A key issue in wireless sensor network applications is how to accurately detect anomalies in an unstable environment and determine whether an event has occurred. This instability includes the harsh environment, node energy insufficiency, hardware and software breakdown, etc. In this paper, a fault-tolerant anomaly detection method (FTAD) is proposed based on the spatial-temporal correlation of sensor networks. This method divides the sensor network into a fault neighborhood, event and fault mixed neighborhood, event boundary neighborhood and other regions for anomaly detection, respectively, to achieve fault tolerance. The results of experiment show that under the condition that 45% of sensor nodes are failing, the hit rate of event detection remains at about 97% and the false negative rate of events is above 92%.Information 2018, 9, 236 2 of 16 based on spatial-temporal correlation. The algorithm consists of two parts: the temporal correlation is used to obtain the probability of event and fault through the time-series data of sensor nodes, then we determine the state of sensor nodes. While according to the neighborhood definition, the sensor network is divided into the fault neighborhood, event and fault mixed neighborhoods, event boundary neighborhoods, and other areas, we use the minimum Bayesian risk decision method to distinguish the event nodes and faulty node. Fault tolerance is realized by abnormity detection to different neighborhoods, and experimental results and analysis show that the method can detect events well even under high fault rates.The contributions of this paper are summarized as follows:(i) In temporal correlation of sensor network, we propose the PCM and interval methods;(ii) In spatial correlation we divide the sensor network into fault neighborhood, event and fault mixed neighborhood, event boundary neighborhood, and other regions for anomaly detection, respectively, to achieve fault tolerance. (iii) We conduct extensive simulations to evaluate the performance of the proposed algorithms.The results demonstrate the effectiveness of the proposed algorithms.The second section introduces the related work and research results. The third section introduces the symbol definitions and network model used in this paper. The fourth section introduces the detection method of fault-tolerance of wireless sensor networks. The fifth section offers the results and analysis of the experiment. The final section concludes the paper.

ResNet has been widely used in the field of machine learning since it was proposed. This network model is successful in extracting features from input data by superimposing multiple layers of neural networks and thus achieves high accuracy in many applications. However, the superposition of multilayer neural networks increases their computational cost. For this reason, we propose a network model compression technique that removes multiple neural network layers from ResNet without decreasing the accuracy rate. The key idea is to provide a priority term to identify the importance of each neural network layer, and then select the unimportant layers to be removed during the training process based on the priority of the neural network layers. In addition, this paper also retrains the network model to avoid the accuracy degradation caused by the deletion of network layers. Experiments demonstrate that the network size can be reduced by 24.00%–42.86% of the number of layers without reducing the classification accuracy when classification is performed on CIFAR-10/100 and ImageNet.