In wireless sensor networks, effective link quality estimation is the basis of topology management and routing control. Effective link quality estimation can guarantee the transmission of data, as well as improve the throughput rate, and hence, extend the life of the entire network. For this reason, a stacked autoencoder-based link quality estimator (LQE-SAE) is proposed. Specifically, the zero-filling method is developed to process the original missing link information. Then, the SAE model is used to extract the asymmetric characteristics of the uplink and downlink from the received signal strength indicator, link quality indicator, and signal-to-noise ratio, respectively. These characteristics are fused by SAE to construct the link features' vectors, which are given as inputs to the support vector classifier (SVC), for which the link quality grade is taken as its label. The experimental results in different scenarios show that the LQE-SAE has better accuracy than link quality estimators based on the SVC, ELM, and WNN. INDEX TERMS Wireless sensor networks, link quality estimation, stacked autoencoder, deep learning, asymmetry of link.
<p class="MsoNormal" style="text-align: left; margin: 0cm 0cm 0pt; layout-grid-mode: char;" align="left"><span class="text"><span style="font-family: ";Arial";,";sans-serif";; font-size: 9pt;">In wireless sensor networks, three-dimensional localization is important for applications. It becomes a challenge with the scale of network getting large. This paper proposes a three-dimensional localization algorithm for large scale WSN on the basis of cluster. Focusing on the MDS-based localization, it adopts the cluster structure and the global coordinate system to represent the whole network logically, and reduces the influence of range measurement errors through decreasing the probability of multi-hop. With the combination of variable power of nodes and the triangle principle, the range measurement errors can be corrected. Through the comparison of three different computations in the algorithm of simulations, correction effects are presented. To address the proposed algorithm, CBLALS, more convincible, the comparison of CBLALS and DV-Distance (3D) is taken. The result shows that the positioning accuracy of CBLALS is much better than the one of DV-Distance (3D). With the increasing of the range measurement error, the positioning error of CBLALS varies gently, and could be controlled within 55% while the range measurement error is 30%.</span></span><span style="font-family: ";Arial";,";sans-serif";; font-size: 9pt;"></span></p>
Wireless sensor networks (WSNs) are resourceconstrained networks, especially when the energy is highly constrained; the application of WSNs is severely restricted. Data fusion can effectively reduce the volume of data transmission in the network, reduce the energy consumption to extend network lifetime and improve bandwidth utilization, as a result, it can overcome the restriction of energy and bandwidth. This paper gives a survey on classical data fusion in wireless sensor networks from the following aspects: constructing an aggregation tree and data correlation processing, etc. And finally the direction of further study on data fusion is also pointed out.
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