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Wireless Signal Strength Analysis in a Home Network

Authors:
Proceeding of 2018 IEEE International Conference on Current Trends toward Converging Technologies, Coimbatore, India
978-1-5386-3702-9/18/$31.00 © 2018 IEEE 1
Wireless Signal Strength Analysis in a
Home Network
Pooja Dhere,
Department of Electronics Engineering,
Vishwakarma Institute of Technology,
Pune, Maharashtra, India.
Pooja.dhere16@vit.edu
Rambabu Vatti,
Department of Electronics Engineering,
Vishwakarma Institute of Technology,
Pune, Maharashtra, India.
rambabu.vatti@vit.edu
Padmavati Chilveri,
Department of Electronics Engineering,
Vishwakarma Institute of Technology,
Pune, Maharashtra, India.
padmavati.chilveri16@vit.edu
Vishwaditya Iyer,
Department of Electronics Engineering,
Vishwakarma Institute of Technology,
Pune, Maharashtra, India.
vishwaditya.iyer15@vit.edu
Kunal Jagdale
Department of Electronics Engineering,
Vishwakarma Institute of Technology,
Pune, Maharashtra, India.
kunal.jagdale15@vit.edu
AbstractWith the advert, invention of wireless
fidelity commonly called as Wi-Fi in the field of
communication and computer networks a new era or
revolution has taken place. Many studies have been
conducted on the Wi-Fi signal and their characteristics
also methods to increase security and to establish a
secure connection in a wireless network has been
studied in great detail. Also in path research have been
carried out to increase throughput, data transfer rate
and Wi-Fi signal strength. In this paper the analysis of
different Wireless Local Area Network [WLAN]
surrounding the home network using different tools is
done. Also an author created a map of area in an app
called as heat mapper, specified Access points, after
which the dead zones and range was analyzed. Also,
Wi-Fi monitor and Wi-Fi analyzer was used to analyze
the signal strength and plot time graph and give
information about mac address, IP address, security
used etc. Authors analyzed the downlink and uplink
speed graph and different methods to increase or
boost signal strength.
Index TermsWi-Fi signal strength, access point,
channel strength, Wi-Fi monitor, link speed, heat map,
IP address
I.INTRODUCTION
With the advert of networking and communication has led
to tremendous speed improvisation, not only that, it has
also improved the accuracy, reliability and reduction of
cost. Further after Wireless network was invented,
demand further increased of these wireless networks
especially in local area networks [LAN]. Naturally
research in the Field of analysis of these type signal has
been carried out. In the past, extensive research has been
carried out on Wi-Fi signal strength analysis and
optimization, some of the work is related to the Wi-Fi
strength optimization and analysis of different parameters.
Wi-Fi has come out as a problem solver to all the wire
related problem and emerged as spearhead in wireless
technologies [1]. Popularity of wireless communication is
only because they are easy available and also reliable and
accurate, these are the factors to be considered while
designing any network [2]. Also, as demand for
bandwidth is increasing in current world, demand for
WLANs and wireless personal area networks [WPAN] is
only going to increasing [3]. In a multichannel
transmission in the Today’s world, with increasing
congestion a wireless network can be a problem solver
[4]. Another import parameter is the throughput along
with Wi-Fi signal strength [5]. Another thing that creates
an interference and effects Wi-Fi signal strength is its
interference with other type of wireless communication
like Bluetooth [6][7]. The more interesting thing is these
all wireless network for industrial, personal etc.,all shares
the same band [8]. Parameters that are affected by Wi-Fi
signal strength are link speed and throughput [9]. The
standard used by Wi-Fi is 802.11 [10]. Which is further
divided into a, b, g and n [11] [12]. The standard 802.11a,
802.11b and 802.11g are old standards consequently less
signal strength which is slowing being replaced by
802.11n [13]. This standard defines the WLAN media
access control and physical layer [14]. Authors Observed
that Wi-Fi signal strength located at navkar residency,
bibwewadi, pune weakens with Distance and this was
causing reduction in the uplink and Downlink speed
therefore to correct this authors created a Heat map of our
Proceeding of 2018 IEEE International Conference on Current Trends toward Converging Technologies, Coimbatore, India
2
home network then analysis of link speed at various
locations of the home network was done and ways to
tackle this problem was analyzed. Analysis of each
method practically was done. There is many Wi-Fi
analyzer software available online both paid and free of
cost, comparing the features, came up with a list of
software’s that have the most Features and is available
across different platforms.
