Towards Realistic Software-Defined Wireless - DCA

Towards Realistic Software-Defined Wireless - DCA
How far can we go? Towards
Realistic Software-Defined Wireless
Networking Experiments
Ramon dos Reis Fontes1 , Mohamed Mahfoudi2 , Walid
Dabbous2 , Thierry Turletti2 and Christian Rothenberg1
University of Campinas (UNICAMP), Campinas, Brazil
Université Côte d’Azur, Inria, France
Email: [email protected]
Software-Defined Wireless Networking (SDWN) is an emerging approach based
on decoupling radio control functions from the radio data plane through
programmatic interfaces. Despite diverse ongoing efforts to realize the vision
of SDWN, many questions remain open from multiple perspectives such as means
to rapid prototype and experiment candidate software solutions applicable to real
world deployments. To this end, emulation of SDWN has the potential to boost
research and development efforts by re-using existing protocol and application
stacks while mimicking the behavior of real wireless networks. In this article,
we provide an in-depth discussion on that matter focusing on the Mininet-WiFi
emulator design to fill a gap in the experimental platform space. We showcase
the applicability of our emulator in an SDN wireless context by illustrating the
support of a number of use cases aiming to address the question on how far we can
go in realistic SDWN experiments, including comparisons to the results obtained
in a wireless testbed. Finally, we discuss the ability to replay packet-level and
radio signal traces captured in the real testbed towards a virtual yet realistic
emulation environment in support of SDWN research.
Keywords: Mininet-WiFi; Software-Defined Wireless Networks; OpenFlow; IEEE 802.11
Received 30 September 2016; revised 24 January 2017
WiFi is ubiquitous and has become an indispensable
part of our daily lives.
IEEE 802.11x standards
are present in almost any portable devices and allow
rolling out wireless infrastructures based on access
point devices at a relative low cost. While the IEEE
802.11x standards keep steadily evolving, there are still
many structural barriers to openness that prevent the
wireless infrastructure to adapt, be customized, allow
for differentiation, and leverage all wireless capacity
around us as consequence of being an infrastructure
closed to innovation [1].
It is believed that the concept of Software-Defined
Networking (SDN) [2] applied to wireless networks,
commonly referred to as Software-Defined Wireless
Networking (SDWN) [3, 4], can break down current
structural barriers and contribute to a more innovative
ecosystem. In spirit of (wired) SDN principles, SDWN
decouples control and data planes, enabling the wireless
network to become programmable by abstracting
the underlying infrastructure from applications and
network services that are offered with higher-level
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Application Programming Interfaces (APIs).
a consequence, wireless network infrastructures are
presented with new deployment paths of research ideas
around seamless communication over heterogeneous
wireless networks [5], high transferring data over
wireless medium [6], energy optimization [7], among
others [3, 4].
The path towards actual roll-out is not without significant challenges, such as hardware availability, protocol implementations in software, resource constraints,
realistic evaluation models, development and testing
costs and efforts, and so on. Simulators, emulators
and testbeds are common tools and approaches used
in experimentally-driven research to evaluate the functionality and performance of networks. Aspects such
as scalability, reproducibility and cost-benefit, among
others, make the simulation and emulation the most
preferred methods [8].
In the context of supporting SDWN research, a
number of alternatives have been developed such as
DCE/ns-3 [9] and OMNeT++ [10]. However, existing
simulation tools face several limitations. For instance,
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OMNeT++ has its own switch/AP implementation and
does not support third-party controllers. DCE/ns-3
uses API-specific glue code for its POSIX and kernel
support, which does not cover system calls for all
applications. Another common weakness of existing
tools is the lack of 802.11 scan mechanisms which are
critical for layer-2 handover mechanisms.
Addressing the gaps in SDWN experimentation tools
from an emulation standpoint, we propose MininetWiFi [11] as an emulator for SDWN that: (i ) allows
the execution of real code with no modification in
either kernel or applications, (ii ) is able to support best
of breed open source technologies (e.g., OpenvSwitch,
any existing OpenFlow controller), (iii ) supports the
Linux mac80211 framework that allows testing most
of the IEEE 802.11 functionality leveraging user space
tools (e.g., hostapd and wpa supplicant), among others.
Mininet-WiFi is a fork of the container-based Mininet
network emulator [12] extended to support WiFi by
adding virtualized WiFi Stations (STAs) and Access
Points (APs). Mininet-WiFi inherits all characteristics
of Mininet, including the container-based emulation,
a category of network emulation which allows the
creation of small emulated networks on commodity
hardware through the use of kernel level virtualization
techniques [13]. Like real testbeds, Mininet-WiFi runs
real code (e.g., OS kernel, external SDN controllers
applications) with real traffic (pcap traces or actual
physical, 802.11-enabled devices). Like simulators, it
supports arbitrary topologies and well-controlled yet
dynamic, real-time environments at low cost.
In this article, we pose the question on how far
can we go in performing realistic SDWN experiments
acknowledging that there is no tool that fits all
scenarios and that the research and deployment path is
dominated by fundamental trade-offs intrinsic to each
approach (i.e., testbed, simulation, emulation). More
specifically, we try to answer the question on delivering
realistic SDWN experiments based on the design
choices and current capabilities of Mininet-WiFi, which
include the support of (user-defined) mobility models,
wireless channel propagation models, the ability to
integrate with physical devices (e.g., smartphones) [14],
among other researcher-friendly features. To assess
our claims of Mininet-WiFi delivering a sweet spot
for realistic and reproducible SDWN experimentation,
we reproduce related work experiments, we present a
novel use case based on SSID-based forwarding, and
we carry extensive evaluation work to validate the
propagation models with traces and results from the
R2lab testbed.3 We also present the ability of replaying
network conditions from the real world by taking into
account observed variations in terms of bandwidth,
latency, and packet loss.
