Circulation in Computer ScienceVol.
Copyright © 2016 The Authors. This is an open-access article distributed under
the terms of the Creative Commons Attribution License 4.0
unrestricted use, distribution, and reproduction in any medium, provided the
original author and source are credited.
Performance Analysis
Nnamdi Azikiwe University Awka
E. O. Nonum Dept. of Computer Science / Maths
Novena University Ogume, Delta State, Nigeria
ABSTRACT
This work centres on the study of campus wifi hotspot
networks in Nigeria universities, using the Nnamdi Azikiwe
University Awka as a case study. A Campus Wide Area
Network (CWN) provides a means of communication and
collaboration in data intensive environments. These are vital
key factors to building a strong knowledge culture and
facilitating collaborative research for any educational
institution. Network stumbler (Wifi Network Analyzer) and
Iperf were installed on different laptop computers in the
respective Access Points (AP) while being monitored from a
dedicated server running on Mikrotik and wireshark. This was
used in collecting useful data needed for the chara
of the UNIZIK wifi hotspot network in terms of Received
signal strength index (RSSI), Data throughput and
latency/network delay. The AP distance from user is carefully
measured with a meter tape. Performance analysis carried out
on this university wifi hotspots shows that the network offers
a delay of 0.1545s by default that increases by a factor of
0.001s; a data throughput of 37.30Mbps that decreases by a
factor of 0.25Mbps for any user added to the network. Also,
that an RSSI of -35.438dBm was obtained at the AP base
station which decreases by a factor of 0.4925dBm for any 1m
distance away from the APand finally that a traditional
hotspot networks based on IEEE 802.11 series lacks
integrated intelligence for services convergence, QoS
performance and in most cases suffers from interoperability
problems.
Keywords Campus, Wide area Network, Wifi, Access Point, Throughput,
Latency, Received Signal Strength Index.
Internal Users
Students
-Need basic Access to Student resources
- Diverse client devices
- Massive User base
The different access and connectivity needs of the user group
present varying challenges to the network infrastructure
design and its administration [3]. In addition, addressing the
challenges has to be done with consideration for the size and
the scope of the CWN.
Circulation in Computer Science Vol. 1, Number 1, pp: (1-9), May 2016
Available online at www.ccsarchive.org
access article distributed under
Creative Commons Attribution License 4.0, which permits
unrestricted use, distribution, and reproduction in any medium, provided the
Analysis of WiFi Hotspot Network
Nnamdi Azikiwe University Awka
Computer Science / Maths,
Nigeria
P. O. OtasowieDept. of Electrical Electronic Engineering,
University of Benin,Benin City,
This work centres on the study of campus wifi hotspot
networks in Nigeria universities, using the Nnamdi Azikiwe
University Awka as a case study. A Campus Wide Area
Network (CWN) provides a means of communication and
collaboration in data intensive environments. These are vital
key factors to building a strong knowledge culture and
facilitating collaborative research for any educational
Wifi Network Analyzer) and
Iperf were installed on different laptop computers in the
respective Access Points (AP) while being monitored from a
dedicated server running on Mikrotik and wireshark. This was
used in collecting useful data needed for the characterisation
of the UNIZIK wifi hotspot network in terms of Received
signal strength index (RSSI), Data throughput and
latency/network delay. The AP distance from user is carefully
measured with a meter tape. Performance analysis carried out
ity wifi hotspots shows that the network offers
a delay of 0.1545s by default that increases by a factor of
0.001s; a data throughput of 37.30Mbps that decreases by a
factor of 0.25Mbps for any user added to the network. Also,
s obtained at the AP base
station which decreases by a factor of 0.4925dBm for any 1m
distance away from the APand finally that a traditional
hotspot networks based on IEEE 802.11 series lacks
integrated intelligence for services convergence, QoS
ce and in most cases suffers from interoperability
Campus, Wide area Network, Wifi, Access Point, Throughput,
1. INTRODUCTION A Campus Wide Area Network (CWN) provides a means of
communication and collaboration in data intensive
environments. These are vital key factors to building a strong
knowledge culture and facilitating collaborative research for
any educational institution. It includes the different kinds of
wireless networks that support IEEE 802.11 as well as IEEE
802.3 standards [1]. The
implementation combines the scalability of virtual LANs and
the flexibility of switched Ethernet with the mobility
wireless LAN access. In this type of network, a suite of
general purpose software tools can be deployed to monitor the
status of the network, measure the bandwidth utilization,
control access to the network, and authenticate users.
