![ICICT4SD2023 10
Proposed Model (2/3)
WIENER II path loss model can be defined as [7],
10 10
log ( ) log
5.0
c
f
PL A d B C X
Motley Keenan free space path loss propagation model can be defined as [7],
4
20 log 20 log
c
O AF AF
f
L d p W k F
c
](https://image.slidesharecdn.com/265-231121040959-eccb1507/85/SDN-based-Signal-Performance-Optimization-in-Campus-Area-Network-10-320.jpg)
![ICICT4SD2023 20
References
[1] W. Jiang, B. Han, M. A. Habibi, and H. D. Schotten, "The Road Towards 6G: A Comprehensive Survey," IEEE Open Journal of the
Communications Society, vol. 2, pp. 334-366, 2021.
[2] K. K. Karmakar, V. Varadharajan, S. Nepal, and U. Tupakula, "SDN-Enabled Secure IoT Architecture," IEEE Internet of Things Journal, vol.
8, no. 8, pp. 6549-6564, 2021.
[3] N. Arman, S. R. Hasan, & M. R. Abedin, "Vehicle Detection Using Deep Learning Method and Adaptive and Dynamic Automated Traffic
System via IoT Using Surveillance Camera," in Proceedings of International Conference on Information and Communication Technology for
Development: ICICTD, Singapore: Springer Nature Singapore, 2023, pp. 15-27.
[4] Y. L. Lee, D. Qin, L. C. Wang, and G. H. Sim, "6G Massive Radio Access Networks: Key Applications, Requirements and Challenges," IEEE
Open Journal of Vehicular Technology, vol. 2, pp. 54-66, 2021.
[5] T. Kurimoto, S. Urushidani, and E. Oki, "Optimization Model for Designing Multiple Virtualized Campus Area Networks Coordinating With
Wide Area Networks," IEEE Transactions on Network and Service Management, vol. 15, no. 4, pp. 1349-1362, 2018.
[6] J. Zhou, H. Jiang, J. Wu, L. Wu, C. Zhu, and W. Li, "SDN-Based Application Framework for Wireless Sensor and Actor Networks," IEEE
Access, vol. 4, pp. 1583-1594, 2016.
[7] Y. A. Zakaria, E. K. Hamad, A. S. Abd Elhamid, & K. M. El-Khatib, "Developed Channel Propagation Models and Path Loss Measurements
for Wireless Communication Systems Using Regression Analysis Techniques," Bulletin of the National Research Centre, vol. 45, no. 1 , pp. 1-11,
2021.](https://image.slidesharecdn.com/265-231121040959-eccb1507/85/SDN-based-Signal-Performance-Optimization-in-Campus-Area-Network-20-320.jpg)
SDN-based Signal Performance Optimization in Campus Area Network
- 1. 2ND INTERNATIONAL CONFERENCE ON INFORMATION AND COMMUNICATION TECHNOLOGY FOR SUSTAINABLE DEVELOPMENT Paper ID: 265 Paper Title: SDN-based Signal Performance Optimization in Campus Area Network Md. Abu Baker Siddiki Abir1, Shahrukh Hossain Rian2, Syed Rakib Hasan3, and Nafiz Arman4 1Institute of Information and Communication Technology 2Department of Electrical Engineering 3Department of Electrical and Electronic Engineering 4Department of Electrical and Computer Engineering 1,3Khulna University of Engineering & Technology, Bangladesh 2University of British Columbia, Okanagan Campus, Canada 4University of Calgary, Calgary, Canada List of the Authors 1
- 2. ICICT4SD2023 2 Presentation Outline Introduction Proposed Model Simulation Setup Result and Discussion Conclusion Future Works
- 3. ICICT4SD2023 3 Motivations 1. Increasing data rate demand 2. Necessary for data rate 3. Minimum latency for communication 4. Better quality of service 5. Secure communication
- 4. ICICT4SD2023 4 Contributions 1. A new approach for modeling AP positioning 2. Minimalistic pathloss profile finding 3. Maximum receiving signal strength getting position 4. Determine signal coverage area
- 5. ICICT4SD2023 5 What is Campus Area Network? A Campus Area Network (CAN) is a type of network that covers a relatively small geographic area, typically within the confines of a single campus or a closely located group of buildings. CANs are designed to provide network connectivity and communication services to organizations such as universities, colleges, corporate campuses, research institutions, and large enterprises. They serve as an intermediate-sized network between Local Area Networks (LANs) and Metropolitan Area Networks (MANs).
