Open AccessUtilizing Genetic Algorithm and Artificial Bee Colony Algorithm to Extend the WSN Lifetime
Author(s) -
Sawsan Alshattnawi,
Lubna Afifi,
Amani Shatnawi,
Malek M. Barhoush
Publication year - 2022
Publication title -
computing
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.184
H-Index - 11
eISSN - 2312-5381
pISSN - 1727-6209
DOI - 10.47839/ijc.21.1.2514
Subject(s) - cluster analysis , computer science , genetic algorithm , wireless sensor network , algorithm , energy consumption , node (physics) , population , transmission (telecommunications) , artificial bee colony algorithm , data transmission , base station , real time computing , computer network , engineering , artificial intelligence , machine learning , electrical engineering , telecommunications , demography , structural engineering , sociology
Extending the lifetime of Wireless Sensor Networks (WSN) is an important issue due to the mission assigned to these networks. The sensors collect data relevant to a specific field. Then, the sensors send the collected data to a base station where it is analyzed, and a suitable reaction can be taken. Sensors in WSN depend on a battery with limited energy to do their work. Data transmission and receiving consume energy, which may lead to the loss of the whole network or some of the essential nodes. For this reason, energy must be preserved as long as possible to prolong the network lifetime. Several types of research were presented with different approaches to minimize power consumption. In this paper, we present a hybrid technique that includes two population-based algorithms: genetic algorithm (GA) and artificial bee colony (ABC) with clustering approaches. This proposed novel technique aims to reduce the dissipation of power consumption per sensor node in the WSN, and as a consequence, the lifetime of the WSN is extended. The ABC algorithm was used to improve an initial population, which was used in the GA. Also, we used two approaches of clustering; clustering based on genetic algorithm and K-means clustering beside LEACH protocol. The experimental results show that the proposed approach approved its efficiency in lifetime extending through an increasing number of the operational nodes per round and transmission.