Software Defined Internet of Everything

The Internet of Things (IoT) is a field that makes smart objects communicate with the Internet. There are four basic components of IoT, i.e., sensors, gateway node, data computing, and end-users. The evolution in distributed computing enhances the processing capabilities at the distributed end. This leads to the generation of a new field called the Internet of Everything (IoE). It evolves from one pillar called things in IoT to four pillars called People, Process, Data, and Things. The IoE is bringing together all these four components in more integrated manner. The evolution of IoE results in a better network having the capacity to turn information into actions. It creates new and exciting opportunities with richer experiences for individuals and organizations. This also brings new challenges such as Data Security, Device Heterogeneity, Compatibility Issues, Intelligent Analysis, and Privacy issues. In this chapter we provide a summarized analysis about the shortcomings in Internet of Things and the key reasons that have led to the emergence of Internet of Everything. Some of the key challenges that need to be overcome to ensure widespread acceptability of IoE are also analyzed.

This chapter aims to introduce the definitions of the main concepts regarding smart cities and Internet of Things (IoT) solutions. In addition to providing context, the chapter should also deal with the theme’s main points, such as applications, technologies, barriers, and goals. It will address how smart cities relate to the connected world through an IoT perspective. Another branch of this chapter is also seeking to qualify smart cities from a holistic and broad view. In this way, it is possible to reflect on how these technologies and innovations will affect society. The chapter also aims to integrate these aspects and detail how Information and Communication Technologies (ICTs) and IoT concepts can improve the control and management of society’s key sectors. Finally, this chapter presents Smart Cities applications from the IoT perspective with the concern of the existing solutions, calling attention to some projects and research.

The life cycle of a network system usually includes four stages: demand investigation, planning and design, deployment and implementation, and operation and maintenance. Based on this cycle, a huge network architecture has now been formed, which has played an important role in promoting economic and social development. However, with the vigorous rise of technologies such as big data, cloud computing, Internet of Things, and mobile Internet, Internet applications are becoming increasingly diversified and business volume is increasing. Therefore, the current network architecture is gradually unable to meet the demand, and the existing problems are becoming increasingly prominent. In general, the core problem is that there is a contradiction between the diverse and changeable network upper-layer applications and business requirements and the current stable and rigid traditional network architecture. In order to meet a specific application requirement, it usually needs to include a large number of hardware devices. However, a noteworthy problem is that network devices produced by different manufacturers usually require different ways to debug and configure. Therefore, in a network that mixes equipment from multiple different vendors, managing and deploying the network is a very big challenge. Moreover, the inability to perform intelligent flow control and visualized network status supervision based on network conditions is also a problem that hinders further development. Based on the above problems, software-defined networking (SDN) is a better solution. In general, SDN has the following three advantages: (1) SDN can change the tightly coupled architecture of applications and networks under traditional networks and improve the level of network resource pooling; (2) SDN networks can realize automatic network deployment and configuration, and support rapid business launch and flexible expansion; (3) By introducing programmable features, automated network services and protocol scheduling can be realized. However, the architecture still has some challenges worth considering, such as: (1) Challenges faced by interface/protocol standardization. At present, the control architecture system of the SDN centralized control concept is not unified, and it is difficult to achieve mutual operation due to the different degrees of vendors’ support for the SDN standard. (2) Security challenges. The core controller of the SDN network may have security problems such as excessive load, single point failure, and vulnerability to network attacks. Therefore, it is necessary to establish a reasonable mechanism to ensure the safe and stable operation of the entire system. (3) Challenges in performance. The existing ASIC chip architecture is based on the traditional IP or Ethernet addressing and forwarding design. Therefore, whether the equipment under the SDN architecture can maintain the theoretical high performance remains to be discussed. To sum up, this chapter will start from the analysis and comparison of the traditional network architecture and the SDN network architecture, summarize the problems in the traditional architecture and the necessity of the development of the SDN architecture, and further analyze the application scenarios and the existence of the SDN architecture challenge.

The current Internet infrastructure is not anticipating such a growth of IoT and increasing the network complexity. New network architecture for the management of IoT data flow and also catering to the Quality of Service of different IoT services is required. The existing incompatible solutions are limited to the early adoption of IoT. The standardization bodies, industries, researches were involved in developing standards to support end-to-end connection, interoperation between devices from different vendors and also provide cost-efficient solutions. The Working Groups (WG) at the IETF introduced new solutions that have allowed the connection of low-power wireless networks to the Internet. In spite of the vast exploration of solutions for deploying IoT, the management of IoT networks requires complex routing topologies with a simplified user operation. This gives rise to the need for centralized network control which is facilitated by Software Defined Networking (SDN). SDN was a standard technology for Wireless Sensor Networks (WSNs) already available which is the early version of IoT as the world knows it today. SDN provides a framework to ease the complexity involved in the management of sophisticated networks. We discuss various protocols present in the architecture along with the research challenges for the future.