The remainder of this article is organized as
The next section provides a primer on
SDWN by introducing relevant concepts to understand
the applicability and relevance of experimental tools.
Section 3 discusses simulators, emulators and testbeds
as candidate platforms to support experimentation in
wireless networks. Section 4 presents the MininetWiFi emulator and the main features allowing
realistic SDWN experiments. Section 5 presents the
experimental outcomes of a real testbed compared
to the results obtained with Mininet-WiFi and the
DCE/ns-3. Section 6 showcases an approach to replay
captured traces supported by Mininet-WiFi. Finally,
Section 7 concludes with some final remarks and
avenues for future work.
Software-Defined Wireless Networking (SDWN) [3, 4]
is based on providing programmatic centralized control
of the network outside wireless-enabled devices (Access
Points - APs) which enforce the data plane instructions
(i.e.. policy decisions) received from the controllers. The
principles of SDWN are similar to those of SoftwareDefined Networking (SDN) [2], i.e., a networking
approach based on a programmatic separation of the
control plane (aka. Network OS) from the data plane
(aka. forwarding- or data-plane). The software-defined
approach allows administrators to specify the behavior
of the network in a logically centralized manner and at
a higher-level through APIs provided by the controller
platform that implements southbound interfaces to the
forwarding devices –the OpenFlow protocol [15] being
the most popular southbound interface (as illustrated
(Service Providers, Operators)
Northbound interface
Wireless SDN Controllers
Control/Management of Wireless
and Mobile functions
(e.g. mobility, authorization, QoS)
Southbound interface
(e.g. OpenFlow, Capwap)
Mobile terminals
Wireless RAN
FIGURE 1. Generalized and Simplified Software-Defined
Wireless Networking Architecture.
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How far can we go? Towards Realistic Software-Defined Wireless Networking Experiments 3
in Figure 1) but not the only one, CAPWAP [16],
FORCES [17], or NETCONF [18] are also candidate
protocols in scope.
SDWN has become an emerging and significant
research branch of SDN, mainly driven by the increased
interest of mobile network operators [19, 20] and
the synergies with Network Function Virtualisation
(NFV) [21]. The separation between control and
data planes has existed in the wireless domain
prior to SDN and OpenFlow.
Indeed, IETF
standardized both LWAPP (Lightweight Access Point
Protocol ) RFC5412 [22] and CAPWAP (Control And
Provisioning of Wireless Access Points) RFC4564 [16].
several years ago. Many enterprise WLAN management
systems use protocols such as LWAPP and CAPWAP to
manage their wireless network systems. LWAPP defines
the control messaging for setup and path authentication
and run-time operations whereas CAPWAP is based
on LWAPP and enables a controller to manage a
collection of wireless access points. Within the Open
Networking Foundation (ONF), the Wireless & Mobile
Working Group (WMWG) is defining a common ground
architectural framework along the necessary OpenFlow
protocol extensions or enhancements to realize the
identified use cases while leveraging related work in
other Standards Developing Organizations (SDOs) such
As per [23], over 15 use cases have been identified,
ranging from flexible and scalable packet core to unified
access networks, encompassing different elements of
OpenFlow-based or OpenFlow-oriented wireless and
mobile network domains.
SDWN research in academia has bloomed over
the last years (refer to [3] for a comprehensive
survey), including proposals such as OpenRoads [1],
Odin [24], OpenRF [25], Ethanol [26], among
others. Architectures such as CloudMac [27] and
Chandelle [28] use CAPWAP in their proposals.
CloudMac describes current WLAN management
protocols such as CAPWAP, as a protocol hard to
extend with new functionalities since CAPWAP AP
controllers are mostly proprietary systems. Chandelle,
instead, proposes a smooth and fast Wi-Fi roaming with
SDN/OpenFlow but suffers from integration challenges
with traditional switches and CAPWAP. One issue with
CAPWAP is that it tries to solve both control and
provisioning/management at once, opening the door
for conflicts due to the split of roles, e.g., consider
the management layer hazards of an AP receiving a
CAPWAP firmware update command.
Identified benefits of integrating WLAN and OpenFlow [23] are commonly related to centralized management and monitoring, unified policies, increased programmability and fine-grained control of WLAN functions. Taking into account these benefits and the limitations associated to CAPWAP –arguably the most advanced (closed) solution today for centralizing wireless
networks management prior to SDN– some questions
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are inevitable: “Is CAPWAP in scope of SDWN?”,
“How to improve the OpenFlow specification to support
centralized management of wireless networks?”, “Are
radical new designs required?” or “How much can be
leveraged from currently deployed infrastructures?”. Although these questions are still majorly open, some
noteworthy initials steps are undergoing. There is IETF
work[29] on extending the CAPWAP protocol to support separate termination points for management, control and data plane tunnels, and the definition of the
role of an AP and its controller(s) in RFC7494 [30].
