The Internet of Vehicles (IoV) is an evolving domain fueled by advancements in vehicular communications and networking. To enhance vehicle coverage, integrating vehicle-to-everything (V2X) networks with cellular networks has become essential, though this integration places increased demand on cellular infrastructure. To address this, we propose a two-tier stable path routing algorithm designed to improve the stability and proactiveness of V2X networks. Our approach divides the coverage area into zones, further segmented into road segments based on road structure. The first tier manages routing within a road segment, while the second tier handles routing between vehicles in adjacent segments. This method improves road awareness, stabilizes topologies, adapts to dynamic changes, and reduces routing overhead. Additionally, the incorporation of Kalman filter-based prediction model further strengthens proactive routing. To validate the proposed approach, we conduct synthetic evaluations across varying vehicular densities with different mobility and traffic scenarios. We compare the traditional centralized routing strategy with the proposed distributed two-tier mechanism to assess execution cost, end-to-end latency, network resource consumption, data rates, packet flow, and packet loss. Quantified results demonstrate that our two-tier approach reduces the average execution cost from 438.61 to 230.48, lowers average latency from 232.34 ms to 129.03 ms, and minimizes average network consumption from 231.26 MB to 129.39 MB. The proposed approach continues to significantly enhance data rates, reduce packet flow processing, decrease packet loss across various routing strategies. Overall, the proposed solution enhances stability, responsiveness, and robustness of V2X communication, making it suitable for future large-scale IoV deployments.

The IBN@speech framework introduces a novel approach to network management by integrating an Intent Translation Engine with speech recognition technology. This system allows users to issue natural language voice commands, which are translated into network configurations and implemented via the Open Network Operating System (ONOS). Unlike traditional methods that rely on fixed databases, IBN @ speech leverages a combination of rule based systems and RASA based conversational AI to offer a user friendly interface, significantly enhancing network automation through real time adjustments and efficient service deployment. Our initial results demonstrate notable improvements in service deployment time and user satisfaction, positioning IBN @ speech as a compelling alternative to conventional network management techniques.

Researchers are investigating new network architectures to achieve seamless global connectivity. One such proposal is the Space-Air-Ground Integrated Network (SAGIN), which integrates space, air, and ground networks. Efficient management of SAGIN layers can be achieved through effective network control, enabled by Software-Defined Network (SDN), where the placement of controller is crucial. This paper presents a heuristic-based SDN controller placement technique for Low Earth Orbit (LEO) satellites to determine the optimal number of controllers. The proposed method initially places controllers based on the average latency of the satellites, followed by iterative improvements using a Z-score-based approach. Different simulations are conducted on Iridium-like LEO satellite constellations demonstrate that the proposed technique significantly reduces the average latency and packet drop rate, even in scenarios where up to$\mathbf{7 5 \%}$of the satellites are unavailable for controller placement.

The recent advancements in network systems, including Software-Defined Networking (SDN), Network Functions Virtualization (NFV), and cloud networking, have revolutionized network management by increasing efficiency and reducing manual effort. This has led to improved agility in deploying new network services, enabling scaling of network resources, making it easier to handle sudden increases in demand, and efficiently accessing new solutions. However, the heterogeneous network infrastructure and the physical links’ capability still impact the performance of interconnected nodes. This work provides a solution to this problem which centers on the use of Intent-Based Networking (IBN) for a high-level definition of service requirements (QoS) tailored to the specifications of each particular node. Additionally, Graph Neural Network (GNN) is integrated into the proposed system to model the overlay topology and understand the behavior of nodes and links. This allows the defined intents to be translated into optimal paths between end-to-end nodes. The network QoS is constantly monitored, and the GNN model regularly updates the path selection to meet the QoS specified by intents. The solution has been implemented as an IBN system design consisting of a manager for intent definition, a GNN model for optimal path selection, an Off-Platform Application (OPA) for policy creation, and a real-time monitoring system for network state assurance.

