . . . . . . . . . . . . . . . .   3
   5.  Examples of ML-based research regarding networking and
       communications  . . . . . . . . . . . . . . . . . . . . . . .   6
     5.1.  Signal classification . . . . . . . . . . . . . . . . . .   7
     5.2.  Data collection and traffic classification for network
           management  . . . . . . . . . . . . . . . . . . . . . . .   7
     5.3.  Network attack prediction . . . . . . . . . . . . . . . .   7
     5.4.  Wireless adaptive streaming . . . . . . . . . . . . . . .   7
     5.5.  Mobile cloud offloading . . . . . . . . . . . . . . . . .   8
   6.  Examples of ML-based research regarding wireless sensor
       networks  . . . . . . . . . . . . . . . . . . . . . . . . . .   8
     6.1.  Channel error diagnostics . . . . . . . . . . . . . . . .   8
     6.2.  Spectrum decision . . . . . . . . . . . . . . . . . . . .   9
     6.3.  Outlier detection . . . . . . . . . . . . . . . . . . . .   9
     6.4.  Indoor localization . . . . . . . . . . . . . . . . . . .   9
     6.5.  Event detection . . . . . . . . . . . . . . . . . . . . .  10
     6.6.  Fault detection . . . . . . . . . . . . . . . . . . . . .  10
     6.7.  Routing . . . . . . . . . . . . . . . . . . . . . . . . .  10
   7.  Concluding Remarks  . . . . . . . . . . . . . . . . . . . . .  10
   8.  IANA Considerations . . . . . . . . . . . . . . . . . . . . .  11
   9.  Security Considerations . . . . . . . . . . . . . . . . . . .  11
   10. Acknowledgement . . . . . . . . . . . . . . . . . . . . . . .  11
   11. References  . . . . . . . . . . . . . . . . . . . . . . . . .  12
     11.1.  Normative References . . . . . . . . . . . . . . . . . .  12
     11.2.  Informative References . . . . . . . . . . . . . . . . .  12
   Authors' Addresses  . . . . . . . . . . . . . . . . . . . . . . .  14

1.  Introduction

   This document shares knowledge and insights regarding applying
   machine learning techniques on wireless sensor networks.  It firstly
   introduces advantages and difficulties in adopting machine learning



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   techniques on wireless sensor networks.  Though dynamicity and
   unpredictability of wireless networks make it difficult to train the
   model with various possible scenarios, it has strong ability in terms
   of flexibility.  This document also overviews several works that
   applied machine learning techniques on diverse research areas
   including networking, communications and lossy environment.  The
   ultimate purpose of this document is to discuss a proper research
   direction aiming the realization of a system that detects, predicts
   and recovers from abnormal situations on wireless sensor networks.

2.  Requirements Language

   The key words "MUST", "MUST NOT", "REQUIRED", "SHALL", "SHALL NOT",
   "SHOULD", "SHOULD NOT", "RECOMMENDED", "MAY", and "OPTIONAL" in this
   document are to be interpreted as described in RFC 2119 [RFC2119].

3.  Terminology

   Abnormal situation : In this document, we use this term on sensor
   network environment.  We define abnormal situation as situation that
   should be corrected by system manager or automated management
   process.  If this situation is not corrected or the proper action is
   not taken in time, the overall system performance will be degraded.
   For example, on sensor network, situations caused by link error,
   energy depletion, memory depletion, routing loop or broken sensor
   should be detected as soon as possible and these situations should be
   considered as abnormal situation.  The best thing is to avoid these
   situations by prediction mechanism.  Even if such abnormal situations
   were happened, a proper action should be taken soon.

4.  Advantages and Difficulties in adopting ML on Wireless Networks

   Recently, machine learning techniques are widely adopted on various
   areas including image, voice, video, public safety, medical, etc.
   With the evolution of more sophisticated computer-related techniques,
   we have a plethora of data stored at a large number of data centers
   and these are analyzed at a speed of real-time.  Machine learning
   techniques can realize the implementation of human-like prediction or
   decision making process.  Ideally, by using machine learning
   techniques, the whole world can be managed autonomously in safe way
   by the system.  For example, the system collects all the information
   produced by each human being and learns everything in the world by
   itself.  One of the strongest advantage of adopting machine learning
   techniques is that it can learn from data continuously over time.
   Even during the operation of the system, it can be continuously
   updated by using newly observed or produced data.  As a result, with
   one machine learning algorithm, different logics are produced with
   different training data.  It means that it can learn continuously



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   from the experience and the system is flexible on its decision making
   process.  Back to the example, when the system detects any dangerous
   or abnormal situation from the world of human beings, it can ring the
   alarm bell or take any action that may be deemed wise and helpful.