The wide use of communication devices and machines over the world turned the legacy Internet into Internet of everything. These devices are controlled and governed by rules and policies, mostly dictated by an organization. Obliged to rules the network management and control are vital tasks that require significant effort and resources. Software Defined Network eases all devices’ control and management by decoupling the control plane from the data plane. Network policies are an essential part of every system that controls the access of devices and users among each other as well as to the data centers. Due to the change in business requirements, these network policies are required to update rules across all the devices. If there are some misconfigurations, the entire IoT system may cause disruption and can lead to unauthorized access to network resources. Several approaches have been adopted to cope with these challenges, automation of network policies, optimizing policy placements, the suitable mechanism to update the policies according to requirements, etc.

An abrupt increase in the workload can be observed nowadays in every aspect of business approach, e.g., e-commerce, financial industry, advertising industry, social networking websites, etc. This surge in the rate of workload traffic can influence the congestion problems, resulting into breakage of various services committed to the end user. To avoid this situation, Cloud, Edge computing approaches are used to handle the workload in an efficient manner. At cloud layer, there are an abundance of resources to handle the workload from the various end devices/users. The data centers (DCs) are centralized at fixed locations, and therefore, latency can be observed while transmitting the workload from end user to the DCs. To overcome the latency issue, a geo-distributed technology, known as edge computing, was introduced to handle the requests of the end users. Whenever, the workload is transmitted from one location to another location, network traffic can be a challenging issue, which further can create bottleneck in the network. Software-Defined Networking (SDN) is the platform by using which the network can be controlled centrally in an efficient manner. The resources are limited in edge computing, and therefore limited services can be provided to the end users. The service provider also interested to provide the services using resources at the edge layer. Due to this, the workload on the resources may not be saturated. To use the resources at its best, in this chapter, different load balancing and routing techniques are discussed thoroughly to handle the generated workload using SDN envisioned environment.

Internet of Things (IoT) is an evolutionary technology in the various fields of the industry, military, etc. There are major issues in IoT-enabled environment, such as energy consumption, congestion, Quality of Service (QoS) and configuration of different nature devices. The energy plays an important role during data transmission from source to destination. The IoT devices are limited in terms of resources, like, battery power, processing power. In this chapter, the challenges of the incorporation of SDN-enabled environment with IoT-based devices are explored with figures and facts. A detailed analysis of the various energy-efficient approaches is highlighted in a proper format to improve the understandability of the concept. A section-wise review of the various authors is discussed to highlight the challenges and solutions to resolve the issues.

This chapter focuses on introducing network function virtualization. In both academia and industry, network function virtualization is abbreviated as NFV. The chapter consists of four main parts: what is NFV, NFV architecture and model, NFV applications and implementations, and resource allocation in NFV-enabled networks.

With the enhancement in the technology and comprehensive growth in Internet of Things (IoT), smart devices are being deployed to provide various modern day services. Smart devices that are deployed and configured at various organizations and applications (like, e-healthcare, intelligent transportation systems, etc.) require seamless services supported through the provisioning of computational and storage resources at the cloud. With the increase in the number of services and devices, an extensive load has been noticed on the cloud platform. Thus, edge devices came into existence and act as a middle layer to provide limited services to the end users. However, a huge data movement has been observed in the three-layered architecture of IoT-Cloud platform. Therefore, a controller is required to manage the traffic in the network for smooth processing of the tasks. Software-defined network (SDN) has the capability to provide programmable interface to the network to handle the incoming traffic intelligently due to its dynamic and scalable behavior. In this chapter, we have discussed various prospectives related to edge–cloud cooperation and how effectively SDN can handle the elephant-like traffic intelligently.

Cloud computing and software-defined networking (SDN) have gained a lot of interest from industry and academia. With the increase in data from applications, data centers require high-speed access networks to fulfill the user demands. Today, the data centers are facing scalability challenges and do not adapt to dynamic application requirements. However, features in SDN facilitate data processing, storage, and transmission of data center applications. This chapter presents an overview of SDN’s importance in cloud computing and to overcome the challenges in data centers. Further, we discuss how SDN in data center networks benefits for managing routing, traffic engineering, and resource management. Moreover, we also present the different methods that have been adopted for SDN-based energy-aware routing strategies to minimize power consumption in data centers.

In this work, the problem of unbalanced data traffic in the presence of heterogeneous Internet of things (IoT) applications in software-defined data center networks (DCNs) is studied. In the existing literature, the presence of heterogeneous flows and switches and mobile IoT devices in software-defined DCN are not considered. Due to heterogeneity, it becomes an NP-hard problem. To address these issues in polynomial time, we proposed a data traffic management scheme, named FASCES, using the single-leader-multiple-followers Stackelberg game. In FASCES, each controller acts as the leader and the IoT applications act as the followers. Each leader and follower aim to achieve an optimal distribution of flow rules and optimal datarate, respectively. We also evaluate the existence of at least one Stackelberg equilibrium solution in FASCES. Furthermore, we evaluated the performance of FASCES while comparing it with the existing schemes through simulation. We observe that FASCES ensures a 16.67–19.45% increase in network throughput and a 4.34–9.43% reduction in network delay. Additionally, using FASCES, the per-flow delay reduces by 27.78–36.67% while ensuring a 15.37–26.91% increase in the per-flow throughput.