With the emergence of Zero Trust (ZT) Architecture, industry leaders have been drawn to the technology because of its potential to handle a high level of security threats. The Zero Trust Architecture (ZTA) is paving the path for a security industrial revolution by eliminating location-based implicant access and focusing on asset, user, and resource security. Software Defined Perimeter (SDP) is a secure overlay network technology that can be used to implement a Zero Trust framework. SDP is a next-generation network technology that allows network architecture to be hidden from the outside world. It also hides the overlay communication from the underlay network by employing encrypted communications. With encrypted information, detecting abnormal behavior of entities on an overlay network becomes exceedingly difficult. Therefore, an automated system is required. We proposed a method in this paper for understanding the normal behavior of deployed polices by mapping network usage behavior to the policy. An Apache Spark collects and processes the streaming overlay monitoring data generated by the built-in fabric API in order to do this mapping. It sends extracted metrics to Prometheus for storage, and then uses the data for machine learning training and prediction. The cluster-id of the link that it belongs to is predicted by the model, and the cluster-ids are mapped onto the policies. To validate the legitimacy of policy, the labeled polices hash is compared to the actual polices hash that is obtained from blockchain. Unverified policies are notified to the SDP controller for additional action, such as defining new policy behavior or marking uncertain policies.

Due to the mega-scale of next-generation networks, dynamic network policy management and assessment is a continuous struggle for network operators. As a result, networks have evolved towards automated Softwarized intent-based policy platforms. IBN (Intent-based Networking) administers high-level interpretation of intents and translates them as policies to the lower-level infrastructures. As a Softwarized platform, it defines an SSOT (Single Source of Truth) where every high-level intention has well-defined policy action at every stage of orchestration from top to bottom. However, due to the dynamic nature of the network, the static quantitative measures may result in excessive reservation or under-allocation of network resources. As network service orchestration is performed for the future, deciding on service parameters per resource availability is crucial. Hence, this work utilizes machine-learning-based resource forecasting to determine quantitative measures for intent activation proactively. This work empowers IBN policy activation through LSTM (Long Short-Term Memory) based compute resource forecasting and RouteNet-driven transport network path link utilization forecasting. The goal of IBN is to ensure service level agreement, as there are two primary service activation rules, GBR and Non-GBR (Guaranteed Bit Rate); hence using forecasting, the proposed model ensures the service agreement accomplishment proactively.

Internet of Vehicles (IoV) is an emerging archetype that is a distributed network of various vehicles armed with sensors, actuators, technologies, and applications to connect and exchange data with each other over the Internet. The primary goal of IoV is to provide a vehicular platform to enable better communication and Quality of Service (QoS) for vehicles, pedestrians, and roadside infrastructure in real-time, through the use of Vehicle-to-Vehicle (V2V), Vehicle-to-Pedestrian (V2P), Vehicle-to-Infrastructure (V2I), Vehicle-to-Network (V2N), and Vehicle-to-Cloud (V2C) channels. However, the increasing number of vehicular services poses serious concerns and challenges to the researchers: real-time traffic forecasting, service placement, security, reliability, and routing. The primary focus of this work is concerned with the challenge of multi-regional forecasting of multi-class traffic. These traffic forecasting models enable pro-activeness in systems by providing real-time and accurate predictions. Also, they can explore traffic densities over spatial and temporal domains for various vehicle types. However, current literature cannot provide forecasts for multi-region and multi-class vehicles at the same time. This study aims at enabling a proactive platform which could make decisions based on the integrated Graph Neural Network (GNN) and Gated Recurrent Unit (GRU) based traffic forecasting model, i.e., Spatio-Temporal GNN (STGNN) based traffic forecasting model. The STGNN-based traffic forecasting model uses the GNN and GRU models to explore spatial and temporal features of varying vehicular multi-class traffic densities. In GNN, spatial data consisting of multi-class traffic densities are utilized for the feature extraction that results in graph embeddings. In the GRU, these graph embeddings are utilized for temporal feature extraction. This approach enables the forecasting of multi-class vehicular traffic densities and the pro-activeness of an IoV platform. In addition, the performance results show that an intelligent platform can be built upon the proposed traffic forecasting model that is capable of inspecting complex and nonlinear traffic accurately.

Zero Trust Networking (ZTN) is a security model where no entity in a network infrastructure is trusted. The first bastion of security for achieving ZTN is to have strong identity verification. Several standard methods for assuring a robust identity exist (E.g., OAuth2.0, OpenID Connect). These standards employ the use of JSON Web Tokens (JWT) during the authentication process. However, the use of JWT for One Time Token (OTT) enrollment has a latent security issue. A JWT can be intercepted by a third party and the information of the payload can be exposed, revealing the details of the enrollment server. Furthermore, an intercepted JWT could be used for enrollment by an impersonator as long as the JWT remains active. Our proposed mechanism aims to secure the ownership of the OTT by including the JWT as encrypted metadata into a Non-Fungible Token (NFT). The mechanism uses the blockchain Public Key of the intended owner for encrypting the JWT, and the blockchain assures the JWT ownership by mapping it to the intended owner's blockchain public address. Our proposed mechanism is applied to an emerging Zero Trust framework (OpenZiti) alongside a permissioned Ethereum blockchain using Hyperledger Besu. The Zero Trust Framework provides the enrollment functionality, while our proposed mechanism based on blockchain and NFT assures the secure distribution of OTTs that is used for the enrollment of identities.