   The reality, however, is that this ideal system is hard to construct.
   The first problem comes from the difficulty in collecting good
   training data that is including various scenarios.  Even though we
   have good machine learning algorithms that can build strong logic and
   analyze the large data set in real-time training the system
   continuously, if we do not have effective data set, we cannot build
   any prediction or decision model.

   Regarding researches on communications or networking, many
   researchers have tried to adopt machine learning techniques for
   decision making process in connection with channel error diagnostics,
   fault detection in wireless sensor networks, routing in wireless
   sensor networks, network attack, etc.  When the environment
   surrounding the model is stable and persistent except some factors
   that are closely related to the output of the model, it is not
   challenging to train the model and make reliable decision.  However,
   if the environment surrounding the model is dynamically changing, the
   algorithm cannot build reliable model that outputs correct decision
   from input data.  This is because with varying condition, it is
   difficult to find consistent patterns from various input data to
   output decision.  Consequently, it is hard to build reliable model.
   Moreover, in this dynamic environment, the system should secure data
   set for training that is including various scenarios.  However, even
   collecting data set for training is difficult due to the lack
   consistent pattern from the data set.

   In recent years, researchers and industries have been paying
   attention on Internet of Things.  With this trend, plenty of research
   groups have been made accelerating a growth of relevant techniques.
   Due to the limited characteristics of constrained devices and low-
   power communication techniques, which are different from that of
   conventional sensor networks, networking and communication techniques
   especially for low power and lossy networks have been received
   especially huge attention compared to that of the other networks.

   To enhance the reliability of communication on low power and lossy
   networks constructed with constrained devices, routing protocol such
   as RPL (Routing Protocol for Low Power and Lossy Network) [RFC6550]
   and special mac protocol such as TSCH (Time Slotted Channel Hopping)
   [IEEE802.15.4e] [RFC7554] have been proposed and widely used as
   standard protocols currently.  To summarize briefly, RPL constructs
   routing paths in simple way and prevents routing loops by
   constructing DODAG structure.  Moreover, topology created for the



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   route management evolves continuously over time considering various
   network conditions and metrics.  With TSCH, various channels can be
   used and problems caused by interference can be overcome.  However,
   these protocols are operated on the basis of local information
   obtained at each node.  Consequently, the problems that are not
   immediately captured and fixed by the protocols that are based on
   local information are still exist.

   With a global view on a network state, various information can be
   used for analyze the current state and the problems that can
   seriously damage the network performance can be detected in advance.
   In sensor network, there are several abnormal situations that should
   be detected before they happen.  For example, on an application
   layer, broken sensors might send wrong sensed values to a connected
   server continuously.  If we do not aware of this, we cannot detect
   the situation where the accident actually happens.  On a network
   layer, though RPL captures the network problem and the topology is
   continuously updated considering network conditions, each node has
   simple decision making process on the basis of local information.  As
   a result, if network traffic is concentrated on a specific node, it
   might not be detected before the problem becomes bigger.  In this
   case, the node with freakishly unbalanced and heavy work load will
   quickly consume its energy and finally it will be powered off.  On a
   link layer, wireless network interface might broke down.  In this
   case, with the global view on the network, all of these situations
   can be detected before the serious accident happens.  The global view
   can be obtained by information collection and traffic monitoring from
   the high-powered root node or the server.

   Following is the list of examples of faults that should be detected
   on a low power and lossy networks.  We aim to construct reliable
   system that manages the network automatically or autonomously.

   - No energy in a node

   - Breakdown on wireless network interface in a node

   - Interference on certain channel

   - Overloaded CPU usage on a node

   - Full memory or buffer on a node

   - Abnormal sensed value

   - Wrong execution of a command for network management

   - Link layer problem falsified data



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   - Traffic overload due to attack (e.g.  DDoS)

   - Energy consumption due to unbalanced traffic load

   We know that one of the benefits from using machine learning
   algorithm is that the system can learn various scenarios including
   unexperienced one over time since the decision making logic is not on
   the basis of predefined static rules.  Accordingly, when we devise
   techniques for detecting abnormal situation described above on low
   power and lossy network, it seems good to have an approach by
   utilizing machine learning algorithms.

