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
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Copyright Notice
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document authors. All rights reserved.
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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
Kim, et al. Expires July 30, 2018 [Page 14]