These networks are found in most tertiary educational
institutions today. A typical campus network interconnects
hundreds of departments across so many buildings, providing
high speed access for both students and staff. Once,
connected, users have access to a wide range of res
such as printers, network file servers, research materials,
lecture notes, tutorials, and even lecture on demand [2].
Other services in CWN includes streaming multimedia,
skyping between classes, peer to peer file sharing.
Applications such as email, discussion forums, bulletin
boards, class schedulers, resource booking systems and
various other administrative applications are also available
through the campus network.
Within the CWN, the users can essentially be partitioned into
three functional groups namely: students, staff and visitors.
These user groups are most often granted different access
rights to resources on the CWN depending on the general
needs of the particular group as shown in Table 1.1
Table 1.1: User groups in a CWN
Internal Users External users
Staff
Need basic Access to Student resources - Need access to teaching and
research resources
- Requires secured access
- long-term user accounts
-Limited access depending on
requirements
- Short term user accounts
-Diverse client devices
The different access and connectivity needs of the user groups
present varying challenges to the network infrastructure
]. In addition, addressing the
challenges has to be done with consideration for the size and
However, the CWN can be achieved with wireless
technologies such as Cellular Systems
3G, B3G, 4G, Satellite Systems, Paging Systems, Cordless
Phone, Wireless LAN, Wireless Local Loop, Wireless Data
Service, Bluetooth, Ultra Wide Band (
Hotspot Network in
Nnamdi Azikiwe University Awka
Otasowie Dept. of Electrical Electronic Engineering,
University of Benin, Benin City, Nigeria
A Campus Wide Area Network (CWN) provides a means of
communication and collaboration in data intensive
environments. These are vital key factors to building a strong
knowledge culture and facilitating collaborative research for
It includes the different kinds of
wireless networks that support IEEE 802.11 as well as IEEE
802.3 standards [1]. The campus-wide wireless
implementation combines the scalability of virtual LANs and
the flexibility of switched Ethernet with the mobility of
wireless LAN access. In this type of network, a suite of
general purpose software tools can be deployed to monitor the
status of the network, measure the bandwidth utilization,
control access to the network, and authenticate users.
und in most tertiary educational
institutions today. A typical campus network interconnects
hundreds of departments across so many buildings, providing
high speed access for both students and staff. Once,
connected, users have access to a wide range of resources
such as printers, network file servers, research materials,
lecture notes, tutorials, and even lecture on demand [2].
Other services in CWN includes streaming multimedia,
skyping between classes, peer to peer file sharing.
l, discussion forums, bulletin
boards, class schedulers, resource booking systems and
various other administrative applications are also available
Within the CWN, the users can essentially be partitioned into
roups namely: students, staff and visitors.
These user groups are most often granted different access
rights to resources on the CWN depending on the general
needs of the particular group as shown in Table 1.1.
External users
Visitors
Limited access depending on
Short term user accounts
Diverse client devices
However, the CWN can be achieved with wireless
technologies such as Cellular Systems-1G, 2G, 2.5G (GPRS),
3G, B3G, 4G, Satellite Systems, Paging Systems, Cordless
Phone, Wireless LAN, Wireless Local Loop, Wireless Data
Service, Bluetooth, Ultra Wide Band (UWB), hotspot, etc [4].
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The rest of this paper is organized as follows. Section II
presents review of related works while section III gives details
of the material and methods used in the research.
2. RELATED RESEARCH EFFORTS Various works were studied in relation to campus hotspots. A
review study was carried in Grid WLAN[5] Cognitive hotspot
radio network based on Minipop access point [6], Fuzzy
TCPWLAN[7], and RTC-GWLAN[8]. In most existing
deployments, these wireless implementation formats faces a
myriad of obstacles, but fundamental to the performance of
any system are the propagation characteristics that restrict
delivery of signal power as well as Quality of Service. Also,
deployment scenarios of these networks create interference.