   However, collecting data set for training the model is challenging.
   Moreover, the network environment on a low power lossy network is
   highly dynamic compared to the other networks.  As a result,
   obtaining good data set that is including various scenarios is
   virtually impossible.

   Though there are many researches on networking or communications on
   the basis of machine learning techniques, only part of that can be
   applied to real systems or devices.  As described above, if the
   environment surrounding the model is huge and changes dynamically, it
   becomes harder and harder in training the model.  Moreover, though
   several methods detecting fault scenarios on a sensor network by
   using machine learning have been proposed, they trained the model
   with too small data set which were made in artificial way or the data
   set has not including various possible scenarios.  Moreover, the
   evaluation and the test scenario were done on rigorously restricted
   environment so it is uncertain whether the constructed model will
   work properly even with the similar but different scenarios.

   In the following sections, we will introduce several works that are
   adopting machine learning techniques on various networks environments
   including wireless sensor networks.  Though adopting machine learning
   on wireless network environment is difficult due to dynamicity and
   unpredictability of wireless network environment, these works are
   valuable and have shown notable performance improvement through their
   evaluation.

5.  Examples of ML-based research regarding networking and
    communications

   We introduce several works that are applying machine learning
   techniques regarding networking or communications.  These methods
   have strong contribution in terms of utilizing machine learning to
   overcome challenging problems caused by fluctuating and unpredictable
   wireless channel state.  Though these works have shown notable
   performance improvement on their evaluation, some works just



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   concentrate on trace-driven simulation or testing using the same data
   set used for training rather than natural real-world experiment.
   Nevertheless, these works are valuable in that they paved the way for
   utilizing various information to construct reliable models to
   overcome the difficulty in predicting the future state of wireless
   channel.

5.1.  Signal classification

   To implement reliable wireless communication, the signal sent by a
   sender should be correctly recognized at a receiver side.  The
   authors of [EX1] are focusing on the impact of interference from
   modulated signals and the influence of realistic wireless channel
   conditions on classification performance.  They proposes machine
   learning approach can be used for classifying the signal on realistic
   wireless environment.  We regards that this work is similar to
   pattern recognition since it classifies the signal which has been
   modified passing through a wireless channel.

5.2.  Data collection and traffic classification for network management

   The authors of [EX2] emphasize the importance of understanding the
   type of data that can be collected in SDNs and the process of
   learning information from that data.  As a first step toward machine
   learning based network control, this work presents a simple
   architecture deployed in an enterprise network that gathers traffic
   data using the OpenFlow protocol.  However, this work just
   concentrates on studying monitoring and classification of traffic
   using data obtained with the OpenFlow protocol without proposing
   sophisticated ML-based system or network management.  Nevertheless,
   this work have paved the way for the use of ML-based network
   management and shown simple examples applying ML techniques.

5.3.  Network attack prediction

   The work [EX3] have proposed the method defining security rules on
   the SDN controller on the basis of machine learning technique.
   Machine learning algorithms are used to predict potential target host
   that can be attacked and the security rules on the SDN controller are
   defined to restrict the access of potential attackers by blocking the
   entire subnetwork.  For the evaluation of the proposed method, the
   same datasets were split for training and testing purpose.

5.4.  Wireless adaptive streaming

   Network conditions fluctuates over time and vary significantly across
   environments.  With this reason, predicting future network condition
   is difficult.  Though many rate adaptation algorithm for high QoE



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   video streaming have been proposed, it is not sufficient and there
   are much room for further improvement.  Several works have proposed
   by adopting machine learning techniques for the video streaming
   services.  [EX4] proposed the system that is implemented on server-
   side, learns critical features and make the best decision on bitrate
   and CDN for the streaming user to optimize QoE.  [EX5] also adopted
   reinforcement learning to generate the best ABR algorithm
   automatically by considering bandwidth, buffer level and video rate.

5.5.  Mobile cloud offloading

   The work [EX6] introduces the use of cloud computing for mobile
   device computation offloading and proposes ML-based dynamic
   algorithm.  It monitors device resources and network parameters and
   makes a decision to offload computation to the cloud.  In this work,
   machine learning technique is used to make a decision on cloud
   computing and network information such as available bandwidth is only
   one of various input values.  Other input values are user input,
   device energy level and CPU usage level and these are definite and
   stable compared to the values influenced by dynamic and unpredictable
   wireless network.  Since the environment surrounding the model is
   stable compared to other works introduced in this document, we
   regards that this model is on better condition in terms of a given
   environment.

