In order to deploy unmanned aerial vehicles (UAVs) in a variety of scenarios and to ensure the performance, reliability, and safety of these systems, a flexible and adaptable core network infrastructure is required. In this regard, a software-defined network (SDN), allows centralized control and coordination using software rather than traditional hardware-based controllers for the operation of UAVs to provide a flexible and scalable platform. On the other hand, digital twin (DT) technology has the potential which can provide real-time monitoring, analysis, and virtualization of network performance with rapid prototyping and testing to revolutionize the way that software-defined UAV networks are designed, implemented, and maintained. Therefore, in this paper, DT integrated approach for software-defined UAV networks is proposed with a routing flow adjustment algorithm. We also demonstrate its capability through the M/M/1 queueing model. Results have shown that our proposal outperforms typical queueing models without the integration of SDN and DT in terms of efficiency, reliability, and agility of UAV networks and provides visualized network analyzing platform with a real-time monitoring feature.

1. 10th International Conference on Signal Processing and Integrated Networks
DATE: 23-24 March 2023
Technically Co-Sponsored by: IEEE Computational Intelligence Society
Organized By: Department of Electronics and Communication Engineering, Amity University, Noida, Delhi-NCR, India
“Digital Twin-based Software-defined UAV Networks Using Queuing Model”
Paper ID: 294
Dr. Mostafa Zaman Chowdhury
Professor
Dept. of Electrical and Electronic Engineering
Khulna University of Engineering & Technology (KUET)
Khulna-9203, Bangladesh
Md. Abu Baker Siddiki Abir
Graduate Student
Institute of Information and Communication Technology
Khulna University of Engineering & Technology (KUET)
Khulna-9203, Bangladesh
Author Co-Author

2. OUTLINES
PRESENTED BY: MD. ABU BAKER SIDDIKI ABIR
1
3
4
5
6
7
8
2
INTRODUCTION
OBJECTIVES OF THIS STUDY
PERFORMANCE EVALUATION
FINAL OUTCOMES
FUTURE WORKS
CONTRIBUTIONS
PROPOSED ARCHITECTURE
STSTEM MODEL

3. INTRODUCTION
PRESENTED BY: MD. ABU BAKER SIDDIKI ABIR 1/20
Mobility [1]
Rapid deployment
Greater coverage
Flexibility [2]
Cost-effective and time saving [3]
WHY UAVS FOR NETWORKING?

4. INTRODUCTION (Cont..)
PRESENTED BY: MD. ABU BAKER SIDDIKI ABIR 2/20
Disaster Area
Campus area networking
Sports stadium
Concert
Traffic jam
Fig 1. Different application scenarios.
TARGETED AREAS

5. INTRODUCTION (Cont..)
PRESENTED BY: MD. ABU BAKER SIDDIKI ABIR 3/20
Centralized Control and
programmability [3]
Remotely configuration and
management
Scalability [4]
Better network security
Enhanced flexibility, agility, and
reliability
Fig 2. SDN vs Traditional Networking.
WHY SDN IN UAV NETWORKS?

6. INTRODUCTION (Cont..)
PRESENTED BY: MD. ABU BAKER SIDDIKI ABIR 4/20
It uses software to manage and control
the communication between UAVs and
ground stations [4]
UAVs can be quickly reconfigured
Administrators can remotely configure
and manage the UAVs
It improves the network's performance
like scalability and reliability
Cost-effective solution
Fig 3. Architecture of a SDUAV networks.
WHAT IS SDUAV NETWORK?

7. INTRODUCTION (Cont..)
PRESENTED BY: MD. ABU BAKER SIDDIKI ABIR 5/20
Simulate, virtualize and analysis the behaviors of the system [5]
Real-time monitoring of the UAVs behavior
Enhance the optimization and decision-making ability [6]
Improve the design, testing, and prototype operation
ROLE OF DIGITAL TWIN (DT) IN SDUAV NETWORK

8. OBJECTIVES OF THIS STUDY
PRESENTED BY: MD. ABU BAKER SIDDIKI ABIR 6/20
Design and develop a digital twin-based SDUAV network
Investigate the use of queuing model in SDUAV network
Explore the potential of integrating DT, queuing model, and SDN
Validate the effectiveness of the proposed approach