In spirit of the longer-term mission and deliverables of
the ONF WMWG, the OpenFlow protocol specification version 1.5 (section B.6.3 [31]) includes a revised
behaviour when sending packets out to incoming ports,
which was a longstanding issue when mapping wireless
interfaces to switch ports.
In addition to CAPWAP-based products, there
are multiple proprietary solutions (e.g., Aerohive,
Aruba, Cisco HDX, Meraki, Ruckus) based on external
controllers to manage a collection of APs. These
commercial solutions introduce a number of extensions
to standardized protocols or define their own APIs
between the controller and APs, presenting differences
in the refactoring of control and data plane functions
in addition to a series of proprietary radio resource
enhancements. While arguably all these solutions
have proven to work well at scale, their cost is often
prohibitive for many deployments and raise concerns
due to their closely integrated nature, the consequent
vendor lock-in, and the inability for in-house or thirdparty innovations.
As today, the most realistic way to experiment with
WiFi and OpenFlow together is using open source
firmware and OS solutions like OpenWRT that allows
turning commodity wireless routers into OpenFlowenabled switches. However, like any real testbed,
such approach is subject to challenges related to the
costs and scale of the experiments, in addition to
reproducibility constraints as well as high setup times.
Wireless SDN simulators and emulators, on the other
hand, are interesting alternatives allowing to work with
multiple devices (e.g. APs and STAs) at reasonable
scale in experimenter-defined environments. Before
detailing our proposed research path based on the
Mininet-WiFi emulator, we next briefly survey the main
available SDWN research platforms.
This section provides an overview of notable simulators,
emulators and testbeds identifying their main characteristics towards a better understanding of the tradeoffs when aiming at supporting realistic SDWN experimentation.
As is well-known but commonly underestimated or
misjudged when choosing an experimental platform
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User Space
Kernel Space
station sta1
station sta2
root ap1
Mobility Models
Propagation Models
wpa_supplicant iw iwconfig
TC tool
AP mode
Configuration management
for wireless
wireless devices
station mode
Creates Virtual WiFi Interfaces
Provides MLME management services with which
drivers can be developed to support softMAC
FIGURE 2. Main components of Mininet-WiFi.
TABLE 1. Ranking of Simulators, Emulators and Testbeds (adapted from [32])
Simulators Emulators Testbeds
Total Cost
Overall Fidelity
Replay Real Traces
Real Applications
Traffic Realism
Timing Realism
to support research efforts, each environment excels
in some aspects but is subject to certain limitations
and/or constraints, as depicted in Figure 3. While
the exact quantification of each characteristic and the
degree of realism ultimately depend on the accuracy
of the model implemented in each specific tool among
other platform aspects that may affect each feature,
Table 1 (adapted from [32]) aims at illustrating the
main strengths and shortcomings typically common to
each type of experimentation approach as a first guide
to choose the best type tool for a given set of research
cost = f(CAPEX, time-to-experiment, complexity, resources, etc.)
Increased Realism/Complexity
Formal Math.
Less real experimental
Less scalability, flexibility,
reproducibility, repeatibility, etc.
Experimental Options
FIGURE 3. Overview of related work and trade-offs of
different wireless experimental platforms.
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goals and constraints.
Turning now the attention to specific wireless
simulators and emulators, Table 2 compares a number
of features including Type (e.g., Simulator/Emulator,
Open/Close source), Programming Language (which
language the solution is written) and finally Supported
Protocols (relevant protocols available by default), Last
Activity (i.e., recent updates). Similarly, Table 3
attempts to categorize and compare relevant wireless
testbeds considering different criteria.
As we can see, there are a couple of alternatives
for OpenFlow-based SDWN experimentation such as
DCE/ns-3, OpenNet, OMNeT++, and the Estinet
and Nitos testbeds. Under the emulation/simulation
space, OpenNet [34] highlights as recent work (arguably
closest to Mininet-WiFi) on combining DCE/ns-3 and
the Mininet SDN emulator [12] to provide rich SDWN
experimentation features by allowing the execution of
external controllers and real applications at the cost
of a strongly coupled solution. In addition, OpenNet
does not provide high-level abstraction APIs for wireless
links nor emulation of wireless nodes (e.g., access points
and stations are not equipped with wireless network
interfaces), and neither mechanisms to select new access
points before disconnection of current link to further
shorten handover latency.
As today, there is few information regarding Estinet,
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TABLE 2. Comparison between Mininet-WiFi and Wireless Simulators & Emulators
Mininet-WiFi [11]
DCE/ns-3 [9]
C++, Python
several different
OpenNet [34]
C++, Python
OMNeT++ [10]
Estinet [35]
ns-2 [36]
C++, TLC
Any (L3 - L7)
IEEE 802.11, 802.3,
IEEE 802.11, LTE,
IEEE 802.2, 802.11
IEEE 802.11, LTE,
IEEE 802.11,
IEEE 802.3, 802.11,
IEEE 802.11, LTE
TABLE 3. Comparative table between Mininet-WiFi and testbeds
Mobility Support
Mininet-WiFi [11]
which according to the developers can be used for many
different scenarios, including SDN. Being a proprietary
solution and due to its testbed nature, the availability
to the wider research community is limited. Nitos, in
turn, supports four OpenFlow switches and allows users
the possibility to conduct experiments in indoor and
outdoor environments.
