=Paper=
{{Paper
|id=Vol-2853/paper38
|storemode=property
|title=Architecture of the Simulator of the Personal Local Wireless Networks: Examples of implementation
|pdfUrl=https://ceur-ws.org/Vol-2853/paper38.pdf
|volume=Vol-2853
|authors=Oleksandr Tymchenko,Bohdana Havrysh,Mariya Nazarkevych,Oleksandr O. Tymchenko,Orest Khamula
|dblpUrl=https://dblp.org/rec/conf/intelitsis/TymchenkoHNTK21
}}
==Architecture of the Simulator of the Personal Local Wireless Networks: Examples of implementation==
https://ceur-ws.org/Vol-2853/paper38.pdf
Architecture of the Simulator of the Personal Local Wireless
Networks: Examples of implementation
Oleksandr Tymchenkoa, Bohdana Havrysh,b, Mariya Nazarkevychb, Oleksandr O.
Tymchenkoc and Orest Khamula c
a
University of Warmia and Mazury Olsztyn, Poland
b
Lviv Polytechnic National University, Lviv, Ukraine
c
Ukrainian Academy of Printing, Lviv, Ukraine
Abstract
The use of existing wireless network simulators requires the large number of preset
simulation settings and node settings. In the case of wireless sensor networks, a large number
of node interaction protocols, methods for constructing a communication graph and ensuring
a given network connectivity are also used. Therefore, it is advisable in the simulation to
abstract to a certain level and not to consider the physical and hardware levels of network
nodes, which will reduce the time spent on the study of network parameters. The architecture
and graphical interface of the developed real-time simulator "SNOW" for research of models
and methods of local networks wireless access construction are considered in the work. The
performance indicators of the simulator for modeling the topology control, construction of
the communication graph, its visualization and determination of the power consumption
parameters of the K-NEIGH type sensor network for variants with sequential and parallel
execution of simulation steps are given.
Keywords 1
Sensor network, construction methods, simulator, simulator architecture, communication
graph
1. Introduction
One of the main tasks that developers of sensor and specialized networks face is to ensure the
scalability and the necessary parameters of reliability, durability and network performance. This is
difficult to achieve without the prior study and analysis of the proposed algorithm characteristics.
Research and evaluation of algorithms and protocols for BSM – the wireless sensor networks can
be done in three ways [1, 2]:
• analytical method – the most difficult way due to the large number of influencing factors;
• modeling on real equipment – the most expensive way, you need ready-made equipment and
high time costs when conducting experiments;
• computer simulation - the best way due to the development of computing capabilities. There
are several ways to model and simulate a complex system.
Including:
• creation and further simulation of a complete model of the system, all its components and
connections between them, together with the negative phenomena that may occur during the
operation of a real network;
IntelITSIS’2021: 2nd International Workshop on Intelligent Information Technologies and Systems of Information Security, March 24–26,
2021, Khmelnytskyi, Ukraine
EMAIL: olexandr.tymchenko@uwm.edu.pl (O. Tymchenko); dana.havrysh@gmail.com (B. Havrysh); mar.nazarkevych@gmail.com (M.
Nazarkevych); olexandr.tymch@gmail.com (O. O. Tymchenko); khamula@gmail.com (O. Khamula)
ORCID: 0000-0001-6315-9375 (O. Tymchenko); 0000-0003-3213-9747 (B. Havrysh); 0000-0002-6528-9867 (M. Nazarkevych);
0000-0003-2774-2138 (O. O. Tymchenko); 0000-0001-7596-0813 (O. Khamula)
© 2021 Copyright for this paper by its authors.
Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
CEUR Workshop Proceedings (CEUR-WS.org)
• creation and subsequent simulation of a model containing stochastic elements. It is assumed
to use random variables to model the consequences of the negative phenomenon, rather than the
negative phenomenon itself (path losses, packet delays, etc).
The first option allows you to simulate the behavior of each of the network nodes in detail over
time, as well as to simulate the movement of packets in detail and their routing.
The second option is optimal for obtaining some general characteristics of the network, such as the
connectivity of the communication graph, the average number of neighbors for each node. This
approach is often used to model topology control methods. To determine the general characteristics of
a network consisting of a large number of nodes (typical for wireless sensor networks), the second
method of modeling is used. Although both the first and the second methods will give the same result
for a large number of elements. The fact is that while using the full model, the modeling time
increases exponentially with increasing number of elements. It is hundreds of times higher than the
time cost of statistical modeling, which has little dependence on changes in the number of network
elements by a thousand elements or more [3-5].