Multiple clouds need to share resources as a service for various reasons, such as overcoming single points of failure or reducing latency. Tunneling serves as the mechanism for multiple clouds to share their resources. Generic Network Virtualization Encapsulation (GENEVE) tunnel has the advantage of a flexible header over other L2 tunnels such as GRE or VXLAN. This enables the GENEVE tunnel to transfer a larger payload in case of smaller header size. The service must be acquired from an optimal Cloud and Instance. An optimal Cloud has the best network performance forecasted. An optimal Instance is forecasted not to be overutilized during resource sharing. For this purpose, monitoring tools must be deployed for metering Instances and network links. Data obtained from monitoring tools can be utilized by Machine Learning for Instances and network performance forecasting. Initially, two clouds are deployed at Trans-Eurasia Internetworking (TEIN), one in Malaysia and the other in South Korea. Monitoring tools are deployed for network and Instance performance monitoring. GENEVE tunnel is then deployed on OVS bridges, after which Instances of a cloud can access Instances of the other Cloud for resource sharing.

5G technology come up with many innovative features compared to legacy networks, such as network slicing that envisioned a wide variety of services from different customers, network operators, and industrial verticals. Network slicing ensures dedicated and isolated resources to each of the services. The autonomous orchestration and management of end-to-end (e2e) network slicing is critical due to the complex network configuration for the underlying infrastructure. On the other side, data analytics seems promising to manage and control the underlying network resources proactively. So, network data analytics function (NWDAF) has been introduced in 5G service-based architecture (SBA), which enables network operators to use various artificial intelligence (AI) and machine learning (ML) techniques. Therefore, this paper presents a closed-loop mechanism that has two parts: 1) an intent-based networking (IBN) mechanism for efficient control, orchestration, and management of e2e network slicing. 2) A data analytics mechanism that uses novel hybrid ensemble learning (EL) algorithms for network resource utilization prediction and anomaly detection and mitigation. The results show that the proposed stacking ensemble learning (STEL) model for resource utilization prediction enhanced accuracy by approximately up to 20% and reduced the error by 45% compared to the state-of-the-art models. In addition, ML models assist the IBN platform in updating and managing the network resources proactively.

Network ecosystems have grown to encompass multiple application domains. SDN and NFV technologies have helped pave the road for the evolution of the core and edge networking systems, allowing for numerous services to be served by the same physical infrastructure. Guaranteeing the operability of the network has become an ever-increasing requirement in order to sustain the underlying services deployed on the network. For this, Intent-Based Networking (IBN) aims to abstract network management by introducing high-level rules/policies that are translated to network configurations per service requirements. By following this principle, we proposed an anomaly detection and mitigation mechanism that exploits the characteristics of IBN for collecting and analyzing flows, using Machine Learning for interpreting traffic patterns, and automatic deployment of high-level policies for corrective actions related to anomalous traffic occurrences. The complete system is deployed on the Korea Advanced Research Network (KOREN), where the abstraction provided by IBN for network anomaly detection and mitigation is a key factor in closing the gap to achieve complete network automation.

Networks slicing in 5G network enables the network operators to accommodate the different quality of service (QoS) to their customers. Moreover, the data analytics in 5G mobile network can be seen as a robust solution to transform the challenging features of 5G into a reality. So, for that, the Third Generation Partnership Project (3GPP) has been introduced a network data analytics function (NWDAF) in 5G service-based architecture (SBA). NWDAF collects the data from different core and management domains and performs analytics on that historical data. It also enables the network operators (NOs) to train their Machine Learning (ML) techniques and use various third-party solutions. On the other side, the automation and management of the end - to-end (e2e) network slicing in a multi -domain environment is a critical task. Therefore, in this manuscript, we have designed a closed-loop Intent-based Networking (IBN) platform, which automatically ensures the commissioning, activation, run-time monitoring, and decommissioning of the network slices. Moreover, we have integrated the newly introduced 3GPP NWDAF with the IBN platform for efficient e2e network slice lifecycle management. By implementing different ML models in the NWDAF function, we can predict the slice load, user mobility, traffic forecasts, and anomaly detection from the network slices.

