Industrial Networks and Intelligent Systems 6th EAI International Conference INISCOM 2020 Hanoi Vietnam August 27 28 2020 Proceedings Nguyen-Son Vo
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Nguyen-Son Vo
Van-Phuc Hoang (Eds.)
334
Industrial Networks
and Intelligent Systems
6th EAI International Conference, INISCOM 2020
Hanoi, Vietnam, August 27–28, 2020
Proceedings
123
Lecture Notes of the Institute
for Computer Sciences, Social Informatics
and Telecommunications Engineering 334
Industrial Networks
and Intelligent Systems
6th EAI International Conference, INISCOM 2020
Hanoi, Vietnam, August 27–28, 2020
Proceedings
123
Editors
Nguyen-Son Vo Van-Phuc Hoang
Faculty of Electrical and Electronics Le Quy Don Technical University
Engineering Hanoi, Vietnam
Duy Tan University
Da Nang, Vietnam
© ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2020
This work is subject to copyright. All rights are reserved by the Publisher, whether the whole or part of the
material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation,
broadcasting, reproduction on microfilms or in any other physical way, and transmission or information
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The registered company address is: Gewerbestrasse 11, 6330 Cham, Switzerland
Preface
We are delighted to introduce the proceedings of the 2020 European Alliance for
Innovation (EAI) International Conference on Industrial Networks and Intelligent
Systems (INISCOM 2020). This conference has brought together researchers, devel-
opers, and practitioners from around the world who are leveraging and developing
industrial networks and intelligent systems. The theme of INISCOM 2020 was
“Computing, Telecommunications Technologies and Applications of 5G-IoT, AI and
Cyber-Security to Improve Citizens’ Lives.”
The technical program of INISCOM 2020 consisted of 25 full papers in oral pre-
sentation sessions at the main conference tracks. The conference tracks were: Track 1 –
Telecommunications Systems and Networks; Track 2 – Hardware, Software, and
Application Designs; Track 3 – Information Processing and Data Analysis; Track 4 –
Industrial Networks and Intelligent Systems; and Track 5 – Security and Privacy. Aside
from the high-quality technical paper presentations, the technical program also featured
one keynote speech. The keynote speaker was Prof. Dong-Seong Kim, from Kumoh
National Institute of Technology, South Korea.
Coordination with the steering chairs, Prof. Imrich Chlamtac, Dr. Vien Ngo, and
Dr. Ta Chi Hieu, was essential for the success of the conference. We sincerely
appreciate their constant support and guidance. It was also a great pleasure to work
with such an excellent Organizing Committee team and we thank them for their hard
work in organizing and supporting the conference. In particular, the Technical Program
Committee (TPC), led by our TPC co-chairs, Dr. Nguyen-Son Vo, Dr. Quoc Tuan
Vien, and Prof. Trung Q. Duong, who completed the peer-review process of technical
papers and made a high-quality technical program. We are also grateful to conference
manager Natasha Onofrei for the support and all the authors who submitted their papers
to INISCOM 2020.
We strongly believe that INISCOM provides a good forum for all researcher,
developers, and practitioners to discuss all science and technology aspects that are
relevant to industrial networks and intelligent systems. We also expect that the future
INISCOM will be as successful and stimulating as indicated by the contributions
presented in this volume.
Steering Committee
Imrich Chlamtac University of Trento, Italy
Vien Ngo Queen’s University Belfast, UK
Ta Chi Hieu Le Quy Don Technical University, Vietnam
Organizing Committee
General Chair
Van-Phuc Hoang Le Quy Don Technical University, Vietnam
General Co-chairs
Cong-Kha Pham The University of Electro-Communications, Japan
Xuan-Nam Tran Le Quy Don Technical University, Vietnam
Local Chairs
Tran Cong Manh Le Quy Don Technical University, Vietnam
Van-Trung Nguyen Le Quy Don Technical University, Vietnam
Do Thanh Quan Le Quy Don Technical University, Vietnam
Workshops Chairs
Koichiro Ishibashi The University of Electro-Communications, Japan
Sylvain Guilley Télécom Paris, France
Xuan-Tu Tran VNU University of Engineering and Technology,
Vietnam
Publications Chairs
Quang Kien Trinh Le Quy Don Technical University, Vietnam
Mai Ngoc Anh Le Quy Don Technical University, Vietnam
Tomoyuki Ohkubo Advanced Institute of Industrial Technology, Japan
Web Chairs
Trong-Thuc Hoang The University of Electro-Communications, Japan
Vu Hoang Gia Le Quy Don Technical University, Vietnam
Panels Chairs
Mai-Khanh Nguyen Ngoc The University of Tokyo, Japan
Le Chung Tran University of Wollongong, Australia
Berk Canberk Istanbul Technical University, Turkey
Demos Chairs
Zoran Hadzi-Velkov Ss. Cyril and Methodius University, Macedonia
Van Sang Doan Kumoh National Institute of Technology, South Korea
Quang Nguyen The Le Quy Don Technical University, Vietnam
Tutorials Chairs
Jean-Luc Danger Télécom Paris, France
Duc Anh Le Center for Open Data in the Humanities, Tokyo, Japan
Tuan Le Middlesex University, UK
c ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2020
Published by Springer Nature Switzerland AG 2020. All Rights Reserved
N.-S. Vo and V.-P. Hoang (Eds.): INISCOM 2020, LNICST 334, pp. 3–18, 2020.
