Optimized TDMA framework for channel interference mitigation in IWSN based on self-adaptive affinity propagation clustering

Abstract

Industrial Wireless Sensor Networks (IWSN) is the cornerstone of the factories of the future. The massive volumes of heterogeneous data generated from large-scale IWSNs still pose challenges to the establishment of predictable, deterministic, and real-time transmission scheduling. One of the major obstacles in wireless sensor networks (IWSNs) is the reduction of collisions caused by adjacent nodes transmitting simultaneously over a single channel. The Optimized TDMA Framework for Optimized Channel Interference Mitigation Algorithm (OCIMA) has been developed in order to prevent transmission collisions. Specifically, the suggested TDMA approach significantly reduces the collision during the data transmission, while simultaneously minimizing the high priority packets transport latency. The nodes are first positioned throughout the experimental area at random. Using the Self-Adaptive Affinity Propagation Clustering (SAAPC) Algorithm, the deployed nodes are clustered to form clusters, with a cluster head selected. Self-adaptive affinity propagation consists of the initial phase, setup phase, and communication phase. After clustering, channel interference can be avoided using the TDMA approach combined with the Gazelle Optimization Algorithm (GOA). To prevent data collisions, each network cluster is given time slots via the TDMA mechanism. The optimal practicable performance of TDMA can be attained by choosing a sufficient amount of time slots for the complete data transfer. For that, GOA optimization is developed to choosing the optimal timeslots. According to the simulation analysis, the OCIMA technique that was created which have 12.4 J residual energy, 94% packet delivery ratio, and 986 s network lifetime. Thus, the proposed approach is the better choice for avoiding the mitigation of TDMA during data transmission.