A New SDN-Based Routing Protocol for Improving Delay in Smart City Environments

The smart city is an ecosystem that interconnects various devices like sensors, actuators, mobiles, and vehicles. The intelligent and connected transportation system (ICTS) is an essential part of this ecosystem that provides new real-time applications. The emerging applications are based on Internet-of-Things (IoT) technologies, which bring out new challenges, such as heterogeneity and scalability, and they require innovative communication solutions. The existing routing protocols cannot achieve these requirements due to the surrounding knowledge supported by individual nodes and their neighbors, displaying partial visibility of the network. However, the issue grew ever more arduous to conceive routing protocols to satisfy the ever-changing network requirements due to its dynamic topology and its heterogeneity. Software-Defined Networking (SDN) offers the latest view of the entire network and the control of the network based on the application’s specifications. Nonetheless, one of the main problems that arise when using SDN is minimizing the transmission delay between ubiquitous nodes. In order to meet this constraint, a well-attended and realistic alternative is to adopt the Machine Learning (ML) algorithms as prediction solutions. In this paper, we propose a new routing protocol based on SDN and Naive Bayes solution to improve the delay. Simulation results show that our routing scheme outperforms the comparative ones in terms of end-to-end delay and packet delivery ratio.

[1]  Jiannong Cao,et al.  SDN-Based Routing for Efficient Message Propagation in VANET , 2015, WASA.

[2]  Yuguang Fang,et al.  Intelligent Data Transportation in Smart Cities: A Spectrum-Aware Approach , 2018, IEEE/ACM Transactions on Networking.

[3]  Guy Pujolle,et al.  NeuRoute: Predictive dynamic routing for software-defined networks , 2017, 2017 13th International Conference on Network and Service Management (CNSM).

[4]  Georgia Aifadopoulou,et al.  A Comparison of Machine Learning Methods for the Prediction of Traffic Speed in Urban Places , 2019, Sustainability.

[5]  Yonggang Wen,et al.  “ A Survey of Software Defined Networking , 2020 .

[6]  F. Richard Yu,et al.  A Survey of Machine Learning Techniques Applied to Software Defined Networking (SDN): Research Issues and Challenges , 2019, IEEE Communications Surveys & Tutorials.

[7]  Yang Yang,et al.  A Supervised Learning Based QoS Assurance Architecture for 5G Networks , 2019, IEEE Access.

[8]  Suat Ozdemir,et al.  Routing in Fog-Enabled IoT Platforms: A Survey and an SDN-Based Solution , 2018, IEEE Internet of Things Journal.

[9]  Sami Souihi,et al.  Distributed SDN Control: Survey, Taxonomy, and Challenges , 2018, IEEE Communications Surveys & Tutorials.

[10]  Federico Chiariotti,et al.  SymbioCity: Smart cities for smarter networks , 2016, Trans. Emerg. Telecommun. Technol..

[11]  Aniruddha S. Gokhale,et al.  Work-in-Progress: Towards Real-Time Smart City Communications using Software Defined Wireless Mesh Networking , 2018, 2018 IEEE Real-Time Systems Symposium (RTSS).

[12]  Xin Yuan,et al.  Machine Learning Aided Load Balance Routing Scheme Considering Queue Utilization , 2019, IEEE Transactions on Vehicular Technology.

[13]  Sukumar Nandi,et al.  Multipath TCP for V2I communication in SDN controlled small cell deployment of smart city , 2019, Veh. Commun..

[14]  David H. Wolpert,et al.  No free lunch theorems for optimization , 1997, IEEE Trans. Evol. Comput..

[15]  Yun-Pang Flötteröd,et al.  Microscopic Traffic Simulation using SUMO , 2018, 2018 21st International Conference on Intelligent Transportation Systems (ITSC).

[16]  A. Laouiti,et al.  Optimized link state routing protocol for ad hoc networks , 2001, Proceedings. IEEE International Multi Topic Conference, 2001. IEEE INMIC 2001. Technology for the 21st Century..

[17]  Enzo Baccarelli,et al.  Memory and memoryless optimal time-window controllers for secondary users in vehicular networks , 2015, 2015 International Symposium on Performance Evaluation of Computer and Telecommunication Systems (SPECTS).

