Synthesis Paper for “Piloting Automated Sensor Applications for
Improving Rice Water Use Efficiency Project (P161216)”
Contents
Overview ....................................................................................................................................................... 2
Summary of Sensors used in Agriculture ...................................................................................................... 3
Water-level Sensors .................................................................................................................................. 6
Basic Concepts on Feasibility Study .............................................................................................................. 9
Basics of Internet of Things (IoT) ................................................................................................................ 11
Fundamentals of IoT Network Connectivity: Design Space Approach ................................................... 12
Wireless Connectivity Technologies ....................................................................................................... 14
Wireless Personal Area Networks (WPAN) ......................................................................................... 16
Wireless Local Area Networks (WLAN) ............................................................................................... 17
Wireless Wide Area Networks (WWAN) ............................................................................................. 17
Mobile Connectivity Expansion............................................................................................................... 22
Synthesis of lessons learned from the sensor application ......................................................................... 22
Challenges of Implementing IoT for Agriculture in Developing Countries ............................................. 22
Practical Challenges from Local Start-ups in Developing Countries ....................................................... 25
TechAguru ........................................................................................................................................... 25
MimosaTEK ......................................................................................................................................... 26
Lessons for Future Work and Scale-up ....................................................................................................... 26
1
Overview
Today’s global agriculture faces unprecedented challenges driven by rapid population growth,
dietary change, resources constraints, climate change, to name a few. One of the ramifications
of these challenges is that the world will need to produce more with less. Indeed, the Food and
Agriculture Organisation (FAO) estimates that the global food production will need to rise by 70%
to meet with projected demand by 20501. These challenges, in turn, imply the urgent need for
the efficient management and the optimized use of natural resources such as water and soil, and
other inputs like fertilizers, pesticides. However, managing these inputs efficiently – let alone
optimizing their usage – is difficult without consistent and precise monitoring. Undoubtedly, for
smallholder farmers, who account for 4/5 of agricultural production from developing regions2,
the production gains could be monumental as many farmers in developing regions still rely on
personal experiences, rather than data for their farming decisions.
Internet of Things (IoT) holds potential to help feed the world’s growing population. IoT refers
to networks of objects – or things – that communicate with other things based on the wireless
connectivity. Simply put, IoT not only enables the inter-communication between the objects,
such as sensors and actuators, but also provides decision analytics based on the precise data
accumulated by these devices. In fact, this concept of improving agricultural decisions based on
the information and data gathered by sensors is not new. Sensors and wireless sensor networks
(WSNs) have long contributed to agriculture, particularly by promoting precision agriculture in
crop production and management3. Precision agriculture (PA) is defined as a holistic strategy in
which farmers can vary input use and cultivation methods to match varying soil and crop
conditions across a field4, enabling farmers "to do the right thing, at the right time, in the right
place, in the right way.5" In this sense, sensors are intrinsic part of PA as they facilitate and enable
its effects. Indeed, the use of sensors and wireless sensor networks made possible the precise
monitoring and management even in the areas in which controlling variables was considered
challenging, such as arable farming and livestock management.
The integration of sensor data and device with various enterprise applications makes IoT
solutions offer much more than simply connecting sensors and devices 6 . Unlike the simple
machine-to-machine (M2M), which relies on direct point-to-point communication, IoT solutions
enable multiple devices to access data through the cloud system where data is stored. This ability
to access data from multiple end-devices, in turn, provides additional benefits of signaling, or
even automating, desired actions to be made based on the information collected. Furthermore,
with the Internet, you can access to the connected devices and take actions whenever and
wherever you are because the data stored in the cloud system is readily accessible through the
Internet. This is where the potential of IoT is realized as sensors can now be more easily
connected to other devices, particularly mobile phones, through the Internet.
Thus, the sensors-based IoT can help farmers by providing the practical knowledge of optimal
input levels required for their field conditions as well as by making an overall farming process
more efficient and effective. While the up-to-date IoT application in agriculture has been visible
mostly in developed countries, it also holds prospects of increasing agricultural productivity in
2
developing countries thanks to the increasing presence of two enabling elements. First, the costs
of sensors, or embedded devices, have rapidly declined over the past decade – as much as 100
times lower7 – contributing to the potential growth in IoT application in agriculture in emerging
economies. Furthermore, the global mobile broadband coverage has rapidly increased in the past
15 years: the proportion of the global population covered by 2G mobile-cellular network grew
from 58% in 2001 to 95% in 20158, by 3G network from 45% in 2011 to 69% in 20159. Thus, the
combination of innovative technologies and high mobile broadband penetration rate serves as
the basis for developing countries to enjoy the benefits of IoT.
Against this background, this synthesis report on the use of wireless sensors and IoT system
in agriculture is prepared as part of the ongoing “Piloting Automated Sensor Applications for
Improving Rice Water Use Efficiency project” in the Agriculture Global Practice. While not
exhaustive, the report aims to provide a review on the practical application of wireless sensors
in agriculture and identify the environment in which the effects of the wireless sensor networks
and IoT system can be best utilized. While each innovative application of sensors across the whole
agricultural value chain is worth noting, the report aims to survey wireless sensors and sensor
networks in crop farming in open fields, with a focus on water-level sensors. This focus on water-
level sensors is to increase the linkage with the feasibility study of deploying sensors and wireless
networks to promote the use of alternate wetting and drying (AWD) irrigation method currently
in progress in Vietnam and India, the main pillar of the project.
