The authors: Leila Ben Saad, Cesar Asensio-Marco and Baltasar Beferull-Lozano
The large number of nodes forming current sensor networks has made essential to introduce distributed mechanisms in many traditional applications. In the emerging field of graph signal processing, the distributed mechanism of information potentials constitutes a distributed graph filtering process that can be used to solve many different problems. An important limitation of this algorithm is that it is inherently iterative, which implies that the nodes incur in a repeated communication cost along the exchange periods of the filtering process. Since the sensor nodes are battery powered and radio communications are energy demanding operations, in this work, we propose to redesign the network topology in order to reduce the total energy consumption of the filtering process. An accurate energy model is proposed and extensive numerical results are presented to show the efficiency of our methodology according to this energy model.
The authors: Leila Ben Saad, Thilina Weerasinghe and Baltasar Beferull-Lozano
Graph filters, which are considered as the workhorses of graph signal analysis in the emerging field of signal processing on graphs, are useful for many applications such as distributed estimation in wireless sensor networks. Many of these tasks are based on basic distributed operators such as consensus, which are carried out by sensor devices under limited energy supply. To cope with the energy constraints, this paper focuses on designing the network topology in order to maximize the network lifetime when applying graph filters. None of the existing works in the literature have studied such problem when graph filters are used. The problem is a complex combinatorial problem and in this work, we propose two efficient heuristic algorithms for solving it. We show by simulations that they provide good performance and increase significantly the network lifetime.