2. Related works
In order to select tools for modeling methods for constructing sensor networks, an assessment of
existing software products was conducted. Figure 1 shows a diagram that allows to compare the
means for simulation on the implemented level abstraction (y-axis) and the maximum possible size of
the simulated network (x-axis).
Рівень
abstract
абстракції
level
Абстрактні
Abstract
алгоритми
algorithms
Atarraya
Високорівневі
High-level Shawn
протоколи
protocols SNOW
Протоколи GloMoSim
низького
Low-level OmNeT++
protocols
рівня
NS-2
SENSE
Апаратний
HArdware
TOSSIM
Фізичний
Phisical
Кількість
number
вузлів
of nodes
103 104 105 106
Figure 1: Existing means of CM simulation
In [6, 8], the NS-2 simulator is used to model the functionality of sensor fences for personal
access. NS-2 (Network Simulator version 2) is a time-discrete simulator developed at the University
of California, Berkeley. NS-2 allows local networks and WAN modeling and supports the detailed
modeling of TCP and UDP protocols, routing in networks with both wired and wireless access.
The NS development began in 1989 is constantly being improved. Purpose of NS is education and
research in network technologies.
The simulator NS-2 has a basic model that implements the IEEE 802.15.4 standard. For ad-hoc
hemming in NS-2, routing protocols AODV, DSDV, DSR and TORA are adopted. They provide an
additional support for securing the flexibility of robots with mobile universities. At the same time,
only the routing protocols can be used in the NS-2, as it is not up to the point to break the special
features of mouthless sensor fences (Figure 2).
Simulation programma: Simulation results:
• Otcl (NS-2) script NS • NAM (NS-2)
• Python or C++ (NS-3) visualization
script Octl (NS-2) interpretator • Pcap (NS-3) trace
files
Library C++:
• Event scheduling
objects
• Network component
objects
• Additional objects
Figure 2: The basic structure of NS-2 and NS-3 simulators
The NS-3 simulator, which is described in [10-13] is much better for stimulating wireless sensor
networks. NS-3 (version 3) is a completely excellent simulator based on NS-2 [7-9]. The main
difference from NS-2 is the absence of OTcl (short for MIT Object Tcl), the use of programming
exclusively in C ++ and Python (Figure 2).
NS uses two programming languages because it has two types of operations to perform. On the
one hand, detailed data exchange protocol simulation requires a programming language that can
efficiently manipulate bytes, packet headers, and at the same time must enable the implementation of
algorithms that work with large data sets. This task requires a high execution speed. Ensuring low
time costs of the development cycle (running the simulation, finding an error in the code, error
correction, recompilation, re-simulation) are less important.
On the other hand, a large amount of research in the field of network technology requires minor
changes in parameters, changes in network configuration or a quick review of possible scenarios. In
this case, the above-mentioned iterative development process is more important, and the speed of
execution does not play a role. Note that in case of both static and mobile sensor network nodes, it is
necessary to investigate not only the network configuration, but also the ways of data transmission,
which is quite difficult in this simulator due to the limited graphical interface.
Shawn simulator described in [14] is a program-simulator of discrete events for large wireless
sensor networks modeling algorithms. Shawn does not provide the same level of modeling detail as,
for example, NS-2, but with the correct construction of the model it gives a convergent result. The
Shawn simulator has a very wide range of possibilities for statistical modeling, however does not
allow modeling of a phenomenon, but simulates the impact of such. For example, you can simulate
the interference of individual packets using a signal propagation model and abstractly set the channel
losses proportional to the number of nodes in the transmitter area.
Also, Shawn simulator requires writing your own processors for wireless network nodes, different
models for messaging, and more. The modular architecture of the simulator allows additions that are
standard for simulators of such types. An alternative approach to the simulation process itself
provides high performance.
TOSSIM (TinyOS) – a system specifically designed for sensor networks [14, 15]. It has a software
model component described in nesC. TinyOS is not an operating system in the traditional sense. It is a
software environment for embedded systems and has a set of components that allow you to create
simulation models for a specific application, such as TOSSIM. The TOSSIM simulator can simulate
networks of up to several thousand nodes, and by analyzing them, predict the behavior of the network
with high accuracy. By modeling networks with possible interferences and errors, the simulator
creates a simple but at the same time effective model of various interactions of nodes in the network.