Table 4 compares Mininet-WiFi and DCE/ns-3.4
In general, in contrast to Mininet-WiFi, DCE/ns-3
does not incorporate real-world network stacks yet and
might not support execution of unmodified applications
and/or without kernel modification. Another important
weakness of DCE/ns-3 is about the development and/or
improvements for IEEE 802.11. DCE/ns-3 depends
on the development of new models while MininetWiFi relies on the mac80211 wireless stack of the
Linux kernel. On the other hand, DCE/ns-3 supports
greater variety of mobility and propagation models
and also Long-Term Evolution (LTE) and because
of this, DCE/ns-3 has been very useful during the
development of Mininet-WiFi, serving as a basis for
reference implementation.
4 Information
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Supported Protocols
Last activity
Any (L3 - L7)
IEEE 802.11, 802.3,
IEEE 802.11, WiMAX, LTE, OpenFlow
IEEE 802.11, LTE
IEEE 802.11
IEEE 802.11, WiMAX, LTE
Bluetooth, ZigBee
sysctl, ifconfig, route
IPv6 address config.
full POSIX
poll implementation
Quagga routing stack
extensive test
real time scheduler
mobility models
propagation models
supported technologies
Comparison between Mininet-WiFi and
Mininet-WiFi is a fork of Mininet [12] extended with
the required classes to add wireless channel emulation,
node mobility, and support of 802.11 through SoftMac,
a MAC layer that provides a flexible environment
for experimenting with MAC protocols. The main
components of the Mininet-WiFi architecture are
illustrated in Figure 2. In the kernel-space, the module
mac80211 hwsim is responsible for creating virtual
Wi-Fi interfaces, enabling the creation of stations
and access points. Still in the kernel-space, MLME
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(Media Access Control Sublayer Management Entity)5
is realized by stations, while hostapd is responsible for
the counterpart task at the user-space side in APs.
Mininet-WiFi relies on a couple of standard Linux
utilities such as iw, iwconfig, and wpa supplicant. The
first two tools are used for interface configuration and
for getting information from wireless interfaces while
the latter is used with Hostapd in order to support
WPA (Wi-Fi Protected Access), among other tasks.
Both infrastructure and ad-hoc networks are supported.
Another fundamental utility to realize the emulation
of the wireless channel is TC (Traffic Control ),6 a
user-space utility program used to configure the Linux
kernel packet scheduler to control packet rate, delay,
latency, and loss. TC applies these attributes in
virtual interfaces of stations and APs, allowing MininetWiFi to replicate with high fidelity the actual packet
behavior as observed in the real world. The mobility
and propagation models do not require any kernel
modification or changes in the applications. More
details about both mobility and propagation models are
discussed below.
Current mobility models supported by MininetWiFi include: Random-Walk, Truncated-Levy Walk,
Random-Direction, Random-Waypoint, Gauss-Markov,
Reference-Point and Time-Variant Community. In
addition to these readily available model, the user is
able to determine all positions that a node have to
pass through and also its speed. This is important,
because the user may have total control over the motion
of the nodes. Basically, the mobility is defined by the
Equation 1:
Propagation Models
In the current version, Mininet-WiFi supports FreeSpace, Log-Distance, Two-Ray Ground, and International Telecommunication Union (ITU) propagation
models. Other propagation models can be easily implemented by extending a single class for propagation
models. Propagation models calculate the power of the
signal received by station (RSSI) and translate them
into “equivalent” network attributes in practice, like the
maximum supported rate or the expected packet loss.
Equation 2 shows how the RSSI is calculated.
P athLoss = P ropagationM odelF ormula
signalStrength = pT + gT + gR − P athLoss
First, the user must set the propagation model
(defined by PropagationModelFormula) to be used
(free-space is defined by default if no propagation model
is set) in order to calculate the path loss (PathLoss).
Diverse parameters are considered in the propagation
models’ formula, such as distance between transmitter
and receiver, frequency, system loss, etc.
the RSSI (signalStrength) is calculated taking into
account the transmission power (pT), antenna gains of
transmitter (gT) and receiver (gR) and the Path Loss.
The RSSI is used to calculate the maximum
supported rate during a communication between two
nodes. We first rely on the technical specification of
the physical devices (e.g., commercial wireless NIC) to
define the custombw thresholds. The link bandwidth
depends on the actual signal strength (RSSI) and
the maximum supported rate for a specific RSSI.
Finally, taking into account the distance between the
transmitter and receiver, the rate is calculated through
Equation 3.
rate = custombw ∗ (1.1−dist )
A similar approach is used to calculate packet loss.
However, as there is no specification for actual packet
loss, we only take into account the distance between
transmitter and receiver as depicted in Equation 4.
intervalT = endT ime − startT ime
posx , posy , posz = initP osx , initP osy , initP osz
f acx = (f inalP osx − initP osx )/intervalT
f acy = (f inalP osy − initP osy )/intervalT
loss = (dist ∗ 0.2)/100
f acz = (f inalP osz − initP osz )/intervalT
posx , posy , posz = posx + f acx , posy + f acy , posz + f acz
First, we determine the interval time (intervalT)
that the node will move to and them we calculate the
motion (that includes the speed of the node) based on
both initial (initPos) and final position (finalPos)
previously defined by the user for getting what we call
by motion factor (fac). Finally, we take both initial
position and motion factor in order to calculate the next
position of the node (defined and updated by pos) until
the final position.
