• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2018.05a
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• pp.419-421
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• 2018

Cloud vehicular networks are a promising paradigm to improve vehicular through distributing computation tasks between remote clouds and local vehicular terminals. Software-Defined Network(SDN) can bring advantages to Intelligent Transportation System(ITS) through its ability to provide flexibility and programmability through a logically centralized controlled cluster that has a full comprehension of view of the network. However, as the SDN paradigm is currently studied in vehicular ad hoc networks(VANETs), adapting it to work on cloud-based vehicular network requires some changes to address particular computation features such as task computation of applications of cloud-based vehicular networks. There has been initial work on briging SDN concepts to vehicular networks to reduce the latency by using the fog computing technology, but most of these studies do not directly tackle the issue of task computation. This paper proposes a Software-Defined Cloud-based vehicular Network called SDCVN framework. In this framework, we study the effectiveness of task computation of applications of cloud-based vehicular networks with vehicular cloud and roadside edge cloud. Considering the edge cloud service migration due to the vehicle mobility, we present an efficient roadside cloud based controller entity scheme where the tasks are adaptively computed through vehicular cloud mode or roadside computing predictive trajectory decision mode. Simulation results show that our proposal demonstrates a stable and low route setup time in case of installing the forwarding rules of the routing applications because the source node needs to contact the controller once to setup the route.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2018.05a
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• pp.238-240
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• 2018

Cloud vehicular networks are a promising paradigm to improve vehicular through distributing computation tasks between remote clouds and local vehicular terminals. Software-Defined Network(SDN) can bring advantages to Intelligent Transportation System(ITS) through its ability to provide flexibility and programmability through a logically centralized controlled cluster that has a full comprehension of view of the network. However, as the SDN paradigm is currently studied in vehicular ad hoc networks(VANETs), adapting it to work on cloud-based vehicular network requires some changes to address particular computation features such as task computation of applications of cloud-based vehicular networks. There has been initial work on briging SDN concepts to vehicular networks to reduce the latency by using the fog computing technology, but most of these studies do not directly tackle the issue of task computation. This paper proposes a Software-Defined Cloud-based vehicular Network called SDCVN framework. In this framework, we study the effectiveness of task computation of applications of cloud-based vehicular networks with vehicular cloud and roadside edge cloud. Considering the edge cloud service migration due to the vehicle mobility, we present an efficient roadside cloud based controller entity scheme where the tasks are adaptively computed through vehicular cloud mode or roadside computing predictive trajectory decision mode. Simulation results show that our proposal demonstrates a stable and low route setup time in case of installing the forwarding rules of the routing applications because the source node needs to contact the controller once to setup the route.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.20 no.3
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• pp.491-498
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• 2016

The software defined networks implement a software network control plane, which is physically separated from the data plane. For wide area software defined network deployments, multiple controllers are required, and the placement of these controllers influences importantly the performance of the software defined networks. This paper proposes a Tabu search algorithm, which is one of the meta heuristic algorithms, for an efficient controller placement in software defined networks. In order to efficiently obtain better results, we propose new neighborhood generating operations, which are called the neighbor position move and the neighbor number move, of the Tabu search algorithm. We evaluate the performances of the proposed algorithm through some experiments in terms of the minimum latency and the execution time of the proposed algorithm. The comparison results show that the proposed algorithm outperforms the existing genetic algorithm and random method under various conditions.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.10
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• pp.4678-4702
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• 2018