Describing a low-power model of TinyOS devices, it simulates the behavior of the sensor node with a
high probability, describing its characteristics and conducting a large number of experiments. For the
convenience of developers, TOSSIM supports a graphical user interface, providing detailed
visualization and reproduction of the actions of the running simulation model, but does not reproduce
the communication graph tied to the environment.
There are also other publicly available network simulators, such as JavaSim, SSFNet, Glomosim
and Qualnet, in which the developers tried to solve the shortcomings of these systems. JavaSim
developers realized the disadvantage of using object-oriented system design and tried to build a
component-oriented architecture. However, the effectiveness of the simulation was limited by the
choice of the Java simulation language [15, 17].
The SENSE simulator is designed as an efficient and powerful sensor network simulator [15, 16].
It uses a component port model, which frees simulations from the interdependence that is common in
object-oriented architecture. The component port model makes simulation models extensible – a new
component can replace an old one if they have compatible interfaces, and advanced users have the
ability to develop new simulation mechanisms. Removing the interdependence between models also
promotes reusability. A component designed for one simulation can be used in another if it meets the
requirements of the latter in terms of interface and semantics. In SENSE, there is a level of reusability
that has been made possible by the widespread use of the C ++ template: a component is usually
declared as a template class so that it can process other types of data. However, SENSE can only use
the parallel simulation mechanism for compatible components. Therefore, only in the case of
sequential simulation can each component in the model repository be reused.
3. Architecture and of the Simulator of the Personal Local Wireless Networks
The analysis of existing software products for simulation of construction methods and control of
sensor topology and actuator networks of wireless access allowed to reveal advantages, lacks and
means of improvement. This led to the development of the "SNOW" simulator (Sensor Network Over
Wireless). The following requirements have been identified as the main ones that will provide the
necessary environment for conducting experiments on the construction and study of sensor networks:
• support of inhomogeneous network structure;
• the possibility of independent description and simultaneous use in experiments of different
components of the network model;
• the maximum approximation of the node behavior description and the protocol to the
description of them in the node software;
• the ability to describe arbitrary methods of construction and data exchange protocols in a
wireless network;
• simplicity of describing the behavior of network nodes;
• ability to expand the simulator by adding new models;
• the ability to change the level of detail for each of the models;
• the ability to remove arbitrary characteristics of the network or individual nodes in real time;
• powerful tools for visualization of results: communication graphs, graphs;
• the ability to save the initial parameters and results of experiments;
• the ability to conduct a series of experiments with different settings;
• no restrictions on the size of the studied network;
• high speed.
The "SNOW" program is a simulator of discrete events in time, designed to study:
• network formation and reconfiguration processes;
• topology control;
• routing methods in the IS;
• distributed algorithms for the operation of nodes and wireless communication protocols
related to the channel, network, transport and session layers of OSI;
• algorithms and methods of building IP, as part of a local area network with wireless access.
The simulator program provides:
• graphical shell to adjust the parameters of the experiment;
• save configuration files and host locations to play the experiment;
• experiment results display in text and graphical formats (graphs of characteristics in real time,
map of nodes, coverage areas, communication graph).
The simulator's ability to scale experiments is limited only by the hardware characteristics of the
PC on which the simulation is performed and the simulation time itself. As for the functional
extension, thanks to the modular architecture it is possible to add any new model for a particular
component.
3.1 Simulator architecture
In the article [18] a general description of the simulator architecture is given. There is briefly
described and explained the relationships between the components of the network model that are
implemented in the developed simulator and the functionality of the simulator kernel.
[18] provides a general description of the simulator architecture, briefly describes the relationships
between the components of the network model implemented in the developed simulator, explains the
functionality of the simulator core. This article describes the structure of classes in the simulator in
more detail and explains the chosen decomposition. In the diagrams mentioned in Figure3 and Figure
4 the main program classes and subclasses of the developed simulator are shown in accordance with:
1. implementation of different types of IP devices for the wireless sensor networks
("Specification of Control Types")
2. implementation of their behavior, ie the exchange of messages in the wireless sensor
networks ("Specification of Stages of Work").
