How do students learn the best? How can we improve classroom and laboratory instruction? Research into student learning clearly points to a critical area of pedagogy – Active learning. In other words, students should play a key role in class room environment. The traditional approach of a professor or instructor giving a lecture makes for a convenient form of instruction but often achieves very little in return when real learning is evaluated. Students can recall material from rote memory but if the aim is to inculcate deeper concepts and critical thinking skills, class room instruction cannot be a one-way street. Optimum learning occurs when students can apply the concepts to real life situations and problems and have a tangible role in designing labratory experiments.
WiseLab, our low-cost wireless sensor network platform is ideally suited to active learning in almost any class room or lab where measurements are made and concepts are tested. Engineering and Science labs are particularly ideal environments, but creative uses can also be found in social sciences and humanities.
WiseLab modules combining the Wisense platform and any suitable sensor or actuator can be used to create new measuring devices or to retool existing instruments. Students can be challenged to come up with new ways of making measurements and collecting data. When students have a stake in the design of the experiment and the instruments, they are naturally more curious and creative. This will also help them in retaining key ideas for a longer time than rote memorization.
Stay tuned for case studies and sample lab ideas to showcase the capability of WiseLab!
Wind energy is one of the most rapidly growing sources of non-fossil fuel based energy. Wind energy accounts for about 3% of the US consumption and is steadily increasing. Installation of both onshore and offshore wind turbine farms is expected to continue to increase worldwid. Large wind farms can easily consist of hundreds of turbines. The wireless based WISENSE MNET platform offers robust low cost solutions for several applications designed for Wind Farms. A couple of solutions are described below:
Real Time Turbine Inflow Monitoring: With the WISENET MNET, hundreds of sensors can be deployed to accurately monitor the characteristics of the inflow air driving the turbines. Given the scalability of our platform, the network can be as dense as desired. Turbulence characteristics of the inflow air and air flowing past turbines can be monitored in real time. The wireless platform is both easy to deploy and use.
Microclimate Surrounding Wind Farms: There is considerable interest in monitoring the microclimate surrounding wind farms, particularly in light of recent research pointing to the impact of the farms on local climate. Accurate monitoring of changes in temperature and humidity over a dense network surrounding wind farms can provide real time guidance and potential ways to mitigate the effects of the farm. Such a sensor network can also be used to design wind farms better.
Buoy mounted sensor network for off shore applications: A wireless platform is also ideally suited to being mounted on buoys for offshore applications. Coupled with a photovoltaic power supply, the sensor network is self sufficient and low-maintenance.
Micrometeorology refers to atmospheric processes that have spatial scales on the order of few hundred meters to a kilometer and temporal scales of under 24 hours. Important phenomena include frontal passages, thunderstorms and pollution dispersion. Measurements of atmospheric variables such as pressure, temperature, humidity and winds when combined with soil characteristics or chemical composition of air can yield important information on conditions in a localized area. It is desirable to have a dense network of measurements to collect real time information that can be analyzed for making useful decisions. Applications for such a dense network of microscale measurements are numerous and we will present a series of case studies in upcoming blog posts.
In a recent article in the bulletin of the American Meteorological Society, Shapiro et al. (2009) describe an observing network deployed by the university of Oklahoma. The primary aim of this network is to provide a hands on experience to students in working with a micormeteorological data network. The network consists of 28 temperature/humidity loggers spaced about 30 m apart. The data are stored onboard dataloggers at 5 minute intervals. Data retrieval must be performed manually and is reported to take about 3 hours every 6-10 weeks.
The WISENSE MNET platform offers significant improvements to the type of network described above. The wireless MNET is low-cost, easy to deploy and most importantly eliminates manual data transfer. As such, the measurements can be made at much higher temporal resolution and are truly real time and can be immediately analyzed and visualized. Our MNET module can replace the manual data loggers or we can supply integrated measurement systems that contain both the weather/chemical sensor and the wireless platform. Furthermore, adding more sensor stations is a breeze and does not add to the data retrieval effort! The WISENSE MNET can also immediately report malfunctioning stations for quick maintenance.