6.  Examples of ML-based research regarding wireless sensor networks

   In this section, we introduce several works that applied machine
   learning techniques on sensor networks.  These methods also have
   strong contribution in terms of utilizing machine learning to
   overcome challenging problems caused by lossy channels and
   constrained devices.  These works are also valuable in that they
   paved the way for utilizing various information to construct reliable
   models to overcome the difficulty in predicting the future state of
   lossy channels.

6.1.  Channel error diagnostics

   ISM band is shared by several protocols such as 802.11, 802.15.4,
   802.15.1, etc.  Here, different systems interfere with each other
   degrading communication performance.  Authors of [EX7] conducted
   extensive experiments to study the error patterns in IEEE 802.15.4
   and found that there are different patterns for major wireless
   scenarios.  Based on this finding, they designed a machine learning
   mechanism to classify the wireless channel errors into different
   categories and proposed the system that diagnoses different troubles
   in IoT networks.




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6.2.  Spectrum decision

   The work [EX8] also points out the pollution of the ISM band and the
   power constraints in sensor nodes.  To overcome this poor
   environment, it proposes machine learning solution for channel
   selection.  By using ML technique, the system predicts a number of
   expected transmission attempts.  It uses the following attributes as
   input data: RSSI, number of transmission attempts, reasons of each
   failed attempt, performance data such as RSSI and LQI from the last
   received packet.  From the output, it selects the best channel and a
   channel with low number of expected transmission attempts is
   considered as better one.

6.3.  Outlier detection

   Since wireless sensor network composed of constrained nodes is
   vulnerable to interference, unstable channel or cyber-intrusion, the
   system performance is degraded and fake data might be provided to
   higher management levels in a system.  This might cause critical
   problems on sensor network systems for public safety or industry
   automation.  Authors of [EX9] have pointed out that the existing
   works for outlier detection require large memory, high computation,
   high energy consumption, communication overhead and does not support
   heavy online data streaming.  To solve the problem, they proposed
   online outliers detection by using a machine learning technique as a
   multi-agent framework.

6.4.  Indoor localization

   Generally, GPS is one of the well-known examples regarding object
   localization.  However, inside a building, it is difficult to
   estimate the correct location of an object due to low received GPS
   signal strength.  With this reason, another approaches are used.  For
   example, several nodes are used as anchor points and these
   information is used to estimate a relative location of a target
   object.  Devising an accurate indoor localization system is important
   since the system can be used to increase the safety in underground
   mines or caves.  However, there still exist interference on wireless
   channel which decreases estimation accuracy.  To overcome the
   problem, the work [EX10] have used seven different machine learning
   techniques on two different architectures to find the algorithm that
   shows the lowest errors and compared the performance.  On testbed,
   the person had a wearable sensor to locate himself within the
   wireless sensor network.







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6.5.  Event detection

   Wireless sensor networks are used for various purposes.  The work
   [EX11] concentrates on detecting pipeline leakage on oil/gas and
   water transportation system.  It uses a pattern recognition algorithm
   and train the sensor network to detect and classify new traces of
   events like leakages.  Here, distributed sensor nodes cooperate to
   identify the leakage event and its size.  Though this work includes
   wireless sensor network, difficulties that comes from using
   unpredictable and vulnerable wireless channel were scarcely
   considered.

6.6.  Fault detection

   Various problems caused by vulnerable and fluctuating wireless
   channel together with cheap sensors make collected data from the sink
   node to be faulty.  Fault data should be detected and the cause of
   the event should be identified to quickly react and manage the
   system.  The work [EX12] developed a statistical approach to detect
   and identify faults in a wireless sensor network on the basis of
   machine learning technique.  It classified fault types into two
   categories: data fault and system fault.  Faults caused by degraded
   or malfunctioning sensor are classified as data fault and the other
   fault types caused by low battery, calibration, communication,
   connection failures are classified as system fault.  Authors of
   [EX13] also studied fault detection on similar environment by using
   machine learning technique.  Here, they classified fault types into
   four categories: offset fault, gain fault, stuck-at fault and out of
   bounds.

6.7.  Routing

   By adopting ML technique, multihop routing protocol can be more
   energy efficient.  The work [EX14] proposed ML-based clustering
   protocol to assign the sensor nodes to the nearest cluster in energy
   efficient way.  The work [EX15] also used ML technique on routing
   method in wireless sensor network.  The purpose of proposed routing
   scheme is to increase network lifetime and transmit information
   packages in shortest possible time.  These works insist that applying
   ML techniques on WSN is beneficial in terms of resource management.