The architecture constructed in this way allows to obtain the necessary flexibility in the description
of all components of the model. It is also possible to study arbitrary methods of topology control for
the network (see “Model of Construction Method” in Figure 4) with simultaneous support of
inhomogeneous devices (see “Model of Network Node” in Figure 3).
Network node model (1)
1. Radio device
2. Network device
3. Power model
4. Control element
Radio device specification (1.1) Control model (1.4)
1. Send a message environtment 1. Type of control
2. Process the message 2. Current and next state
environtment
Specification of control 3. Transmitter power Transmitter power
element types (1.4.1) 4. Massage buffer specification (1.1.3)
5. Current and next reciver states
1. Simple 1. Current
2. Complex 2. Minimal
Network device specification (1.2)
3. Super 3. Maximum
1. Identifier
2. Properties (2.4) Specification of the states of
3. Stages of work (2.5) the control element (1.4.2)
Power model (1.3) 1. Not rannsng
2. Sleeping
1. Battery charge 3. Working
2. Costs 4. Stopped
Figure 3: Control element – the network node model and its components.
“Network Node Model”, in particular, can be described arbitrary un combinations of the following
components:
• Radio Device – sending and receiving data from the air, allows to adjust the power of the
transmitter and the sensitivity of the receiver and to control its activation;
• Network Device – network identification; storing, updating and accumulating information
about neighbors, routes, etc;
• Power model – control of battery charge, data transmission and reception costs;
• Control Element – implements the machine states of the node operation and the transition to
them in accordance with the current method of the nod work; the basic set includes four states of
operation of the node; Each of the components described above can be supplemented with new
properties and allows to make changes to existing ones [19, 20].
For the “Topology Control Model” we can describe arbitrary:
• types and structure of messages;
• protocol states and functionality of each of the states;
• incoming message handlers;
• communication radius assignment function.
Model of construction method (2)
1. Message types
2. Message structure
3. Protocol conditions
4. Properties
5. Stage of work
6. Incoming message processing
7. Communication radius assignment function
Specification of protocol Specification of protocol states
stages (2.1) (2.3)
Specification of work
1. Internal condition 1. Internal condition
stages (2.5)
2. Simulator protocol status 2. Simulator protocol status
1. Initialization
2. Functioning Specification of the message Specification of the construction
3. State switching structure (2.2) method properties (2.4)
1. Sender 1. Message counters (by simulator
2. Transmitted power message types)
3. Received power 2. List of neighbors
4. Simulator package type 3. List of routes
5. (Internal variables) 4. Current and next internal
protocol states
Figure 4: Behavior description – a model of the construction method and its components.
Figure 5 shows the relationships between the main classes of the program. The figure does not
show the graphics subsystem.
{} SIM4
{} SIM4.NetworkComponents
Network {} SIM4.NetworkComponents.Nodes
Environment {} SIM4.NetworkComponents.Interfases
{} SIM4.NetworkComponents.TopologyControls
Figure 5: The main classes of the simulator.
Figure 6 – Figure 8 shows the interfaces described for the components of the network model as
part of the simulator.
{} SIM4.NetworkComponents
{} SIM4.NetworkComponents.Nodes
SuperNode RegularNode ExtendedNode
Figure 6: Types of nodes.
{} SIM4.NetworkComponents
{} SIM4.NetworkComponents.Interfases
RealTimeClockMethods IRealTimeClock
NetworkDeviceType IRadioDevise
MessageToEnvirontment IProcessor
ITopologyControlMethods IPacket
ISimulatorTopologyControl IBatteryDevice
ISimulatorPacket INetworkDevice
ITopologyControlProperties
Figure 7: Simulator interfaces.
AssignNodes {} SIM4.NetworkComponents
Environment
Network PassMessageToNodesInRange
ProcessMessageFromNode
Figure 8: Environment model and its connections.
3.2 Simulator graphical interface
The Table 1 shows an example of setting parameters for sensor networks simulation.
