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Network slicing in 5G is a solution to accommodate a wide range of services. It also enables the network operators to establish multiple end-to-end (e2e) logically isolated and customized networks with shared or dedicated resources over the same infrastructure. Although, many tools and platforms have been developed to accomplish the management and orchestration (MANO) of e2e network slicing automatically, it is still challenging. Each of these platforms requires expertise and manual effort to define the requirements for the provisioning of the resources. The other issue is the generation of well-defined network slice configurations with lifecycle parameters. To this end, this paper proposes an efficient solution that automates the configuration process and performs the management and orchestration of network slices. This solution contains a one-touch Intent-based Networking (IBN) platform that effectively orchestrates and manages the lifecycle of multi-domain slice resources. IBN automates the process of slice configuration generation, service provisioning, service update, and service assurance by eliminating experts and manual effort. Furthermore, it has an intelligent Deep Learning (DL) based resource update and assurance mechanism which handles the run-time resource scalability and assurance.

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.

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.

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.

Network establishment and management in the data center need a considerable investment of time and money. To increase the cost productivity of networks, maximizing the utilization of network resources is mandatory. Many network inconsistencies may occur due to the utilization of maximum link capacity. Nowadays, Fat-Tree topology is a popular network architecture in data center networks therefore many researchers are designing routing algorithms for fat-tree topology by adopting load balancing methods. The inefficient way of obtaining network traffic statistics from network devices is a major problem to achieve load balancing traditionally. Software-Defined Network (SDN) platform provides researchers, an environment to develop a user-defined routing algorithm that can be flexible as well as cost-efficient. In this paper, an SDN based system has been proposed for Fat-Tree topology that provides a solution that enables networks to make effective resource utilization and minimize the maximum link capacity utilization of network using intent-based networking (IBN). The proposed system provides a permanent unique path from source to destination that allows traffic to flow uniformly and enables all the links to be offloaded by providing them with equal traffic share proactively. By taking a proactive approach which is inherence of an IBN, the proposed system provides the secure channel to provision the host in network with load balancing capability in the running network by restricting illegal in-premise access to the network.

By the prediction of future location for a vehicle in Internet of Vehicles (IoV), data forwarding schemes can be further improved. Major parameters for vehicle position prediction includes traffic density, motion, road conditions, and vehicle current position. In this paper, therefore, our proposed system enforces the accurate prediction with the help of real-time traffic from the vehicles. In addition, the proposed Neural Network Model assists Edge Controller and centralized controller to compute and predict vehicle future position inside and outside of the vicinity, respectively. Last but not least, in order to get real-time data, and to maintain a quality of experience, the edge controller is explored with Software Defined Internet of Vehicles. In order to evaluate our framework, SUMO simulator with Open Street map is considered and the results prove the importance of vehicle position prediction for 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.

The goal of 5th generation networks is to provide network services to everything, i.e. moving beyond mobile devices. This extension of the network domain led to an exponential raise in the complexity in terms of architecture, infrastructure, and deployment. Due to the vast number of new applications, each having a specific set of requirements, requiring proficient experts incorporating manual multi-level and domain specific operations for the design deployment and orchestration of networks. Due to the complexity and specificness of configurations the manual approaches are highly erroneous and uncertain. In addition, they don't confirm an optimal solution. Hence, this manuscript describes two possible solutions. The first is the automatic configuration for the deployment and management of the network using an intent-based networking application, while the second introduced a network-slicing scenario using a network slice selection procedure by enhancing existing Long-Term Evolution (LTE) network architecture. The M-CORD platform is used for the implementation of the network slicing scenario.

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 requirements of next-generation of networks have imposed a multi-dimensional/ directional complexity over network management. As a result, the only way forward is through the automation of administrative and management procedures of networks. However, the best solution for having an automation system is through Machine Learning. The next-generation network services have complex requirements that can be determined using service graphs. However, autonomous translation of high-level user requirements to a service graph is itself a complex problem and requires an intelligent solution. Hence in this manuscript, a GAN (generative adversarial network) based fake service graph generation and DRL (Deep reinforcement learning) based optimized graph selection mechanism is considered for automatic intent translation.