Most hotspots in Nigerian tertiary institutions are faced with
several performance issues as the IEEE 802.11 infrastructures
are not optimized for high density services such as cloud
services [4]. The purpose of this case study is to ascertain the
behaviour and performance of user traffic over the radio
environment of CWN.
3. MATERIALS AND METHODS
3.1 Traditional CWN System Analysis In this section, an analysis of the traditional CWN network
will be carried out so as to help us portray their limitations
Consequently,the Nnamdi Azikiwe University, Awka campus
was visited, its key design features/parameters were obtained
and the performance analysis was carried out.
3.2 UNIZIK Campus Network Testbed Considering the peculiarity of UNIZIK environment, this
work characterizes a real life traditional hotspot testbed in
terms of Received Signal Strength (RSS), Mobile Node
distance, latency and throughput using Mikrotik-AP
Infrastructure in Nnamdi Azikiwe University, Awka. Data
collection was carried out in Awka environment.The main
campus as shown in Figure 2.1 is located on 378 hectares
(3.78km2) of hilly savannah in the town of Awka, about
eighty two kilometers south-west of Enugu and twenty three
kilometers north south of Awka. The Awka campus houses
the Faculties of Agriculture, Arts, Biological Sciences,
Management Sciences, Education, Engineering, Physical
Sciences, Social Sciences, and many research centres.
Figure 2.1: NAU Campus Wifi Traditional WLAN Testbed
The hotspot environment derives network connectivity via the
Mikrotik TR-5800 Series (broadband wireless backhaul
radios) and 2400 TR-AP hotspot technology respectively. The
2400 TR-AP outdoor Wi-Fi base stations runs with 802.11n
in 2.4 GHz and 5 GHz unlicensed bands and in 700MHz
licensed band, offering end-to-end solutions including access,
backhaul, CPEs, management and service provisioning tools.
With up to nine embedded radios, 3x3 MIMO and three
spatial data streams, 2400 TR-AP base stations can offers
theoretical 450Mbps data speeds. The specification used for
the access points are: 2400 TR-AP Base Station, 15806101,
5.8GHz spatially adaptive, multi-radio base station, with an
array of 3 sectorial antennas, 5.8GHz self backhaul, PoE input
(Power over Ethernet injector), Pole mount kit, FCC /TUV
compliant. An integration of the 2400 TR-AP base station
with the 5800 backhaul radio is referred to as a Mikrotik
access point (TR-AP).
In the characterization, consideration was made on a large
area of different locations as basic setting. Due to a potentially
high number of users, several mikrotik TR-APs based on
802.11 that operate on different IEEE channels are placed
within this area. Users appear in these locations at different
points in time and at different places. Firstly, looking at figure
2.1, there have several locations where several respective
Mikrotik-APs are deployed, namely; Admin block,
Engineering Faculty, Law faculty, Chike Okoli, etc. Each of
the backhaul AP terminates in the NOC switching system as
shown in Figure 2.2.
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Figure 2.2: NAU Switching System (Source NAU NOC Admin Block)
Figure 2.3: NAU Battery Bank System (Chike Okoli Centre)
3.2.1. Tools for measurement and Test Places These programs were used viz: Network stumbler (Wifi
Network Analyzer) and Iperf were installed on different
laptop computers in the respective APs while being monitored
from a dedicated server running on Mikrotik and wireshark.
Network stumbler (Wifi Network Analyzer)installed in
android phone captures among other parameters; Received
Signal Strength, signal to noise ratio, latency, but throughput
– used Iperf program while the AP distance from user is
measured with a meter tape. Considering the settings, to
understand the basic performance of the IEEE802.11n during
the test, we disabled Dynamic Host Configuration Protocol
(DHCP), firewalls and other security settings. Static setting
was used to avoid the additional resource consumption on the
server, which is required to create a secure and flexible
wireless connection.
The approach here is to find the bestplace with good
receptivity for various frame sizes; hence, the first experiment
was conducted in NOC (Admin-Block) as shown in figure 2.2.