Table 1
Parameters for sensor networks simulation
Parameter Meaning
Number of nodes 100
Number of monitors 1
Minimum radius, maximum radius, step 4, 30, 2
Launch devices Simultaneous
Target logical connectivity 3
Initial energy, transmission costs, reception
costs per cycle 30000, 2, 3
Number of retransmissions 5
The size of the placement area 100×100
Type of accommodation Random (same for both experiments)
Device types "Super", "Simple"
The simulator interface consists of the following windows:
1. Main window (Figure 9)
2. The window for generating the number and type of nodes (Figure 10)
3. Node settings change window (Figure 11)
4. Communication graph view window (Figure 12)
Main window
Figure 9: Main window:
1. display elements of calculations and simulation results;
2. the main means of setting up and controlling the simulation (left-right): network generation,
experiment setup, start the simulation, display the communication graph;
3. save the current and load the existing configuration of the location.
Node generation window
Figure 10: Node generation window:
1. distribution of nodes number by type;
2. the size of the location;
3. distribution of nodes on the plane.
Settings change window
Figure 11: Settings change window:
1. energy consumption parameters;
2. target connectivity;
3. coverage area parameters for the node;
4. type of start of knots;
5. retransmission parameters;
6. change the distribution of nodes by type;
7. choice of construction method.
Simulation window
Figure 12: Node generation window:
1. tabs of the removed characteristics;
2. area of real-time graphs characteristics display;
3. the progress of the experiment;
4. time spent on the simulation;
5. simulation control.
Communication graph view window
Figure 13: Node generation window:
1. scaling control;
2. display area of communication graphs;
3. display control.
4. Experimental results
The main results of the development and application of the SNOW simulator will be demonstrated
by examples of the study of real sensor networks.
Simulation time for the K-NEIGH network topology method of construction and control
Table 2 shows the duration of 100 simulation steps for experiments with different numbers of
nodes. The size of the side of the nodes square area was changed to maintain the same density of the
location. The side of the square region is calculated by the formula:
|N |
r= (1)
q
where N is the set of nodes, r is the side of the nodes square area, q is the density of the nodes; the
density is equal to 0.01; r is rounded to the nearest larger number that is a multiple of 10.
We believe that devices in the network can be of two types: "Simple Node" (limited autonomous
power supply) and "Super Node" (unlimited power).
Instead of assigning the same communication radius to all nodes, we use the function of assigning
the communication radius: a gradual increase in the communication radius from the minimum value
until we achieve the desired connectivity of the node. For the first method of construction, it is
physical, and for the second – logical connectivity of knot.
A number of experiments were performed for networks of different sizes, which aimed to
determine the simulator performance on the example of the K-NEIGH topology control method.
The K-NEIGH (K-Neighbors) method involves building a network based on a certain minimum
required number for each node neighbors, which ensures the connectivity of the communication
graph.
Experiments were performed with off (sequential simulation), partially on and full on
parallelization of processes in the simulator core. Table 2 shows the obtained data.
Table 2
Simulation time in different modes of the simulator
Network size R, units T, sequential, ms T, partially T, parallel, ms
parallel, ms
100 100 916 1389 914
200 140 1978 2347 1663
500 220 8343 7647 5073
800 280 20047 16726 11886
1000 320 30350 22228 17243
1200 350 42924 30352 24575
1500 390 67447 44069 39762
1700 410 88466 55174 47393
2000 450 117378 74771 66867
2500 500 186351 110318 98441
3000 550 264805 155691 142234
Sequential simulation here means the use of sequential cycles when performing both state
machines of all nodes and when processing (redirecting) messages by the environment. With a partial
parallelization, message processing is carried out by the medium and the parallel cycle.
Improving the speed of the simulator – work in parallel mode
One way to increase the productivity is more sparse performance. In the conducted experiments,
the removal of all characteristics occurred every 10 steps of the simulation with a total number of
steps equal to 200. More frequent removal of characteristics provides more accurate intermediate
results and, accordingly, smoothed graphs of the obtained characteristics over time.
Also note, that the speed experiments were performed in the program debugging mode;
eliminating the collection of debugging information saves up to 30% of the time. To test this
assumption, experiments were repeated for the cases listed in Table 2 color. It was confirmed that
when you run the program in normal operation, the gain ranges from 26.6% to 33.8%.
Time costs for experiments in different modes are shown in Figure14.
Figure 14: The results of experiments to determine the effect of parallel computing of the simulator
performance.
Scaling capabilities and graphical interface of simulation results
As you can see from the Figure14, the use of parallel calculations in the simulator allows you to
significantly reduce the time spent on the simulation, while achieving the same results.
Figure 15: Communication graph after the simulation of the K-NEIGH method in a 3000 elements
network.
