Open AccessAn Energy-Aware and Load-balancing Routing scheme for Wireless Sensor Networks
Author(s) -
Omar Adil Mahdi,
Yusor Rafid Bahar Al-Mayouf,
Ahmed Basil Ghazi,
Mazin Abed Mohammed,
Ainuddin Wahid Abdul Wahab,
Mohd Yamani Bin Idris
Publication year - 2018
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.v12.i3.pp1312-1319
Subject(s) - computer science , wireless sensor network , computer network , load balancing (electrical power) , geographic routing , dynamic source routing , multipath routing , energy consumption , routing protocol , distributed computing , routing (electronic design automation) , static routing , efficient energy use , engineering , grid , mathematics , geometry , electrical engineering
Energy and memory limitations are considerable constraints of sensor nodes in wireless sensor networks (WSNs). The limited energy supplied to network nodes causes WSNs to face crucial functional limitations. Therefore, the problem of limited energy resource on sensor nodes can only be addressed by using them efficiently. In this research work, an energy-balancing routing scheme for in-network data aggregation is presented. This scheme is referred to as Energy-aware and load-Balancing Routing scheme for Data Aggregation (hereinafter referred to as EBR-DA). The EBRDA aims to provide an energy efficient multiple-hop routing to the destination on the basis of the quality of the links between the source and destination. In view of this goal, a link cost function is introduced to assess the quality of the links by considering the new multi-criteria node weight metric, in which energy and load balancing are considered. The node weight is considered in constructing and updating the routing tree to achieve dynamic behavior for event-driven WSNs. The proposed EBR-DA was evaluated and validated by simulation, and the results were compared with those of InFRA and DRINA by using performance metrics for dense static networks.