A Cluster-Based Data Aggregation in IoT Sensor Networks Using the Firefly Optimization Algorithm

Data are typically collected from sensors distributed across the network and transmitted for analysis and processing to a central base station (BS). However, a significant challenge in Internet of Things (IoT) sensor networks is the efficient aggregation of data from multiple sensors to increase network longevity and reduce the consumption of energy. During the aggregation of data, sensor nodes often transmit redundant data due to multiple factors, including overlapping distribution. The network should gather redundant packets and convert them into aggregated data. Aggregation is necessary to remove duplicate data and convert it into unified data, a task that requires large amounts of energy. In this research paper, we suggest a technique for aggregating data in IoT sensor networks, using clustering with an optimized firefly algorithm (FA), taking into consideration both energy consumed and distance. In this approach, a particular number of nodes are identified in each round. These nodes have a proximate node with a distance less than the threshold. After that cluster heads (CHs) are elected strategically based on brighter fireflies (nodes with higher fitness). The FA is employed for this purpose, where fireflies represent the sensor nodes, and their attractiveness is determined by their fitness, representing the quality of their solutions. The simulation outcomes, executed in MATLAB 2023b, indicated that the suggested method, the firefly optimization algorithm (FOA), outperformed the FA and LEACH in improving the quality-of-service parameters. Furthermore, the ANOVA testing of the simulation result demonstrated the superiority of the proposed approach as well.