Sensing technology has been widely investigated and utilized for gas detection. Due to the different applicability and inherent limitations of different gas sensing technologies, researchers have been working on different scenarios with enhanced gas sensor calibration. This paper reviews the descriptions, evaluation, comparison and recent developments in existing gas sensing technologies. A classification of sensing technologies is given, based on the variation of electrical and other properties. Detailed introduction to sensing methods based on electrical variation is discussed through further classification according to sensing materials, including metal oxide semiconductors, polymers, carbon nanotubes, and moisture absorbing materials. Methods based on other kinds of variations such as optical, calorimetric, acoustic and gas-chromatographic, are presented in a general way. Several suggestions related to future development are also discussed. Furthermore, this paper focuses on sensitivity and selectivity for performance indicators to compare different sensing technologies, analyzes the factors that influence these two indicators, and lists several corresponding improved approaches.

The massive data generated by the Internet of Things (IoT) are considered of high business value, and data mining algorithms can be applied to IoT to extract hidden information from data. In this paper, we give a systematic way to review data mining in knowledge view, technique view, and application view, including classification, clustering, association analysis, time series analysis and outlier analysis. And the latest application cases are also surveyed. As more and more devices connected to IoT, large volume of data should be analyzed, the latest algorithms should be modified to apply to big data. We reviewed these algorithms and discussed challenges and open research issues. At last a suggested big data mining system is proposed.

The advances in wireless communication techniques, mobile cloud computing, and context- aware technologies boost a growing interest in the design, development, and deployment of vehicular networks for emerging applications. This leads to an increasing evolutionary tendency to change from vehicular networks toward cloud-assisted context-aware vehicular cyberphysical systems. In this article, we first propose a multi-layered context-aware architecture and introduce two crucial service components, vehicular social networks and context-aware vehicular security. Then we propose an application scenario regarding the context-aware dynamic parking services by illuminating the cloud-assisted architecture and logic flow. Finally, we investigate the challenges and possible solutions, including context-aware safety hazard prediction, context-aware dynamic vehicle routing, and context-aware vehicular clouds.

The proliferation of the Internet of Things (IoT) has fostered growing attention to real-time locating systems (RTLSs) using radio frequency identification (RFID) for asset management, which can automatically identify and track physical objects within indoor or confined environments. Various RFID indoor locating systems have been proposed. However, most of them are inappropriate for large-scale IoT applications owing to severe radio multipath, diffraction, and reflection. In this paper, we propose a newly fashioned RTLS using active RFID for the IoT, i.e., iLocate, which locates objects at high levels of accuracy up to 30 cm with ultralong distance transmission. To achieve fine-grained localization accuracy, iLocate presents the concept of virtual reference tags. To overcome signal multipath, iLocate employs a frequency-hopping technique to schedule RFID communication. To support large-scale RFID networks, iLocate leverages the ZigBee. We implement all hardware using 2.45-GHz RFID chips so that each active tag can communicate with readers that are around 1000 m away in a free space. Our empirical study and real project deployment show the superiority of the proposed system with respect to the localization accuracy and the data transmission rate for large-scale active RFID networks.