This effect increases as the size of the network increases. N the Fig. 15, there is given an example
of a communication graph for a 3000 elements network by the K-NEIGH construction method, which
is obtained by means of the "SNOW" simulator.
5. Conclusion
The wireless network simulator building method adapted for the research on the process of
building, forming and reconfiguring the network, topology control, routing methods and maintaining
wireless network connectivity, is considered. The simulator allows to explore the distributed
algorithms of the node operation and wireless sensor network protocols related to the channel,
network, transport and session levels of OSI.
The simulator implements all the requirements (which are determined from the analysis of the
advantages and disadvantages of existing simulators and described in section 3), which provides the
necessary environment for experiments to build and study sensor networks.
The structure of the simulator program in which the network model is implemented is described in
details. Components, classes, interface, environment model and simulator implementation are
described. Thanks to the modular architecture, it is possible to use any new model for a particular
component. There is a graphical shell for the study parameters adjustment and text displaying in the
textual and graphical form (graphs of characteristics in real time, location map of nodes, coverage
areas, communication graph).
Examples of work with the simulator are given. One of the simplest ways to build a wireless
network is taken as an example. Methods of optimization of the described method of network
construction are proposed.
6. Acknowledgements
The authors are appreciative to colleagues for their support and appropriate suggestions, which
allowed them to improve the materials of the article.
7. References
[1] M. Zhou, Y. Li, X. Huang, Q. Pu and H. Yuan, Indoor WLAN Intrusion Detection Using Intra-
class Transfer Learning with Low Effort, in: Proceedings of the 2019 IEEE 30th Annual
International Symposium on Personal, Indoor and Mobile Radio Communications, PIMRC,
Istanbul, Turkey, 2019, pp. 1-6. doi: 10.1109/PIMRC.2019.8904445.
[2] R. Hartung, U. Kulau and L. Wolf, Demo: PotatoScope – Scalable and Dependable Distributed
Energy Measurement for WSNs, in: Proceedings of the 2016 13th Annual IEEE International
Conference on Sensing, Communication, and Networking, SECON, London, 2016, pp. 1-3. doi:
10.1109/SAHCN.2016.7732981.
[3] C. Schmitt, A. Freitag and G. Carle, CoMaDa: An adaptive framework with graphical support for
Configuration, Management, and Data handling tasks for wireless sensor networks, in:
Proceedings of the 9th International Conference on Network and Service Management, CNSM’
2013, Zurich, 2013, pp. 211-218. doi: 10.1109/CNSM.2013.6727839.
[4] S. H. Gade and S. Deb, HyWin: Hybrid Wireless NoC with Sandboxed Sub-Networks for
CPU/GPU Architectures, IEEE Transactions on Computers 66 7 (2017) 1145-1158. doi:
10.1109/TC.2016.2643668.
[5] C. Peng, K. Qian and C. Wang, Design and Application of a VOC-Monitoring System Based on
a ZigBee Wireless Sensor Network, IEEE Sensors Journal 15 4 (2015) 2255-2268. doi:
10.1109/JSEN.2014.2374156.
[6] M. R. M. Kassim and A. N. Harun, "Using Wireless Sensor Network to determine pollination
readiness of oil palm flower, in: Proceedings of the 2015 9th International Conference on
Sensing Technology, ICST, Auckland, 2015, pp. 59-64. doi: 10.1109/ICSensT.2015.7438365.
[7] B. Durnyak, B. Havrysh, O. Tymchenko, M. Zelyanovsky, O. O. Tymchenko and O. Khamula,
Intelligent System for Sensor Wireless Network Access: Modeling Methods of Network
Construction, in: Proceedings of the 2018 IEEE 4th International Symposium on Wireless
Systems within the International Conferences on Intelligent Data Acquisition and Advanced
Computing Systems, IDAACS, Lviv, 2018, pp. 93-97. doi: 10.1109/IDAACS-
SWS.2018.8525792.
[8] M. Logoyda, M. Nazarkevych, Y. Voznyi, S. Dmytruk, O. Smotr, Identification of Biometric
Images using Latent Elements. CEUR WS 2488 (2019) 99-108.
[9] M. M. R. Mozumdar, A. Ganesan and A. Ameri, Synthesizing Sensor Networks Backbone
Architecture for Smart Buildings, IEEE Sensors Journal 14 12 (2014) 4273-4283. doi:
10.1109/JSEN.2014.2346654.
