Dr. Maohui Zheng, Tongji University, Shanghai, will stay as a visiting professor with the Geosensor Networks Lab for a year. His current research interests are in air pollution monitoring and prediction via simulation models and the use of in situ sensors. Welcome, Maohui!
This sounds like an April fool’ss joke, especially the part of putting 5000 bees to sleep, then shaving (some of) them, putting a sensor on their back, and letting them fly again. Nevertheless, a first wide scale deployment of miniaturized sensor technology. However, it looks like the scientists only track the trajectory of the bees using ‘e-tags’ (on the bees) and (potentially) readers dispersed in the environment. It is unclear if there are actually sensors on the bees that actively sense.
“CSIRO is working with the University of Tasmania, beekeepers and fruit growers to trial the monitoring technology, in an attempt to improve honey bee pollination and productivity”.
read more about the Australian CSIRO project here.
Bee sensor network
Sensors and apps for the non-green thumb gardeners. (I still talk to my plants, but I would be interested if this app is combined with a water dispensing I-robot).
Parrot Flower Power Plant Sensor
With 2014 arriving, it is time to redefine the metaphor of scientific discovery.
In the fall 2013 semester, the first SIE598 Geosemsor Networks course took place at the University of Maine, School of Computing and Information Science with instructor Silvia Nittel. The course covered an excursion from a 1940s vision paper by Vannevar Bush, to tiny platforms, TinyOS, networking and routing protocols, database systems for wireless sensor networks and geosensor networks. Last Friday, 3 teams of students presented their final projects for the course.
This paper is published at the International Workshop on Geostreaming, in conjunction with SIGSPATIAL 2013, Orlando, 2013.
Paper link: IWGS13_CR_whittier_nittel
Towards Window Stream Queries Over Continuous Phenomena
J.C. Whittier, S. Nittel, Q. Liang, M. Plummer
Technological advances have created an unprecedented availability of inexpensive sensors capable of streaming environmental data in real-time. Data stream engines (DSE) with tuple processing rates of around 500k tuples/s have demonstrated their ability to both keep up with large numbers of spatio-temporal data streams, and execute stream window queries over them efficiently. Typically, geographically distributed sensors take samples asynchronously; however, when approximating the reality of a continuous phenomenon – such as the radiation field over an urban region- the objective is to integrate their values correctly over space as well as over time. This paper presents an approach to extend DSEs with support enabling sliding window queries over dynamic continuous phenomena, which return both spatio-temporal snapshot and movies as window query results. We introduce a novel grid-pane index as a main memory index structure shared between multi-queries over a phenomenon and an adaptive, data driven kNN algorithm for efficiently approximating cells based on available stream data samples. AkNN implements a spatio-temporal inverse distance weighting interpolation (IDW) method that integrates time with space via an anisotropic ratio. Further, we introduce the shell list template that allows quick calculation of NN cells by distance in a space-time (ST) cuboid. We performed extensive performance evaluations using the Fukushima nuclear event in March 2011 as a test data set.
Interesting progress for self assembling robots.