Multiple objective optimization-based DV-Hop localization for spiral deployed wireless sensor networks using Non-inertial Opposition-based Class Topper Optimization (NOCTO)

Abstract

The problem of localization is one of most important issues in wireless sensor networks. Furthermore, it is critical to monitor and evaluate the data gathered. For a variety of factors, such as upkeep, lifespan, and breakdown, the fixed density of these beacons may be increased or decreased. Because of its robustness, flexibility, and economic viability, a well-known technique for locating wireless sensor network nodes is the Distance Vector-Hop (DV-Hop) algorithm. As a result, researchers continue to look for ways to develop it. A new Non-inertial Opposition based Class Topper Optimization (NOCTO) based enhanced DV-Hop localization algorithm is proposed. It also focuses through an optimized formulation to compute the average hop-size with weight of beacon nodes in order to reduce the localization error with estimated distance between the beacon and the dumb node, due to improved localization accuracy. For spiral deployed 2D wireless sensor networks, this paper proposes a multi-objective NOCTO-based DV-Hop localization. The simulation results indicate that our suggested multi-objective function outperforms some existing techniques.