https://doi.org/10.1007/978-3-030-63083-6_1
4 M. R. Erol and B. Canberk
1 Introduction
Low Altitude Platforms (LAVs), also called drones, are rapidly developing and
becoming extremely useful in a variety of areas, from civil applications to mil-
itary missions due to the structural advantages and moving flexibility on air.
Surveillance, search and rescue missions, delivery of goods, construction, and
natural disaster monitoring are most standing out applications of drones [1].
The achievement of these applications depends on improvements in network
performance. Hence, there are a significant number of challenges in aerial net-
works to increase network performance [2–4]. In this aspect, to provide reliable,
efficient, and stable drone to drone networks, monitoring resources of aerial sys-
tems is a crucial mission because that minimizes the cost of maintenance of data
flow. Thus, we focused on the topic of resource monitoring, which is channel
utilization for the drone to drone (D2D) networks.
D2D network complexity is dramatically expanding in terms of services and
topology, which causes challenging network management problems on network
resources. Hence, the diagnosing channel utilization as resource monitoring takes
crucial place in D2D networks. As mentioned in [5], monitoring characteristics of
wireless networks is critical to many management tasks such as fault diagnosis
and resource management. Also, in that work, monitoring types are introduced
as PHY and MAC behaviors. In this aspect, we focus on the discovery of channel
utilization for D2D networks in the field of smart city applications. It is known
that smart cities enhance life quality with intelligent things. Therefore, drone
collaboration and D2D networks play a vital role in supporting a lot of smart
city applications such as D2D communication and network resource management
[6]. Thus, in this work, we work on monitoring of channel utilization as resource
management of D2D networks in smart cities.
There exist many studies in the recent literature about evaluating channel uti-
lization in many ways for D2D networks. In [7], MIT LL has developed a data col-
lection and visualization framework to monitor and analyze the performance of
a high-capacity backbone (HCB) network, which is an example of Mobile AdHoc
Networks (MANETs). In that work, the monitoring implemented at various lay-
ers of the OSI stack. Furthermore, the channel utilization can be measured with
PHY(physical) layer methods. In [8], with the proposed Channel Quality Indica-
tor (CQI) feedback scheme, each cellular-UAV can evaluate link quality by the
reference signal. Also, in [9], Negative Acknowledgement (NACK)-related regu-
lar feedback system is considered. In this work, if Signal to Interference and Noise
Ratio (SINR) is less than the threshold of a special Modulation and Coding Scheme
(MCS), the user transmits NACK back to the base station. Moreover, [10] provides
novel channel feedback schemes that solve the problem of finding the right feed-
back mechanism to convey channel information. With this scheme, it is possible
to measure channel quality for wireless networks.
None of these works presented on PHY layer are accurate and reliable mea-
surement methods for link quality because PHY layer can be affected by other
signals or signal cannot reach the destination due to shadowing effect and
mobility of UAVs. Also, it is impossible to obtain any information about chan-
Intelligent Channel Utilization Discovery in D2D Networks for Smart Cities 5
nel quality for the base station, if no NACK is sent. The flow management
and logical connection are necessary for more accurate and reliable monitoring
of channel utilization; however, these works do not provide these MAC layer
properties. Shortly, to be sure about there is a communication in the channel,
the MAC layer protocol based approach is needed. Moreover, they have a very
complicated implementation of monitoring channel utilization.
Consequently, keeping these studies in mind, we propose a novel monitoring
approach of D2D network channel utilization and network traffic type in the
field where drones are actively communicating with the IEEE 802.11 protocol.
Also, our model works on the MAC layer with flow management and logical con-
nection advantages. Even though the most preferred way of calculating channel
utilization is the sensing channel always on the PHY layer, we present a protocol-
based method that uses the NAV vector, which is originated from IEEE 802.11
RTS/CTS enabled protocol. In our approach, we calculate channel utilization
using the duration field of the frames, which determines the NAV vector size.
