Empowering Software-Defined
Wi-Fi Networks
QoS-Aware Load Balancing for High Density Software
Defined Wi-Fi Networks
MUHAMMAD FAIZAN
Mirpur University of Science and Technology, Pakistan
Email: mailto:mfaizan
Phone: +92 349 6930770
LinkedIn: linkedin.com/muhammad-faizan
TABLE OF CONTENTS
TABLE OF CONTENTS ............................................................................................................. 2
EXECUTIVE SUMMARY .......................................................................................................... 3
PROJECT CONCEPT ................................................................................................................. 4
TARGETED AUDIENCE | MARKET ....................................................................................... 5
CHALLENGES AND GOALS .................................................................................................... 6
THE BUSINESS PLAN ................................................................................................................ 6
CORE TECHNOLOGIES ........................................................................................................... 7
TERMS AND CONDITIONS ........................................................................................................ 8
AGREEMENT ................................................................................................................................ 9
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EXECUTIVE SUMMARY
High-density Wi-Fi networks struggle with congestion and uneven load distribution,
leading to degraded user experience and inefficient resource utilization. Our project
introduces QDLB, a “QoS through Dynamic Load Balancing” scheme utilizing SDN and
machine learning to dynamically balance network load across OpenFlow-enabled Access
Points (OAPs). QDLB innovates by Multi-metric Analysis, Centralized Management and
Hardware Agnostics.
The Team | QDLB Innovators (MUST)
QDLB (QoS through Dynamic Load Balancing). From the bustling halls of Mirpur
University, we, B.Sc. Electrical Engineering students, aren't just learning - we're
reimagining the network future. Inspired by our mentor, Dr. Sohaib Manzoor, we
tackled the toughest challenges: load balancing, hand-off delays, and ensuring flawless
QoS.
The Imagine Cup ignited a different flame. We mustered our skills, unified our vision,
and transformed into QDLB Innovators MUST. Our QDLB scheme orchestrates a digital
symphony, where every packet flows seamlessly, delivering exceptional user
experience. We're not just a team, we're a family, fueled by curiosity and driven by the
desire to leave our mark.
I Muhammad Faizan is currently enrolled in the B.Sc. degree in Electrical Engineering
at Mirpur University of Science and Technology, Pakistan. My journey in this realm
began with the thrill of crafting code in C++ and Python, then evolved into meticulously
configuring network simulations with OMNeT++. This hands-on approach, coupled with
deep dives into tools like Wireshark and the prowess of MATLAB, has equipped me with
a comprehensive understanding of network behaviour and optimization techniques. But
my true passion lies in pushing the boundaries of SDN. Currently, I'm engrossed in
research that delves into the intricate dance of network efficiency, scalability, and
adaptability. Optimizing load balancing, minimizing hand-off times, and achieving
unwavering quality of service – these are the challenges that ignite my fire. The Imagine
Cup Hackathon is more than just a competition for me; it's a stage to channel my
passion and expertise into collaboration and innovation. I envision joining forces with
brilliant minds, sharing insights, and building solutions that can transform how we
experience the connected world.
Mentor– Our project is proudly guided by Dr. Sohaib Manzoor, a renowned leader
and mentor in the field of information and communication engineering. Dr. Manzoor
brings a formidable combination of academic excellence, with a Gold Medal in his B.Sc.,
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a Distinction in his M.S. from Coventry University, and Academic Excellence Award from
Huazhong University, China in his Ph.D. He is a recipient of numerous best paper and
presentation awards in international forums. Recently, he received the Prestigious
Science Award from the Ministry of Science and Technology, China. His expertise,
encompassing SDNs, wireless LANs, edge computing, machine learning, and
programming, directly aligns with our project goals. Notably, his publications on load
balancing in multi-controller SDN Wi-Fi networks and reducing handoff times in Wi-Fi
networks using SDN provide invaluable insights for our research. Working with Dr.
Manzoor is not only a privilege but a catalyst for our growth. His deep knowledge,
professional approach, and unwavering support create the perfect environment for us
to thrive. We are confident that under his guidance, our project will reach its full
potential, making a significant impact on the field of SDNs.