In the network softwarization, Network Function Virtualisation (NFV) has shifted the standard of network services deployment and management for telecommunication. However, the allocation of physical resources to the Virtualised Network Functions (VNFs) dynamically, efficiently, and autonomously is one of the key tasks to achieve this objective. Network designing is a serious factor for the deployment of VNFs to create a Service Function Chaining (SFC) with efficient resource utilization and optimal path in the edge computing environment. The development of a self-driven Software-Defined Network (SDN) also requires an intelligent network design, especially to find optimum routing patterns for traffic steering that meet the goals set by administrators. But unfortunately, the current network designing methods do not fulfill the desired requirement for precise estimations of related performance metrics. Recently, the application of Artificial Intelligence (AI) is considered by the research community to operate and control the network. The Graph Neural Network (GNN) is adopted as an AI solution in the field of the network because GNN can understand the complex relationship between network traffic features, routing, and topology to produce an accurate estimation of relevant performance metrics. In this paper, we propose the implementation of the Knowledge-Defined Networking (KDN) system based on Graph Neural Network (GNN) to predict the optimal path for SFC deployment and traffic steering. The proposed system is evaluated using the complete virtualized environment which is composed of ONOS, OpenStack, and open-source MANO (OSM).

The fifth-generation mobile network presents a wide range of services which have different requirements in terms of performance, bandwidth, reliability, and latency. The legacy networks are not capable to handle these diverse services with the same physical infrastructure. In this way, network virtualization presents a reliable solution named network slicing that supports service heterogeneity and provides differentiated resources to each service. Network slicing enables network operators to create multiple logical networks over a common physical infrastructure. In this research article, we have designed and implemented an intent-based network slicing system that can slice and manage the core network and radio access network (RAN) resources efficiently. It is an automated system, where users just need to provide higher-level network configurations in the form of intents/contracts for a network slice, and in return, our system deploys and configures the requested resources accordingly. Further, our system grants the automation of the network configurations process and reduces the manual effort. It has an intent-based networking (IBN) tool which can control, manage, and monitor the network slice resources properly. Moreover, a deep learning model, the generative adversarial neural network (GAN), has been used for the management of network resources. Several tests have been carried out with our system by creating three slices, which shows better performance in terms of bandwidth and latency.

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.

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.

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.

From the last decade, pharmaceutical companies are facing difficulties in tracking their products during the supply chain process, allowing the counterfeiters to add their fake medicines into the market. Counterfeit drugs are analyzed as a very big challenge for the pharmaceutical industry worldwide. As indicated by the statistics, yearly business loss of around $200 billion is reported by US pharmaceutical companies due to these counterfeit drugs. These drugs may not help the patients to recover the disease but have many other dangerous side effects. According to the World Health Organization (WHO) survey report, in under-developed countries every 10th drug use by the consumers is counterfeit and has low quality. Hence, a system that can trace and track drug delivery at every phase is needed to solve the counterfeiting problem. The blockchain has the full potential to handle and track the supply chain process very efficiently. In this paper, we have proposed and implemented a novel blockchain and machine learning-based drug supply chain management and recommendation system (DSCMR). Our proposed system consists of two main modules: blockchain-based drug supply chain management and machine learning-based drug recommendation system for consumers. In the first module, the drug supply chain management system is deployed using Hyperledger fabrics which is capable of continuously monitor and track the drug delivery process in the smart pharmaceutical industry. On the other hand, the N-gram, LightGBM models are used in the machine learning module to recommend the top-rated or best medicines to the customers of the pharmaceutical industry. These models have trained on well known publicly available drug reviews dataset provided by the UCI: an open-source machine learning repository. Moreover, the machine learning module is integrated with this blockchain system with the help of the REST API. Finally, we also perform several tests to check the efficiency and usability of our proposed system.

