Energy-Efficient-Based Optimizing Cluster Head Selection by Geometric-Based Mechanism and Implementation Using Soft Computing Techniques

摘要

The joint technique used is geometric-based analysis and transitional probability for the cluster head selection in wireless sensor networks with the combination of the threshold analysis and the trust factor for the determination of the optimized values by using soft computing techniques. In this paper, we present a geometric and the transitional probability for the cluster head selection in wireless sensor networks. Therefore, a continuous semi-Markov chain-based node behavior prediction process is incorporated for identifying the trust parameter that integrates the energy factor estimated previously based on fuzzy probability. The proposed algorithm is the fuzzy-based geometric translational probability (FGTP) for cluster head selection. The semi-Markov chain is implemented by using fuzzy logic for determining the cluster head selection process. The implementation of the proposed FGTCH scheme is investigated based on various performance metrics such as the Packet delivery ratio, Delay, Energy difference, Throughput and quantified trust. The trust parameter is incorporated in the proposed FGTCH scheme for optimizing the cluster head selection through soft computing techniques. To evaluate the proposed FGTCH model, extensive simulations were carried out in MATLAB. The number of nodes in a network ranges from 70 to 100 nodes. The simulation is conducted to evaluate the performance of the proposed FGTCH, and it is compared with existing LEACH for clusters head formation, the average end-to-end delay, packet delivery ratio, and lifetime computation. When the number of nodes is 30, the percentage of average end-to-end delay (s) of cluster formation using fuzzy-based geometric translational probability for cluster head selection algorithm is decreased by 4.87% than LEACH. When the number of nodes is 30, the percentage of packet delivery ratio of cluster head selection using fuzzy-based geometric translational probability algorithm is increased by 6.60% than LEACH. When the number of nodes is 80, the percentage of packet delivery ratio of cluster formation using fuzzy-based geometric translational probability algorithm is decreased by 10.42% than LEACH and the existing methods.