## 6 IoT Leveraging SDN  
IoT plays an important role in the development of intelligent systems such as smart health-care [179], smart transportation systems [180], and smart energy systems [181] using large scale distributed systems. These distributed systems connect billions of RFID nodes and sensors, and hence there is a need for designing a secure, efficient, intelligent, and cost-effective, scalable IoT architecture [182]. A sensing network is made up of sensing nodes capable of collecting and monitoring information from the environment, such as temperature, pressure, humidity, and motion. These sensing nodes usually have scarce resources such as limited computing power, battery, and bandwidth, which raise issues of heterogeneity and network configurations. SDN is an effective solution for network management and network configurations. SDN is integrated with the sensing network for sustainability and efficiency of the sensing network [183]. In paper [119], a model of a software-defined wireless sensor network (SDWSN) is proposed to solve heterogeneity and network configurations of scarce resources. Large-scale distributed systems will generate massive data; machine learning techniques are applied for the predictive analytics of such massive data. Predictive machine learning models are trained using a centralized approach; however, transferring such a massive amount of data to a centralized SDN controller requires large network bandwidth [74]. For low bandwidth consumption, EC-based solutions are used to preprocess the data and improve the system response time. The prepared data are then transferred to the SDN controller to speed up the machine learning training process. To improve IoT services' response time, these trained models are deployed on edge servers [184]. Machine learning and SDN hybrid techniques are applied for route optimization, data analysis,event detection, node clustering, localization [66], intrusion, and fault detection [185] in IoT applications. In Figure 3, a conceptual IoT architecture based on intelligent SDN architecture with machine learning modules is presented. The benefits of SDN in IoT architecture are discussed in detail by [PERSON] et al. [186]. SDN allows the dynamic configurations to define the policies and rules for different data planes. They also discussed IoT architecture requirements such as common service layer, big data management, QoS, and network access management. In paper [187], a restful application programming interfaces (APIs) based IoT architecture consisting of several modules such as northbound and southbound APIs is proposed. The control plane consists of a processor and database modules. SBIs module deals with protocols such as HTTP, COAP, and LoRaWAN. In literature, free scale SDN platforms such as vortiQa open network switch director and vortiQa open network are discussed [188]. SDN-based Majord' home management platform is proposed in [189]. Due to the extensive use of the internet, device management has become a difficult task. In [PERSON]' home management, the connected object is represented by CO, and coCO represents a community of the connected objects. The virtual object is represented using Vo, and avatar is used for the management of VOs. [PERSON] et al. [190] expand the proposed home automation platform further using a generalization of the concept to any smart environment. CO is generalized to a home device, VO is the abstract view of a device, whereas coVO is a community of the virtual objects. Proposed SDN architecture is composed of four layers, a management layer spanning vertically and three horizontal layers. The data layer performs data generation without routing and forwarding functions. The Control layer is composed of a network controller, CO controller, and coVO controller. The management layer consists of a network manager, application, and VO manager. In paper [191], SDN and distributed data service are applied to IoT architecture.