The amount of data generation is exponentially increasing over the past decade due to the widespread use of multimedia applications and social media platforms. Advanced real-time applications such as virtual reality, augmented reality, automated vehicles, smart homes, and intelligent traffic control systems have increased the demand for low latency. Many of these applications are delay-sensitive and put enormous stress on the core network to respond in real-time. CDN (Content Delivery Network) brings storage service to end-users proximity to provide low latency, high data throughput, and low traffic pressure to handle the problems mentioned above. Due to the limited storage capacity of the edge, only in-demand content should cache. Therefore, to optimally utilized the cache space, an efficient content caching and replacement policy is needed. To this end, in this paper, we propose an optimal content replacement algorithm. In this algorithm, a video request pattern is first generated based on a publicly available dataset. After that, a machine learning model is trained on cache logs data. As a result, the predicted video is deleted from the edge to make space for new videos. A real-time testbed is built on KOREN to check the performance of our model. The results based on MAE, MSE, and R-2 show that our model performs well in real-time scenarios.

The expansion of infrastructure and services in the 5th generation networks resulted in complex configuration management throughout the network lifecycle. To this end, network automation replaces existing traditional manual administrative approaches with software-driven repetitive and reliable applications. Since network expansion is in multiple dimensions, including multi-services, domains, and platforms, it is challenging to resolve such a vast infrastructure through a single automation solution. Hence this paper proposed applying an intent-based solution for achieving automatic orchestration for vastly spreading network services. Intent-based solution not only considers network automation but also performs service assurance throughout the network service lifecycle. The proposed IBN (Intent-Based Networking) solution implements a closed-loop network lifecycle management using a single abstracted software platform. It translates high-level requirements to the infrastructure irrespective of the various underlying platforms and domains, and it includes intelligence-driven monitoring and updates for service assurance. A multi-model machine learning approach is proposed in this work to control the network infrastructure reliably. To this end LSTM (Long Short-Term Memory) algorithm is applied for compute-resource prediction and the Route-Net model for optimized service path routing. The infrastructure includes FlexRAN deployed as the access network controller, OSM (OpenSource MANO) resides at the core, and the KOREN network serves as a high-speed transport network.

Nowadays, intelligence is paving its ways into the IoV domain. With the need of improvement in intelligence in this area, the need of self-organizing network management systems for providing V2X communication is also of vital importance, which current systems lack. Current routing solutions for IoV are complex and require intelligence embedded in the form of closed-loop systems. To this, an intent-based system is designed, which takes high level requirements for routing in IoV.The routing approach is road-aware and widens the scope of road-awareness to vehicles from multiple edge domains. Another problem with the current routing schemes in IoV is that their network management systems are not self-organizing. A self-organizing management system is of high importance and requires a closed-loop system. To this, an intent-based approach is followed integrated with a reinforcement learning model that supports the creation of a routing policy, which is further applied to the orchestrator and enables a road-aware and hierarchical routing approach. The proposed system is shown to be efficient in terms of data rate and the number of packet flows managed per unit time, in the management of vehicular networks.

Intent-Based Networking (IBN) is a model that enables proactive network control and automation to satisfy high-level demands. It follows a closed-loop mechanism that abstracts the network complexity and dynamically allows updates to the network monitoring and intelligence. Hence, it eliminates the traditionally practiced error-prone manual network control. In contrast with the traditional reactive approach, IBN-based approach promotes the proactive and dynamic solution. To this end, IBN uses machine learning (ML) to predict the future and keeps a balance between the intended and actual state. Hence, this work considers an IBN-driven routing mechanism to ensure service provisioning with the desired quality of service (QoS). In contrast to the traditional static routing scheme, this work considers AI-driven dynamic and proactively updatable routing schemes to ensure QoS. This work demonstrates AI-driven service route control mechanism for ensuring quality of service (QoS) on top of Korea Advanced Research Network-Software Defined Infrastructure (KOREN-SDI). More precisely, an ML-based approach is used to find the best path between source and destination nodes. In addition, it incorporates IBN closed-loop process to monitor and update service routes proactively based on predicted future link utilization. This novel approach introduces proactive updates on runtime to avoid future failures and ensures seamless service provisioning with the fulfillment of QoS demands by changing traffic routes dynamically.