5 Some of the functions performed by MLME are authentication, association, sending and receiving beacons, etc.
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Equations 3 and 4 are based on generic approaches
that by default try to mimic the behavior presented
by R2lab. In the future, we plan to further improve
the models by supporting minstrel,7 a mac80211 rate
control algorithm, and wmediumd,8 related work that
provides enhancements on the wireless medium for
mac80211 hwsim.
Scenario Diversity
As previously discussed, Mininet-WiFi supports both
infrastructure and ad-hoc networks modes, i.e., it
supports APs where stations may connect to and
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How far can we go? Towards Realistic Software-Defined Wireless Networking Experiments 7
(a) Sample Topology
Throughput (Mbits/sec)
stations are also able to directly connect to each
other. Both infrastructure and ad-hoc modes can
coexist in the same topology/experiment, extending the
variety of possible experimentation scenarios. As an
example of ad-hoc networks, Mininet-WiFi supports
VANET (Vehicular Ad Hoc Networks) scenarios
through an integration with SUMO (Simulation of
Urban MObility) providing the node mobility patterns.
Further features in scope of diverse SDWN scenarios
include support of Wi-Fi Direct (or Wi-Fi P2P), a
technology developed by the Wi-Fi Alliance to replace
the Wi-Fi ad-hoc mode, user-defined control on node
mobility to allow limiting the region of motion (even
when default mobility models are used), replaying
network conditions (see Sec. 6.3); and allowing
hybrid environments blending real physical nodes
(e.g. smartphones) with the emulated environment as
publicly demonstrated [14] and further discussed in the
use case experiments (see Sec. 5.3). Further diverse
scenarios enabled by the experimenter-friendly features
are being driven by the user community.9
with bicasting
Mininet-WiFi is under continuous development driven
by SDWN experiments that define the feature roadmap.
As of today, a number of limitations worth to
note include: (i ) lack of mechanisms for channel
contention (e.g. CSMA-CA), as well as for MAC layer
retransmission and interference; (ii ) WDS - Wireless
Distribution System support;10 (iii ) although beacons
can be captured using any packet sniffer, changes
on the RSSI by the propagation models in place are
not included in the packet header yet; and (iv ) any
inherited limitations from Mininet, mainly concerning
performance fidelity under high workloads and overall
scalability in terms of total amount of nodes interfaces
before performance fidelity degrades which ultimately
depends on the system platform (e.g., number of cores,
memory, etc.).
with bicasting
This section showcases how Mininet-WiFi facilitates
fast prototyping and experimentation of SDWN use
cases along its ability to reproduce experiments from
the literature. All code and instructions to reproduce
the four selected use cases (and further ones) are
publicly available in the source code repository11 and
documented in a comprehensive user manual.
10 Required improvements of mac80211 hwsim are underway;
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no bicasting
Time (seconds)
(b) Bandwidth Measurement between STA3 and H4
FIGURE 4. Bicasting over WiFi
Wireless n-Casting
This use case aims at replicating OpenFlow wireless
work [41] on bicasting over multiple wireless links.
The original experiment consisted of a video streaming
application running in a mobile station, equipped with
two WiFi and one WiMax interface, and receiving
the packet flow over multiple radios simultaneously
(n-casting). The mobile station was attached to the
multiple APs using the same SSID and OpenFlow rules
were used to duplicate packets in the wired network and
re-write the L2/L3 headers at the radio access points.
As a result, the quality of experience is improved by
compensating packet loss over one wireless link by the
duplicated stream received over the alternative links(s).
In the strawman scenario shown in Figure 4(a), we
use Iperf to measure the bandwidth between STA3 and
H4 during 60s. STA3 has two wireless interfaces and
both interfaces are connected to different APs (AP1 and
AP2), which are connected to an OpenFlow controller
as well as the switch S1. OpenFlow rules ensure that
packets are copied and sent through the different paths
to the stations. During the first 20s the measured
bandwidth is about 40Mbps, which seems coherent with
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(a) Topology setup
mininet-wifi> h10 ifstat
KB/s in KB/s out
30592.15 886.37
30592.50 872.26
30594.53 870.65
30590.97 870.02
30594.11 870.48
30592.60 869.30
30593.90 868.02
30591.12 869.98
30594.98 871.94
30590.90 867.48
mininet-wifi> sta1 ifstat
KB/s in KB/s out KB/s in KB/s out
246.04 5047.28 632.20 25951.69
245.37 5045.90 624.49 25949.27
246.96 5045.91 625.94 25950.82
245.09 5047.38 624.06 25949.16
245.19 5047.35 624.34 25950.53
247.08 5045.96 625.22 25949.61
245.21 5047.43 625.50 25950.95
246.84 5046.30 624.95 25948.97
244.79 5047.37 625.49 25950.64
246.59 5045.86 624.63 25950.56
(b) Results
FIGURE 5. MultiPath TCP
the 54Mbps limit of each interface in mode g. When the
time reaches 20s one wireless interface is disconnected
(from AP2) causing a traffic decrease. The bandwidth
at the station increases again when the wireless interface
is connected back to AP2 at 40s (Figure 4(b)).
