Every researcher’s goal is to obtain usable field data for the entire duration of a study. A good data set is one a scientist can use to draw conclusions or learn something about the behavior of environmental factors in a particular application. However, as many researchers have painfully discovered, getting good data is not as simple as installing sensors, leaving them in the field, and returning to find an accurate record. Those who don’t plan ahead, check the data often, and troubleshoot regularly often come back to find unpleasant surprises such as unplugged data logger cables, sensor cables damaged by rodents, or worse: that they don’t have enough data to interpret their results. Fortunately, most data collection mishaps are avoidable with quality equipment, some careful forethought, and a small amount of preparation.
Before selecting a site, scientists should clearly define their goals for gathering data.
Make no mistake, it will cost you
Below are some common mistakes people make when designing a study that cost them time and money and may prevent their data from being usable.
Site characterization: Not enough is known about the site, its variability, or other influential environmental factors that guide data interpretation
Sensor location: Sensors are installed in a location that doesn’t address the goals of the study (i.e., in soils, both the geographic location of the sensors and the location in the soil profile must be applicable to the research question)
Sensor installation: Sensors are not installed correctly, causing inaccurate readings
Data collection: Sensors and logger are not protected, and data are not checked regularly to maintain a continuous and accurate data record
Data dissemination: Data cannot be understood or replicated by other scientists
When designing a study, use the following best practices to simplify data collection and avoid oversights that keep data from being usable and ultimately, publishable.
Dr. Yossi Osroosh, Precision Ag Engineer in the Department of Biological Systems Engineering at Washington State University, discusses where and why IoT fits into irrigation water management. In addition, he explores possible price, range, power, and infrastructure road blocks.
Wireless sensor networks collect detailed data on plants in areas of the field that behave differently.
Studies show there is a potential for water savings of over 50% with sensor-based irrigation scheduling methods. Informed irrigation decisions require real-time data from networks of soil and weather sensors at desired resolution and a reasonable cost. Wireless sensor networks can collect data on plants in a lot of detail in areas of the field that behave differently. The need for wireless sensors and actuators has led to the development of IoT (Internet of Things) solutions referred to as Low-Power Wide-Area Networking or LPWAN. IoT simply means wireless communication and connecting to some data management system for further analysis. LPWAN technologies are intended to connect low-cost, low-power sensors to cloud-based services. Today, there are a wide range of wireless and IoT connectivity solutions available raising the question of which LPWAN technology best suits the application?
IoT Irrigation Management Scenarios
The following are scenarios for implementing IoT:
buying a sensor that is going to connect to a wireless network that you own (i.e., customer supplied like Wi-Fi, Bluetooth),
buying the infrastructure or at least pieces of it to install onsite (i.e., vendor managed LPWAN such as LoRaWAN, Symphony Link), and
relying on the infrastructure from a network operator LPWAN (e.g., LTE Cat-M1, NB-IOT, Sigfox, Ingenu, LoRWAN).
This is how cellular network operators or cellular IoT works. LPWAN technology fits well into agricultural settings where sensors need to send small data over a wide area while relying on batteries for many years. This distinguishes LPWAN from Bluetooth, ZigBee, or traditional cellular networks with limited range and higher power requirements. However, like any emerging technology, certain limitations still exist with LPWAN.
Individual sensor subscription fees in cellular IoT may add up and make it very expensive where many sensors are needed.
IoT Strengths and Limitations
The average data rate in cellular IoT can be 20 times faster than LoRa or Symphony Link, making it ideal for applications that require higher data rates. LTE Cat-M1 (aka LTE-M), for example, is like a Ferrari in terms of speed compared to other IoT technologies. At the same time, sensor data usage is the most important driver of the cost in using cellular IoT. Individual sensor subscription fee in cellular IoT may add up and make it very expensive where many sensors are needed. This means using existing wireless technologies like traditional cellular or ZigBee to complement LPWAN. One-to-many architecture is a common approach with respect to wireless communication and can help save the most money. Existing wireless technologies like Bluetooth LE, WiFi or ZigBee can be exploited to collect in-field data. In this case, data could be transmitted in-and-out of the field through existing communication infrastructure like a traditional cellular network (e.g., 3G, 4G) or LAN. Alternatively, private or public LPWAN solutions such as LoRaWAN gateways or cellular IoT can be used to push data to the cloud. Combination of Bluetooth, radio or WiFi with cellular IoT means you will have fewer bills to pay. It is anticipated that, with more integrations, the IoT market will mature, and costs will drop further.
