A water environmental monitoring system based on a wireless sensor network is proposed. It consists of three parts: data monitoring nodes, data base station and remote monitoring center. This system is suitable for the complex and large-scale water environment monitoring, such as for reservoirs, lakes, rivers, swamps, and shallow or deep groundwaters. This paper is devoted to the explanation and illustration for our new water environment monitoring system design. The system had successfully accomplished the online auto-monitoring of the water temperature and pH value environment of an artificial lake. The system's measurement capacity ranges from 0 to 80 °C for water temperature, with an accuracy of ±0.5 °C; from 0 to 14 on pH value, with an accuracy of ±0.05 pH units. Sensors applicable to different water quality scenarios should be installed at the nodes to meet the monitoring demands for a variety of water environments and to obtain different parameters. The monitoring system thus promises broad applicability prospects.

Wireless sensor networks (WSNs) are an important tool for monitoring distributed remote environments. As one of the key technologies involved in WSNs, node fault detection is indispensable in most WSN applications. It is well known that the distributed fault detection (DFD) scheme checks out the failed nodes by exchanging data and mutually testing among neighbor nodes in this network., but the fault detection accuracy of a DFD scheme would decrease rapidly when the number of neighbor nodes to be diagnosed is small and the node’s failure ratio is high. In this paper, an improved DFD scheme is proposed by defining new detection criteria. Simulation results demonstrate that the improved DFD scheme performs well in the above situation and can increase the fault detection accuracy greatly.

Various types of sensor data can be collected by IoT. Each sensor node has spatial attributes and may also be associated with a large number of measurement data that evolve over time; therefore, these high-dimensional sensor data are inherently large scale. Detecting outliers in large-scale IoT sensor data is a challenging task. Most existing anomaly detection methods are based on a vector representation. However, large-scale IoT sensor data have characteristics that make tensor methods more efficient for extracting information. The vector-based methods can destroy original structural information and correlation within large-scale sensor data, resulting in the problem of the “curse of dimensionality,” and some outliers hence cannot be detected. In this paper, we propose a one-class support Tucker machine (OCSTuM) and an OCSTuM based on tensor Tucker factorization and a genetic algorithm called GA-OCSTuM. These methods extend one-class support vector machines to tensor space. OCSTuM and GA-OCSTuM are unsupervised anomaly detection approaches for big sensor data. They retain the structural information of data while improving the accuracy and efficiency of anomaly detection. The experimental evaluations on real datasets demonstrate that our proposed method improves the accuracy and efficiency of anomaly detection while retaining the intrinsic structure of big sensor data.

In this study, data-driven fault diagnosis model dealing with chemical imbalanced data streams is investigated. Different faults occur with different frequencies by continuous arriving in chemical plants while this issue has scarcely been addressed in developing a diagnosis model in chemical processes. A novel incremental imbalance modified deep neural network (Incremental-IMDNN) is proposed to promote the fault diagnosis for imbalanced data stream. The first step in designing the incremental-IMDNN is to the employment of an imbalance modified method combined with active learning for the extraction and generation of valuable information in view of model feedback rather than blindness. Then for the continuously arriving of new fault modes, DNN is promoted in an incremental hierarchical way. Unlike the traditional model that trained on a static snapshot of data, instead, the model inherits the existing knowledge and hierarchically expands the diagnosis model by similarity of faults. The similar faults that judge by fuzzy clustering merge to a superclass and every sub-model share the same architecture succeed in the previous knowledge that can be trained in parallel. We validate the performance of proposed method in TE dataset and the simulation results indicate that the proposed incremental IM-DNN considerably better than the existing methods.