FIGURE 6. Realistic experiment in hybrid physicalvirtual environments
interfaces of sta1 (sta1-wlan0 and sta1-wlan1 ) and the
amount of data received by the sta1-wlan0 interface
is less than sta1-wlan1 (around 1.97Mbps and 5Mbps,
respectively), due to the different modes of operation
(IEEE 802.11g and IEEE 802.11n) used in each
interface. Following the configuration provided by the
AP TP-Link TLWR740N13 chosen for this experiment,
theoretically, up to 54Mbps over 802.11g and up to
130Mbps over 802.11n can be provided when the signal
is up to -68 dBm. Considering the signal strength
measured at the station '-34.3 dBm over IEEE 802.11g
and '-19.1 dBm over IEEE 802.11n, the maximum data
rate capacity is consumed.14 The maximum data rate
is calculated by taking into account the rate supported
by TP-Link TLWR740N and Equation 3.
Multipath TCP
We now showcase the use of MPTCP (MultiPath
TCP),12 a TCP extension that allows end-hosts to use
multiple paths to maximize network utilization and
increase redundancy.
Previous work [42] presented the integration of
Mininet and physical wireless devices in order to
evaluate the gains of MPTCP in conjunction with
SDN path control. We show the ability of MininetWiFi to reproduce similar experiments using only a
virtual environment, including wireless devices, such
as laptops acting as stations and APs. Allowing APs
to be controlled by external OpenFlow controllers,
different from previous work, both wired switches and
the APs are unifiedly managed through the common
flow abstraction.
The topology setup (Figure 5a) consists of 1 station
(sta1) acting as a client and 2 access points (ap2 and
ap3), with the distance between sta1 and ap2 being
8.06 meters and 1.41 meters between sta1 and ap3 ; 1
host (h10) acting as a server; and 1 OpenFlow controller
responsible for the flow control based on a networkwide view. In order to obtain the results presented in
Figure 5b, we measure the bandwidth between sta1 and
h10 using ifstat to report network interfaces bandwith
for each packet transmission. As expected, the results
show that the data is transmitted by the two Wi-Fi
The Computer Journal,
Hybrid Physical-Virtual Environment
This use case experiment was first presented in [14] and
features a hybrid physical-virtual environment where
real users connect their 802.11-enabled smartphones to
interact with the virtualized infrastructure, including
nodes forming a mesh subnetwork, and access the global
Internet after having its traffic processed through a
multi-hop OpenFlow network. Figure 6 illustrates the
scenario, where virtual and physical mobile devices
are able to interact with each other with the switch
behaviour being defined by an OpenFlow Controller.
The OpenFlow controller initially discovers the
topology and installs the required L2 flow entries to
allow connectivity between the APs. Next, the user
connects to AP1’s SSID and tries to access any Internet
Web page via HTTP. The OpenFlow controller installs
one rule to re-write the IP destination address and to redirect all user’s HTTP traffic to a captive portal where
the user is expected to authenticate in order to get
Internet access and unlock the initial bandwidth limits
enforced through OpenFlow 1.3 metering actions. The
user can also communicate (e.g. PING) with mobile
nodes forming a mesh network.
13 Mininet-WiFi supports technical specifications of several
14 TLWR740N.html#specificationsf
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How far can we go? Towards Realistic Software-Defined Wireless Networking Experiments 9
SSID-based Flow Abstraction
This use case illustrates the ability of fostering SDWN
research and experimentation by prototyping and trying
out ideas around SSID-based packet forwarding as
illustrated in Figure 7. This scenario is similar to the
one described in [43] but is novel in the OpenFlow
implementation choice. We mimic the case presented
by the authors by assigning unique Service Set-Identifier
(SSID) for each user (or group of users) and managing
all flows through an OpenFlow controller that defines
different bandwidth limit on an SSID basis. It is very
common for an organization to have multiple SSIDs in
their wireless network for various purposes, including:
(i ) to provide different security mechanisms such as
WPA2-Enterprise for your employees and an “open”
network with a captive portal for guests; (ii ) to split
bandwidth among different types of service; or (iii ) to
reduce costs by reducing the amount of physical access
Using multiple SSIDs requires the AP to map each
station to a different network connection. Traditionally,
this fixed mapping is accomplished through VLAN
tagging. In our case, we use the OpenFlow protocol
to apply rules based on input/output ports as instances
of the SSIDs abstractions. Multiple SSIDs are created
in APs and each SSID is linked to separated sub/virtual
OpenFlow rules defined how traffic is
being handled and allowed through different SSIDs.
By acting on ports, no changes were required to the
OpenFlow protocol in order to support this use case.
One drawback in our current implementation is the
WiFi NIC limit of 8 sub/virtual interfaces per virtual
interface constraining each AP to handle up to 8
different SSIDs whereas commercial WLAN solutions
are known to be able to create up to 64 SSIDs per AP.
To the best of our knowledge, this is the first time
that traffic based on a specific WLAN attribute (SSID)
is defined through OpenFlow rules. The technique
is an example that may lay the groundwork for the
implementation and evaluation of more sophisticated
scenarios using Mininet-WiFi, for instance: (a) WiFi Hotspots for Public Wireless Access [44]; (b)
Community Wi-Fi [44]; and (c) virtual Service
Providers (vSPs) [45].