The location is an open building with several offices whose
ground floor is used for the measurement. Measurement was
carried out up to 100m from the building to the point where
the access point (TR-AP) is mounted. The NAU hotspot
network is an open or flat network where IPs are assigned to
devices dynamically. For each client TR-AP, maximum
allowable user is 1005 owing to the switch capacity.
From the Admin block; NOC, measurements on various
network metrics from users (mobile hosts) scattering at
different locations within the building and to other TR-APs.
Signal measurements were taken by following the network
map of figure 2.1.
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3.2.2 Measurement Infrastructure Equipment
Description To analyse the Unizik CWN, the following devices with their
configurations and settings were used, viz:
i. CWN Switching DataCenter System:
For the testbed, the Mikrotik APs terminates at the NOC
switching modules. The various APs were configured for user
access. Figure 2.2 show NAU main campus switching system
in the setup with the battery bank as shown in Fig 2.3.
ii. Mikrotik AP Antenna
The AP-TR 5800 series which is a broadband wireless
backhaul radios provide high data throughput over long
distances. This was configured to drive data rates up to
54Mbps at Half-Duplex speeds.
Operating in the license-free 5 GHz frequencies. The TR-5800
Series was used in Point-to-Point (PtP) wireless links and can
also be used in Line-of-Sight (LoS) running at 5.3 to 5.8 GHz
backhaul point-to-point solution. The TR 5800 beamforming
technology is:
- Based on TR-AP’s Beamforming Wi-Fi chip
- Leverages up to 3 radio and 3 high-gain antennas for
optimal performance beamforming signal.
- Performs per-packet true beamforming in both the
uplink and downlink
- Exploits multi-path for its advantage by coherently
combining all reflections, thus creating optimal signal
at the receiver with up to 10 dB beamforming gain.
This doubles the range, improves in building
penetration, and increases aggregated base station
capacity as air transmissions are more efficient
- Inherently suppresses interferences by an average of
8dB, thus providing significant advantage in noisy
environments
- Supports off-the-shelf standard Wi-Fi 802.11a/b/g/n
CPEs.
- TR-AP base stations leverage on following unique
technologies and capabilities which further enhance the
technological lead:
- Space Division Multiple Access (SDMA) technology –
enables transmitting to multiple users simultaneously,
thus further increasing the base station capacity
- Dynamic Interference Handling (DIH) capability –
enables continuous adaptation of the air access
parameters according to the noise and interference
level, thus maximizing the base stations capacity in
noisy environments
- Down-tilted antennas – further reduce the noise and
interference levels
- Automatic Channel Selection (ACS) capability –
automatically selects the clearest radio channel for best
radio performance
- Carrier grade – robust mechanical solution with
bottom-up IP-67 design for reliable operation in
extreme outdoor conditions, easy and fast installation,
low power consumption, and enhanced management
solution. Figure 2.5 shows the TR-AP nanostation used
in thetraditional CWN analysis. Figure 2.6 shows the
5800 Backhaul Mikrotik nanostation with mount kit.
Figure 2.5: 5800 Client Mikrotik Nanostations (Author’s testbed)
Figure 2.6: 5800 Backhaul Mikrotik Nanostation with mount kit (Authors testbed)
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Figure 2.7a: Antenna mast with Mikrotik Figure 2.7b: Sectorial Antenna
Backhaul radios (Author’s testbed) (Author’s testbed)
Admin Block (NOC) Ping Connectivity System
Configurations
i. Computer 1 (IEEE802.11g):
- Operating System: Windows Vista Home Premium (6.0,
Build 6002);
- Processor: AMD Turion (tm) 64x2 (2CPUs), ~2,0GHz;
- Memory: 2046MB RAM;
- Wireless g adapter: Atheros AR5007EG Wireless Network
Adapter.
- IEEE802.11g settings: additional channel coding speed up to
¾, OFDM symbol guard interval 800ns and 20 MHz wide
channel.
Android Mobile Phone (Mobile Network: HSDPA):
- Operating System: GINGERBREAD~Android
version 2.3.6 (BV: S5300XXLF5, KV: 2.6.35.7);
- Processor: armeabi @832.9MHz (1 CPU), ~832
MHz;
- Memory: 2GB RAM;
- Wireless HSDPA software: The Broadcom
BCM4329 802.11 network software.