Some of them are as follows:
1. Wi-Fi Analyzer
2. Wi-Fi monitor
3. Net spot
4. Heat mapper
5. InSSiDer
6. Vistumbler
7. Wi-Fi commander
8. Wireshark
9. Wi-Fi scout
10. Wi-Fi tool
Out of these Wi-Fi analyzer apps listed above and many
more available Wi-Fi monitor, Wi-Fi analyzer was
selected and even heat mapper was selected for their
cross-platform availability and wide array of features. The
Wi-Fi monitor has many features such as time graph,
channel strength and signal strength graph and these
applications are available free of cost.
II.RELATED WORK
Analysis of Wi-Fi SSID information and security
information was done in [15]. Analysis of Wi-Signal
strength for indoor localization countered hardship was
done in [16]. Wi-Fi signal strength at receiver end and
also transmitter signal strength along with parameter such
as noise was analyzed for an indoor network in [17]. Wi-
Fi signal characteristics analysis was done using a mobile
phone, further positioning of access point was suggested
[18]. In an indoor network a signal path was traced and
positioning for optimal signal strength was done in [19].
A Wi-Fi localization method for an indoor network was
suggested [20]. Wi-Fi and related wireless
communication’s energy consumption was predicted for a
small area in [21]. A survey was done on Wi-Fi access
point positioning using different methods in [22]. Wi-Fi
positioning and location of access point for good signal
strength was studied in depth in [23]. IEEE 802.15.4 is a
relatively new standard specialized for low rate wireless
personal area network in [24]. Performance of WLAN
signal strength was increased using deep learning and
neural network techniques in [25]. A review of 802.11
was done in [26]. Comparison between different wireless
standards was done in [27]. Signal strength loss that
occurs due to room walls and metal object was done in
[28]. Positioning and design of Wi-Fi access point in the
terrain of Inner Mongolia University was performed in
[29].
Hence summary obtained from the above papers is that
the focus was mainly on the positioning of the access
point and different ways to improve the throughput and
energy was done but there was less focus on the on the
analysis part also focus was less on signal strength more
emphasis was there on throughput. In this paper the main
focus was on this aspect of wireless networks. Also, the
focus in the above papers was on wireless networks in
general, our focus is mainly wireless networks used in a
home network or small area.
III.EXPERIMENTATION
To start with first, analysis of available Wi-Fi analyzer
apps on the internet was done and a comparison based on
features, availability across different platforms and cost
was done. Shortlisting of three applications was done to
perform analysis they are heat mapper, Wi-Fi analyzer
and Wi-Fi monitor based on different parameters. First
Wi-Fi monitor was selected and analyzed in networks
available in our home network as shown in Fig.1 Then
Wi-Fi analyzer was used to see the information of the
wireless networks as shown in Table I. Then Wi-Fi signal
strength graph was plotted as shown in Fig.2. Then heat
mapper application was used to create a heat map of the
home network as shown in Fig.6
Fig.1 Wi-Fi network available in network
TABLE I. Wi-Fi network information
Frequency
2412 MHz
Security
Wpa2-psk
SSID
Skyworth_33B060
MAC address
88:cc: 45:03: a1: b1
Channel
1
Service provider
Skyworth digital tech
The Wi-Fi network used for further analysis and its
information presented in the Wi-Fi analyzer application.
Proceeding of 2018 IEEE International Conference on Current Trends toward Converging Technologies, Coimbatore, India
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Fig.2 Channel no and strength of signal
Signal strength of various wireless signals present in the
home network with the channel which each wireless
network occupies presented. Then Wi-Fi monitor was
used to plot the real time graph as shown in Fig.3
Fig.3 Real time graph
Graph showing the real time Wi-Fi signal strength along
with the signal strength of Skyworth presented in orange
color and analyzed with the help of Wi-Fi monitor
application as shown in Fig.3. With the help of this
application the uplink and downlink speed of the network
can also be observed and analyzed as shown in Fig.5 Then
fig.4 shows the channel rating with channel number and
access point count.
Fig.4 Channel rating
Channel rating of various wireless networks presented as
ten-star rating with more star representing better channel.
Fig.5 Link speed
Fig.5 shows the downlink and uplink speed of the
network under consideration in kbps, but this represents
the maximum speed not average speed which is less
because of the signal strength which directly affects this
speed.
Fig.6 Heat map of network
Proceeding of 2018 IEEE International Conference on Current Trends toward Converging Technologies, Coimbatore, India
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The Fig.6 represents heat map of the network and the
outline or terrain of the home network analyzed with
different color representing the signal strength. The color
and their strength representation are listed below:
1. Dark green- excellent strength
2. Light green- good strength
3. Yellow- fair strength
4. Red- dead zone.
Fig.7 Signal strength graph
Fig.7 graph lists the various signal strength measured at
different locations of the home network.
