Correlative Dynamic Mapping based Optimization (CDMO) for Optimal Allocation in Cognitive Radio Sensor Network

The data communication in the sensor networking system with cognitive radio sensor network increases the speed of packet transmission by the optimal spectrum allocation among available channel. For a large size of network architecture, the routing system needs to manage the packet transmission in an efficient routing path with optimal allocation of channel to the sensor nodes. In these optimal network allocation and routing system, the methods need to be modeled based on the parameters of channel capacity and other routing properties. In this paper, a novel optimization algorithm was proposed to estimate the dynamic change of network parameters. This can be achieved by the enhanced model of Correlative Dynamic Mapping based Optimization (CDMO). In this, it analyse traffic level in a network path and the availability of spectrum channel present at each time instant for the sensor nodes and estimate the availability if channel in it. The proposed system estimates the correlation factor between the sensor nodes and with the weight value of dynamic traffic occurs in the routing path. From these estimated properties, the optimization method selects the best attributes from overall feature set of the node arrangement. The CDMO algorithm performs the prediction of relative feature attributes and form as a clustering architecture to represent the high data transmission by reusing the spectrum of channel. The best fitness value of the convergence graph at every iteration of the optimization algorithm represents the best routing path. From the fitness of the objective function in CDMO, the best routing path and the availability of spectrum for a node can be analysed at each time instant. The experimental result justifies the efficiency of proposed work comparing to other state-of-art methods for routing and channel allocation system.