3.2.3 Data Collection and Analysis The switching in the NOC was configured to supports over
10,000 user session and over 7,000 are students with 2 major
servers among the 6 servers in stock (HP proliant 380G5,
370G5 server cluster model running on Linux), hundreds of
switches which rank from 64 to 16 ports were implemented in
NAU campus Wifi open network, and two routers. The server
cluster, on Mikrotik RouterOS V2.5 coordinates the traffic
flow on the NOC. Essentially, users connect to network from
their respective Mikrotik TR-APs from 8am-6pm in the NOC.
The servers maintain reservations across the network and
allocate tenant requests in an on-line fashion. Given our focus
on quantifying the traffic performance in traditional WLAN,
this work then used Wireshark to capture network statistics
alongside with the Mikrotik on the two-server IPs. In this
work, over 1000 data captures were gathered from Admin
block, Management Sciences and School of Arts, but the
average values were used for our traditional hotspot study.
The parameters for data collection system is depicted in Table
2.1. Extended discussions was detailed in section ??
Table 2.1: Traditional WLAN Parameters
Unizik CWN Parameters Specifications
Link Connection TR-AP PtP
Switch ports Two Ports
Terminal Test node Snifers 2 major nodes
Network Architecture WLAN ESS
Network Topology Servers-In-Stars
Wimax AP NO
Server Clustering NO
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Storage Area Network (SAN) Yes
Virtualization NO
RSVP NO
TCP SACK
Coverage 300-500m
Table 2.2 shows the average of data captured from the various faculties, Nnamdi Azikiwe University, Awka
Table 2.2: Performance Metrics DataSet for UNIZIK
DISTANCE
From AP (m)
SNR (db RSSI (dBm) Latency
(Secs)
User Density Throughput
(BYTES/SEC)
5 35.5 -44 0.0000 10 26.21
10 38.5 -46 0.011338 20 67.32
15 38.4 -40 0.01308 30 0.00
20 43.5 -45 0.016744 40 2112.06
25 30.5 -47 0.03401 50 5.31
30 39.5 -48 0.039216 60 0.00
35 46 -55 0.045707 70 746.64
40 44.5 -52 0.59716 80 6.24
45 52.5 -54 0.67431 90 9.81
50 40 -60 0.68394 100 37.61
55 20.5 -59 0.072659 110 183113.37
60 38.5 -64 0.80217 120 2804.63
65 32.5 -68 0.82303 130 4990.89
70 27.5 -66 0.0835 140 8929.90
75 7.5 -70 0.092646 150 179.96
80 15 -78 0.97584 160 11.88
85 19 -72 0.102671 170 27.81
90 15 -81 0.105997 180 40.33
95 23 -84 0.107058 190 1171.10
100 29 -88 0.109879 200 21.56
105 15 -92 0.11 210 0.00
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Figure 2.8: A Plot of Traditional Throughput Behaviour for UNIZIK wifi hotspot
The equation that characterises the behaviour of Data
Throughput as the user density increases is given by:
y = -4.1066x + 9277
with a variance of R² = 4E-05bps.
therefore, the standard deviation (SD) = 0.0063bps. This
means that the network offers a data throughput of 0.9277bps
when no servive user has been added to the network but the
network data throughput decreases by a factor of 4.1066s for
any one user added. The plot of Received signal strength
behaviour is shown in Figure 2.9. Basically, in
telecommunications, received signal strength index (RSSI) is
a measurement of the power present in a received radio signal.
RSSI is usually invisible to a user of a receiving device.
However, because signal strength can vary greatly and impact
on functionality in hotspot scenarios, the IEEE 802.11 devices
often make the measure available to users as captured with the
sniffing tool above. Figure 2.10: shows the network latency
behaviour.
