Research On Routing Algorithm In Real-time For The Internet Of Things

With the development of a variety of communication technologies, the networkis not only used by computer through the Internet, but also used by all the machines,everything can be getting through the net. That is, the Internet of Things (IOT). TheInternet of Things provides customers a series of service, which include datacollection, transmission, processing and management. Above all, the Internet ofThings will be widely used.Between the Internet of Things and the physical world is a sensor network,which collects and transfers data, so the Internet of Things is considered as "the lastmile of physical world". However, the Internet of Things is more complex than thesensor network, which consists of different types of things and does different work.There are differences in many ways such as physical characteristics, storage capacityand computing power. These features raised some new requirements in transmission.During the data transmission, the complexity of the data structure will besheltered in the hierarchy model, the leader of the nodes in the hierarchical modelundertakes the job of integrating and handling data, it plays a crucial role. Thealgorithms existed always focus on energy consumption of nodes, because of thecapability of data process is not need to measure. However, under the environment ofthe Internet of Things, it will help to make full use of the resources of thehigh-performance nodes, meanwhile, the efficiency of transmission will be raised,and the delay will be reduced.Nodes with different types served as collection and transmission in the same area.Different requirements of event will get different sensitivity of the time. When theurgent data cannot get priority to transfer, it will become invalid. Therefore, in orderto satisfy the environment of the Internet of Things, it’s necessary to improve thestrategies of channel allocation.For the issues raised, the main research works are listed as follows:Firstly, designed an algorithm based on the ability of computing. Through the mode of self-learning, the BS records and maintains the parameter values for eachtype of nodes computing capacity. One node with high computing ability equivalentto many low ones. In this way, nodes with high ability of computing will probabilityto be the cluster-head. It will make full use of the resources of the high-performancenodes, and computes more data during the same time.Secondly, designed a dynamic strategy of channel allocation based on thedifference timeliness. Under the Internet of Things, nodes with different tasks will beassigned different ideal thresholds. The length of slot depends on the thresholds, therate of sending and the amount of data. The less threshold, the higher rate and themore data it is, the longer slot it allocated, as a result, the data of the sort will betransmitted in a shorter time. By this way, we can distinguish timeliness of thedifferent types of nodes.In the last, we do simulation to verify the algorithm.The cluster-head algorithmbased on the ability of computing can be increased the amount of data are delivered.The dynamic strategy of channel allocation ensured the urgent data can be deliveredin short time, it’s able to distinct the nodes with different functions and differentrequirements.