This section delves into experiments to assess the
realism of the wireless channel emulation and overall
end-to-end system provided by Mininet-WiFi. To this
end, we conduct a series of experiments in R2lab,15 an
anechoic wireless testbed that will allow us to compare
the results obtained in the physical testbed to the
results provided by Mininet-WiFi. In a nutshell, R2lab
consists in a set of thirty-seven nodes on the ceiling
of a room of approximately 90m2 distributed in mesh
The Computer Journal,
FIGURE 7. Forwarding by SSID
(a) Overview of R2lab’s chamber
(b) Sample Topology
FIGURE 8. Experimental validation by using R2lab
layout to offer an advanced simulation Wi-Fi site (see
R2Lab Testbed
Mininet-WiFi (Free-Space)
Mininet-WiFi (Log-Distance)
Mininet-WiFi (ITU)
log(distance) - meters
9 10
FIGURE 9. Signal Propagation in R2lab and in different
indoor propagation models implemented in Mininet-WiFi
Vol. ??,
No. ??,
R. F. Ramon
Figure 8). Being an RF anechoic chamber built into a
screened room, it provides a suitable environment for
high-fidelity, reproducible Wi-Fi experimentation.
Propagation Model
In order to evaluate the propagation models implemented in Mininet-WiFi, this case experiment uses
R2lab nodes to obtain the signal received by the stations with varying distances.
6.1.1. Approach
We first choose some nodes (3, 8, 13, 17, 21, 24
and 28) as reference nodes, where node 3 acts as the
transmitting station and the other nodes as receivers
(APs) located at different distances.16 All the nodes
were configured with specific rate mask limited to
11Mbps, running in mode b, channel 6 and transmission
power equal to 15dBm.
Then, we used readily
available instrumentation (wireshark ) to record the
RSSI between the communicating nodes and compared
them with the RSSI values provided by the three
supported indoor propagation models in Mininet-WiFi.
Parameters for each propagation model include:
Free-Space: system loss = 2
Log-Distance: exponent = 3; system loss = 2
ITU: power loss coefficient = 32; floor penetration
loss factor = 0; number of floors = 0
Note that variations in these parameters directly
influence the outcome of each propagation model.
System loss is a factor which is not related to
propagation; exponent represents the path loss
exponent whose value is normally in the range
of 2 to 4 depending of the type of environment;
power loss coefficient represents the quantity that
expresses the loss of signal power with distance; floor
penetration loss factor is an empirical constant
dependent on the number of floors the waves need
to penetrate; and number of floors represents the
number of floors. Being user-defined parameters in
the propagation class of Mininet-WiFi, the parameters
can be tuned according to observed in particular
scenarios with “snowflake” characteristics (e.g., room
geometry, obstacles, etc.), a fact that can be exploited
when aiming to reproduce physical experiments using
6.1.2. Results
Figure 9 illustrates the results obtained for the same
experiment setup in R2lab and Mininet-WiFi. We can
observe the relationship between RSSI and distance,
i.e., the signal decrease as the distance between the
transmitter and receiver increases. Based on these
results, we conclude that the ITU propagation model is
more appropriate. Furthermore, based on the measured
RSSI values, the model can be calibrated to reflect with
more accuracy the conditions of the target environment,
R2lab in our case for the follow-up experiments.
All logs from R2lab experiments can be found
Simple File Transfer
We now aim at assessing the end-to-end
user/application experience in a real setup, ns-3
and in Mininet-WiFi. The experiment consists of
transferring a single file between two nodes in the
R2lab testbed (Figure 10) and measure the transferring time, throughput, latency and packet loss. Then,
we replicate the same scenario (e.g., node distance,
WiFi modes, etc.) in Mininet-WiFi and ns-3 in order
to compare the similarity of the obtained results and
hence assess the realism that the different tools provide
compared to a testbed experiment.
6.2.1. Approach
We select node 13 as the AP and nodes 3 and 24 as
the server and client, respectively (see Figure 10). We
then transfer a 62.6MB file between server and client
by using wget over TCP and repeat this process for 10
times and measure the total transfer time, throughput,
delay, and packet loss. Relevant setup details of this
experiment towards reproducibility include: a) nodes
working on channel 1, transmission power equals to
15dBm and IEEE 802.11b enabled; b) distance between
nodes 3 and 13, and 13 and 24 equals to 2.72m and
4.08m, respectively.
6.2.2. Results
As we can observe from the results presented in Table 5,
the experiment run in Mininet-WiFi yields results very
close to those obtained in the R2lab testbed. The
observed difference of the average transfer time of 10
runs (stdev) is only 5 seconds of a total of about
180 seconds. The measured bandwidth and end-to-end
latency are also quite similar. When compared to the
results obtained in ns-3 for the same configuration17
we observe higher throughput over the wireless channel
but less goodput. Inspecting the pcap capture we
observe a high amount of TCP retransmissions and also
17 Information about the file configuration can be found at the
Mininet-WiFi’s manual
16 Node 33 was not chosen due to observed misbehavior at the
time of the experiments.
The Computer Journal,
FIGURE 10. Sample Topology
Vol. ??,
No. ??,
How far can we go? Towards Realistic Software-Defined Wireless Networking Experiments11
Packet Loss
Transf. Time
TABLE 5. Mean values of measures obtained from tests
out-of-order and duplicated packets corresponding to
approximately 16% of the total packets transferred to
complete the file transfer. The observed retransmissions
explain why ns-3 provides higher throughput, but
finalizing the file transferring in almost 4 minutes. We
do not have a clear explanation for the deviations
obtained in the ns-3 results, once we tried to our best to
configure the simulation parameters to match the same
experiment setup run in R2lab and ns-3.
