Energy Efficient Data Gathering Schema for Wireless Sensor Network: A Matrix Completion Based Approach

Abstract

In this paper, we seek to address the data gathering in the continually growing Wireless Sensor Networks (WSNs) with the intention to save the nodes' energy. In order to address usual WSN problems, such as data losses, collisions and re-transmissions, a twofold data compression pattern is proposed. We consider that a restricted number of sensor nodes are selected to be active and represent the whole network, while the rest of nodes remain idle and do not participate at all in the data sensing and transmission. Furthermore, the set of active nodes' readings is efficiently reduced, in each time slot, according to the cluster scheduling. Relying on the existing Matrix Completion (MC) techniques, the sink node is unable to recover the entire data matrix due to the existence of completely empty rows that correspond to the inactive nodes, which can be considered as absent nodes for a very long period, or nodes that do not exist at all. Thereby, we propose a complementary interpolation technique, based on a minimization problem that benefits from sensor nodes inter-correlation, to guarantee the reconstruction of all the empty rows, despite their large number. The simulations confirm the efficiency of the proposed approach and show that it outperforms the existing one by up to 70.101% of Normalized Mean Absolute Error on all missed elements, when the number of active nodes is of about 10% of the total number of sensor nodes.

The Energy-Aware Matrix Completion-Based Data Gathering Scheme for Wireless Sensor Networks

“…Therefrom, several papers have mainly focused on data compression in order to minimize the amount of data readings to be forwarded. Without the need of any extra communication and computational overheads, the Matrix Completion (MC) technique, viewed as an extension of Compressive Sensing (CS), reduces the number of active agents, in each time slot, while all the missing data can still be recovered [7]- [11].…”

“…In this paper we focus on the twofold data compression scenario that had been addressed in our previous work [7] with the Cluster-based MC data gathering approach (CBMC), where the wireless network gets denser. This atypical data sampling scenario consists on choosing a significant number of sensor nodes to remain inactive during the whole sensing period, whilst the rest of nodes serve as the representative of the entire network.…”

“…Indeed, each time slot, a subset of the representative nodes is picked to get the role of source nodes. In [7], relying on a MC-based approach, the sink is able to recover all the missing readings, even the fully empty data rows that correspond to the inactive nodes, thanks to a complementary minimization problem based on an interpolation technique that is annexed to the MC one. Different from [7], where the representative nodes are chosen randomly, in this paper, we focus on the node selection process taking into account the reconstruction quality as well as the energy efficiency.…”

“…In [7], relying on a MC-based approach, the sink is able to recover all the missing readings, even the fully empty data rows that correspond to the inactive nodes, thanks to a complementary minimization problem based on an interpolation technique that is annexed to the MC one. Different from [7], where the representative nodes are chosen randomly, in this paper, we focus on the node selection process taking into account the reconstruction quality as well as the energy efficiency. In order to neatly represent the entire network and reach an interesting data reconstruction quality, despite the challenging addressed scenarios, correlation among sensors has been calculated and those holding the greatest informative values are better ranked to be chosen as representative nodes.…”

“…• As a sequel of [7], the selection process of the representative sensor nodes is no longer random. It is rather deterministic.…”