Abstract:

Wireless sensor network (WSN) has been widely deployed in precision agriculture to improve the crop production. However, we still face many challenges for large-scale farmland habitat monitoring, the energy shortage for battery-powered sensor nodes, the complicated propagation environment for wireless signals during different growing-up periods of the crops, the optimization of the coverage of the sensor nodes, etc. In order to guarantee the holeless coverage of the sensor nodes during the whole life of the crops, the WSNs are typically deployed with high density. Therefore, some of the sensors in the network are redundant in certain growing-up periods of the crops. And also the data collected by each sensor node may have strong temporal correlation. Recently, compressive sensing (CS) has received much attention due to its capability of reconstructing sparse signals with the number of measurements much lower than that of the Nyquist sampling rate. With the rapid progress of CS based sparse representation, matrix completion (MC) theory was proposed very recently. According to the MC theory, a low-rank matrix can be accurately rebuilt with a few number of entries in the matrix. Matrix completion provides the advantage of sampling small set of data at sensor nodes without requiring excessive computational and traffic loads, which meets the requirement of energy-efficient data gathering and transmitting in WSNs. In this research, by considering the characteristics that the sensor nodes are redundant in some growing-up periods of the crops and the data collected by sensor-nodes usually share a strong spatial and temporal correlation among them in WSNs for large-scale farmland habitat monitoring, we put forward a MC based two-step energy saving optimization algorithm to reduce both the energy consumption of the data acquisition and data transmission process in WSNs and achieved the purpose of prolonging the network lifetime. Firstly, through the measurement of the sensor node's data information, we found the non-redundant nodes by considering the spatial correlation of the data from the sensor nodes. We would close the data acquisition units of the remaining redundant nodes and make them only transmit data as relay nodes. Secondly, we took advantage of the partial sampling scheme in matrix completion to further reduce the quantity of data. Thereby, we could reduce the energy consumption on both data collection and transmission process of the wireless sensor network. The experiment results show that the proposed algorithm reduces 83% working nodes in the network, and therefore reducing the energy cost of the network.

Key words: wireless sensor networks, temporal and spatial correlations, matrix completion, large-scale farmland, habitat monitoring