As the global Internet of things increasingly is popular with consumers and business environment, network flow management has become an important topic to optimize the performance on Internet of Things. The rigid existing Internet of things (IoT) architecture blocks current traffic management technology to provide a real differentiated service for large-scale IoT. Software-defined Internet of Things (SD-IoT) is a new computing paradigm that separates control plane and data plane, and enables centralized logic control. In this paper, we first present a general framework for SD-IoT, which consists of two main components: SD-IoT controllers and SD-IoT switches. The controllers of SD-IoT uses resource pooling technology, and the pool is responsible for the centralized control of the entire network. The switches of SD-IoT integrate with the gateway functions, which is responsible for data access and forwarding. The SD-IoT controller pool is designed as a vertical control architecture, which includes the main control layer and the base control layer. The controller (main controller) of the main control layer interacts upward with the application layer, interacts with the base control layer downwards, and the controller (base controller) of the basic control layer interacts with the data forwarding layer. We propose a dynamic balancing algorithm of the main controller based on election mechanism and a dynamic load balancing algorithm of the basic controller based on the balanced delay, respectively. The experimental results show that the dynamic balancing algorithm based on the election mechanism can ensure the consistency of the messages between the main controllers, and the dynamic load balancing algorithm based on the balanced delay can balance between these different workloads in the basic controllers.
In order to achieve dynamic load balancing based on data fl ow level, in this paper, we apply SDN technology to the cloud data center, and propose a dynamic load balancing method of cloud center based on SDN. The approach of using the SDN technology in the current task scheduling flexibility, accomplish real-time monitoring of the service node flow and load condition by the Open Flow protocol. When the load of system is imbalanced, the controller can allocate globally network resources. What’s more, by using dynamic correction, the load of the system is not obvious tilt in the long run. The results of simulation show that this approach can realize and ensure that the load will not tilt over a long period of time, and improve the system throughput. Existing research on data collection using wireless mobile vehicle network emphasizes the reliable delivery of information. However, other performance requirements such as life cycle of nodes, stability and security are not set as primary design objectives. This makes data collection ability of vehicular nodes in real application environment inferior. By considering the features of nodes in wireless IoV, such as large scales of deployment, volatility and low time delay, an efficient data collection algorithm is proposed for mobile vehicle network environment. An adaptive sensing model is designed to establish vehicular data collection protocol. The protocol adopts group management in model communication. The vehicular sensing node in group can adjust network sensing chain according to sensing distance threshold with surrounding nodes. It will dynamically choose a combination of network sensing chains on basis of remaining energy and location characteristics of surrounding nodes. In addition, secure data collection between sensing nodes is undertaken as well. The simulation and experiments show that the vehicular node can realize secure and real-time data collection. Moreover, the proposed algorithm is superior in vehicular network life cycle, power consumption and reliability of data collection by comparing to other algorithms.