Device-free Passive (DfP) detection has received increasing attention for its ability to support various pervasive applications. Instead of relying on variable Received Signal Strength (RSS), most recent studies rely on finer-grained Channel State Information (CSI). However, existing methods have some limitations, in that they are effective only in the Line-Of-Sight (LOS) or for more than one moving individual. In this paper, we analyze the human motion effect on CSI and propose a novel scheme for Robust Passive Motion Detection (R-PMD). Since traditional low-pass filtering has a number of limitations with respect to data denoising, we adopt a novel Principal Component Analysis (PCA)-based filtering technique to capture the representative signals of human motion and extract the variance profile as the sensitive metric for human detection. In addition, existing schemes simply aggregate CSI values over all the antennas in MIMO systems. Instead, we investigate the sensing quality of each antenna and aggregate the best combination of antennas to achieve more accurate and robust detection. The R-PMD prototype uses off-the-shelf WiFi devices and the experimental results demonstrate that R-PMD achieves an average detection rate of 96.33% with a false alarm rate of 3.67%.

Quality of Service (QoS)-based service selection is the key to large-scale service-oriented Internet of Things (IOT), due to the increasing emergence of massive services with various QoS. Current methods either have low selection accuracy or are highly time-consuming (e.g., exponential time complexity), neither of which are desirable in large-scale IOT applications. We investigate a QoS-based service selection method to solve this problem. The main challenges are that we need to not only improve the selection accuracy but also decrease the time complexity to make them suitable for large-scale IOT applications. We address these challenges with the following three basic ideas. First, we present a lightweight description method to describe the QoS, dramatically decreasing the time complexity of service selection. Further more, based on this QoS description, we decompose the complex problem of QoS-based service selection into a simple and basic sub-problem. Finally, based on this problem decomposition, we present a QoS-based service matching algorithm, which greatly improves selection accuracy by considering the whole meaning of the predicates. The traces-driven simulations show that our method can increase the matching precision by 69% and the recall rate by 20% in comparison with current methods. Moreover, theoretical analysis illustrates that our method has polynomial time complexity, i.e., $O\mathsf{}\mathrm{(}{m}^{\mathrm{2}}\mathrm{\times }n\mathrm{)}$, where $m$ and $n$ denote the number of predicates and services, respectively.