Network slicing is an important pillar of 5G networks that empowers the network operators to provide the different quality of services (QoS) to the users. It enables network operators to split the physical network into multiple logical networks to meet different QoS requirements. In this research paper, we have designed an intent-based network slicing framework that can slice and manage the core network and radio access network (RAN) resources efficiently. It is an automated system, where users just needs to provide higher-level information in the form of intents/contracts for a network slice, and in return our system deploys and configures the requested resources. Moreover, a deep learning model Generative Adversarial Neural Network (GAN) has been used for the management of network resources. Several tests have been performed by creating three slices with our system, which shows better performance in terms of bandwidth and latency.

5G claims the service provisioning for a variety of new applications increasing orchestration complexity. Additionally, many different orchestrators and platforms have been developed over recent years to handle and control the network infrastructures. However, each of the orchestration platforms requires different expertise for handling them. The diversity and dynamicity in terms of requirements made it very complex for the operators to handle runtime operations. As with the increasing number of devices, the amount of traffic streamed over network deviates drastically making it impossible for network operators to handle the network operations manually. Hence automated platforms are a dire need, this work proposed a generic intent-based system that can automatically orchestrate and manage network lifecycle over multiple domains, sites and orchestrators. This system will include the orchestration over multiple domains access, transport and core and it also includes the use of Machine Learning for prediction of resource status for proactive decision making to manage the slice lifecycle automatically.

To achieve high availability of traffic and maximum coverage in the 5G network, clusters of small cells approach to maximize the system capacity. In this approach, the Core Network (CN) encounters a large number of signals due to the frequent hand-off between small cells in the event of an increase in the speed of user movement and users within certain limits of small cells. The present 5G system executes all handoff signals in the traditional complex hierarchical sequence which leads the system toward the bad performance due to redundant signaling. NO Stack architecture has emerged as a promising methodology for radio access network (RAN) in the context of reducing the redundant signaling during X2-based handover by using control logic centrally and flat protocols. This paper proposes a system that is based on NO Stack architecture for reducing handoff requests in CN by assigning the dedicated mobility controller to each cluster of small cells for locally managing the mobility. The dedicated cluster controller specially designs to manage and control the cluster of small cells as well as maintain the forwarding information of user equipment locally. In this proposed system, the dedicated controller resides at the nearest edge cloud, therefore a distributed edge cloud network is developed for high computing infrastructure and low latency access for the X2-based handover request.

Flying networks are resource constraints while the nature of nodes' mobility is very dynamic and unpredicted. Therefore, these networks are very prone to link failure and performance degradation. By considering the existing limitations, this work proposes a new approach consists of proactive and reactive network failure mitigation techniques that have been named as a hybrid approach. In the proposed architecture, the SDN controllers are distributed where each one controls its local domain nodes. The controller node continuously monitors the network state information and proactively adjusts the near-future changes to the topology. Each local domain also contains a sink node that directly connects to the controller. The sink node is used to forward the network state information to the controller and keep the controller defined flow rules for local domain nodes. The sink node can also request a new path in case of any link failure or any topology updates cause by nodes' movement. Besides, a distributed routing protocol also runs on domain nodes to establish connectivity toward the sink node.

5G networks are aimed at provisioning of a wide range of sophisticated services with uninterrupted user experience. In addition, it is very challenging to manage all the services due the variety in the service requirement and a very large number of users causing dynamic changes in traffic streamed over the network. Currently, the networks are managed manually and requires experts to control the behavior of network. Due to increase in network domain it is an esteem requirement to manage and control network autonomously. In this paper we have introduced and Intent-Based networking approach which on one side abstracts and automates the network configuration also it assures the network resource state stability by using machine learning. We have used an IBN abstraction layer and M-CORD as an next generation testbed for the implementation of this work.

The enormous increase in the number of communicating vehicles every year imposes many challenges for efficient resource allocation in IoV (Internet of Vehicles). Additionally, with advancements, many new communication services have been added to the vehicles, each requiring a unique set of resources. The vehicle communication services include simple video streaming, multimedia, and navigation to mission-critical services in self-driving cars. In addition to that, the vehicular technology is rapidly shifting towards the electric vehicle to help the green energy revolution and reduction of carbon footprints. Similarly, communication services are also consuming high-energy so IoE (Internet of Energy) has also become vital to optimize resource utilization for reduction of energy consumption. Due to the complexity of the service requirements in IoV, an energy-efficient and intelligent resource allocation system is essential. To this end, this paper proposes a solution that provides an efficient and proactive resource orchestration for IoV services while considering edge-cloud infrastructure. Machine Learning (ML) approach has been used to manage the resources by predicting network traffic at the edge, and VNF resource utilization at the core.

