Social Group Optimization Based Cluster Head Identification in Underwater Acoustic Sensor Networks

Due to the advancements in ocean monitoring and underwater exploration, it has become increasingly important to incorporate a method of underwater sensor network that would be cost effective and energy efficient, reducing maintenance efforts and increasing longevity. Underwater Sensor Network applications based on acoustic, optical and RF communication are gaining popularity, especially acoustic communication in this area for applications such as offshore oil and gas extraction, military surveillance, mine detection, tsunami, and hurricane forecasts to name a few. One of the simpler architectural implementations of the sensor network is the 2D – Underwater Sensor Network, where a group of sensor networks are anchored underwater to the surface, with a cluster head nominated for communication within the cluster nodes and the surface buoyant nodes. There exist certain optimization algorithms that address the problem of optimal cluster head selection to minimize resources consumed and increase the lifespan of the cluster. One such technique that we use in our research is Social Group Optimization (SGO). It is based on the human behavior of learning and solving complex problems. This report uses this SGO technique to choose the cluster head among a set of clusters, in which number of solutions are taken and calculated to yield a robust solution where the sacrifices of time and complexity are minimized.