Figure 2.9: A Plot of Traditional Signal Strength Behaviour
The equation that characterises the behaviour of the received
signal strength as as the distance from the access points
increases is given by: y = -0.4925x - 35.438 with a variance of
R² = 0.9545dB. therefore, the standard deviation (SD) =
0.9770s. This means that the received signal strength at the
access point base station is -35.438dB but decreases by a
factor of 0.4925dB for any distance of 1m away from the
access point
y = 4.1066x + 9277
R² = 4E-05
0.00
20000.00
40000.00
60000.00
80000.00
100000.00
120000.00
140000.00
160000.00
180000.00
200000.00
0 50 100 150 200 250
Av
g.T
hro
ug
hp
ut
(By
tes/
Se
c)
Hotspot User Density
Throughput (BYTES/SEC) Vesus Hotspot User Density
Throughput (BYTES/SEC)
Linear (Throughput (BYTES/SEC))
y = -0.4925x - 35.438
R² = 0.9545
-100
-80
-60
-40
-20
0
0 20 40 60 80 100 120
Re
ceiv
ed
Sig
na
l S
tre
ng
th I
nd
ex
(d
BM
)
Distance from the Access Points (m)
RSSI (dBm)
RSSI (dBm)
Linear (RSSI (dBm))
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Figure 2.10: A Plot of Traditional Latency Behaviour
The equation that characterises the behaviourof Network
delay / Latency as the user density increases is given by: y =
0.001x + 0.1545 with a variance of R² = 0.0335s. therefore,
the standard deviation (SD) = 0.5788s. This means that the
network offers a delay of 0.1545s when no servive suer has
been added to the network but the network delay to data
traffic increases by a factor of 0.001s for any one user added.
4. DISCUSSION OF RESULTS From UNIZIK testbed, the following results were obtain:
As shown in figure 2.8, as the user density increases, the
average throughput starts to drop from its peak threshold. This
is peculiar with traditional WLANs From Figure 2.8, poor
quality of service usually results owing to high congestion rate
at peak traffic periods with high network density. Usually,
when the number of users on the network is below a certain
threshold, the network can perform optimally up till the peak
point. Afterwards, a gradual degradation of the output is
observed. This is regardless of the hotspot maximum buffer
configuration. Dynamic controls and intelligent user density
load control is practically difficult to obtain. Poor throughput
in bandwidth limited hotspot network like in Unizik case
study results from 100 users and above, but will eventually
return to its steady state condition with extreme user
dissatisfaction orchestrated by packet drops, network crash,
etc
From the plot of Figure 2.9, as the users move away from the
base station access point, the RSSI drops proportionally which
show that the hotspot devices cannot dynamically manage
user mobility.
In this research, after the investigations and analysis on the
selected testbed, the following were the identified challenges
with the traditional hotspot testbeds, viz: poor scalability,
impaired throughput behaviour, High infrastructure Economy
and peak network congestion.
This findings now makes it imperative to evolve an improved
services convergence network that will offer maasive
scalabiliy, reliable QoS as well addressing interoperability
problems.
5. CONCLUSION In today’s fast paced cloud computing era, QoS in high
density CWN deployment is perceived as a critical asset
considering the end user perspective. The traditional hotspot
networks based on IEEE 802.11 series lacks integrated
intelligence for service, convergence,reliable QoS
performance and in most cases suffers from interoperability
problems. Actual performance results greatly vary particularly
with (a) line of sight issues; (b) Fresnel zone issues; (c)
towers heights (d) noise floors; and: (e) and spectrum issues
such as interference.The challenge of traditional WLAN IEEE
802.11 in transmission delay of sensitive realtime voice, video
and data in this era of cloud computing can be devastating.
Packet drop and QoS degradation in existing CWN is
completely unacceptable. Again, flexibility and excellent QoS
is the desire of every hotspot user in CWN generally. In order
to achieve these, a cognitive hotspot WiMAX driven CWN
that is backward compatible with IEEE 802.11x (IEEE
802.11a, IEEE 802.11b, IEEE 802.11g, and IEEE 802.11n) is
proposed. This is intended for high density Campus hotspot
environment.
6. ACKNOWLEDGEMENT The authors wish to appreciate the ICT unit of Nnamdi
Azikiwe University, Awka, and Kswitche consult for their
supports in providing the relevant materials for this study.
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y = 0.001x + 0.1545
R² = 0.0335
0
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0 50 100 150 200 250
Av
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