Water quality monitoring using Wireless Sensor Networks (WSNs) is essential in aquaculture water quality management. In the field of water quality monitoring, dissolved oxygen (DO) is a key parameter, and its prediction can provide decision support for aquaculture production, thereby reducing farming risk. However, it is difficult to build a precise prediction model, and existing methods of DO prediction neglect the importance of analyzing DO content. To address this problem, this study proposes a hybrid DO prediction model, named KIG-ELM, which is composed of K-means, improved genetic algorithm (IGA), and extreme learning machine (ELM). This model is based on edge computing architecture, in which data acquisition, processing and dissolved oxygen prediction are distributed in sensing nodes, routing nodes and server respectively. Sensing technique and clustering operation are applied in the process of data acquisition and processing. Meanwhile, an optimized extreme learning machine is implemented for DO prediction. We evaluate the efficiency and accuracy of proposed prediction approach in a practical aquaculture on massive water quality data. Experimental results show that the hybrid model achieves significant prediction results and can meet the needs of practical production and management.

For monitoring the aquaculture parameters in pond with wireless sensor networks (WSN), high accuracy of fault detection and high precision of error correction are essential. However, collecting accurate data from WSN to server or cloud is a bottleneck because of the data faults of WSN, especially in aquaculture applications, limits their further development. When the data fault occurs, data fusion mechanism can help to obtain corrected data to replace abnormal one. In this paper, we propose a data fusion method using a novel function that is Dynamic Time Warping time series strategy improved support degree (DTWS-ISD) for enhancing data quality, which employs a Dynamic Time Warping (DTW) time series segmentation strategy to the improved support degree (ISD) function. We use the DTW distance to replace Euclidean distance, which can explore the continuity and fuzziness of data streams, and the time series segmentation strategy is adopted to reduce the computation dimension of DTW algorithm. Unlike Gauss support function, ISD function obtains mutual support degree of sensors without the exponent calculation. Several experiments were finished to evaluate the accuracy and efficiency of DTWS-ISD with different performance metrics. The experimental results demonstrated that DTWS-ISD achieved better fusion precision than three existing functions in a real-world WSN water quality monitoring application.

Data missing problems often occur on the Internet-of-Things domains. This article proposes a missing type-aware interpolation framework (IMA) for data loss problems in city-wide environmental monitoring systems that contain many scattered stations. To interpolate data as accurately as possible, IMA considers three aspects of information, i.e., spatiotemporal, all attributes of one measurement, and all values and accordingly develop three methods to estimate the missing data. First, we develop an improved multiviewer method, which uses the spatiotemporal correlation of data from neighbor stations to estimate random missing values. Second, we propose a new multi-eXtreme Gradient Boosting (multi-XGBoost) method that uses the values of the co-occurring and correlated correct attributes to predict the value of the missing attribute. Third, we take advantage of matrix factorization to estimate the missing parts if the data of the interpolation matrix are not all missing. To avoid the influence of uncorrelated data, IMA calculates Pearson's correlation coefficient between data of each station and uses those data from its top k highest correlation neighbors to form an interpolation matrix. Furthermore, due to the complexity of missing cases, IMA uses confidence levels in each of the three data prediction methods. For example, if the multiviewer method fails, IMA weights all valid results with confidence levels. We conduct our experiments on two real-world datasets from air quality monitoring stations in Beijing. Both datasets contain numerous missing measurements. Experimental results show that IMA outperforms other counterpart methods in interpolating the missing measurements, in terms of accuracy and effectiveness. Compared with the most related method, IMA improves the interpolation accuracy from 0.818 to 0.849 in a small dataset and from 0.214 to 0.759 in a large one.

Forest fires result in severe disaster, causing significant ecological damage and substantial economic losses. Flames and smoke represent the predominant characteristics of forest fires. However, these flames and smoke often exhibit irregular shapes, rendering them susceptible to erroneous positive or negative identifications, consequently compromising the overall performance of detection systems. To enhance the average precision and recall rates of detection, this paper introduces an enhanced iteration of the You Only Look Once version 5 (YOLOv5) algorithm. This advanced algorithm aims to achieve more effective fire detection. First, we use Switchable Atrous Convolution (SAC) in the backbone network of the traditional YOLOv5 to enhance the capture of a larger receptive field. Then, we introduce Polarized Self-Attention (PSA) to improve the modeling of long-range dependencies. Finally, we incorporate Soft Non-Maximum Suppression (Soft-NMS) to address issues related to missed detections and repeated detections of flames and smoke by the algorithm. Among the plethora of models explored, our proposed algorithm achieves a 2.0% improvement in mean Average Precision@0.5 (mAP⁵⁰) and a 3.1% enhancement in Recall when compared with the YOLOv5 algorithm. The integration of SAC, PSA, and Soft-NMS significantly enhances the precision and efficiency of the detection algorithm. Moreover, the comprehensive algorithm proposed here can identify and detect key changes in various monitoring scenarios.

The accurate and efficient detection of printed circuit board (PCB) surface defects is crucial to the electronic information manufacturing industry. However, current approaches to PCB defect detection face challenges, including large model sizes and difficulties in balancing detection accuracy with speed. To address these challenges, this paper proposes a novel PCB surface defect detection algorithm, named DVCW-YOLO. First, all standard convolutions in the backbone and neck networks of YOLOv8n are replaced with lightweight DWConv convolutions. In addition, a self-designed C2fCBAM module is introduced to the backbone network for extracting features. Next, within the neck structure, the C2f module is substituted with the more lightweight VOVGSCSP module, thereby reducing model redundancy, simplifying model complexity, and enhancing detection speed. By enhancing prominent features and suppressing less important ones, this modification allows the model to better focus on key regions, thereby improving feature representation capabilities. Finally, the WIoU loss function is implemented to replace the traditional CIoU function in YOLOv8n. This adjustment addresses issues related to low generalization and poor detection performance for small objects or complex backgrounds, while also mitigating the impact of low-quality or extreme samples on model accuracy. Experimental results demonstrate that the DVCW-YOLO model achieves a mean average precision (mAP) of 99.3% and a detection speed of 43.3 frames per second (FPS), which represent improvements of 4% and 4.08%, respectively, over the YOLOv8n model. These results confirm that the proposed model meets the real-time PCB defect detection requirements of small and medium-sized enterprises.

Wireless sensor networks (WSNs) are susceptible to faults in sensor data. Outlier detection is crucial for ensuring the quality of data analysis in WSNs. This paper proposes a novel improved support vector data description method (ID-SVDD) to effectively detect outliers of sensor data. ID-SVDD utilizes the density distribution of data to compensate SVDD. The Parzen-window algorithm is applied to calculate the relative density for each data point in a data set. Meanwhile, we use Mahalanobis distance (MD) to improve the Gaussian function in Parzen-window density estimation. Through combining new relative density weight with SVDD, this approach can efficiently map the data points from sparse space to high-density space. In order to assess the outlier detection performance, the ID-SVDD algorithm was implemented on several datasets. The experimental results demonstrated that ID-SVDD achieved high performance, and could be applied in real water quality monitoring.