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.

In the Wireless Sensor Networks (WSNs), ensuring long-term survival of the sensor devices is crucial, especially for non-energy harvesting networks where the sensors have to deal with the available limited power. Thus, there is a huge need to efficiently select, in each time-slot, a small set of source nodes to monitor the network area and deliver their data to the sink. Note that there is a trade-off between energy efficiency, achieved through data-compression, and the informative quality received by the sink. Moreover, although applying a high data compression ratio extremely reduces the overall network energy consumption, the network lifetime is not necessarily extended due to the uneven energy depletion of the nodes' batteries. To this end, in this paper, we propose the Energy-Aware Matrix Completion based data gathering approach (EAMC), which designates the active nodes according to their residual energy levels. To collect data readings, the proposed EAMC relies on a nodes clustering phase and a MC based data sampling. Then, the interpolation of all the missing data is performed by the sink thanks to a Three-stage MC based recovery framework. Since we are interested in high data loss scenarios, the limited amount of delivered data must be sufficient in terms of informative quality it holds in order to reach a satisfactory recovery accuracy for the entire data. Hence, the EAMC selects the nodes depending on their inter-correlation as well as the network energy efficiency, with the use of a combined energy-aware and correlation-based metric. This introduced active node cost function changes with the type of application one wants to perform with the intention to reach a longer lifespan for the network. Therewith, the numerical results show that the EAMC achieves an attractive and competitive trade-off between the data reconstruction quality and the network lifetime for all the investigated scenarios.