Energy optimization strategy for wireless sensor networks in large-scale farmland habitat monitoring

doi:10.12133/j.smartag.2019.1.2.201812-SA024

Abstract

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

CLC Number:

Zhang Xiaohan, Yin Changchuan, Wu Huarui. Energy optimization strategy for wireless sensor networks in large-scale farmland habitat monitoring[J]. Smart Agriculture, 2019, 1(2): 55-63.

Figures/Tables 6

Fig. 1

Table 1

Matrix completion based node selection algorithm for wireless sensor network

1	初始化非冗余节点集Q_k：k=1,将任意一个节点加入到节点集,节点集中包含的节点数目n_k=1（n_k的最大值为NMAX）,t=0
2	将节点集中节点的数据填充到原始数据矩阵 $X (t)$ 中, $X (t) ∈ R N × T$
3	以采样率 $p ∈ 0,1$ 对 $X (t)$ 进行采样观测得到观测矩阵 $M t$ ,采样集合为 $Ω$ ,其大小为 $Ω = pNT$
4	对 $M t$ 运用矩阵补全算法得到恢复矩阵 $X ? (t)$
5	if $n k ≤ NMAX$ then
6	随机选择下一个节点且k增加1,将该节点数据和节点集中所有节点的数据都填充到新的原始数据矩阵 $X (t + 1)$ 中,并运用矩阵补全算法得到新的恢复矩阵 $X ? (t + 1)$
7	根据式(7)计算该节点的信息量 $INFO$
8	if $INFO > μ$ then
9	将该节点加入到节点集 $Q k$ 中且 $n k$ 增加1
10	end if
11	$t = t + 1$ ,返回第5步
12	end if

Table 1

Fig.2

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