As shown in Fig.7 graph the signal strength follows a
certain pattern because of the positioning of the router
near the kitchen and passage. The reason that the signal
strength is less at certain places in the network was
analyzed and based on it the possible reasons was jotted.
One obvious reason that comes to one’s mind is because
of wall and other objects obstructing the signal also range
of the signal. Also the transmitter output power. Also
interference because of other wireless networks in the
home network also the router standard which plays an
import role. Frequency of transmission which affects the
Signal strength. Another important parameter neglected
by the network designer is the antenna used for
transmission.
Generally, for monetary reasons the antenna used in the
router is of poor quality, the user based on his needs can
replace it with a better one for more range and signal
strength. Based on Fig.7 graph, distance of the router
from different places in the house can also be roughly
inferred.
IV.PERFORMANCE AND EXPERIMENTS
Based on the above analysis performed there are many
ways to improve your Wi-Fi signal strength for better
signal strength which in turn will give better throughput
The methods analyzed by us are listed below:
1. Changing the Wi-Fi standard and frequency of
operation. That is use 802.11n instead of
802.11a/b/g also if possible change frequency to 5
GHz
2. Choosing 40 MHz channel bandwidth instead of 20
MHz which enables a throughput of 300mbps
3. Choosing a channel with no interference from
neighboring networks or less RF noise.
4. iperf tool was used to test the throughput of the
output as done.
5. Use of repeaters or range extenders where ever
there is a dead zone and no signal was observed.
6. increasing the Wi-Fi router transmit power, it was
configured to an optimal 70mw because increasing
too much can at times worsen the signal strength.
7. Use of different antenna instead of traditional
antenna.
V. CONCLUSION
Performance of the above analysis for a home network
was carried out and the conclusion is that using repeaters
range can be considerably improved. Also using newer
standard and using different frequency of operation also
helps. If there are more than one signal sharing a channel
changing channel can also be done. Also, observed that
uplink and downlink speed is greatly affected by Wi-Fi
signal strength which greatly reduces as distance from
nearest access point increases which can be corrected by
suitable positioning of repeaters or range extenders. Also
a heat map was developed using layout of the area to
identify weak signal strength areas. Analysis was done on
different methods to improve Wi-Fi signal strength and
gave feedback based on each method. Based on the above
analysis conclusion can be drawn that there is significant
improvement by changing of antenna, standard of router
and frequency of operation has significant improvement
also throughput was checked using iperf tool. Channel
change can also be done based on rating. If your network
is a big estate than use of range extenders can be used
because of the cost involved.
Performing the above experiment concludes that wireless
network in spite of having the advantage of speed,
reliability and cost has drawback of range but this can be
corrected and reduced by employing smart tactics to solve
them.
VI. FUTURE SCOPE
Everyone knows the future in wireless networks like Ad
hoc network is going to play an important role in human
development where this analysis can play a vital and
powerful role in solving problem related to signal
strength. Yacine Mezali et al, “on indoor wifi signal
stastical properties” IEEE wireless and mobile networking
conference on 26-28 oct 2011
Proceeding of 2018 IEEE International Conference on Current Trends toward Converging Technologies, Coimbatore, India
5
REFERENCES
[1]. Hui-Tang Lin, Ying-You Lin et al, “An Integrated
WiMAX/WiFi Architecture with QoS Consistency over
Broadband Wireless Networks” in Proc. of IEEE 978-1-
42442309-5, 2009.
[2]. Cavalcanti D, et al, “Issues in Integrating Cellular
Networks WLANs, and MANETs: a Futuristic
Heterogeneous Wireless Network”, IEEE Wireless
Commun. Mag., vol. 12, no. 3, pp. 30-41, 2005
[3]. Pedro Neves, Susana SargentoRui, L. Aguiar, “Support
of Real-Time Services over Integrated 802.16
Metropolitan and Local Area Networks”, in Proc. of
IEEE.ISCC, pp.15-22,2006.
[4]. F. Ye, H. Yang and B. Sikdar, "Enhancing MAC
Coordination to Boost Spatial Reuse in IEEE802.11 Ad
Hoc Networks," presented at IEEE International
Conference on Communications, 2006, pp.3814-3819.
[5]. Rambabu Vatti, Arun Gaikwad, “Variable Rate and
Adaptive Traffic Tuning Technique to improve
Throughput of IEEE 802.15.4 based Wireless
Networks,” SRATE- International Journal of Research in
Application Technologies, Vol.6, Issue.1, pp.15-20. June
2016.
[6]. I. Howitt, "Bluetooth performance in the presence of
802.11b WLAN, "IEEE Transactions on Vehicular
Technology, Vol.51, no. 6, pp. 16401651, 2002.
