Modeling of Tuna Swarm Algorithm Based Unequal Clustering Approach on Internet of Things Assisted Networks

Internet of Things (IoT)-assisted Wireless Sensor Networks (WSNs) integrate traditional WSNs
with the expansive ecosystem of IoT devices. This integration enables sensor nodes (SNs) to connect to the
internet, facilitating seamless data exchange, remote monitoring, and real-time control of physical
environments. IoT-assisted WSNs are crucial in various fields, including industrial automation, smart cities,
healthcare, and environmental monitoring. In these networks, sensor nodes near the base station (BS) are
responsible for relaying data to nearby nodes and the BS itself, a process that consumes significant energy.
This issue, known as the "hotspot problem," arises when certain nodes deplete their energy faster than
others. Unequal clustering techniques address this challenge by distributing the energy load more effectively,
allowing nodes with higher energy reserves to take on more tasks while conserving the energy of nodes with
lower reserves. This study introduces the Tuna Swarm Algorithm-based Energy Efficient Unequal Clustering
Approach (TSA-EEUCA) to enhance the performance of IoT-assisted WSNs. The proposed method aims to
improve energy efficiency and extend network lifetime by organizing nodes into clusters of unequal sizes. The core of this approach is the Tuna Swarm Algorithm (TSA), inspired by the cooperative foraging behavior
of tuna swarms. Unequal cluster formation and cluster head selection are determined by a fitness function
that considers both energy levels and distance metrics. To validate the effectiveness of the proposed method,
a series of simulations were conducted. The results showed that the proposed method outperforms existing
techniques, offering a more efficient and longer-lasting solution for IoT-assisted WSNs.