Publication

Abstract : In wireless sensor networks (WSNs), mobile Sink based data gathering and routing is considered an emerging topic. MS-based routing is developed to achieve enhanced network performance and sensed data gathering from each region. To address the issues related to energy consumption and to improve flexibility, energy-efficient data gathering and routing are proposed with hybridized models. The energy-efficient routing process for data transfer involves clustering, cluster head selection, data gathering, and routing. Initially, sensor nodes (SNs) in the network are clustered using an Improved Fuzzy C Mean (I-FCM) clustering algorithm based on the distance between nodes. For each cluster, Cluster heads (CHs) are selected based on the energy and distance parameters using Hybridized Glow-worm Swarm and Monarch Butterfly Optimization (Hybrid GS-MBO). The data collection in CH follows Time Division multiple accesses (TDMA)-based scheduling, which provides time slots for data gathering. TDMA based scheduling is significant for avoiding data collision and packet retransmission. Finally, optimal routing is performed among the CHs and MS using the adaptive Euclidean Distance-based Crow Search algorithm (AEDCS), which offers the shortest path with reduced time complexity. The proposed approach is simulated in Network Simulator 3 (NS-3) and the performance is evaluated with standard performance metrics. The performance of the proposed approach is improved with the energy consumption of 40 mJ, packet delivery ratio of 99.5%, packet loss ratio of 1.8%, throughput rate of 0.85 Mbps, etc. When compared with the existing approaches, the performance of the proposed approach is high, and it indicates the superiority of the approach.