Furthermore, only one participant can communicate in the channel with IEEE
802.11 RTS/CTS (see Fig. 1); thus, calculating the channel utilization with our
method becomes applicable. With our model, we prevent the sensing channel on
the PHY layer, which is not an optimal approach due to the power consumption
of the searching drone and noise in the channel. The MAC protocol-based system
we offered shows there exists absolutely communication in the MAC layer, which
6 M. R. Erol and B. Canberk
is a more exact sensing way rather than blindly sensing the channel. Further-
more, we propose a visualization method using the Voronoi diagram in our work
to show channel utilization in the area. Due to a Voronoi map that can be used
to find the largest empty circle amid a collection of points, the drone environ-
ment where drones are communicating can be represented this method in a more
precise way. With this method, drone groups can be visualized more centralized
manner within regions because we use one of the unsupervised machine learning
algorithms called the k-means clustering method according to drone coordinates.
This algorithm clusters drone coordinates and helps to create Voronoi regions.
Shortly, the main contributions of this paper include the following:
our proposed model. Finally, we conclude the paper by summarizing the achieve-
ments in Sect. 6.
2 Network Architecture
The network topology for our model consists of n drones with one searching
drone. These drones are communicating with the IEEE 802.11 wireless net-
working protocol with RTS/CTS (Request To Send/Clear To Send) mechanism.
The RTS/CTS mechanism is created for avoiding the hidden terminal problem
in wireless networks and allows only one pair to communicate in the channel.
Drones can fly at different heights with the specific moving pattern. However, the
searching drone always flies at the pre-determined height. All drones repeat their
flying patterns after reaching destination coordinates. Furthermore, we assume
that all drones are completed authentication stage for wireless communication.
Hence, they communicate directly with each other on the same channel without
authentication messages. Moreover, the channel is always busy, and the frame
size is randomly generated in the network. We represent the whole network
architecture and the component models of the searching drone in Fig. 2.
3 System Model
We divide the proposed system model into two coherent sub-layers titled Search-
ing & Finding Sub-layer and Functional Sub-layer. Searching & Finding Sub-
layer is responsible for searching on the area with a specific movement pattern
and gathering information from D2D communication. Moreover, we dedicate the
Functional Sub-layer to process information belongs to the Searching & Finding
Sub-layer. Each of the sub-layers additionally owns some modules. Searching
& Finding Sub-layer has two modules entitled Sensing and Data Classification;
furthermore, the Functional Sub-layer has two modules entitled Calculation and
Visualization. In Fig. 3, we represent the entire system model and the associa-
tions between its segments.
Sensing Module. This module handles the movement pattern of the search-
ing drone and operations of collecting data from the channel with sensing. This
information contains coordinates of communicating drones, source and destina-
tion address of the frame, Duration ID in the frame to keep Network Allocation
Vector (NAV) timer and frame types such as RTS, CTS, DATA or ACK. After
the sensing channel for gathering this information, this module transfers col-
lected data to the next layer called Data Classification.
8 M. R. Erol and B. Canberk
Data Classification Module. This module exists at the end of the Searching
& Finding Sub-layer. The transferred data from Sensing Module is classified here
to match that data with Drone Data sections. The coming information from the
below layer is assigned to Drone Data if there exists. In the case of a new drone
whose information does not exist in the Drone Data section, is discovered, then
the new part is created in the Data Classification Module. All other information
about this drone will be assigned this section in the future data gathering. This
module’s main aim is grouping collected data with corresponding drones to make
it easier for calculations in the future. After all these operations, the classified
data is transferred to the upper layer named Calculation Module.
The communication sequence diagram of the area can be seen in the below part
of the figure. In our model, we concentrate on the repeated cycle of RTS/CTS
mechanism denoted as tslottime and natural outcome of IEEE 802.11 RTS/CTS
protocol, known as sequentially repeated cycles like in the Fig. 4b. It can be
understood that tslottime can be calculated focusing on starting time with the
RTS frame after the Backoff timer until the ACK frame is transferred, which
showed in Fig. 4b. In this interval, only one drone pair can transmit, and the
total transmission time for data always has the same sub-time intervals except
for the data frame size. Other time intervals like DIFS duration denoted as tDIF S
are constant values determined by the protocol as in work [12]. Hence, tslottime
can be denoted as following:
where tBackof f is a random value between [1, CW], and other time intervals are
the part of the IEEE 802.11 RTS/CTS protocol. The CW(contention window) is
an integer between CWmin = 32 and CWmax = 1024. In our model, we consider
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