- 6. ICICT4SD2023 6 Challenges of CAN 1. Latency and Reliability 2. Signal Interference 3. Bandwidth Limitations 4. Compatibility and Integration 5. Power Consumption 6. Cost
- 7. ICICT4SD2023 7 Software-defined Networking According to Open Network Foundation (ONF) “The physical separation of the network control plane from the forwarding plane, and where a control plane controls several devices.” Fig. 1. SDN vs Traditional Networking
- 8. ICICT4SD2023 8 Features of SDN 1. Centralized Control and 2. Programmability 3. Remotely configuration and management 4. Scalability 5. Better network security 6. Enhanced flexibility, agility, and reliability
- 9. ICICT4SD2023 9 Proposed Model (1/3) Fig. 2. Common scenario of CAN in KUET south side.
- 10. ICICT4SD2023 10 Proposed Model (2/3) WIENER II path loss model can be defined as [7], 10 10 log ( ) log 5.0 c f PL A d B C X Motley Keenan free space path loss propagation model can be defined as [7], 4 20 log 20 log c O AF AF f L d p W k F c
- 11. ICICT4SD2023 11 Proposed Model (3/3) Fig. 3. Simulated map diagram of KUET south side.
- 12. ICICT4SD2023 12 Simulation Setup Parameters Values Frequency, fc 5 GHz Path loss exponent, A 18.7 Distance from one block to another block ~100 m Wall attenuation factor, WAF 12 Speed of light, c 3×108 m/s Frequency dependent parameter, C 20 Wall attenuation factor, FAF 30 dB Intercept, B 46.8 TABLE I. KEY ASSUMPTION OF THE PARAMETERS.
- 13. ICICT4SD2023 13 Result and Discussions (1/5) Fig. 4. The user input transmitted point defined as red star.
- 14. ICICT4SD2023 14 Fig. 5. Path loss of the signal from Tx point. Result and Discussions (2/5)
- 15. ICICT4SD2023 15 Fig. 6. Top view of the path loss from Tx point. Result and Discussions (3/5)
- 16. ICICT4SD2023 16 Fig. 7. Receiver signal strength of the signal Tx point. Result and Discussions (4/5)
- 17. ICICT4SD2023 17 Fig. 8. Top view of the receiver signal strength from Tx point. Result and Discussions (5/5)
- 18. ICICT4SD2023 18 Conclusions 1. Improves the quality as well as performance of signals 2. Improvers the probability of channel gain 3. Improve the receiving signal strength and path loss
- 19. ICICT4SD2023 19 Future Work Different area of applications, types, and more analytical parameters will be considered
- 20. ICICT4SD2023 20 References [1] W. Jiang, B. Han, M. A. Habibi, and H. D. Schotten, "The Road Towards 6G: A Comprehensive Survey," IEEE Open Journal of the Communications Society, vol. 2, pp. 334-366, 2021. [2] K. K. Karmakar, V. Varadharajan, S. Nepal, and U. Tupakula, "SDN-Enabled Secure IoT Architecture," IEEE Internet of Things Journal, vol. 8, no. 8, pp. 6549-6564, 2021. [3] N. Arman, S. R. Hasan, & M. R. Abedin, "Vehicle Detection Using Deep Learning Method and Adaptive and Dynamic Automated Traffic System via IoT Using Surveillance Camera," in Proceedings of International Conference on Information and Communication Technology for Development: ICICTD, Singapore: Springer Nature Singapore, 2023, pp. 15-27. [4] Y. L. Lee, D. Qin, L. C. Wang, and G. H. Sim, "6G Massive Radio Access Networks: Key Applications, Requirements and Challenges," IEEE Open Journal of Vehicular Technology, vol. 2, pp. 54-66, 2021. [5] T. Kurimoto, S. Urushidani, and E. Oki, "Optimization Model for Designing Multiple Virtualized Campus Area Networks Coordinating With Wide Area Networks," IEEE Transactions on Network and Service Management, vol. 15, no. 4, pp. 1349-1362, 2018. [6] J. Zhou, H. Jiang, J. Wu, L. Wu, C. Zhu, and W. Li, "SDN-Based Application Framework for Wireless Sensor and Actor Networks," IEEE Access, vol. 4, pp. 1583-1594, 2016. [7] Y. A. Zakaria, E. K. Hamad, A. S. Abd Elhamid, & K. M. El-Khatib, "Developed Channel Propagation Models and Path Loss Measurements for Wireless Communication Systems Using Regression Analysis Techniques," Bulletin of the National Research Centre, vol. 45, no. 1 , pp. 1-11, 2021.
- 21. 21 Thank You