Working on web-applications and geosensor network development
Steve Liang is a researcher, teacher and entrepreneur. Steve is currently an associate professor at the University of Calgary and director of the GeoSensorWeb Laboratory. Steve’s goal is to disrupt the silos of the Internet of Things and to empower anyone to build connected applications by using the information generated from the world around them. For example, Steve is the editor of the Open Geospatial Consortium SensorThings API and the result of the standard work is to provide a uniform way to expose the full potential of the Internet of Things.
Steve has been an invited speaker at universities and industries in 11 countries. In 2013, Steve was chosen one of Calgary’s Top 40 Under 40 by the Avenue Magazine. Steve held the AITF-Microsoft Industry Research Chair on the Open Sensor Web from 2011 to 2014.
Geosensor Network Data Integration
I am a Postdoctoral fellow in the GeoSensor Web lab, at the University of Calgary. I received my Ph.D. in 2013, from Multi Mobile Sensor System research team, Geomatics Engineering, at the University of Calgary. My interests are primarily in the field of sensor fusion and data mining in location-based services. Sensor technologies are used almost everywhere, and connecting them over a network is recently growing very fast. However, there is still a gap between the multi-sensor networks and quality/usability of their data in real-time applications. Multi-sensor integration leverages the individual sensor data to get a more accurate and reliable view of the raw data. Sensor data, along with the IoT’s access to the cloud-based processing resources, will lead to a tremendous expansion in the on-line delivery of context-aware services customized for any given situation. Services could be triggered or customized based on the context of what an individual user needs, what machines are doing, what the infrastructure is doing, what nature is doing, or all of the above in various combinations. This research creates a unique opportunity to integrate the multi-sensor data to improve the efficiency and quality of the Geosensor network services.
New technologies have allowed us to conduct research at larger and larger scales, but our ability to manage such scales becomes more complex and difficult. By re-using some of the lessons learned by major technology companies like Facebook, Yahoo!, Etsy, and Bloomberg, we can take configuration management from cloud tech and apply it to sensor device tech. By managing our devices with a centralized tool, we can set up devices with less work. Other web tech can be used to simulate and test geo-sensor networks, allowing us to design and build more robust networks that can respond to changing network and environmental conditions.
IoT architecture for Vegetation Recovery Monitoring in the Boreal
Recent innovations in an internet of things(IoT) technology have allowed researchers to collect large and highly accurate datasets on the environment while vastly decreasing the time and cost of gathering such data. However, researchers need more stable, scalable, lower cost sensor monitoring network and more powerful monitor data processing and analysis system. By nature IoT applications are dynamic and, thus, complex and being difficult to build. Devices can be lost at any time. So does the network connection. Therefore, a new IoT architecture should be intended to meet these requirements. Based on existed technology, is possible to design an IoT architecture, which suits the environmental monitoring, using open source hardware and software. The IoT architecture contains several components. 1) Devices: It records the ecological parameters with different kinds of field sensors and actuators; 2) Gateway: It connects sensors to the world and manage the hardware and software running at the edge.3) Cloud: It is a place where process and analyze the field data; 4) User-front-end: Researchers can see the general monitoring information and data analysis result. This information will appear in a variety of charts and integrates with map.
University of Calgary
IoT sensor fusion for Context-Aware Recommender Systems
My interest research topic is primarily in the field of Context-Awareness and Recommender Systems. Context-Aware Systems are intelligent systems that recommend users with adaptive service choices which are appropriate to their profile preferences and contextual situations. Recently by the popularity of IoT smart devices as well as the availability of the wireless network, a massive volume of data is available to gain useful insights into user profile which contains dynamic contextual information. IoT sensors continuously generate enormous amounts of data. However, in order to add value to raw sensor data, we need to understand them and use them in a context-aware computing platform. On the other hand, Recommender Systems can be considered as an effective solution for fighting information overload. So, the integration of Context-Awareness, Recommender Systems, and other technologies including IoT, Social Networks, and Ubiquitous Computing opens new windows of opportunities to propose more efficient services.