PROJECT CONCEPT
In traditional Wi-Fi networks, the load balancing is often uneven due to wireless stations
making association decisions themselves, leading to congestion and degraded QoS. In
this proposal we introduces the new paradigm QoS through Dynamic Load Balancing
strategy for software-defined Wi-Fi networks (SD-Wi-Fi) to address the challenges of
load imbalance, congestion, and degraded QoS in high-density environments. The
proposed strategy involves designing two modules, one for the OpenFlow enabled
access points (OAPs) and one for the controller. The OAPs dynamically adjust their load
level according to the network conditions and make association decisions based on a
multi-metric criteria (packet loss rate, RSSI, and throughput) to satisfy the least loaded
conditions. The controller module monitors the network conditions and provides
feedback to the OAPs to ensure that the network load is balanced and QoS is
maintained. The proposed strategy does not require any hardware changes and is
applicable to any wireless device that supports the OpenFlow standards. A prototype
testbed is designed to perform real-time experiments, taking into account factors such
as signal fading and channel interference, which are often ignored in emulation. The
results from the emulation and testbed experiments demonstrate the efficiency of the
proposed strategy. The strategy outperforms existing load balancing methods in terms
of normalized throughput, turn-around time for AP association and de-association
processes, and overall QoS.
This is just the first movement of QDLB's symphony. We envision a future where QDLB
integrates with mobility management, allowing users to roam seamlessly between OAPs
without hiccups. We're also exploring adaptive load balancing strategies, constantly
refining QDLB's ability to orchestrate the perfect digital flow.
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TARGETED AUDIENCE | MARKET
We believe that our findings represent a major advance in the area information and
communication technologies. This is the first and standardised method of enhancement
and optimization of load balancing and quality of service (QoS) in high-density softwaredefined Wi-Fi (SD-Wi-Fi) networks. Specifically, the project aims to improve the
distribution of network traffic, reduce congestion, and enhance overall network
performance in environments with a high density of Wi-Fi devices.
Personas
The strategy can help ensure that Every Wi-Fi User experiences improved connectivity,
reduced latency, and minimal packet loss, leading to a more reliable and seamless user
experience. The proposed strategy can be marketed to a wide range of industries and
organizations that rely on Wi-Fi networks to provide reliable and high-quality
connectivity to their customers, employees, and stakeholders. Some potential markets
for the project include:
1. Hospitality Industry: Hotels, resorts, and other hospitality businesses often face
the challenge of providing reliable Wi-Fi connectivity to a large number of guests
in a high-density environment.
2. Education Sector: Schools, colleges, and universities often have large numbers of
students, faculty, and staff using Wi-Fi networks simultaneously, leading to
network congestion and performance issues.
3. Healthcare Industry: Hospitals, clinics, and other healthcare facilities rely on WiFi networks to provide critical services such as patient monitoring, electronic
health records, and communication between healthcare professionals.
4. Retail Industry: Retail businesses often use Wi-Fi networks to provide in-store
connectivity to customers and employees.
5. Transportation Industry: Airports, train stations, and other transportation hubs
often face the challenge of providing reliable Wi-Fi connectivity to a large number
of passengers in a high-density environment.
The proposed project can help Industries and Education Sectors optimize their Wi-Fi
networks, improve performance and reliability and provide a better learning and
working experience for their users.
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Overall, the proposed project can be marketed to any industry or organization that
relies on Wi-Fi networks to provide reliable and high-quality connectivity to their users
in a high-density environment.
CHALLENGES AND GOALS
Challenges
Goals and Objectives
Industry Adoption: Convincing
industries to adopt a new approach to
Wi-Fi network management, especially
in high-density environments, may pose
a challenge due to existing
infrastructure and established practices.
Education and Awareness: To educate
potential stakeholders about the benefits of
the proposed strategy, emphasizing
improved network performance, enhanced
user experience, and long-term cost
savings.
Scalability and Adaptability: Highlight the
Cost and Investment: Implementing the scalability and adaptability of the strategy
proposed strategy may require initial
to cater to diverse industry needs,
investment in hardware, software, and emphasizing its potential to address the
expertise, which could be a deterrent.
challenges of high-density Wi-Fi networks
across various sectors.
