Genetic algorithm based protocols to select cluster heads and find multi-hop path in wireless sensor networks: review

Abstract

A wireless sensor network (WSN) is a modern technology in radio communication. A WSN comprises a number of sensor nodes that are randomly spread in a specific area for sensing and monitoring physical attributes that are difficult to monitor by humans, such as temperature, fire, and pressure. Many problems, including data transmission, power consumption and selecting cluster heads, may occur due to the nature of WSNs. Various protocols have been conducted to resolve these issues. Most of the proposed protocols are based on the Genetic Algorithm as an optimization technique to select the Cluster Heads (CHs) or to find a multihop path for sending the data from the CHs to the Base Station (BS). This paper presents a comprehensive study of the protocols for WSNs that are proposed to come up with these issues. This study emphasises on CHs selection protocols and multi-hop path finding protocols and their strengths and weaknesses. A new taxonomy is presented to discuss these protocols on the basis of different classes. A complete comparison of the main features and behaviors of the protocols is conducted. This study will give basic guidelines for the researchers those have a motivation to develop a new CHs selection protocol or a multi-hop path finding protocol.

Machine and deep learning techniques for detecting internet protocol version six attacks: a review

“…Genetic algorithm is based on evolutionary principles and is one of the most used ML techniques, utilizing biological evaluation to solve different optimization problems [46]. A normal behavior profile is created as a baseline to learn from and compare with unknown patterns to make decisions using a genetic strategy.…”

Optimization by Genetic Algorithm in Wireless Sensor Networks Utilizing Multiple Sinks

“…GA is the meta-heuristic evolutionary algorithm, which mimic, the natural selection process. The GA is utilized, mostly, for the optimization, where it finds the most optimal solution from the available solutions [6][7][8]…”