Many competing approaches exist in evaluating sensor network solutions differing by levels of ease of use, cost, control, and realism. Existing work concentrates on simulating network protocols or emulating processing units at the machine cycle level. However, little has been done to emulate the sensors and the physical environments that they monitor. The main contribution of this work is the design of WiserEmulator, an emulation framework for structural health monitoring, which gracefully balances the trade-offs between realism, controllability, and cost. WiserEmulator consists of two main components - a testbed of wireless sensor nodes and a software emulation environment. To emulate the excitation and response of piezo-electric transducers, as well as the wave propagation inside concrete structures, the COMSOL Multi-Physics software was utilized. Digitized sensing output from COMSOL was played back via a multi-channel Digital-to-Analog Converter (DAC) connected to the wireless sensor testbed. In addition to the emulation of concrete structures, WiSeREmulator also allows users to choose pre-stored data collected from field experiments and synthesized data. A user-friendly Graphical User Interface (GUI) was developed that facilitates intuitive configurations of experimental settings, control of the on-set and progression of the experiments, and real-time visualization of experimental results. We have implemented WiSeREmulator in MATLAB. This work advances the state of the art in providing low cost solutions to evaluating Cyber Physical Systems such as wireless structural health monitoring networks.
Cyber-Physical System (CPS) and Cyber-Physical-Social System (CPSS) computing are now challenging existing research in many realms, including Intelligent Transportation Systems (ITS). In this survey, we highlight some advances in the coevolution of CPS, CPSS, and ITS, with an emphasis on traffic data. We first explain the hierarchical architecture of CPS-ITS in terms of five layers: perception, communication, computing, control, and application. Then, we analyze the characteristics of traffic data in CPS-ITS, and enumerate some new technologies for data operation and management. Two typical cases of CPS-ITS, vehicular-communication-based traffic control systems and smart parking systems, are illustrated to describe how CPS is changing our lives and influencing the development of future ITS.
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?(m2×n), where m and n denote the number of predicates and services, respectively.
Mass spectrometry is one of the widely utilized important methods to study protein functions and components. The challenge of mono-isotope pattern recognition from large scale protein mass spectral data needs computational algorithms and tools to speed up the analysis and improve the analytic results. We utilized nave Bayes network as the classifier with the assumption that the selected features are independent to predict mono-isotope pattern from mass spectrometry. Mono-isotopes detected from validated theoretical spectra were used as prior information in the Bayes method. Three main features extracted from the dataset were employed as independent variables in our model. The application of the proposed algorithm to publicMo dataset demonstrates that our nave Bayes classifier is advantageous over existing methods in both accuracy and sensitivity.
Gene expression is a critical process in biological system that is influenced and modulated by many factors including genetic variation. Expression Quantitative Trait Loci (eQTL) analysis provides a powerful way to understand how genetic variants affect gene expression. For genome wide eQTL analysis, the number of genetic variants and that of genes are large and thus the search space is tremendous. Therefore, eQTL analysis brings about computational and statistical challenges. In this paper, we provide a comprehensive review of recent advances in methods for eQTL analysis in population-based studies. We first present traditional pairwise association methods, which are widely used in human genetics. To account for expression heterogeneity, we investigate the methods for correcting confounding factors. Next, we discuss newly developed statistical learning methods including Lasso-based models. In the conclusion, we provide an overview of future method development in analyzing eQTL associations. Although we focus on human genetics in this review, the methods are applicable to many other organisms.