Technical Complexity: The technical
intricacies of software-defined
networking and multi-metric load
balancing may present a barrier to
adoption for organizations without
extensive expertise in network
management.
Demonstrated Value: Showcase the
tangible benefits of the strategy through
case studies, pilot implementations, and
performance metrics to build confidence in
its effectiveness.
THE BUSINESS PLAN
1. Industry Partnerships: Our team aspire to collaborate with industry leaders in
hospitality, education, healthcare, retail, and transportation to pilot the strategy
in real-world environments, demonstrating its effectiveness and building
credibility.
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2. Thought Leadership: We are establishing the project team as thought leaders in
the field of high-density Wi-Fi network optimizing through publications,
conference presentations, and industry events to gain visibility and credibility.
3. Training and Support: We can offer training programs and ongoing support to
help organizations implement and manage the strategy effectively, addressing
concerns about technical complexity.
By addressing these challenges, aligning with key goals, and implementing targeted
strategies for market entry, the proposed QoS-aware load balancing strategy can be
effectively brought to the market, offering tangible benefits to a wide range of
industries reliant on high-density Wi-Fi networks.
CORE TECHNOLOGIES
The proposed QoS through Dynamic Load Balancing (QDLB) strategy for high-density
software-defined Wi-Fi networks uses several platforms and paradigm technologies,
including:
1. Software-Defined Networking (SDN): The strategy leverages the SDN
architecture to separate the control plane from the data plane, allowing for
centralized network management and control. This enables the SDN controller to
collect information from the OpenFlow enabled Access Points (OAPs) and make
informed decisions about load balancing and network optimization.
2. OpenFlow Standards: The strategy utilizes the OpenFlow standards to enable
communication between the SDN controller and the OAPs. This allows for a
unified interface for network administrators to add new applications and
functions to the load balancing strategy without changing the original hardware
and software framework.
3. Multi-Metric Criteria: The strategy uses multi-metric criteria, including packet
loss rate, Received Signal Strength Indicator (RSSI), and throughput, to select the
best under loaded destination OAP for wireless stations to re-associate. This
ensures that the load balancing decisions are based on multiple factors, leading
to more efficient and effective load distribution.
4. Programmable Wi-Fi Systems: The strategy proposes the use of programmable
Wi-Fi systems, such as Zynq-based systems, to design and implement an
intelligent load balancing scheme in a software/hardware co-design approach.
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This allows for greater control over wireless parameters and further optimization
of the load balancing strategy.
TERMS AND CONDITIONS
By now you’ve outlined the challenges the reader is facing and how you can help. The final
stage is to summarize the overall agreement you’re entering into, and close the deal. Once
you’ve reached this stage, you’re looking to lock in the client to a legal agreement, so it’s very
important to make this section thorough, clear and accurate. Get legal advice if you need it, to
make sure you’ve covered all your bases.
Having a concise and digestible terms and conditions leaves less room for misunderstanding at
a later stage, so make sure you capture details such as:



Project timeline and milestones
Dates for review as needed
Payment terms, dates and methods
Here’s an example to consider - add and edit to make sure your final business proposal covers
everything you need for your specific project:
This [product/service] Business Proposal Contract outlines the terms and conditions that govern
the contractual agreement between [Your Company] and [client company]. Both [Your
Company] and [Client company] agree to be bound by the terms laid out in this Business
Proposal Contract.
whereas, the Seller agrees to deliver [product/service]
whereas, the Purchaser agrees to purchase [product/service] according to the terms and
conditions laid out in this contract.
Therefore, in consideration of the mutual agreement made by the parties hereto, the Seller and
the Purchaser agree to the following:
Insert your terms and conditions here.
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AGREEMENT
In signing this document below, [Your Name] and [Client Name] confirm their agreement to the
terms and conditions laid out in this business proposal and form a binding contractual
agreement beginning on the date of signing.
[Your Company]
[Client Company]
Signature
Signature
Date
Date
[Your Name]
[Client Name]
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