The automatic control over the network platforms is an esteem requirement of the operators. Recently, 5G with its aim to attain the internet of everything, it challenged researchers for the achievement of automatic control over the network platform. Furthermore, the 5G system consists of multi-domain network applications and platforms which make it complex to control and configure the network. Hence, the focus of this research is to enable easy configuration through high-level instructions and the use of machine learning for automatic control over the network infrastructure. The overall system consists of Intent-Base application that includes a machine learning model and it configures and controls the M-CORD-based network slicing test-bed.

Investigation and collection of real-time data plays a very crucial part in the orchestration of network resources. Selection of the correct data is very important as it decides to auto-scale the resources. In cloud & SDN environments such as NFV, auto-scaling becomes more critical in terms of precision and accuracy. In our case, we propose a solution for auto-scaling the network resources based on the calculations made for every action's execution-time [1] of respective instances of a VNF. The instances for each VNF are auto-scaled on the basis of execution-times per time slot, and the number of cores that are assigned by the usage of weight factor [2] used for virtual/physical cores. Hence by using the proposed solution, we are able to enhance the proper resource provisioning to fulfill the dynamic demands [3] of future mobile networks.

Network Function Virtualization (VNF) is one of the main drivers for the next generation of mobile networks. It allows the creation of multiple and diverse functionalities over a single physical infrastructure thus fulfilling the main requirements for 5G mobile networks that focus on serving heterogeneous ecosystems. The research presented in this literature focuses on a Network Slice Selection Function (NSSF) for the selection of Data Plane network slices, which are created and configured with specific QoS that cater to the need of network users. As the complexity of managing multiple Virtual Network Functions (VNF) grows with the number of Data Plane Network Slices, a system that abstracts the configuration of the mobile network is needed. This paper positions the NSSF as part of a three-layer Virtual Mobile Network System that contains an Application Layer for Policy creation, a Management Layer for Orchestration of VNFs and a Physical Layer for the deployment of network functions.

Network slicing has been considered as one of the fundamental technologies in the fifth generation (5G) mobile networks. Slicing mechanism enables virtual networks while providing customized services on demand. There are several papers attempting to narrow down the architectural requirement for 5G networks. Many of them consider the aspect of slice creation as a central point, sometimes strengthening slices with slice isolation. The aim of this paper is to focus more on network slice management, slicing of transport network (TN) and challenges to elaborate technological options and enablers, irrespective of the slicing mechanism. Our proposed framework creates, manages and associates the slice to user equipment (UE) upon the request made by different applications. Proposed framework exploits the key features offered by CORD including OpenStack and ONOS as well as modules based on M-CORD such as vBBU, NSSF and vEPC for establishing a network slice scenario.

In this paper, the performance analysis of a hierarchical routing protocol for mobile ad hoc networks (MANETs) called Location‐aware Grid‐based Hierarchical Routing (LGHR) is performed. In LGHR, the network comprises nonoverlapping zones and each zone is further partitioned into smaller grids. Although LGHR is a location‐aware routing protocol, the routing mechanism is similar to the link‐state routing. The protocol overcomes some of the weaknesses of other existing location‐based routing protocols such as Zone‐based Hierarchical Link State (ZHLS) and GRID. A detailed analysis of the LGHR routing protocol is performed and its performance is compared with both the above‐mentioned protocols. The comparison shows that LGHR works better than ZHLS in terms of storage overhead as well as communication overhead, whereas LGHR is more stable than GRID especially in scenarios where wireless nodes are moving with very high velocities.

Intelligent Transportation System (ITS) have been attracting a significant attention among the researchers due to its numerous applications that will pave the path towards the faster, safer and enjoyable journeys. However, these applications require a reliable routing protocol along with a robust, secure and fast connection to infrastructure. In this paper, we propose an Infrastructure-assisted hybrid road-aware routing (IHRAR) and QoS Provisioning for VANETs. IHRAR utilities RSUs to efficiently relay packets towards remote vehicles and IP users. Furthermore, we have proposed an exclusive QoS routing algorithm by opting an approach of localization between intersections of a road segment.

Monitoring of infrastructural resources against a huge number of virtual machines in clouds is a challenging task to system administrators. System management tools are used to collect and provide the utilization of a variety of performance counters of each virtual machine. Such dispersed views cannot reveal the actual performance behavior of virtual machines. To this purpose, a monitoring framework is necessary particularly since cloud hosts are subject to varying load conditions. In this paper, we propose a framework based on sFlow to interpret the resource utilization correctly and in real-time to ease the workload of system administrators.