On-demand service is the main feature of the 5G network, and Network Function Virtualization (NFV) provides it by virtualizing the existing 5G network infrastructure. NFV crafts various virtual networks on a shared physical network, but one of the core challenges in future 5G networks is to automate the modeling of Virtualized Network Functions (VNFs) and end-to-end Network Service (NS) orchestration with less human interaction. Traditionally, the descriptor of VNF and NS is created manually, which requires expert-level skills. This manual approach has a big threat of human error, which can be avoided by using the Intent-Based Networking (IBN) approach. The IBN approach eliminates the requirement of expertise for designing VNFs and NS by taking users&rsquo, intentions as an input. In this paper, the proposed system presents the Intent Management System for VNF modeling and end-to-end NS orchestration for multi-platforms. This system takes the high-level information related to a specific service, configures it accordingly, and converts it into the selected platform. The proposed system is tested using Mobile Central Office Re-architected as Data Center (M-CORD) and Open-Source Management and Orchestration (OSM) orchestrators. The results section shows that the proposed system reduces the effort of the end-user in creating network slices and provides seamless end-to-end service orchestration.

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.

Nowadays, the continuous increase in the high data traffic demand and providing the different quality of services (QOS) to the customer is a very challenging task for all network operators. In the last few years, mobile data traffic is increased to a significant extent. To overcome the increasing traffic demand WiFi offloading is the best option to reduce the burden of cellular networks. So, by aggregating existing indoor WiFi technology to the cellular network is increased the network capacity and provides better QoS to customers. In this article, we proposed and implement the LTE-WiFi aggregation system which is very similar to the 3GPP LWA system. Furthermore, the proposed system is integrated with the M-CORD platform which is SDN, NFV, and cloud-based open-source solutions for providing a 5G environment. The implementation of the proposed system is based on the OpenAirInterface OAI software-defined radio (SDR) platform where WLAN AP is directly connected to eNodeB. The aggregation of both technologies is done at the PDCP layer. Moreover, we test our proposed system with three kinds of policies for UDP and TCP traffic known as LTE only, WiFi only and LTE-WiFi aggregated. The experimental result shows that our proposed LTE-WiFi aggregated system gives better performance and provides high bandwidth as compared to single LTE.

Management of data plays an important role in the healthcare systems and has impacts on the performance of the software applications used. Current healthcare systems don’t utilize the facilities provided by the latest equipment for their stakeholders to interact with the system. The flexibility for pharmacists to interact with the current systems is not as efficient as it should be. Apart from this, we propose a system based on android and a web application by which a doctor has the ability to prescribe patients through an android application using stylus pen and other users such as patients, receptionists, pharmacists and admins interact with the system via their web accessibility. The proposed system is based on integration of Internet of Things and cloud computing technologies using Arduino and E-Health sensors. The users utilize the registered IoT devices which facilitate the healthcare stakeholders, making the system efficient, user-friendly and less error prone. Overall, we propose a better solution of managing the healthcare data as well as provide an easy way to interact with the system.

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.

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.

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.

Proposing an optimal routing protocol for internet of vehicles with reduced overhead has endured to be a challenge owing to the incompetence of the current architecture to manage flexibility and scalability. The proposed architecture, therefore, consolidates an evolving network standard named as software defined networking in internet of vehicles. Which enables it to handle highly dynamic networks in an abstract way by dividing the data plane from the control plane. Firstly, road-aware routing strategy is introduced: a performance-enhanced routing protocol designed specifically for infrastructure-assisted vehicular networks. In which roads are divided into road segments, with road side units for multi-hop communication. A unique property of the proposed protocol is that it explores the cellular network to relay control messages to and from the controller with low latency. The concept of edge controller is introduced as an operational backbone of the vehicle grid in internet of vehicles, to have a real-time vehicle topology. Last but not least, a novel mathematical model is estimated which assists primary controller in a way to find not only a shortest but a durable path. The results illustrate the significant performance of the proposed protocol in terms of availability with limited routing overhead. In addition, we also found that edge controller contributes mainly to minimizes the path failure in the network.