9. CONTRIBUTIONS
PRESENTED BY: MD. ABU BAKER SIDDIKI ABIR 7/20
DT integrated holistic SDUAV network is proposed
A M/M/1 queueing model is implemented
A routing adjustment algorithm is designed
Demonstrate its effectiveness for various network parameters

10. PROPOSED ARCHITECTURE
PRESENTED BY: MD. ABU BAKER SIDDIKI ABIR 8/20
 SDN is considered as the core
network
 UAV is considered as the
backhaul network
 DT is integrated for the virtual
representation
 Queueing model is integrated for
the network analysis
Fig 4. Architecture of the proposed model.

11. PROPOSED ARCHITECTURE (CONT.)
PRESENTED BY: MD. ABU BAKER SIDDIKI ABIR 9/20
Fig 5. Communication flow diagram.
1. Hello message
2. Packet_in
3. Packet_out
4. Administrative_role
5. Feature_request
Type of communication message
Link establishment
Routing
and
Switching
Initiative

12. SYSTEM MODEL
PRESENTED BY: MD. ABU BAKER SIDDIKI ABIR 10/20
Fig 6. Queueing model of the proposed model.
Name of the parameter Symbol
Total packet probability at controller 𝑷𝒄
Total packet probability at DT 𝑷𝒅
Total packet probability at UAV 𝑷𝒖
Packet arrival rate at the UAV 𝝀𝟎
SDN controller's service rate µ𝒄
DT's service rate µ𝒅
UAV’s service rate µ𝒖
Table 1. Notations used in the analysis

13. SYSTEM MODEL (CONT.)
PRESENTED BY: MD. ABU BAKER SIDDIKI ABIR 11/20
 Traffic arrival follows the Poisson distribution.
 Service rate follows the Exponential
distribution.
 Upon the packet arrival rate the Markov
chain is determined.
𝝆 =
𝝀𝟎
𝝁𝒖
𝑵𝒑 =
𝝆
𝟏 − 𝝆
𝑫𝒄 =
𝟏
𝝁𝒄 − 𝟏
∞
𝝀𝒖
3. Packet processing delay at the
controller,
1. Utilization of the system,
2. The total amount of packet,
Fig 7. Markov chain of the proposed M/M/1 model.

14. SYSTEM MODEL (CONT..)
PRESENTED BY: MD. ABU BAKER SIDDIKI ABIR 12/20
M/M/1 Based Mathematical Modal
Part Total arrival rate Packet processing delay
UAV 𝝋𝒖 = 𝟏 + 𝑷𝒄 + 𝑷𝒅 − 𝑷𝒄𝑷𝒅 𝝀𝟎 𝑫𝒖 =
𝟏
µ𝒖 − 𝟏 + 𝑷𝒄 + 𝑷𝒅 − 𝑷𝒄𝑷𝒅 𝝀𝟎
DT 𝝋𝒅 = 𝑷𝒅𝝀𝟎 𝑫𝒅 =
𝟏
µ𝒅 − 𝑷𝒅𝝀𝟎
Controller 𝝋𝒄 = 𝑷𝒅𝝀𝟎 + 𝑷𝒄 + 𝑷𝒅 − 𝑷𝒄𝑷𝒅 𝝀𝟎 𝑫𝒄 =
𝟏
µ𝒄 − 𝟐𝑷𝒄 + 𝑷𝒅 − 𝑷𝒄𝑷𝒅 𝝀𝟎
Table 2. Queueing model and its corresponding equations

15. PROPOSED ROUTING ALGORITHM
PRESENTED BY: MD. ABU BAKER SIDDIKI ABIR 13/20
Fig 8. Flow chat of the proposed algorithm.
Routing Adjustment Algorithm
Input: Network status from the Data plane
Output: Optimal routing scheme to the UAVs
Step 1: Network status arrives at the SDN controller.
Step 2: Test and verify different scheme at the DT.
Step 3: Successful simulation at the DT?
Step 4: if yes then apply optimal routing scheme to the UAVs.
Step 5: if no then fix routing error is fixed and send it to the
controller.
Step 6: Repeat Step 1.
Step 7: End.