Road traffic information is considered vital for drivers and passengers in a VANET. Recently many approaches have been proposed for sharing the road information among the vehicles on the roads. Applications that use hovering information for road safety consider TTL and a radial distance from a center point to disseminate the information in a geographically limited area. These approaches generally do not define any technique for the routing of content. In this paper, a context-aware routing mechanism is proposed for the content that hovers in a predefined geographic region. Road probability model is presented to calculate the probability of content to be disseminated on the roads. The vehicles use an efficient low overhead routing mechanism to forward the content/information towards the destination/anchor region. Proposed protocol is evaluated over a road network and the results show that it can be helpful in the design and deployment of VANETs.

Data aggregation for VANETs is considered a vital building block for many applications due to its bandwidth saving potential. In this paper, we propose a novel context aware data aggregation technique for VANETs. The data aggregation is done by the cluster head based on the context i.e. vehicle speed and density to reduces the required bandwidth for periodic data dissemination. The proposed technique is validated through extensive simulations and results show that context aware data aggregation can significantly reduce communication cost.

Elephant flows in data center networks have a tendency to utilize a lot of bandwidth, leaving latency-sensitive mice flows choked behind them. This causes degradation of application performance running on the network. Therefore, a data center network should be able to not only identify elephant flows, but also offer Quality of Service (QoS) provisioning. SDN makes network operating systems to achieve greater governance of the control plane within a given network. In order to detect elephant flows, we use a framework based on sFlow sampling technology. We show an approach to guarantee QoS that is administered and defined by a centralized network controller by using SDN and specifications offered by OpenFlow. Although there have been efforts in this area over the past but in the scope of this paper, we will only focus on how Traffic Shaping (rate limiting) based classification technique can be used within an SDN network.

Vehicular Ad Hoc Network (VANET) is a subclass of Mobile Ad Hoc Network (MANET). QoS routing protocols are fundamental part of VANETs that aim to provide services with guaranteed quality. Existing routing protocols of MANET can be applied to VANETs as well but they do not perform well under the dynamic conditions of VANETs such as, fast moving nodes and frequent disconnections in the network. In this paper we propose a novel QoS routing protocol for VANETs. The proposed scheme takes a localization approach by a road segment as a road between intersections. Every road segment can calculate candidate paths with QoS parameters, and vehicles on intersections serve as the gateway nodes to calculate the weights for the routes on local road segments and select reliable route based on these weights to connect the road segments. Our proposed scheme is designed to improve the delivery ratio and end to end delay with guaranteed quality in highly mobile VANET environments. The simulation results show that our proposed scheme performs better than AODV in aforementioned performance metrics.

Recently, OpenFlow and FlowVisor are the most promising architecture components of the software-defined networks (SDNs). The evolution of these components brought the revolution of utilizing network elements for the shared and virtual network infrastructure, however from the aspects of production networks, it still lacks of some key component. For example, policy-based services provisioning for user-defined requirement is an indispensable part. Hence, innovative aggregation management design for virtual network management system framework must be developed and tested. This paper describes an initial design to user-defined network infrastructure in order to make it capable of running policy-based engineering related experiments. We developed the framework regarding the real use cases upon the shared and virtual network in KREONET infrastructure. Moreover, we also presented perflow-user-defined specifications which include max rate limiter and dynamic priority assignment strategy to enhance the validation performance of our system for production networks.

This paper provides insights into the traffic flow monitoring system of OF@TEIN (OpenFlow@Trans Eurasia Information Network) testbed. OF@TEIN is software defined networking (SDN) testbed adapted by KOREN (KOrea advanced REsearch Network) and integrated with the research and education networks of several Asian nations including Japan, Malaysia, Thailand, Vietnam, Philippine, etc. Traditional traffic monitoring solutions such as NetFlow, RSPAN ports, Network Packet Brokers (NPBs) can't provide network-wide visibility in OF@TEIN because OF@TEIN is a large multi-tenant testbed deployed over high speed research networks across several countries. Therefore, we have implemented a new sFlow-based flow monitoring system that is tailored to OF@TEIN requirements and can provide real-time L2 to L7 network wide visibility. OF@TEIN uses SDN-based network virtualization to slice the network among multiple concurrent experimenters. Machines in a network slice (or VLAN) communicate with each other using GRE tunnels. Our traffic monitoring system enables monitoring flow spaces of VLANs as well as physical provider network. It utilizes northbound interfaces (NBIs) exposed by sFlow-RT analytics engine and FloodLight SDN Controller. It periodically fetches flows statistics from sFlow-RT and stores them in time-series format in Whisper RRD database. Graphite real-time charting tool is used to plot the statistics stored in Whisper.

