Genetic Algorithm for Cross-Layer-Based Energy Hole Minimization in Wireless Sensor Networks

Abstract

Wireless sensor networks encompass wireless connectivity, low energy consumption protocols, and many algorithms. The sensor node battery is marked as a striking problem in such resource-constrained networks. Once the sensor nodes are mounted in an area, the batteries present in nodes are difficult to repair or replace. Many algorithms and protocols are implemented to progressively minimize the usage of energy in nodes. The sensor in charge of scheduling packets is significantly essential to reduce the processing overhead, energy consumption. Maximum-largest weighted delay first (M-LWDF) and packet dependent (PD) scheduling algorithms have been investigated in the multiuser multiservice systems. Efficiency and performance are better especially for multiuser multiservice to nonloss transmission during resource lag. Proposed work of cross-layer approach using genetic algorithm improves energy quality and efficiency by 24.7% compared to the existing methods and finds useful applications are low.