Traffic Matrix estimation has always caught attention from researchers for better network management and future planning. With the advent of high traffic loads due to Cloud Computing platforms and Software Defined Networking based tunable routing and traffic management algorithms on the Internet, it is more necessary as ever to be able to predict current and future traffic volumes on the network. For large networks such origin-destination traffic prediction problem takes the form of a large under- constrained and under-determined system of equations with a dynamic measurement matrix. Previously, the researchers had relied on the assumption that the measurement (routing) matrix is stationary due to which the schemes are not suitable for modern software defined networks. In this work, we present our Compressed Sensing with Dynamic Model Estimation (CS-DME) architecture suitable for modern software defined networks. Our main contributions are: (1) we formulate an approach in which measurement matrix in the compressed sensing scheme can be accurately and dynamically estimated through a reformulation of the problem based on traffic demands. (2) We show that the problem formulation using a dynamic measurement matrix based on instantaneous traffic demands may be used instead of a stationary binary routing matrix which is more suitable to modern Software Defined Networks that are constantly evolving in terms of routing by inspection of its Eigen Spectrum using two real world datasets. (3) We also show that linking this compressed measurement matrix dynamically with the measured parameters can lead to acceptable estimation of Origin Destination (OD) Traffic flows with marginally poor results with other state-of-art schemes relying on fixed measurement matrices. (4) Furthermore, using this compressed reformulated problem, a new strategy for selection of vantage points for most efficient traffic matrix estimation is also presented through a secondary compression technique based on subset of link measurements. Experimental evaluation of proposed technique using real world datasets Abilene and GEANT shows that the technique is practical to be used in modern software defined networks. Further, the performance of the scheme is compared with recent state of the art techniques proposed in research literature.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.12 no.3
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• pp.1063-1082
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• 2018

The concept of SDN (Software Defined Networking) endows the network with programmability and significantly improves the flexibility and extensibility of networks. Currently a plenty of research works on introducing SDN into wireless networks. Most of them focus on the innovation of the SDN based architectures but few consider how to realize the global perception of the network through the controller. In order to address this problem, a software defined carrier grade Wi-Fi framework called SWAN, is proposed firstly. Then based on the proposed SWAN architecture, a blueprint of introducing the traditional NSA (Network Situation Awareness) into SWAN is proposed and described in detail. Through perceiving various network data by a decentralized architecture and making comprehension and prediction on the perceived data, the proposed blueprint endows the controllers with the capability to aware of the current network situation and predict the near future situation. Meanwhile, the extensibility of the proposed blueprint makes it a universal solution for software defined wireless networks SDWNs rather than just for one case. Then we further research one typical use case of proposed NSA blueprint: network performance awareness (NPA). The subsequent comparison with other methods and result analysis not only well prove the effectiveness of proposed NPA but further provide a strong proof of the feasibility of proposed NSA blueprint.

• International Journal of Computer Science & Network Security
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• v.22 no.9
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• pp.376-388
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• 2022

The expansion of new applications and business models is being significantly fueled by the development of Fifth Generation (5G) networks, which are becoming more widely accessible. The creation of the newest intelligent vehicular networks and applications is made possible by the use of Vehicular Ad hoc Networks (VANETs) and Software Defined Networking (SDN). Researchers have been concentrating on the integration of SDN and VANET in recent years, and they have examined a variety of issues connected to the architecture, the advantages of software-defined VANET services, and the new features that can be added to them. However, the overall architecture's security and robustness are still in doubt and have received little attention. Furthermore, new security threats and vulnerabilities are brought about by the deployment and integration of novel entities and a number of architectural components. In this study, we comprehensively examine the good and negative effects of the most recent SDN-enabled vehicular network topologies, focusing on security and privacy. We examine various security flaws and attacks based on the existing SDVN architecture. Finally, a thorough discussion of the unresolved concerns and potential future study directions is provided.

• International Journal of Computer Science & Network Security
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• v.22 no.9
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• pp.231-243
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• 2022

The expansion of new applications and business models is being significantly fueled by the development of Fifth Generation (5G) networks, which are becoming more widely accessible. The creation of the newest intelligent vehicular net- works and applications is made possible by the use of Vehicular Ad hoc Networks (VANETs) and Software Defined Networking (SDN). Researchers have been concentrating on the integration of SDN and VANET in recent years, and they have examined a variety of issues connected to the architecture, the advantages of software defined VANET services, and the new features that can be added to them. However, the overall architecture's security and robustness are still in doubt and have received little attention. Furthermore, new security threats and vulnerabilities are brought about by the deployment and integration of novel entities and several architectural components. In this study, we comprehensively examine the good and negative effects of the most recent SDN-enabled vehicular network topologies, focusing on security and privacy. We examine various security flaws and attacks based on the existing SDVN architecture. Finally, a thorough discussion of the unresolved concerns and potential future study directions is provided.