[7]. N. Golmie, R. E. V. Dyck, A. Soltanian, A. Tonnerre,
and O. Rébala,"Interference evaluation of Bluetooth and
IEEE 802.11b systems,"Wireless Networks, vol. 9, no. 3,
pp. 201-211, 2003.
[8]. Rambvau A. Vatti, Arun N. Gaikwad, ““Frame
Converter for cooperative coexistence between IEEE
802.15.4 Wireless Sensor Networks and WiFi”, Springer
Link - Proceedings of 3rd International Conference on
Advanced Computing, Networking and Informatics,
vol.44, pp.151-157. 2016.
[9]. Rambabu A. Vatti, Arun N. Gaikwad, “Throughput
Improvement of Randomly Deployed Wireless Personal
Area Networks”, 2013 International Conference on
Applied Computing, Computer Science, and Computer
Engineering, Published by Elsevier B.V.pp.42-48.
[10]. IEEE 802.11, Standard 2012, Part 1 1: Wireless LAN
Medium Access Control (MAC) and Physical Layer
(PHY) Specifications, IEEE-SA Standard Board (2012).
[11]. IEEE Std 802.11-2012. Part 11: Wireless LAN Medium
Access Control (MAC) and Physical Layer (PHY)
specifications.
[12]. IEEE Std 802.11b-1999 (Supplement to IEEE
Std802.11-1999). Part 11: Wireless LAN Medium
Access Control (MAC) and Physical Layer (PHY)
specifications: High-speed Physical Layer in the 2.4 GHz
Band.
[13]. IEEE Std 802.11g-2003. Part 11: Wireless LAN Medium
Access Control (MAC) and Physical Layer (PHY)
specifications Amendment 4: Further Higher Data Rate
Extension in the 2.4 GHz Band.
[14]. IEEE Std 802.11n-2008. Part 11: Wireless LAN Medium
Access Control (MAC) and Physical Layer (PHY)
specifications Amendment 5: Enhancement for Higher
Throughput.
[15]. Haishen Peng, “wifi network information security
analysis research”
[16]. IEEE conference on consumer electronics (CECNet) on
21 23 april 2012
[17]. Yongduo Wang et al, “Adaptive room level localization
system with crowd sourced wifi data” IEEE conference
on SAS intelligent systems on 10-11 nov 15
[18]. Yacine Mezali et al, “on indoor wifi signal stastical
properties” IEEE wireless and mobile networking
conference on 26-28 oct 2011
[19]. Ling Yang et al, “wifi signal characteristics analysis for
indoor positioning using mobile phone” springer- china
satellite navigation conference on 3 may 2017
[20]. Sheng Su et al, “path matching indoor positioning with
wifi signal strength” springer 2015 international
conference on communication, signal processing and
systems pp577-5
[21]. Yao zhou et al, “FIMO A novel wifi localization
method” springer- asia -pacific web conference pp 437-
448
[22]. Enrico Eugenio et al, “wifi related energy consumption
analysis of mobile devices in a walable area by abstract
interpretation” springer international conference on
distributed computing and internet technology pp 27-39
[23]. Ahmed Makki et al, “survey of wifi positioning using
time based techniques” elsevier – computer networks
volume 88 9 september 2015 pp 218-283
[24]. David Munoz et al, “position location techniques and
applications” elsevier on 15 april 2009
[25]. IEEE. 802.15.4., Standard 2006, Part 15.4: “Wireless
Medium Access Control (MAC) and Physical Layer
(PHY) Specifications for Low Rate Wireless Personal
Area Networks (LR WPANs)”, IEEE –SA Standards
Board 2006W.-K. Chen, Linear Networks and Systems
(Book style). Belmont, CA: Wadsworth, 1993, pp. 123
135.
[26]. David Plets et al “surrogate modeling based cognitive
decision engine For Optimization of WLAN
performance”
[27]. springer- wireless networks November 2017, volume 23,
issue 8, pp 2347-2359
[28]. Vikram Singh et al “A review paper on IEEE 802.11
WLAN Springer-Proceedings of international conference
on internet computing and information communications
pp 251-256
[29]. Tjensvold, J.M. “Comparison of the IEEE
802.11,802.15.1,802.15.4 and 82.15.6 wireless
standards” IEEE-2007
[30]. Anshu Bhuwania et al, “Positioning wifi access points
using particle swarm optimization” 2016 IEEE second
international conference on research in computational
intelligence and communication networks[ICRCICN] pp
112-115
[31]. Juanjuan wang et al “wireless campus network design
and optimization based on OPNET” IEEE cyber-enabled
distributed computing and knowledge discovery 2015
international conference.
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