For pervasive applications of the artificial neural networks (ANN), the autonomous control usually affects the overall processing and analysis of data in the healthcare systems. Though, involvement of several malicious activities by the intruders may drastically affect the overall communication system. Recently, more and more engineers have applied a trusted and secure Artificial Internet of Things (AIoT) healthcare system is used to analyze and process the accurate control for overall benefits. However, the lack of security and trust in IoT devices results in accidental control of risks. The aim of this chapter is to propose an ANN-based secure network to analyze the legitimacy of IoT devices by categorizing through back propagation and Bayesian rule schemes. The proposed system can efficiently recognize the illegal activity of malicious IoT devices used to record, manage, and store the sensitive information of healthcare centers. The proposed phenomenon is proposed over various security metrics against conventional scheme. Further, we have discussed the Software-Defined Networks (SDNs) architectures that provide better solutions by removing the decision making capabilities from intermediate nodes in the network.

Software-defined network (SDN) is an emerging networking paradigm that segregates functionalities of control and data plane to reduce their complexity and provides more control, scalability, and centralized management. OpenFlow (OF) is a widely used protocol that builds a global and shared view of the network. Therefore, for SDN applications, the correctness of the topology view has a critical impact on the flow-based communication and provision of services. However, recently identified vulnerabilities in Open Flow Discovery Protocol (OFDP) reveal that malicious hosts or data plane switches can poison the global view of the network, and an intruder can launch man-in-the-middle or denial of service attacks. Existing passive approach-based solutions work well for known attacks. Some solutions use an active approach to identify the fake links or malicious hosts by sending Stealthy Probing Verification (SPV) packets. However, due to the use of probing mechanism, it faces scalability and bandwidth consumption issues in the case of large data centers networks and resource limited networks. The proposed technique is based on the SPV mechanism, however, to counter the scalability and bandwidth issues, the probing packets are only initiated when triggered updates of a new link or network node are received by the SDN controller. The probing traffic has been reduced by 40%. Hence consume less bandwidth and identifies a malicious host in less than 90 ms. The results indicate that the Enhance Stealthy Probing Verification (ESPV) is a more scalable and suitable solution to detect and identify fake links or malicious hosts in large data center networks and resource limited networks such as Wireless Sensor Networks (WSNs).

Software-defined network (SDN) is designed to make the central network more efficient and improve the control network. Its architecture is fully controlled designed and configured as programmable. The controller of SDN controls the entire network of SDN. In this chapter, some SDN protocols are implemented to reduce security risks. So many devices are launched in the market with the new technology as per the requirement of the new era. SDN gives more benefits due to centralized control and network control programmability. The simple network uses switch and routers to manage all the client requests as a centralized network. To identify the fake links and requests by sending SPV (Stealthy Probing Verification) packets. Sometimes, it detects the fake links but does not detect them all the time.

Vehicular ad hoc networks (VANETs) are one of the key technologies used for developing an Intelligent Transportation System (ITS). They are expected to provide seamless network connectivity to moving vehicles while supporting various services for effective transportation medium. There have been a large number of new technological integrations in VANETs for achieving this objective. Software-Defined Networking (SDN) is also one of the key technologies that is being used for resolving numerous issues of VANETs. Here, we have proposed a smart communication system based on software-defined vehicular networks that will enable vehicles moving at high speed on express highways to avail multiple services to help them commute effortlessly through the expressway. The proposed system provides a captive network for every vehicle that is moving along the expressway that will assist them in moving at high speeds by providing periodic status messages about the road conditions. The proposed communication model uses a two-level hierarchy where Road Side Unit-based controllers are at a lower level that are used to distribute the load of centralized SDN controller that constitutes the upper layer of the model. The lower level controllers work under the assistance of centralized controller only and due to their distributed structure are unable to take decisions on their own. This centralized network architecture provides control of the whole network by providing various services to vehicles. The chapter also discusses a case study that can open the way towards development of the proposed data communication system to support ITS services.

To improve the level of industrial management through image processing in the production and processing, the image processing technology based on deep learning is explored. The network architecture of traditional industrial Internet of Things (IIoT) is not flexible enough. The present work designs and implements a dynamic computing framework in the environment of IIoT to achieve more flexible and efficient transmission and computing process. First, the image processing system of the machine vision module in IIoT is explored, and second, the application of image processing technology based on deep learning in the system is discussed. Through training the natural images in the data set, the image processing based on deep learning is applied to the detection of welding defects in industrial production. It is found that the deep learning algorithm is effective in a wider range of image processing tasks, and the image processing based on deep learning proposed is effective in the image classification of welding defects of industrial production. In SMOTE data set, deep convolution neural network (DCNN2) features based on DCNN have the strongest classification ability, and the classification accuracy can reach 97.5%. However, in RUS data set, the classification accuracy of DCNN1 and DCNN2 ranges from 75% to 80%, which is significantly lower than that of Stacked AutoEncoder (SSAE), which is 87.9%. The software-defined network (SDN) is combined with IIoT to construct a network environment suitable for IIoT, and a dynamic computing framework configured in the transmission and calculation process is realized. It provides a convenient and powerful guarantee for the effective supervision of equipment and processing technology in the production of IIoT.