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.
Highlights
With the rapid progress achieved in the information technology fields, the Internet of Things (IoT) has been emerging and promising to revolutionize the life quality by deploying massively the sensor nodes
In this paper we focus on the twofold data compression scenario that had been addressed in our previous work [7] with the Cluster-based Matrix Completion (MC) data gathering approach (CBMC), where the wireless network gets denser
In light of the importance of the energy saving and lifetime for wireless sensor nodes that are suffering from a limited power capacity, in this paper, we have presented an adaptive data collecting scheme, called the Energy-Aware Matrix Completion based data gathering approach (EAMC)
Summary
With the rapid progress achieved in the information technology fields, the Internet of Things (IoT) has been emerging and promising to revolutionize the life quality by deploying massively the sensor nodes. 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. Withal, these active nodes do not send, every time slot, their raw data to the sink.
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