Open AccessEnergy efficient WSN using hybrid modification PEGASIS with ant lion optimization
Author(s) -
Ahmed Abdul Azeez Asmael,
Basman M. AlNedawe
Publication year - 2021
Publication title -
indonesian journal of electrical engineering and computer science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.241
H-Index - 17
eISSN - 2502-4760
pISSN - 2502-4752
DOI - 10.11591/ijeecs.v23.i1.pp273-284
Subject(s) - wireless sensor network , ant colony optimization algorithms , computer science , energy consumption , latency (audio) , node (physics) , ant colony , computer network , duration (music) , cluster analysis , real time computing , engineering , electrical engineering , telecommunications , artificial intelligence , art , literature , structural engineering
Wireless sensor nodes consist of tiny electronic devices that can sense, transmit, and measure data from physical environments such as the field of minter surveillance. These sensor nodes significantly depend on batteries to gain energy which is used to operations associated with communication and computation. Generally, designing communication protocols is feasible to achieve effective usage of these energy resources of the sensor node. Both reported medium access control and routing can achieve energy-saving that supporting real time functionality. This paper emphasizes the use of hybrid modified PEGASIS-Ant lion optimization. Several steps are entailed in this research. First is random distribution of node followed by clustering the map as a circular region. Then, the nodes are connected to the closest node in that region. In consequence, PEGASIS-Ant lion optimization is applied to enhance the connection of the nodes and accomplish the maximum life batter of the sensor. At last, the experiments performed in this work demonstrate that the proposed optimization technique operates well in terms of network latency, power duration and energy’s consumption. Furthermore, the life span of the nodes has improved greatly by 87% over the original algorithm that accomplished a rate of life nodes of 60%.