• Journal of information and communication convergence engineering
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• v.20 no.1
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• pp.22-30
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• 2022

The reinforcement learning algorithm has proven its potential in solving sequential decision-making problems under uncertainties, such as finding paths to route data packets in wireless sensor networks. With reinforcement learning, the computation of the optimum path requires careful definition of the so-called reward function, which is defined as a linear function that aggregates multiple objective functions into a single objective to compute a numerical value (reward) to be maximized. In a typical defined linear reward function, the multiple objectives to be optimized are integrated in the form of a weighted sum with fixed weighting factors for all learning agents. This study proposes a reinforcement learning -based routing protocol for wireless sensor network, where different learning agents prioritize different objective goals by assigning weighting factors to the aggregated objectives of the reward function. We assign appropriate weighting factors to the objectives in the reward function of a sensor node according to its hop-count distance to the sink node. We expect this approach to enhance the effectiveness of multi-objective reinforcement learning for wireless sensor networks with a balanced trade-off among competing parameters. Furthermore, we propose SDN (Software Defined Networks) architecture with multiple controllers for constant network monitoring to allow learning agents to adapt according to the dynamics of the network conditions. Simulation results show that our proposed scheme enhances the performance of wireless sensor network under varied conditions, such as the node density and traffic intensity, with a good trade-off among competing performance metrics.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2018.10a
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• pp.62-65
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• 2018

This paper comes out with the study on sensing data collection strategy in a Software-Defined Mobile Edge vehicular networking. The two cooperative data dissemination are Direct Vehicular cloud mode and edge cell trajectory prediction decision mode. In direct vehicular cloud, the vehicle observe its neighboring vehicles and sets up vehicular cloud for cooperative sensing data collection, the data collection output can be transmitted from vehicles participating in the cooperative sensing data collection computation to the vehicle on which the sensing data collection request originate through V2V communication. The vehicle on which computation originate will reassemble the computation out-put and send to the closest RSU. The SDMEVN (Software Defined Mobile Edge Vehicular Network) Controller determines how much effort the sensing data collection request requires and calculates the number of RSUs required to support coverage of one RSU to the other. We set up a simulation scenario based on realistic traffic and communication features and demonstrate the scalability of the proposed solution.

• Journal of Internet Computing and Services
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• v.22 no.5
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• pp.27-33
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• 2021

With the broad adoption of the Internet of Things (IoT) in a variety of scenarios and application services, management and orchestration entities require upgrading the traditional architecture and develop intelligent models with ultra-reliable methods. In a heterogeneous network environment, mission-critical IoT applications are significant to consider. With erroneous priorities and high failure rates, catastrophic losses in terms of human lives, great business assets, and privacy leakage will occur in emergent scenarios. In this paper, an efficient resource slicing scheme for optimizing federated learning in software-defined IoT (SDIoT) is proposed. The decentralized support vector regression (SVR) based controllers predict the IoT slices via packet inspection data during peak hour central congestion to achieve a time-sensitive condition. In off-peak hour intervals, a centralized deep neural networks (DNN) model is used within computation-intensive aspects on fine-grained slicing and remodified decentralized controller outputs. With known slice and prioritization, federated learning communications iteratively process through the adjusted resources by virtual network functions forwarding graph (VNFFG) descriptor set up in software-defined networking (SDN) and network functions virtualization (NFV) enabled architecture. To demonstrate the theoretical approach, Mininet emulator was conducted to evaluate between reference and proposed schemes by capturing the key Quality of Service (QoS) performance metrics.