This article presents a report on APNOMS2015, which was held August 19---21, 2015 in Busan, Korea. The theme of APNOMS2015 was "Managing a Very Connected World."

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.

Despite the fact that traffic engineering techniques have been comprehensively utilized in the past to enhance the performance of communication networks, the distinctive characteristics of Software Defined Networking (SDN) demand new traffic engineering techniques for better traffic control and management. Considering the behavior of traffic, large flows normally carry out transfers of large blocks of data and are naturally packet latency insensitive. However, small flows are often latency-sensitive. Without intelligent traffic engineering, these small flows may be blocked in the same queue behind megabytes of file transfer traffic. So it is very important to identify large flows for different applications. In the scope of this paper, we present an approach to detect large flows in real-time without even a short delay. After the detection of large flows, the next problem is how to control these large flows effectively and prevent network jam. In order to address this issue, we propose an approach in which when the controller is enabled, the large flow is mitigated the moment it hits the predefined threshold value in the control application. This real-time detection, marking, and controlling of large flows will assure an optimize usage of an overall network.

Long-lived flows termed as elephant flows normally transport large volumes of data in enterprise networks, particularly data center networks. These flows tend to consume a lot of bandwidth and fill up network buffers end-to-end. This causes non-trivial delays for short-lived flows referred to as mice flows which are usually delay-sensitive. Therefore, identifying and handling elephant flows is important for QoS provisioning. In this paper, we present a framework for real-time detection and visualization of elephant flows in SDN-based networks using sFlow. Using our proposed framework, network operators can examine elephant flows through each switch by double-clicking the switch node in the topology visualization UI. Although not in the scope of this paper, but in order to meet traffic engineering requirements, the elephant flows detected and visualized by our proposed framework can be reprioritized, re-scheduled, or routed via dedicated high speed links. We evaluate the proposed framework by using a physical SDN testbed as well as a Mininet-based testbed.

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 er\joyable 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.

In recent years, numerous design parameters have been considered to evaluate the performance of different routing protocols in VANETs. These parameters include end-to-end delay, packet delivery ratio and path duration. Path duration is one of the important design parameter for diverse environments. In this paper, we have proposed a probabilistical model for path duration estimation using highway mobility model for our Two-level Hierarchical routing protocol. Although, various analytical models have been exhibited for different protocols in VANETs, but a detailed model for VANETs is not available in literature. Specifically, the connectivity model for Highways and the urban areas has not been presented. Our proposed model has been validated by comparing analytical and experimental results obtained from MATLAB and ns-3.

Rapid growth in Internet of Things (IoT) has resulted in the need for effective management and provisioning of resources in an IoT-based system. The resource may be an entity (virtual or physical) that constitutes the IoT system, such as network elements, gateways, cloud data stores, and sensors. The dynamic and constantly changing nature of request by a typical user for resource allocation demands end-to-end slices of an IoT system. In order to meet such requests, end-to-end slices need to be integrated, ultimately making it necessary to control and aggregate diverse types of resources from IoT, cloud infrastructures, and network functions. To that end, in this paper, we propose a framework for IoT resources management and provisioning. Our proposed approach enables to automate resource management and provisioning for users across IoT, clouds, and network functions by virtualizing access to underlying IoT resources and utilizing APIs to employ those resources. Furthermore, our proposed approach aims to establish and manage end-to-end slices of various types of resources from distributed and diverse infrastructure. In this paper, we present multiFIA — multi-dimensional Future Internet Architecture — which permits IoT system designers to accommodate multiple user demands by establishing end-to-end slices of the overall IoT network. We outline multiFIA components and layers and also describe our approach to an implementation of multiFIA.

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.