[10] M. J. Saikia, G. Cay, J. V. Gyllinsky and K. Mankodiya, "A Configurable Wireless Optical Brain
Monitor Based on Internet-of-Things Services, in: Proceedings of the 2018 International
Conference on Electrical, Electronics, Communication, Computer, and Optimization Techniques
(ICEECCOT), Msyuru, India, 2018, pp. 42-48. doi: 10.1109/ICEECCOT43722.2018.9001456.
[11] M. Kemal, F. Iov, R. Olsen, T. Le Fevre and C. Apostolopoulos, On-line configuration of
network emulator for intelligent energy system testbed applications, in: Proceedings of the
AFRICON 2015, Addis Ababa, 2015, pp. 1-4. doi: 10.1109/AFRCON.2015.7331979.
[12] H. Saidi, M. Turki, Z. Marrakchi, M. S. Ben Saleh and M. Abid, Embedded FPGA accelerator
for Wireless Sensor Network nodes, in: Proceeedings of the 2016 11th International Design &
Test Symposium (IDT), Hammamet, 2016, pp. 313-318, doi: 10.1109/IDT.2016.7843061.
[13] M. B. Duggimpudi, A. Moursy, E. Ali and V. V. Raghavan, An Ontology-Based Architecture for
Providing Insights in Wireless Networks Domain, in: Proceedings of the 2016 IEEE/WIC/ACM
International Conference on Web Intelligence (WI), Omaha, NE, 2016, pp. 473-478. doi:
10.1109/WI.2016.0078.
[14] O. Tymchenko, O. O. Tymchenko, B. Havrysh, O. Khamula, O. Sosnovska and S. Vasiuta,
Efficient Calculation Methods of Subtraction Signals Convolution, in: Proceedings of the 2019
IEEE 15th International Conference on the Experience of Designing and Application of CAD
Systems (CADSM), Polyana, Ukraine, 2019, pp. 1-4, doi: 10.1109/CADSM.2019.8779250.
[15] A. Kasture, A. Raut and S. Thool, Visualization of Wireless Sensor Network by a Java
Framework for Security in Defense Surveillance, in: Proceedings of the 2014 International
Conference on Electronic Systems, Signal Processing and Computing Technologies, Nagpur,
2014, pp. 256-261. doi: 10.1109/ICESC.2014.49.
[16] A. Guerrieri, L. Geretti, G. Fortino and A. Abramo, A service-oriented gateway for remote
monitoring of building sensor networks, in: Proceedings of the 2013 IEEE 18th International
Workshop on Computer Aided Modeling and Design of Communication Links and Networks
(CAMAD), Berlin, 2013, pp. 139-143. doi: 10.1109/CAMAD.2013.6708105.
[17] T. Yilmaz and O. B. Akan, Utilizing terahertz band for local and personal area wireless
communication systems, in: Proceedings of the 2014 IEEE 19th International Workshop on
Computer Aided Modeling and Design of Communication Links and Networks, CAMAD’2014,
Athens, 2014, pp. 330-334. doi: 10.1109/CAMAD.2014.7033260.
[18] Z. Fan, Z. Bai, X. Zhang, S. Rahardja and J. Chen, AUC Optimization for Deep Learning Based
Voice Activity Detection, in: Proceedings of the 2019 IEEE International Conference on
Acoustics, Speech and Signal Processing ICASSP’2019, Brighton, United Kingdom, 2019, pp.
6760-6764. doi: 10.1109/ICASSP.2019.8682803.
[19] F. Wu, C. W. Tan, M. Sarvi, C. Rudiger and M. R. Yuce, Design and Implementation of a Low-
Power Wireless Sensor Network Platform Based on XBee, in: Proceedings of the 2017 IEEE
85th Vehicular Technology Conference (VTC Spring), Sydney, NSW, Australia, 2017, pp. 1-5.
doi: 10.1109/VTCSpring.2017.8108667.
[20] T. Nishio, M. Morikura and K. Yamamoto, Heterogeneous media communications for future
wireless local area networks, in: Proceedings of the 2015 IEEE International Conference on
Consumer Electronics, ICCE, Las Vegas, NV, 2015, pp. 637-640. doi:
10.1109/ICCE.2015.7066560.