CLUTERING IN WSN USING CUCKOO SEARCH OPTIMIZATION

Abstract

The wireless sensor network (WSN) features that separate data for field of application by distributing nodes are the foundation of the IoT. IoT-WSN sensor nodes generally have a wide range of properties. There aren’t many nodes with more energy or even data aggregation capabilities. Because the Internet of Things is vast and extremely complex, creating energy-efficient solutions to address its difficulties is essential. Furthermore, attackers may pose a threat to the security of these networks. In order for sensitive data to be transmitted often over a multihop path, the sensor must periodically transmit sensitive data to the BS. Therefore, it is crucial to create a secure WSN that can effectively identify cunning attackers. The main motive of this research is to use the most recent, efficient algorithms to extend the life of communication networks reduce energy usage. Industry 4.0 is strongly dependent on WSN and IoT. Numerous control systems, such as those for home automation, chemical or biological assault identification, or environmental monitoring, use IoT or WSN. The information that is obtained from WSN modules is processed and sent to remote destinations via IoT devices or apps. One method that academics frequently employ to extend the lifetime of networks is clustering. One of the crucial steps in this procedure is choosing the head, which needs to be done very carefully. Within the current approach, the heads are select only on the basis of distance, with no consideration given to other considerations like node energy. Furthermore, the convergence speed of sun flower optimization is reduced. Second, only a single hop is used to send information from the CH to the BS, which uses a lot of energy. Since the energy consumption of a single hop is directly correlated with the distance among two nodes, this value typically tends to be larger. The suggested study aims to apply firefly optimization method to the data transmission operation and Cuckoo search optimization for CH selection process.