Replaying Network Conditions
The wireless medium is known for the frequent
variations in networking conditions change due to
multiple reasons, such as cross traffic and many sources
that contribute to fluctuations of the physical medium.
We now focus on the ability of Mininet-WiFi to
dynamically change the parameters of the network links
(e.g. bandwidth, packet loss, latency and delay) based
on the distance between the communicating nodes and
eventually augmented with observations from an actual
experiment in the real world.
Being able to replay real networking conditions
based on traffic observations in real environments
is useful to predict network performance under
certain conditions, reason about the observed network
behavior, and perform fair comparisons between
alternative algorithms’ implementations subject to
the mirrors of the physical network.
works have explored this approach [46, 47], some of
the including wireless scenarios, e.g., TraceR [48],
OMNeT++ [10], SimGrid [49], and others. To the best
of our knowledge, Mininet-WiFi pioneers the use of this
technique in SDWN.
6.3.1. Approach
In this scenario, we transfer a 62.6MB file between two
nodes in R2lab and collect real traffic using wireshark.
By filtering by TCP protocol, we record information
about bandwidth and latency. We then replay these
information (traces) in Mininet-WiFi by dynamically
redefining link bandwidth and latency using Linux
TC and measure the results using both 802.11b and
6.3.2. Results
The results are presented in Figure 11. As expected,
the total transfer time was less for 802.11g than
802.11b due to the higher transmission speed. MininetWiFi outputs a similar behaviour but with slightly
The Computer Journal,
higher throughput, and, consequently, finalizes the file
transfer file process before R2lab. We believe that
the difference is due to the amount of ACK frames
generated during the communication by both receiver
and transmitter, where in Mininet-WiFi those frames
are sent out to a specific interface called hwsim0 by
default. This behaviour differs from the real world
where wireless network interfaces are responsible for
processing any packet including ACK frames. If more
accurate results are desired, the parameters of the
wireless channel emulation can be tuned, for instance to
reduce the bandwidth (axis X) as needed. In the case
of IEEE 802.11g, the average consumed bandwidth in
Mininet-WiFi and R2lab during the file transferring was
1.66Mbps and 1.56Mbps, respectively. Thus, reducing
the bandwidth provided by Mininet-WiFi in 0.1Mbps
(i.e., about 1%) should be enough for getting further
closer results when aiming at reproducing experiments
for this specific use case scenario.
Regarding latency and packet loss both R2lab and
Mininet-WiFi provide very similar results. Altogether,
we believe that the results are accurate within an
order of magnitude, which seems to be a “good
enough” result for the wireless channel emulation
sufficient to support SDWN research commonly focused
on the higher-layer control and management features.
Nevertheless, we plan to keep improving our approach
to emulate the wireless medium with increased fidelity
and reproducibility (aka “replay”) features in order
to provide more realistic experimentation options and
deliver accurate results comparable to those from a real
This article revolves around the question of carrying
realistic SDWN experiments, and in particular, the
advances reached by our Mininet-WiFi emulation
Extending the success story of Mininet for wired
SDN into wireless, Mininet-WiFi introduces itself
as a powerful tool for SDWN research and WiFi
experiments by supporting arbitrary Linux-based
applications, OpenFlow programmability, external
SDN controllers, mobility and propagation models
among other experimenter-friendly features.
We believe that a lightweight virtualization together
with good enough wireless channel emulation capabilities, mobility models and overall experimental scenario
reproducibility are steps forwards towards cost-effective
and realistic SDWN experimentation. Can we go further? Certainly.
With Mininet-WiFi we have achieved significant
steps as validated through the prototype use cases
presented and the proof-of-concept experiments to
validate our claims of realistic SDWN emulation. To
this end we conducted experiments in a real testbed
and compared the results with those from the MininetVol. ??,
No. ??,
R. F. Ramon
Latency (ms)
Bandwidth (Mbps)
Captured Traces - R2lab
Replay Captured Traces - Mininet-WiFi
Time (seconds)
Captured Traces - R2lab
Replay Captured Traces - Mininet-WiFi
Latency (ms)
Captured Traces - R2lab
Replay Captured Traces - Mininet-WiFi
Bandwidth (Mbps)
(a) IEEE 802.11b
Captured Traces - R2lab
Replay Captured Traces - Mininet-WiFi
Time (seconds)
(b) IEEE 802.11g
FIGURE 11. Replaying Network Conditions
WiFi propagation models. Furthermore, we showed
the ability to replay network conditions from the real
testbed to reproduce in the emulated environment the
expected behavior from a real world experiment.
Altogether, the obtained results suggest that
Mininet-WiFi is able to mimic the real world
with enough fidelity to support meaningful SDWN
experiments. All necessary code and instructions to
reproduce each of the experiments presented in this
article and additional ones are available in the project
In order to reach further, some of our ongoing efforts
include (i ) further validation (including scalability limits) via experiments of more complex scenarios, (ii ) improve the replaying features of captured traces in order
to provide more accurate results and facilitate reproducibility (e.g., configure statistics/results collection),
and (iii ) keep adding features and support of scenarios
as requested by the increasing user community via the
mailing list and the public code repository.19
This research was partially supported by FAPESP grant
# 14/18482-4. The R2lab wireless testbed at Inria has
been funded by the ANR Equipex FIT 6165.
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