Open AccessWDAT‐OMS: A two‐level scheme for efficient data gathering in mobile‐sink wireless sensor networks using compressive sensing theory
Author(s) -
Tirani Shima Pakdaman,
Avokh Avid,
Azar Sahebeh
Publication year - 2020
Publication title -
iet communications
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.355
H-Index - 62
eISSN - 1751-8636
pISSN - 1751-8628
DOI - 10.1049/iet-com.2019.0433
Subject(s) - wireless sensor network , computer science , data aggregator , energy consumption , traverse , network packet , scalability , computer network , sink (geography) , efficient energy use , cluster analysis , data collection , compressed sensing , real time computing , distributed computing , algorithm , mathematics , statistics , ecology , cartography , geodesy , engineering , database , machine learning , electrical engineering , biology , geography
This study addresses the problems of energy and delay in wireless sensor networks equipped with mobile sinks. The authors jointly consider the compressive sensing (CS) theory, cluster‐based routing, and sink mobility to propose a data collection method named ‘weighted data aggregation trees with optimal mobile sink(s) (WDAT‐OMS)’. The proposed scheme relies on a two‐level architecture in which sensors are clustered at the first level. WDAT‐OMS uses the CS theory along with load‐balanced data aggregation trees to route packets from sensors to the corresponding cluster heads (CHs). In this regard, they present an efficient metric named ‘energy‐and distance‐aware CH selection’ to fairly distribute the energy consumption among different sensors. At the second level, one or more sinks traverse the network to collect the aggregated data of CHs. As an advantage, WDAT‐OMS not only balances the energy consumption among different sensors but also increases the network scalability. Numerical results demonstrate that the proposed algorithm reduces energy consumption in comparison with ‘centralised clustering algorithm’, ‘energy‐aware CS‐based data aggregation’, and ‘energy‐balanced high‐level data aggregation tree ‘by 66%, 62%, and 63% for an average number of clusters, respectively. It also decreases the sink delay in comparison with the ‘single‐hop data‐gathering problem’ by 10%.