Hybrid of COOT Optimization Algorithm with Genetic Algorithm for Sensor Nodes Clustering Using Software Defined Network.

The main issue in wireless sensor networks (WSNs) is the energy consumption of the nodes. Each sensor node communicates directly with other nodes in its transmission range or uses other nodes to forward the message to nodes outside its range. Software-defined networking (SDN) is a good solution for WSNs by separating the control logic from nodes/drivers. The advantage of SDN-WSNs is that SDN has centralized control over the whole network and deployment of network management protocols and applications becomes easy. In this paper, a new clustering model based on SDN-WSNs is proposed that uses the COOT optimization algorithm and Genetic Algorithm (GA). GA is used in the proposed model to improve COOT. COOT-GA is embedded in the SDN controller and is responsible for forming clusters with optimal structures. The SDN controller sends commands to the sensor nodes and finds the best clustering and energy consumption mode by repeated operations. The COOT-GA model is evaluated by the number of alive nodes, energy consumption, and packet delivery rate. The COOT-GA model is evaluated in two scenarios with 100 and 200 nodes. According to the results, with 100 nodes, the COOT-GA model has reduced energy consumption by 8.55%, 6.23%, and 3.90% compared to the Sine Cosine Algorithm (SCA), Harris Hawks Optimization (HHO) and COOT. According to the results, with 200 nodes, the COOT-GA model has reduced energy consumption by 13.54%, 10.32%, and 4.51% compared to SCA, HHO, and COOT. [ABSTRACT FROM AUTHOR]