The application of swarm intelligence algorithms to wireless sensor networks (WSNs) deployment has been the focus of research community for past few years. One such algorithm is ant colony optimization (ACO), whose application in reducing the cost of wireless sensor networks in terms of deployed sensor nodes has recently attracted attention of the researchers. In this letter, we propose an ACO based framework for wireless sensor network deployment in a realistic 3-D environment, by making modifications to the standard ACO algorithm. The simulation results lead to the conclusion that the proposed framework achieves better performance compared to the state-of-the-art ACO based algorithms in terms of size of the solution for node deployment. In addition, in a 3-D environment, time overhead problem arises in standard ACO based algorithms since they require a large number of iterations to achieve better solutions. In contrast, the performance of the proposed approach does not degrade with reduction in number of iterations, which enables the algorithm to achieve quick convergence.

Online defect detection in small industrial parts is of paramount importance for building closed loop intelligent manufacturing systems. However, high-efficiency and high-precision detection of surface defects in these manufacturing systems is a difficult task and poses a major research challenge. The small sample size of industrial parts available for training machine learning algorithms and the low accuracy of computer vision-based inspection algorithms are the bottlenecks that restrict the development of efficient online defect detection technology. To address these issues, we propose a small sample gear face defect detection method based on a Deep Convolutional Generative Adversarial Network (DCGAN) and a lightweight Convolutional Neural Network (CNN) in this paper. Initially, we perform data augmentation by using DCGAN and traditional data enhancement methods which effectively increase the size of the training data. In the next stage, we perform defect classification by using a lightweight CNN model which is based on the state-of-the-art Vgg11 network. We introduce the Leaky ReLU activation function and a dropout layer in the proposed CNN. In the experimental evaluation, the proposed framework achieves a high score of 98.40%, which is better than that of the classic Vgg11 network model. The method proposed in this paper is helpful for the detection of defects in industrial parts when the available sample size for training is small.