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

Abstract

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.

Robust Data Recovery in Wireless Sensor Network: A Learning-Based Matrix Completion Framework

“…In Section 7 , the proposed minimization-based interpolation technique ( 11 ) of stage 3 has been investigated and then updated when compared to the one of our previous works ([ 2 ] Equation ( 8 )) and ([ 4 ] Equation ( 5 )), as we have mentioned. Figure 5 illustrates a performance comparison in terms of between the two methods for different values of and with respect to the regularization parameter .…”

“…S represents the spatial constraint matrix, whose computation steps will be detailed hereafter, and are two tuning parameters, and is the final reconstructed data matrix. It is noteworthy that the above proposed minimization-based interpolation technique has been updated when compared to the one of our previous works ([ 2 ] Equation ( 8 )) and ([ 4 ] Equation ( 5 )), and, through simulations, we found out that the updated minimization significantly enhances the data reconstruction quality of the nodes. Note that the resolution of this optimization problem can be easily accomplished while using the semidefinite programming (SDP).…”

“…This strategy not only minimizes the energy cost and extend the network lifetime, but it also helps to avoid other problems, such as the traffic congestion collapse [ 3 ]. It is true that in [ 4 ], the choice of the active nodes follows a deterministic based metric. However, unlike [ 4 ], in this work, we explain, in detail, each building block of the introduced structured MC-based data gathering framework (the representative nodes selection process and the network clustering phase).…”

“…It is true that in [ 4 ], the choice of the active nodes follows a deterministic based metric. However, unlike [ 4 ], in this work, we explain, in detail, each building block of the introduced structured MC-based data gathering framework (the representative nodes selection process and the network clustering phase). Subsequently, we separately evaluate them in the numerical results section in order to illustrate the benefits of each building block of the proposed technique.…”

“…Finally, we recover the rows of the sensor nodes using a minimization problem interpolation-based technique with a spatial correlation matrix. In this paper, the third stage of data recovery pattern has been re-investigated and improved to be more efficient when compared to the one used in papers [ 2 , 4 ], i.e., providing a lower data recovery error for the nodes. In the numerical results section, we evaluate the two techniques with respect to a tuning parameter, and we show that the proposed minimization problem interpolation-based method significantly enhances the data recovery performance.…”