16. PERFOMANCE EVALUATION
PRESENTED BY: MD. ABU BAKER SIDDIKI ABIR 14/20
Platform
1. MATLAB’s “SIMEVENTS” module
2. Mininet
3. OpenDaylight SDN Controller
Key performance indicators Threshold value
Total packet probability at controller, 𝑷𝒄 0.05
Total packet probability at DT, 𝑷𝒅 0.4
Total packet probability at UAV, 𝑷𝒖 0.5
Packet arrival rate at the UAV, 𝝀𝟎 10000 packets/sec
SDN controller's service rate, µ𝒄 7500 packets/sec
DT's service rate, µ𝒅 9000 packets/sec
UAV’s service rate, µ𝒖 8500 packets/sec
Table 3. Baseline variables for the experiment

17. PERFOMANCE EVALUATION (Cont.)
PRESENTED BY: MD. ABU BAKER SIDDIKI ABIR 15/20
Fig 9. Impact of arrival rate on the packet processing time.
 Refer to the rate at which data is being
generated by the UAVs and sent to the
SDN controller
 Response steadily and Shows less packet
processing time than the other model

18. PERFOMANCE EVALUATION (Cont.)
PRESENTED BY: MD. ABU BAKER SIDDIKI ABIR 16/20
 Refer to the rate at which the controller
is processing the data received from the
UAVs
 Overall throughput of the network has
improved in the proposed model
Fig 10. Impact of service rate on the packet processing time.

19. PERFOMANCE EVALUATION (Cont.)
PRESENTED BY: MD. ABU BAKER SIDDIKI ABIR 17/20
 Can determine the optimal number of
UAVs
 Shows that the proposed model have less
processing time with the changing
number of UAV nodes
 Optimize the network design and
improve its efficiency
Fig 11. Controller’s performance with the changing number of UAV nodes.

20. FINAL OUTCOMES
PRESENTED BY: MD. ABU BAKER SIDDIKI ABIR 18/20
The proposed model enhances the scalability and capabilities of
wireless network
The M/M/1 model can monitor and predict changes networking
parameters
The routing algorithm illustrates how the controller installs
administrative policies
The proposed model can predict more precisely about different
traffic parameters variation

21. FUTURE WORKS
PRESENTED BY: MD. ABU BAKER SIDDIKI ABIR 19/20
Master-slave SDN-controller approach can be applied
Intelligent Reflecting Surface technology can be deployed
Machine learning can be integrated

22. REFERENCES
PRESENTED BY: MD. ABU BAKER SIDDIKI ABIR 20/20
M. Z. Chowdhury, M. Shahjalal, S. Ahmed, and Y. M. Jang, "6G Wireless Communication Systems: Applications,
Requirements, Technologies, Challenges, and Research Directions," IEEE Open Journal of the Communications Society, vol. 1,
pp. 957-975, 2020.
M. M. Azari, G. Geraci, A. Garcia-Rodriguez, and S. Pollin, "UAV-to-UAV Communications in Cellular Networks," IEEE
Transactions on Wireless Communications, vol. 19, no. 9, pp. 6130-6144, Sept. 2020.
O. Sami Oubbati, M. Atiquzzaman, T. Ahamed Ahanger, and A. Ibrahim, "Softwarization of UAV Networks: A Survey of
Applications and Future Trends," IEEE Access, vol. 8, pp. 98073-98125, 2020.
Z. Lv, D. Chen, H. Feng, H. Zhu, and H. Lv, "Digital Twins in Unmanned Aerial Vehicles for Rapid Medical Resource Delivery in
Epidemics," IEEE Transactions on Intelligent Transportation Systems, vol. 23, no. 12, pp. 25106-25114, 2022.
A. Fahmin, Y. C. Lai, M. S. Hossain, Y. D. Lin, and D. Saha, "Performance Modeling of SDN with NFV Under or Aside the
Controller," in proc. of International Conference on Future Internet of Things and Cloud Workshops (FiCloudW), Prague,
Czech Republic, 2017.
J. Raychev, G. Hristov, D. Kinaneva, and P. Zahariev, "Modelling and Evaluation of Software Defined Network Architecture
Based on Queueing Theory," in proc. of EAEEIE Annual Conference (EAEEIE), Hafnarfjordur, Iceland, 2018.
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