Many of LPWAN technologies currently have a very limited network coverage in the U.S. LTE Cat-M1 by far has the largest coverage. Ingenu, which is a legacy technology, Sigfox and NB-IOT have very limited U.S. coverage. Some private companies are currently using subscription-free, crowd-funded LoRaWAN networks to provide service to U.S. growers: however, with a very limited network footprint. Currently, cellular IoT does not perform well in rural areas without strong cellular data coverage.
In two weeks: Dr. Osroosh continues to discuss IoT strengths and limitations in part 2.
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What can you do when you need data from all over the world in a short amount of time? Many scientists, including ones at JPL/NASA, are crowdsourcing their data collection.
Projects range from ground truthing NASA satellite data, to spotting migration patterns, to collecting microbes.
Darlene Cavalier, Professor of Practice at Arizona State University is the founder of SciStarter, a website where scientists make data collection requests to a community of volunteers who are interested in collecting and analyzing data for scientific research.
Who Collects the Data?
SciStarter was an outgrowth of Cavalier’s University of Pennsylvania graduate school project where she sought to connect people who didn’t have formal science degrees with scientists who needed their help. She says, “We know from various National Science Foundation reports that many people without science degrees are interested in participating in and learning about science. The challenge was that there was no easy way to find those opportunities.”
One project invites UK citizens to find and take pictures of orchids.
Cavalier started SciStarter, in part, to create a “one-stop shop” resource where people could easily search and find projects best suited to their locations and interests. She says, “We have over 1,600 projects and events. Projects range from ground truthingNASA satellite data, to spotting migration patterns, to collecting microbes.” One project,sponsored by the National History Museum in London, invites UK citizens to find and take pictures of orchids with their smartphones, so scientists can study the effect of climate change on UK flowering times.
How Are Volunteers Recruited?
Volunteers are recruited through SciStarter’s partnerships with the National Science Teachers Association, Discover Magazine, the United Nations, PBS and more. One of the most visible ways that volunteers are enlisted is through an organization Cavalier started called Science Cheerleader. The organization consists of 300 current and former NFL and NBA cheerleaders who are scientists and engineers. These role models visit youth sports groups, go to science festivals, and talk in schools. During their appearances they engage people of all ages in actual citizen science projects. Darlene says, “This is our way of casting a wide net and making new audiences aware of these opportunities.”
Science cheerleader consists of 300 current and former NFL and NBA cheerleaders who are now scientists and engineers.
What’s the Ultimate Goal?
Cavalier is determined to create pathways between citizen science and citizen science policy. She says, “The hope is after people engage in citizen science projects, they will want to participate in deliberations around related science policy. Or perhaps policy decision makers will want to be part of the discovery process by contributing or analyzing scientific data.” Darlene has partnered with Arizona State University and other organizers to form a very active network called Expert and Citizen Assessment of Science and Technology (ECAST). This group seeks to unite citizens, scientific experts, and government decision makers in discussions evaluating science policy. Cavaliers says, “The process allows us to discover ethical and societal issues that may not come up if there were only scientists and policy makers in a room. It’s a network which allows us to take these conversations out of Washington D.C. The conversations may originate and ultimately circle back there, but the actual public deliberations are held across the country, so we get a cross-section of input from different Americans.” ECAST has been contracted by NASA, NOAA, the Department of Energy, and others to explore specific policy questions that would benefit from the public’s input.
ECAST is a network which allows us to take science policy conversations out of Washington D.C.
Cavalier says the SciStarter team constantly works to remove challenges and impediments to public participation. She explains, “We’ve found it can be difficult to articulate the geographic bounds of a project because when a researcher says, “this project can be done in a watershed,” it doesn’t mean anything to most people. So SciStarter spent time developing a system of “Open Streetmap and USGS databases that show land-type coverage.”
Another obstacle to some types of research is access to instrumentation. Darlene comments, “The NASA Soil Moisture Active Passive (SMAP) project really opened our eyes to how many obstacles can exist between the spectrum of recruiting, training, equipping, and fully engaging a participant.” This year, SciStarter is building a database of citizen science tools and instruments and will begin to create the digital infrastructure to map tools to people and projects through a “Build, Borrow, Buy” function on project pages.
“The NASA Soil Moisture Active Passive (SMAP) project really opened our eyes to how many obstacles can exist to full engagement.”
Darlene says that sometimes scientists who want accurate data without knowing about or identifying a particular sensor for participants to use often create room for data errors. To address this problem, SciStarter and Arizona State University will be hosting a summit this fall where scientists, citizen scientists, and commercial developers of instrumentation will meet to determine if it’s possible to fill gaps to develop and scale access to inexpensive, modular instruments that could be used in different types of research. You can learn more about crowdsourcing your data collection with SciStarter here.
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