VANET plays an important role in dissemination of traffic and emergency information among vehicles moving on roads. This information is used by various vehicular applications like safety applications, route guiding applications etc. However, because of high mobility of vehicular nodes, maintenance of routing tables generates high network traffic. In this paper we have proposed an optimization of Zone based Hierarchical Link State (ZHLS) routing protocol for VANET. The two tier topology of ZHLS allows building of a relatively stable routing path, however because of its proactive nature ZHLS creates huge network overhead when nodes are increased. In order to overcome this issue of network overhead that every proactive protocol causes we have optimized routing parameters of ZHLS based on traffic awareness and network performance. The proposed traffic aware optimized framework allows each vehicle to update its time interval for dissemination of routing information based on its context. Our experiments have proved our claim that by optimizing routing parameters of ZHLS a better performance in term of packet delivery ratio with low network overhead can be achieved.

The data aggregation process can be considered a problem of multi-objective optimization which reduces the size of data in such a way that its relevance with original data remains as closer as possible. Data Aggregation is of great importance in Wireless Sensor Networks, Vehicular Ad-hoc Networks to transmit the recorded data in time over low bandwidth. In this regard, data aggregation solutions have been developed; however, their actual usage has been limited, for the reason of low accuracy and high processing time. In this paper, particle swarm optimization (PSO) is used to optimize process of multi-objective data aggregation in vehicular ad-hoc network. In our work processing time for aggregation and aggregation quality have been set as objectives. The proposed method has been compared with state of the art existing aggregation techniques. Experimental results show that our method simplifies aggregation effectively and obtains a higher aggregation accuracy compared to the other data aggregation methods.

Several peer-to-peer solutions have been newly suggested in recent years to resolve the problems in the existing P2P architecture such as Soribada and Napster as well as the client/server architecture. We design a content-sharing application based on the JXTA that is a P2P solution of the Sun Microsystems. It is for a community activity and has seven modules to share several kinds of contents with peers such as file, web-contents, picture, video and real-time video as well as for chatting and group management. And, we are developing to share the 3D streaming as well as 3D avatars. Peers on this application can make groups to easily share their own contents. We have proven it is scalable with simulation results.

With research effort of Ubiquitous computing development, Wireless Sensor Networks have also been studied and deployed in many places. Recently various works for sharing the existing sensor data have been tried. In this paper we propose a P2P based system for sharing sensor data. It uses a structured P2P - Pastry to provide DHT based routing without server and has been deployed in KOREN. We classify the sensor data into real-time data and past data, and Scribe in Pastry is applied to multicast the real-time data and PAST in Pastry is used to store and provide the past data. Also as Pastry itself does not provide the search function, we propose to use PAST for providing search function using keywords.

Policy-based network management (PBNM) in mobile ad-hoc networks (MANETs) requires additional reliable and efficient mechanisms over PBNM in wired networks. Thus, it is important that the management system in MANETs should cluster the moving nodes and manage their movements in an effective manner. In the paper, a scalable framework is proposed for the policy-based management in ad hoc networks in which we use k-hop clustering with extended COPS-PR. And, we discuss methods for policy enforcement points (PEPs) to discover autonomously the policy decision point (PDP) and set the management area in the framework. Also, three regions are suggested to effectively maintain PDP/PEP clusters in the PBNM system. Finally, we discuss the results achieved through simulations.

Policy-based network management (PBNM) in the mobile ad-hoc networks (MANETs) requires additional reliable and efficient mechanism over PBNM in wired networks. Thus, it is important that the management system in MANETs should cluster the moving nodes and manage their movements in an effective manner. In this paper, we propose a mechanism for the policy-based management in ad hoc networks in which we discuss methods to discover the policy decision point (PDP), set the management area, and manage the movements of nodes in the PBNM system. Moreover, COPS-PR is extended for the mechanism. Finally, we discuss the results through simulations

There is; an emergence of Internet applications that have real-time requirements. These applications require IP to support guaranteed capacity, higher priority and lower packet loss rate. To address this, the Internet Engineering Task Force (IETF) is developing a set of protocols and standards for Integrated Services on the Internet. Using RSVP and policies to manage the allocation of network resources in order to provide different levels of service is a topic of great interest to service providers. Currently, it is not possible to dynamically reallocate resources during an application's session. This paper discusses how policies in conjunction with new service class can provide a more enhanced network resource allocation by allowing for this dynamic reallocation.