WDAT‐OMS: A two‐level scheme for efficient data gathering in mobile‐sink wireless sensor networks using compressive sensing theory

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%.