Intelligent occupancy sensing is becoming a vital underpinning for various emerging applications in smart homes, such as security surveillance and human behavior analysis. However, prevailing approaches mainly rely on video camera, ambient sensors or wearable devices, which either requires arduous deployment or arouses privacy concerns. In this paper, we present a novel real-time, device-free and privacy-preserving WiFi-enabled IoT platform for occupancy sensing, which can promote a myriad of emerging applications. It is designed to achieve an optimal tradeoff between performance and scalability. Our system empowers commercial off-the-shelf (COTS) WiFi routers to collect Channel State Information (CSI) measurements and provides an efficient cloud server for computing via a lightweight communication protocol. To demonstrate the usefulness of our platform, an occupancy detection system is developed by exploiting the CSI curve of human presence. Furthermore, we also design an innovative activity recognition system based on our platform and machine learning techniques with high availability and extensibility. In the evaluation, the experimental results show that our platform enables these applications efficiently, with the accuracy of 96.8% and 90.6% in terms of occupancy detection and recognition respectively.

The detection of insulators with cluttered backgrounds in aerial images is a challenging task for automatic transmission line inspection system. In this paper, we propose an effective and reliable insulator detection method based on deep learning technique for the aerial images. In the proposed deep detection approach, the single shot multibox detector (SSD), a powerful deep meta-architecture, is incorporated with a strategy of two-stage fine-tuning. The SSD based model can realize automatic multi-level feature extractor from aerial images instead of manually extracting features. Inspired by transfer learning, a two-stage fine-tuning strategy is implemented using separate training sets. In the first stage, the basic insulator model is obtained by fine-tuning the COCO model with aerial images including different types of insulators and various backgrounds. In the second stage, the basic model is fine-tuned by the training sets of the specific insulator types and specific situations to be detected. After the two-stage fine-tuning, the well-trained SSD model can directly and accurately identify the insulator by feeding the aerial images. The results show that both the porcelain insulator and composite insulator can be quickly and accurately identified in the aerial images with complex background. The proposed approach can enhance the accuracy, efficiency and robustness significantly.

Indoor localization has attracted more and more attention because of its importance in many applications. One of the most popular techniques for indoor localization is the received signal strength indicator (RSSI) based fingerprinting approach. Since RSSI values are very complicated and noisy, conventional machine learning algorithms often suffer from limited performance. Recently developed deep learning algorithms have been shown to be powerful for the analysis of complex data. In this paper, we propose a local feature-based deep long short-term memory (LF-DLSTM) approach for WiFi fingerprinting indoor localization. The local feature extractor attempts to reduce the noise effect and extract robust local features. The DLSTM network is able to encode temporal dependencies and learn high-level representations for the extracted sequential local features. Real experiments have been conducted in two different environments, i.e., a research lab and an office. We also compare the proposed approach with some state-of-the-art methods for indoor localization. The results show that the proposed approach achieves the best localization performance with mean localization errors of 1.48 and 1.75 m under the research lab and office environments, respectively. The improvements of our proposed approach over the state-of-the-art methods range from $\text{18.98}\text{\%}$ to $\text{53.46}\text{\%}$ .

Insulator fault in the transmission lines is a main factor of power transmission accident. The images captured from aerial inspection can be utilized to detect the fault of insulators for further maintenance. For automatic transmission lines inspection system, the insulator fault detection is an interesting and challenging task due to the complex background and diversified insulators. In this paper, we propose a novel insulator fault detection method based on multi-level perception for aerial images. The multi-level perception is implemented by an ensemble architecture which combines three single-level perceptions. These single-level perceptions include the low level, middle level and high level that are named by the attention to the insulator fault. They detect the insulator fault in the entire image, multi-insulator image and single-insulator image respectively. To address the filtering problem in the combination of three single-level perceptions, an ensemble method is proposed for generating final results. For training the detection models employed in the multi-level perception, a powerful deep meta-architecture so-called Single Shot multibox Detector (SSD) is utilized. The well-trained SSD models can automatically extract high quality features from aerial images instead of manually extracting features. By using the multi-level perception, the advantages of global and local information can achieve a favorable balance. Moreover, the limited inspection images are fully utilized by the proposed method. Fault detection recall and precision of the proposed method are 93.69% and 91.23% testing in the practical inspection data, and insulator fault under various conditions can be correctly detected in the aerial images. Experimental results show that the proposed method can enhance the accuracy and robustness significantly

The location and contextual status (indoor or outdoor) is fundamental and critical information for upper-layer applications, such as activity recognition and location-based services (LBS) for individuals. In addition, optimizations of building management systems (BMS), such as the pre-cooling or heating process of the air-conditioning system according to the human traffic entering or exiting a building, can utilize the information, as well. The emerging mobile devices, which are equipped with various sensors, become a feasible and flexible platform to perform indoor-outdoor (IO) detection. However, power-hungry sensors, such as GPS and WiFi, should be used with caution due to the constrained battery storage on mobile device. We propose BlueDetect: an accurate, fast response and energy-efficient scheme for IO detection and seamless LBS running on the mobile device based on the emerging low-power iBeacon technology. By leveraging the on-broad Bluetooth module and our proposed algorithms, BlueDetect provides a precise IO detection service that can turn on/off on-board power-hungry sensors smartly and automatically, optimize their performances and reduce the power consumption of mobile devices simultaneously. Moreover, seamless positioning and navigation services can be realized by it, especially in a semi-outdoor environment, which cannot be achieved by GPS or an indoor positioning system (IPS) easily. We prototype BlueDetect on Android mobile devices and evaluate its performance comprehensively. The experimental results have validated the superiority of BlueDetect in terms of IO detection accuracy, localization accuracy and energy consumption.

Fault diagnosis of insulators in aerial images is an essential task of power line inspection to maintain the reliability, safety, and sustainability of power transmission. This paper develops a novel method for intelligent diagnosis of electrical insulators based on deep learning, termed Box-Point Detector, which consists of a deep convolutional neural network followed by two parallel branches of convolutional heads. These two branches are utilized to locate the fault region and estimate insulator endpoints, which presents a new representation for insulator faults. Endpoints of the faulty insulator string can provide detailed and correlative information for enhancing the diagnosis capability of component-dependent faults that occur on component bodies. The proposed Box-Point Detector implements all predictions including region and endpoint into one network thus forms an efficient end-to-end structure, and adopts a smaller downsampling ratio to generate high resolution feature-maps in order to preserve more original information for small size faults. Experimental results indicate that Box-Point Detector can accurately diagnose high-voltage insulator faults in real-time under various conditions. Compared with some previous works using Faster R-CNN, SSD, and cascading network, our Box-Point Detector shows more competitively capabilities with high accuracy and robustness.