Being a subclass of mobile ad hoc networks (MANETs), vehicular ad hoc networks (VANETs) have received considerable attention in recent years. Existing routing protocols of MANETs do not perform well for VANETs due to many different characteristics such as, fast moving nodes and frequent disconnections in the network. We present a context-aware hierarchical routing protocol based on road connectivity and vehicle context in VANETs called Context-aware Two-level Hierarchical Routing (CTHR) designed to fulfill QoS requirements in inter-vehicle communication in both urban and highway environments. CTHR's distinguished property is to not only find the available paths between source and destination but also to select the best available path based on QoS parameters. QoS parameters are calculated using the vehicle context such as, velocity. Unlike other routing protocols, the path from source to destination is made by road id's. CTHR uses gateway vehicles to locate the destination, forward the packets and to calculate QoS parameters. For the evaluation of CTHR protocol, we perform extensive simulations and results show that CTHR can help in design and deployment of VANETs.

Vehicular ad hoc networks have received considerable attention in recent years. Being a subclass of mobile ad hoc networks, VANETs have some different characteristics than MANETs such as, fast moving nodes and frequent disconnections in the network. Existing routing protocols of MANETs do not perform well under these dynamic conditions in VANETs. We present a two-level hierarchical routing protocol based on road connectivity in VANETs called Hybrid Road-Aware Routing (HRAR) designed specifically for inter- vehicle communication in urban and/or highway environments. A distinguished property of HRAR is it's ability to not only discover the available paths between source and destination pairs but also to re-adjust the paths on the fly, without starting a new destination discovery process. Unlike other routing protocols, the path from source to destination is made by road id's. Gateway nodes help in locating the destination and forwarding the packets from one road segment to other. For the evaluation of HRAR protocol, we perform extensive simulations and results show that HRAR can help in 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.

The traffic in research network for the new structure of the network and new service research has the various properties. From the perspective of a specific traffic point of view, transmission of traffic with different requirements using a single routing protocol is an obstacle to satisfy requirements. In this study, we classify the properties of the traffic into two types. We propose the way that we can get the overall optimization effect, the experiment proves it by providing independent multiple forwarding path by applying optimized algorithm by types. In order to distinguish each type of traffic we use the ports on the switch and in order to implement independent path we apply OpenFlow system. In other words, we present the measure that can be implemented to improve the satisfaction of the traffic by making multiple paths by OpenFlow controller according to the type of traffic and by enforcing in Forwarder.

The PBNM on MANET is being researched to ensure the reliability and efficiency between mobile nodes. Therefore, it is essential to determine the cluster effectively which will perceive the movements of nodes and distribute the policies. In PBNM mechanism, to determine the node cluster for PDP and manage PEP nodes, Active PDP Discovery Protocol is proposed as a mechanism which is more efficient than preexistent techniques. While k-hop cluster selects the PEP nodes which PDP node manages, Active PDP Discovery actively selects the PDP node among the moving PEP node. This method prevents orphan nodes that are not connected to PDP and reduces continual broadcasting messages. This paper implements Active PDP Discovery which determines cluster in the real networks and analyzes its capability, expanding COPS-PR to detect the movement of nodes and adding MNL to PDP node.

This paper is primarily concerned with multi-path routing in Mobile Ad hoc Networks (MANETs). We propose a novel associativity-based on-demand source routing protocol for MANETs that attempts to establish relatively stable path(s) between the source and the destination. We introduce a new notion for gauging the temporal and spatial stability of nodes, and hence the paths interconnecting them. The proposed protocol is compared with other unipath (DSDV and AODV) and multi-path (AOMDV) routing protocols. We investigate the performance in terms of throughput, normalized routing overhead, packet delivery ratio etc. All on-demand protocols show good performance in mobile environments with less traffic overhead compared to proactive approaches, but they are prone to longer end-to-end delays due to route discovery and maintenance.

