Field testing of Soil Moisture Geosensor Network

After building it in the lab over the winter, and indoor testing, we deploy the soil moisture geosensor networks for field testing outdoors today.

soil_moisture_SN

waterproof_GSN_UMaine

Soil moisture geosensor network in weatherproof containers

setup

Sensor setup in weatherproof box

GSN_Umaine_sensor_install

Field testing of wireless soil moisture sensor network

GSN_Umaine_sensor_install2

Installing the solar panel to power the node and sensors

GSN_Umaine_sensor_2

Node 1: transition between full sun and full shade

GSN_Umaine_sensor_3

Node 2: mostly shade (but enough sun for solar panel)

GSN_Umaine_sensor_1

Node 3: South location, full sun

GSN_Umaine_basestationLR

Raspberry Pi base station using Xbee to sensor nodes, and internet to upload data to a database.

Note, that node 4 (with sensors 400, 401, 402) is in the lab. Nodes update every 6 minutes.

Wild_blueberries_3

Next step: blueberry barren

Sensing a color and display the RGB color via the LED

Today’s second Arduino summer camp was about reverse engineering resistors and LEDs that show different colors. One project was about a 3-part RGB sensor and displaying the sensed RGB color as LED light.

Katie taping her changing color LED.

Katie taping her changing color LED.

LED_2

RGB_LED_lr

Joel’s RGB sensor and LED.

Arduino Summer Camp Kickoff

Today, we kicked off an Arduino learning summer camp. J.C., our resident expert, showed us to get started with an Arduino starter kit, and get the first sensor sensing and the first LED blinking. However, it felt a bit like a flash back to electrical engineering classes, with resistors, pins, and analog inputs…. anyway.

We have 3 CS undergraduates, one SIE graduate and a few faculty participating.

Ard_5_setup

Ard_6_setup

Ard_9_conf

Arduino blinking

CFP: Special Issue “Geosensor Networks and the Sensor Web”, International Journal of Geo-Information

Guest Editor
Prof. Dr. Silvia Nittel
Spatial Informatics, School of Computing and Information Science & National Center of Geographic Information and Analysis, University of Maine, Orono, ME 04473, USA
E-Mail: nittel@spatial.maine.edu

Dear Colleagues,

The last two decades have seen unprecedented advances in the development and miniaturization of a variety of sensors, as well as inexpensive, small computing platforms, and a plethora of wireless communication media. These technological developments have lead to the related research areas of geosensor networks and the sensor web.

Geosensor networks are wireless, ad hoc sensor networks that employ recent research progress from electrical engineering, computer science, and spatial information science to create small devices, running compact, space and time-aware algorithms for live, in-place analytics. Sensors can range from stationary environmental sensors to drones or autonomous vehicles collecting imagery data, or even to humans acting as sensors using smartphones. The sensor web, on the other hand, realizes the idea of a standardized, interoperable platform for everyone to easily share, find, and access sensor data that is based on space, time, and other attributes, similar to easily searching for and sharing information on the Internet. Today, we see further growth in the availability of massive numbers of real-time sensor streams, precipitating a need for real-time analysis. From a practical perspective, geosensor networks can be simply defined as “networked geosensors”, or networks of sensor nodes deployed in geographic space with various communication topologies. Such geosensor networks enable us to observe, reason about, and react to events in space and time in near real-time. To truly leverage this ubiquitous sensing infrastructure, research advances relating to the sensor web are of utmost importance, enabling easy access, sharing, and interoperability.

We invite original research contributions on all aspects of geosensor networks, the sensor web, and their applications, and, particularly, encourage submissions focusing on the following themes for this Special Issue.

  • ž   Formal foundations of geosensor networks
  • ž   Decentralized spatial computing and spatial self-organization
  • ž   Languages for describing spatial tasks and patterns
  • ž   Real-time sensor data streams
  • ž   Integration of real-time sensor streams and historic streams
  • ž   Data management for Big Sensor Data
  • ž   Integration of heterogenous sensor streams
  • ž   Analytics of sensor data streams
  • ž   Crowdsensing for emergency applications and humans as sensors
  • ž   Cooperative sensing using drones, and UAVs
  • ž   Experiences and lessons learned deploying geosensor networks
  • ž   Geosensor network and sensor web use cases: government, participatory
  • ž   GIS, health, energy, water, climate change, etc.
  • ž   Platforms, architectures and open source software for geosensor
  • ž   Networks and the sensor web
  • ž   Geosensor networks, ontologies and standards
  • ž   Benchmarking geosensor networks
  • ž   Ethical and societal impacts of geosensor networks

Submission deadline: January 31 2016.

For submission instruction, please see the Journal’s website.

Successful PhD defense by Qinghan Liang

Today, Qinghan successfully presented and defended his PhD thesis.  Congratulations!

qinghan

A beer well-earned!

beers

Beers at the Orono Brewing Company to toast to Qinghan’s successful defense.

PhD defense by Qinghan Liang, May 5th 2015

Qinghan will defend his dissertations on May 5 2015, 1pm in 336 Boardman Hall, University of Maine.

Title: Towards the Continuous Spatio-Temporal Field Model for Sensor Data Streams

Today, with the availability of inexpensive, wireless enabled sensor nodes, we encounter a massive amount of geo-referenced sensor streams, which are collected continuously, spatially dense, and in real-time. Continuous geographic phenomena such as pollen distribution, extreme weather events, a toxic chemical leak or radioactive fallout now can be observed live and needs to be analyzed in real-time. However, the high volume of continuous sensor data streams pushes the capabilities of traditional sensor data management beyond their limits. Over the last decade, data stream engines (DSEs) have been introduced as data management technology, which provide real-time query support for applications with very high throughput rates. However, users are better supported if they would be able to interact with higher-level abstractions of the real-world phenomena, rather than analyzing observations based on individual measurement streams. Dealing with individual streams requires that users need to write code that not only copes with the real-time nature of streams but also that fact that the streams need to be integrated and analyzed, continuously, which is a non-trivial task.

This dissertation introduces the Stream Field Data Model, a DSE data model extension that is based on the concept of a field to represent continuous phenomena over space and time and is formally integrated with the relational and relational-based stream models. Using the high-level abstraction of fields provides an easy-to-use, flexible, mathematically defined and concise data model support for both sensor data streams as well as continuous phenomena. Furthermore, a Stream Query Language for the Field Stream Data Model is proposed with a novel set of stream query operators specifically for spatio-temporal fields. The approach is to lift relational operator to fields, and the semantics of this set of operators are discussed and formalized. The feasibility of extending DSEs for visualizing fields in near real-time based on 100,000 of streams has been investigated. This dissertation proposes and evaluates different strategies to optimize a pipelined stream operator framework to achieve near real-time spatial interpolation throughput, considering the memory footprint, run-time efficiency and interpolation quality.

Mobile Mapping with DroneDeploy

Today, DroneDeploy, a start-up poised to make farms and other businesses significantly more efficient, launches its mobile app and announced it will be compatible with one of the world’s most popular drones.

dropdeploy

“With DroneDeploy a drone can be easily told to fly on autopilot over a farmer’s fields. Shortly after the drone lands a farmer will already be able to review the maps and data gathered by the drone. With NDVI data, a farmer can what portions of his fields are healthy, and what portions aren’t.

He can then download a geotagged image out of DroneDeploy and upload that into his usual farming software to apply a dose of fertilizer to only the area of his field that is in need.”  The software steers the drone, maps a path, stitches the collected data into a map, and visualizes the new image on a tablet.

See more in this article.

dropdeploy1

Real-time sensing, streaming and analysis, 1 step closer.

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