WDAT‐OMS: A two‐level scheme for efficient data gathering in mobile‐sink wireless sensor networks using compressive sensing theory. Issue 11 (1st July 2020)

• Record Type: Journal Article
• Authors: Tirani, Shima Pakdaman; Avokh, Avid; Azar, Sahebeh
• Is Part Of: IET communications. Volume 14:Issue 11(2020)
• Page Start: 1826
• Page End: 1837
• Publication Date: 2020-07-01
• Subjects: energy consumption -- wireless sensor networks -- pattern clustering -- trees (mathematics) -- compressed sensing -- data aggregation -- mobility management (mobile radio) -- resource allocation -- telecommunication network routing; WDAT‐OMS -- two‐level scheme -- mobile‐sink wireless sensor networks -- data collection method -- two‐level architecture -- CS theory -- energy consumption -- network scalability -- centralised clustering algorithm -- energy‐aware CS‐based data aggregation -- sink delay -- single‐hop data‐gathering problem -- efficient data gathering -- compressive sensing theory -- cluster‐based routing -- sink mobility -- weighted data aggregation trees with optimal mobile sink -- energy‐balanced high‐level data aggregation tree -- load‐balanced data aggregation trees -- cluster heads; Telecommunication systems -- Periodicals; Speech processing systems -- Periodicals; 621.38205
• DOI: 10.1049/iet-com.2019.0433
• Languages: English
• ISSNs: 1751-8628

Abstract

Abstract : 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%.