[18]  Jia Wu,et al.  A Correlation-Based Feature Weighting Filter for Naive Bayes , 2019, IEEE Transactions on Knowledge and Data Engineering.

[19]  Hamed Haddadi,et al.  Deep Learning in Mobile and Wireless Networking: A Survey , 2018, IEEE Communications Surveys & Tutorials.

[20]  Aniruddha S. Gokhale,et al.  Publish/subscribe-enabled software defined networking for efficient and scalable IoT communications , 2015, IEEE Communications Magazine.

[21]  Yong Wang,et al.  A supervised learning approach for routing optimizations in wireless sensor networks , 2006, REALMAN '06.

[22]  Salaja Silas,et al.  A Review of SDN-Based Next Generation Smart Networks , 2019, 2019 3rd International Conference on Computing and Communications Technologies (ICCCT).

[23]  Aniruddha S. Gokhale,et al.  Enabling Software-Defined Networking for Wireless Mesh Networks in smart environments , 2016, 2016 IEEE 15th International Symposium on Network Computing and Applications (NCA).

[24]  Wang-Cheol Song,et al.  SD-IoV: SDN enabled routing for internet of vehicles in road-aware approach , 2020, J. Ambient Intell. Humaniz. Comput..

[25]  Mohammed Al-Maitah,et al.  Intelligent Traffic Engineering in Software-Defined Vehicular Networking Based on Multi-Path Routing , 2020, IEEE Access.

[26]  Min Chen,et al.  TIDE: Time-relevant deep reinforcement learning for routing optimization , 2019, Future Gener. Comput. Syst..

[27]  Francesco Musumeci,et al.  Machine Learning-Based Routing and Wavelength Assignment in Software-Defined Optical Networks , 2019, IEEE Transactions on Network and Service Management.

[28]  Theodore B. Zahariadis,et al.  Hybrid Clouds for Data-Intensive, 5G-Enabled IoT Applications: An Overview, Key Issues and Relevant Architecture , 2019, Sensors.

[29]  Abdullah Baz,et al.  Bayesian Machine Learning Algorithm for Flow Prediction in SDN Switches , 2018, 2018 1st International Conference on Computer Applications & Information Security (ICCAIS).

[30]  Osisanwo F.Y,et al.  Supervised Machine Learning Algorithms: Classification and Comparison , 2017 .

[31]  Katia Jaffrès-Runser,et al.  QMR: Q-learning based Multi-objective optimization Routing protocol for Flying Ad Hoc Networks , 2020, Comput. Commun..

[32]  Nael B. Abu-Ghazaleh,et al.  Wireless Software Defined Networking: A Survey and Taxonomy , 2016, IEEE Communications Surveys & Tutorials.

[33]  Wen Wu,et al.  Delay-Minimization Routing for Heterogeneous VANETs With Machine Learning Based Mobility Prediction , 2019, IEEE Transactions on Vehicular Technology.

[34]  Maode Ma,et al.  Link Stability Based Optimized Routing Framework for Software Defined Vehicular Networks , 2019, IEEE Transactions on Vehicular Technology.

[35]  Brendan Jennings,et al.  Software Defined Networks-Based Smart Grid Communication: A Comprehensive Survey , 2018, IEEE Communications Surveys & Tutorials.

[36]  Manoj Kumar Singh,et al.  Moving towards smart cities: Solutions that lead to the Smart City Transformation Framework , 2020 .

[37]  Nazrul Islam,et al.  Performance analysis of OpenFlow based software defined wired and wireless network , 2017, 2017 20th International Conference of Computer and Information Technology (ICCIT).

[38]  Moshe Zukerman,et al.  Naïve Bayes Classifier-Assisted Least Loaded Routing for Circuit-Switched Networks , 2019, IEEE Access.

[39]  Walid Dabbous,et al.  How Far Can We Go? Towards Realistic Software-Defined Wireless Networking Experiments , 2017, Comput. J..

[40]  Dharani Kumari Nooji Venkatramana,et al.  SCGRP: SDN-enabled connectivity-aware geographical routing protocol of VANETs for urban environment , 2017, IET Networks.