7.  Concluding Remarks

   From this documents, we introduced advantages and difficulties in
   adopting machine learning techniques on wireless sensor network
   environment.  With machine learning algorithm, we can design a
   flexible system that learns and evolves continuously through
   experience.  However, it is difficult to obtain training data that



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   includes various scenarios.  Moreover, due to dynamicity and
   unpredictability of wireless multi hop network, it is hard to find
   any regular pattern from previously experienced data.  Besides, even
   if we have decision model, it is uncertain whether it will work
   properly or not in the future where the network environment
   continuously changing dynamically.

   With this reason, it is difficult to devise a system that detects,
   predicts and recovers from abnormal situation on wireless sensor
   networks on the basis of machine learning technique.  Nevertheless,
   as long as we have this system, managing and operating IoT network
   become easier establishing a foothold in IoT system.

   As concluding remarks, we would like to raise the issue of devising
   IoT network management systems that can detect, predict and recover
   from abnormal situation on wireless network environment.

   Until now, several works have adopted various machine learning
   techniques on diverse research areas including networking or
   communications over wireless channel.  These works have shown the
   potential that machine learning can be used together with the
   conventional approaches to improve system performance in efficient
   way.  However, it is still difficult to obtain training data that
   includes various scenarios on dynamic and unpredictable environment.
   Moreover, if we make a simple model due to difficulty in training,
   the application of constructed model is confined with very narrow
   limits.  Nevertheless, these works have strong contribution in terms
   of adopting ML techniques to improve the performance of corresponding
   system and paved the way for utilizing ML to construct model that is
   surrounded by dynamic and unpredictable environment.

   We expect many researchers actively discuss on this topic to devise
   resilient and automatic recovery systems on IoT network.

8.  IANA Considerations

   There are no IANA considerations related to this document.

9.  Security Considerations

   In this document, security is just considered as an example of
   abnormal situation (e.g.  DDoS).

10.  Acknowledgement

   This work was supported by Institute for Information and
   communications Technology Promotion(IITP) grant funded by the Korea
   government(MSIT) (No.2015-0-00557, Resilient/Fault-Tolerant Autonomic



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   Networking Based on Physicality, Relationship and Service Semantic of
   IoT Devices) and the MSIP(Ministry of Science, ICT and Future
   Planning), Korea, under the ITRC(Information Technology Research
   Center) support program (IITP-2017-2015-0-00378) supervised by the
   IITP(Institute for Information and communications Technology
   Promotion).

11.  References

11.1.  Normative References

   [IEEE802.15.4e]
              IEEE, "IEEE Std 802.15.4e-2012 (Amendment to IEEE Std
              802.15.4-2011) - IEEE Standard for Local and metropolitan
              area networks--Part 15.4: Low-Rate Wireless Personal Area
              Networks (LR-WPANs) Amendment 1: MAC sublayer", April
              2012, <https://standards.ieee.org/findstds/
              standard/802.15.4e-2012.html>.

   [RFC2119]  Bradner, S., "Key words for use in RFCs to Indicate
              Requirement Levels", BCP 14, RFC 2119,
              DOI 10.17487/RFC2119, March 1997,
              <https://www.rfc-editor.org/info/rfc2119>.

   [RFC6550]  Winter, T., Ed., Thubert, P., Ed., Brandt, A., Hui, J.,
              Kelsey, R., Levis, P., Pister, K., Struik, R., Vasseur,
              JP., and R. Alexander, "RPL: IPv6 Routing Protocol for
              Low-Power and Lossy Networks", RFC 6550,
              DOI 10.17487/RFC6550, March 2012,
              <https://www.rfc-editor.org/info/rfc6550>.

   [RFC7554]  Watteyne, T., Ed., Palattella, M., and L. Grieco, "Using
              IEEE 802.15.4e Time-Slotted Channel Hopping (TSCH) in the
              Internet of Things (IoT): Problem Statement", RFC 7554,
              DOI 10.17487/RFC7554, May 2015,
              <https://www.rfc-editor.org/info/rfc7554>.

11.2.  Informative References

   [EX1]      Arnau Mata Llenas, Janne Riihijarvi, Marina Petrova,
              "Performance Evaluation of Machine Learning Based Signal
              ClassificationUsing Statistical and Multiscale Entropy
              Features", May 2017,
              <http://ieeexplore.ieee.org/document/7925865/>.







