Agriculture Reference
In-Depth Information
on. To save energy on communication, some push-down filter methods are used to
preprocess raw data before transmitting (Diao et al., 2005; Ganesan et al., 2004).
This is especially useful for multimedia WSNs with large data sets including images,
audio, and/or video streams. TinyDB, BBQ (Deshpande et al., 2004), and Direct
Diffusion (Intanagonwiwat et al., 2003) provide tools for continuous queries to the
current data. Another method, Acquisitional Query Processing (AQP), offers func-
tionality to determine which nodes, which parameters, and at what time to collect
data (Ganesan et al., 2004). In many WSN applications, data collected by WSNs are
streamed to a remote traditional database through various long-distance communi-
cation networks. End users can query stored “historical” data any time. Data mining,
artificial intelligence, and multivariate statistical analysis methods can be used to
process, analyze, and query the data. Some new energy-efficient query methods and
database management are still under development that view the WSNs as a database
supporting archival query processing (Diao et al., 2005).
13.5 EXAMPLES OF WORKSITE MANAGEMENT SYSTEMS
A mobile field data acquisition system was developed by Gomide et al. (2001) to collect
data for crop management and spatial-variability studies. The system consisted of a data
collection vehicle, a manager vehicle, and data acquisition and control systems on farm
machines. The system was able to conduct local field surveys and to collect data of soil
water availability, soil compaction, soil fertility, biomass yield, leaf area index, leaf tem-
perature, leaf chlorophyll content, plant water status, local climate data, insect-disease-
weed infestation, grain yield, etc. The data collection vehicle retrieved data from farm
machines via a WLAN and analyzed, stored, and transmitted the data to the manager
vehicle wirelessly. The manager and engineers in the manager vehicle monitored the
performances of the farm machines and the data acquisition systems, and troubleshoot
problems based on received data. Lee et al. (2002) developed a silage yield mapping
system, which included a GPS, load cells, a moisture sensor, and a Bluetooth wire-
less communication module. The moisture sensor and the Bluetooth transmitter were
installed on the chopper. The signal from the moisture sensor was sent to a Bluetooth
receiver on a host PC at a data rate of 115 Kbps and was used to correct the yield data.
Li et al. (2011) reported a hybrid soil sensor network (HSSN) designed and
deployed for
in situ
, real-time soil property monitoring (Figure 13.4). The HSSN
included a local wireless sensor network, which was formed by multiple sensor nodes
installed at preselected locations in the field to acquire readings from soil property
sensors buried underground at four depths and transmit the data wirelessly to a data
sink installed on the edge of the field; and a long-distance cellular communication
network (LCCN). The field data were transmitted to a remote web server through
a GPRS data transfer service provided by a commercial cellular provider. The data
sink functioned as a gateway that received data from all sensor nodes; repacked the
data; buffered the data according to a cellular communication schedule; and trans-
mitted the data packets to LCCN. A web server was implemented on a PC to receive,
store, process, and display the real-time field data. Data packets were transmitted
based on an energy-aware self-organized routing algorithm. The data packet deliv-
ery rate was above 90% for most of the nodes.