A Policy-based Network Management (PBNM) in Mobile Ad-hoc Networks (MANETs) should be efficient and reliable. In this letter, we propose a mechanism for the policy-based management in ad hoc networks and we discuss methods to discover the Policy Decision Point (PDP), set the management area, and manage the movements of nodes in the PBNM system. Finally, we assess the results through simulations.

Geocast routing in vehicular ad-hoc networks has become an area of interest for researchers as it is an essential part of ITS (intelligent transport system). Many vehicular applications require geocast i.e. fast and reliable warning message dissemination to particular regions. Several proposed approaches, consider a geocast region as a circular area with a center point and a radius. However, in case of VANETs, only road segments should be considered as geocast regions to deliver the safety information efficiently for many scenarios such as accidents, deviations and congestion. In this paper, we propose a map-aware delay based novel geocast routing protocol to ensure reliable delivery of warning messages to all nearby vehicles on different road segments. Our approach is robust and scalable over highly mobile and dynamic vehicular conditions with low rebroadcast overhead. Roads are selected as geocast regions based on the road probabilities calculated using number of lanes around each intersection. Moreover, using the neighbor knowledge, a circle of forwarders is selected and messages are rebroadcasted by assigning certain delays to vehicles to avoid unnecessary rebroadcasts. Vehicles use two different modes for message forwarding to safeguard message delivery to all vehicles present in the geocast regions. We evaluate our approach through extensive and realistic simulations and results show that our routing algorithm achieves high packet delivery ratio and low packet rebroadcast ratio for highway scenario compared with existing geocast routing method.

Recently, several approaches to share road conditions and/or digital contents through VANETs have been proposed, and such approaches have generally considered the radial distance from the information source as well as the TTL to provision an ephemeral, geographically-limited information sharing service. However, they implement general MANETs and have not been tailored to the constrained movement of vehicles on roads that are mostly linear. In this paper, we propose a novel application-level mechanism that can be used to share road conditions, including accidents, detours and congestion, through a VANET. We assign probabilities to roads around each of the intersections in the neighborhood road network. We then use the graph representation of the road network to build a spanning tree of roads with the information source as the root node. Nodes below the root represent junctions, and the edges represent inter-connecting road segments. Messages propagate along the branches of the tree, and as the information propagates down the branches, the probability of replication decreases. The information is replicated until a threshold probability has been reached, and our method also ensures that messages are not delivered to irrelevant vehicles, independently of their proximity to the source. We evaluated the success rate and performance of this approach using NS-3 simulations, and we used IDM car following and MOBIL lane change models to provide realistic modeling of the vehicle mobility.

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.

Geocast is a communication technique to disseminate information in specific geographic regions instead of node addresses. Geocast routing in VANETs have become an area of interest for researchers. Traffic congestion, accidents and local hazards are examples of use cases of information sharing in VANETs. As geocast is an important part of ITS (intelligent transport system), many geocast routing protocols have been proposed in the past. Yet, geocast region selection techniques for VANET routing have not been studied in detail so far. In this paper we propose a map aware geocast routing protocol for information dissemination in VANETs. The target regions are selected by constructing a probability tree and information is disseminated along the branches of tree. Vehicles use two different packet forwarding modes based on their direction towards the hazardous road. Packets are forwarded towards the target regions by selecting the fastest neighbor vehicles. Moreover, packets broadcasted from the vehicles at intersections are given priority to disseminate information on all roads across the intersection. We evaluate our approach through extensive and realistic simulations and results show that our geocast routing protocol shows better delivery ratio and improved end to end delay as compared to existing method.

We have proposed the methods how to control the slope of CMOS inverter`s characteristic and how to shift it in y axis. We control the MOS transistor threshold voltage for these methods. By observing that two transistors are in saturation region at the center of the CMOS inverter`s characteristic, we have presented how to make the characteristic for one pole neuron. The circuit level simulation is used for verifying the proposed method. PSpice(OrCAD Co.) is used for circuit level simulation.