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   [EX10]     Eduardo Carvalho, Bruno S. Faical, Geraldo P. R. Filho,
              Patricia A. Vargas, Jo Ueyama, Gustavo Pessin (ICIT'16),
              "Exploiting the use of machine learning in two different
              sensor networkarchitectures for indoor localization", May
              2016, <http://ieeexplore.ieee.org/document/7474826/ >.

   [EX11]     Sidra Rashid, Usman Akram, Saad Qaisar, Shoab Ahmed Khan,
              Emad Felemban (iThings, GreenCom, CPSCom), "Wireless
              Sensor Network for Distributed Event Detection Based on
              Machine Learning", March 2015, < >.

   [EX12]     Ehsan Ullah Warriach, Kenji Tei (IEEE 16th CSE), "Fault
              Detection in Wireless Sensor Networks: A Machine Learning
              Approach", March 2014, <
              http://ieeexplore.ieee.org/document/6755296/>.

   [EX13]     Salah Zidi, Tarek Moulahi, Bechir Alaya (IEEE Sensor
              Journal), "Fault Detection in Wireless Sensor Networks
              Through SVM Classifier", November 2017,
              <http://ieeexplore.ieee.org/document/8101556/ >.

   [EX14]     Feeza Khan; Saira Memon; Sana Hoor Jokhio (ICRAI'16),
              "Support vector machine based energy aware routing in
              wireless sensor networks", December 2016,
              <http://ieeexplore.ieee.org/document/7791218/ >.

   [EX15]     Kaveri Kadam, Navin Srivastava (ISPTS-1), "Application of
              machine learning (reinforcement learning) for routing in
              Wireless Sensor Networks (WSNs)", August 2012,
              <http://ieeexplore.ieee.org/document/6260967/ >.

   [EX2]      Pedro Amaral, Joao Dinis, Paulo Pinto, Luis Bernardo, Joao
              Tavares, Henrique S. Mamede(ICNP), "Machine Learning in
              Software Defined Networks: Data collection and traffic
              classification", December 2016,
              <http://ieeexplore.ieee.org/document/7785327/ >.

   [EX3]      Saurav Nanda, Faheem Zafari, Casimer DeCusatis, Eric
              Wedaa, Baijian Yang (WCNC), "Predicting network attack
              patterns in SDN using machine learning approach", May
              2017, < http://ieeexplore.ieee.org/document/7919493/ >.

   [EX4]      Junchen Jiang, Vyas Sekar, Henry Milner, Davis Shepherd,
              Ion Stoica (NSDI'16), "CFA: A Practical Prediction System
              for Video QoE Optimization", March 2016,
              <https://www.usenix.org/node/194919 >.





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   [EX5]      Hongzi Mao, Ravi Netravali (ACM SIGCOMM'17), "Neural
              Adaptive Video Streaming with Pensieve", August 2017,
              <https://dl.acm.org/citation.cfm?id=3098843 >.

   [EX6]      S M Azharul Karim, John J Prevost (Computing Conference),
              "A machine learning based approach to mobile cloud
              offloading", January 2018,
              <http://ieeexplore.ieee.org/document/8252168/ >.

   [EX7]      Su Yi, Hao Wang, Jun Tian, Wenqian Xue, Leifei Wang,
              Xiaojing Fan, Ryuichi Matsukura (VTC Spring), "Machine
              Learning Based Channel Error Diagnostics in Wireless
              Sensor Networks", November 2017,
              <http://ieeexplore.ieee.org/document/8108196/ >.

   [EX8]      Vinicius F. Silva, Daniel F. Macedo, Jesse L. Leoni
              (Brazilian Symposium on Computer Networks and Distributed
              Systems), "Spectrum Decision in Wireless Sensor Networks
              Employing Machine Learning", October 2014,
              <http://ieeexplore.ieee.org/document/6927158/ >.

   [EX9]      Hugo Martins, Fabio Januario, Luis Palma, Alberto Cardoso,
              Paulo Gil (IECON 2015), "A machine learning technique in a
              multi-agent framework for online outliers detection in
              Wireless Sensor Networks", January 2016,
              <http://ieeexplore.ieee.org/document/7392180/>.

Authors' Addresses

   Seohyang Kim
   Seoul National University

   Email: shkim@popeye.snu.ac.kr


   Nguyen Duc Lam
   Seoul National University

   Email: lam@popeye.snu.ac.kr


   Chong-Kwon Kim
   Seoul National University

   Email: ckim@snu.ac.kr






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