Recently, researchers are focusing on a new use of the Internet called the Internet of Things (IoT), in which enabled electronic devices can be remotely accessed over the Internet. As the realization of IoT concept is still in its early stages, manufacturers of Internet-connected devices and IoT web service providers are defining their proprietary protocols based on their targeted applications. Consequently, IoT becomes heterogeneous in terms of hardware capabilities and communication protocols. Addressing these heterogeneities by following open standards is a necessary step to communicate with various IoT devices. In this research, we assess the feasibility of applying existing open standards on resource-constrained IoT devices. The standard protocols developed in this research are OGC PUCK over Bluetooth, TinySOS, SOS over CoAP, and OGC SensorThings API. We believe that by hosting open standard protocols on IoT devices, not only do the devices become self-describable, self-contained, and interoperable, but innovative applications can also be easily developed with standardized interfaces. In addition, we use memory consumption, request message size, response message size, and response latency to benchmark the efficiency of the implemented protocols. In all, this research presents and evaluates standard-based solutions to better understand the feasibility of applying existing standards to the IoT vision.

The world-wide sensor web has become a very useful technique for monitoring the physical world at spatial and temporal scales that were previously impossible. Yet we believe that the full potential of sensor web has thus far not been revealed. In order to harvest the world-wide sensor web’s full potential, a geospatial cyberinfrastructure is needed to store, process, and deliver large amount of sensor data collected worldwide. In this paper, we first define the issue of the sensor web long tail followed by our view of the world-wide sensor web architecture. Then, we introduce the Geospatial Cyberinfrastructure for Environmental Sensing (GeoCENS) architecture and explain each of its components. Finally, with demonstration of three real-world powered-by-GeoCENS sensor web applications, we believe that the GeoCENS architecture can successfully address the sensor web long tail issue and consequently realize the world-wide sensor web vision.

Smart city applications integrate the human, physical, and digital systems in a built environment with Internet of Things (IoT) resources, city models, and domain models. However, existing methods for the integration are suitable for individual applications and lack interoperability among application modules. This study analyzed existing integration strategies and developed an ontology for integrating the data modeling standards of the Open Geospatial Consortium (OGC) CityGML, IndoorGML, and SensorThings API. To cope with the broad definition of “things” in the IoT, the proposed ontology supports multiple views of things, including the a-building-as-a-thing, a-room-as-a-thing, an-opening-as-a-thing, and a-device-as-a-thing views. Thus, the proposed ontology relates information from these three standards and supports semantic queries. We demonstrated the proposed solution in smart home, smart security, smart health care, and fire evacuation systems. Overall, the proposed solution can facilitate the integration of standard-based IoT resources and city models to support smart city applications.

Background: Stroke is a major cause of disability in the elderly and considerably increases the risk of dementia, which is another important source of disability. This population-based study aimed to examine the risk of dementia in patients with stroke compared with non-stroke cases with similar comorbidities. Methods: Using the Taiwan National Health Insurance databank covering the period 2001-2007, this retrospective cohort study evaluated the risk of dementia in 10,884 patients with first stroke who had no history of dementia. In this study, we performed a 1:5 case-control matched analysis, in which cases were matched to controls based on their estimated propensity scores, which were estimated with demographics and associated risk factors. This approach reduced selection bias. Cox proportional hazards regression analysis was then used to estimate the risk of dementia in stroke patients. Results: During the 5-year follow-up period, 1,487 (13.74%) stroke and 1,402 (2.59%) non-stroke patients suffered dementia. Stroke was independently associated with a 6.09 (95% confidence interval [CI], 5.66 to 6.55) times greater risk of dementia 5 years after stroke. Older age was associated with a higher incidence of dementia after stroke. Each stroke type had different impacts on the occurrence of dementia. The hazard ratio of dementia among hemorrhagic stroke patients was much higher than those of ischemic stroke and controls. Conclusion: The findings of this study suggest that stroke confers an increased risk of dementia, especially in the elderly and in patients with hemorrhagic stroke. We advocate the need for close observation and enhanced health education programs to benefit patients with stroke.

Thermal infrared (TIR) satellite images are generally employed to retrieve land surface temperature (LST) data in remote sensing. LST data have been widely used in evapotranspiration (ET) estimation based on satellite observations over broad regions, as well as the surface dryness associated with vegetation index. Landsat-8 Operational Land Imager (OLI) and Thermal Infrared Sensor (TIRS) can provide LST data with a 30-m spatial resolution. However, rapid changes in environmental factors, such as temperature, humidity, wind speed, and soil moisture, will affect the dynamics of ET. Therefore, ET estimation needs a high temporal resolution as well as a high spatial resolution for daily, diurnal, or even hourly analysis. A challenge with satellite observations is that higher-spatial-resolution sensors have a lower temporal resolution, and vice versa. Previous studies solved this limitation by developing a spatial and temporal adaptive reflectance fusion model (STARFM) for visible images. In this study, with the primary mechanism (thermal emission) of TIRS, surface emissivity is used in the proposed spatial and temporal adaptive emissivity fusion model (STAEFM) as a modification of the original STARFM for fusing TIR images instead of reflectance. For high a temporal resolution, the advanced Himawari imager (AHI) onboard the Himawari-8 satellite is explored. Thus, Landsat-like TIR images with a 10-minute temporal resolution can be synthesized by fusing TIR images of Himawari-8 AHI and Landsat-8 TIRS. The performance of the STAEFM to retrieve LST was compared with the STARFM and enhanced STARFM (ESTARFM) based on the similarity to the observed Landsat image and differences with air temperature. The peak signal-to-noise ratio (PSNR) value of the STAEFM image is more than 42 dB, while the values for STARFM and ESTARFM images are around 31 and 38 dB, respectively. The differences of LST and air temperature data collected from five meteorological stations are 1.53 °C to 4.93 °C, which are smaller compared with STARFM’s and ESATRFM’s. The examination of the case study showed reasonable results of hourly LST, dryness index, and ET retrieval, indicating significant potential for the proposed STAEFM to provide very-high-spatiotemporal-resolution (30 m every 10 min) TIR images for surface dryness and ET monitoring.

With the advance of the World-Wide Web (WWW) technology, people can easily share content on the Web, including geospatial data and web services. Thus, the “big geospatial data management” issues start attracting attention. Among the big geospatial data issues, this research focuses on discovering distributed geospatial resources. As resources are scattered on the WWW, users cannot find resources of their interests efficiently. While the WWW has Web search engines addressing web resource discovery issues, we envision that the geospatial Web (i.e., GeoWeb) also requires GeoWeb search engines. To realize a GeoWeb search engine, one of the first steps is to proactively discover GeoWeb resources on the WWW. Hence, in this study, we propose the GeoWeb Crawler, an extensible Web crawling framework that can find various types of GeoWeb resources, such as Open Geospatial Consortium (OGC) web services, Keyhole Markup Language (KML) and Environmental Systems Research Institute, Inc (ESRI) Shapefiles. In addition, we apply the distributed computing concept to promote the performance of the GeoWeb Crawler. The result shows that for 10 targeted resources types, the GeoWeb Crawler discovered 7351 geospatial services and 194,003 datasets. As a result, the proposed GeoWeb Crawler framework is proven to be extensible and scalable to provide a comprehensive index of GeoWeb.