Also, the report surveys the basic components that comprise of the IoT network system,
particularly on connectivity. Understanding wireless connectivity solutions will shed light on how
the global expansion of the mobile broadband networks and mobile phone usage can spur the
benefits of IoT to the farmers in developing regions.
Summary of Sensors used in Agriculture
The application of sensors in agriculture is widely ranged, from collection of weather and crop
information to differentiated usage of fertilizer and water. As wide as the range of the sensors
applied to agriculture, the technical design of these sensors also varies according to their purpose
of use and application context. Some of the most common types of the sensors used in
agriculture include capacitance, ultrasonic, infrared, and tensiometric sensors. The non-
exhaustive list of sensor types used in agriculture is provided with a brief description and sample
usage in Table 1.
Table 1. List of Sensor Types Used in Agriculture
Technical Types Sensor Description Sample Usage
Dynamic measurements to estimate
Measure distance to a target by illuminating fruit-tree leaf area; Combined with
Lidar sensors
that target with a laser light GPS have been applied for 3D map
generation in vine plantations
3
Fourier Transform
Obtain an infrared spectrum of absorption or Assessment of structural differences
Infrared
emission of a solid, liquid or gas of celluloses of various origins
Spectroscopy (FTIR)
Work as a capacitor coupled with a capacitance
Soft water level- Hydrological behavior in agriculture
to frequency converter and measures water
based sensors catchments
level at an adjustable time step acquisition
Use capacitance to measure the dielectric
Capacitance probes Soil moisture
permittivity of a surrounding medium
Reflectometers Measure the transit time of waves along a
(Frequency Domain probe in the soil based on the properties of Soil moisture content
Reflectometry, FDR) electromagnetic waves10
Determine the distance to a target by emitting
Ultrasonic ranging an ultrasonic pulse and measuring the elapsed
Analyzing apple tree canopies
sensors time between the emission and return of the
pulse as an echo reflected from the target11
Produce an electrical signal proportional to the
Optoelectronic amount of light incident on its active area and Weed detection in wide row crops to
sensors respond to light to recognize patterns, images, detect accuracy and feasibility
motion, intensity, and color12
Measurements of variables in the soil
pH soil-based
Measure soil pH level oriented toward crop
sensors
productivity
Infrared Detect infrared by receiving heat energy Measurement of the leaf
thermocouple radiated from the objects and measure surface temperature of forests or agricultural
(IRt/c) temperatures of materials without contact13 plants
Optical sensors observe visible lights and
infrared rays (near infrared, intermediate
infrared, thermal infrared); Microwave sensors
Optical and
receive microwaves, which is longer wavelength Characterizing olive grove canopies
microwave sensors
than visible light and infrared rays, and
observation is not affected by day, night or
weather14
Source: Pajares, G., Advances in Sensors Applied to Agriculture and Forestry, MDPI, 2011
The application of the sensors in precision agriculture can be further categorized by the
agricultural application purposes, such as irrigation, fertilization, and pest control, or by the
target inputs, such as temperature, humidity, moisture, that the sensors are used to measure. In
addition, the types of sensors can be also broken down into the technical functions that they
serve – environmental, chemical, mechanical, acoustic, ultrasonic, electric, optical, computer
vision systems, biological, Micro-Electro-Mechanical (MEMS), radio-frequency identification
(RFID)15 – or the measurement platforms to which sensors are attached or mounted, including
remote platforms such as unmanned aerial vehicles (UAVs) like drones, mobile proximal ones
such as agricultural machines and robots, and in-situ proximal, fixed sensors16.
4
The existing literature17,18 categorizes the WSN application in agriculture into three main
areas, soil, ambient, and plant. A few types of sensors are applied and tested in measuring soil
properties: electromagnetic, optical, mechanical, electrochemical, airflow, and acoustic19. A brief
explanation for each sensor type is summarized below:
• Electromagnetic Sensors use electric circuits to measure the capability for soil
particles to conduct or accumulate electrical charge by making soil part of an
electromagnetic circuit. Electromagnetic soil properties are mostly affected by soil
texture, salinity, organic matters, and moisture content;
• Optical sensors use light reflectance to characterize soil, simulating the human
eye as well as measuring near-infrared, mid-infrared, or polarized light reflectance.
They are usually mounted to vehicles or airborne platforms for remote sensing;
• Mechanical Sensors can be used to estimate soil mechanical resistance such as
compacting by using a mechanism that penetrates or cuts through the soil and
recording the force measured by strain gauges or load cells;
• Electrochemical Sensors can measure soil nutrient levels and pH, an essential type
of information needed for precision agriculture, whereby electrodes detect the
activity of specific ions in the soil;
• Airflow sensors measure soil air permeability on the go. The pressure required to
squeeze a given volume of air into the soil at fixed depth could be compared to
several soil properties;
• Acoustic sensors can determine soil texture by measuring the change in noise
level based on the interaction of a tool with soil particles
Table 2. Summary of In-field Sensor Applications in Agriculture
Sensor Usage Sensor Application Types of Sensors
Soil moisture
Rain/water flow
Soil Water level
Soil temperature Electromagnetic
Conductivity Optical
Salinity Mechanical
Electrochemical
Humidity
Airflow
Ambient temperature
Acoustic
Atmospheric pressure
Ambient Wind speed
Wind direction
Rain fall
5
Solar radiation
Moisture
Temperature
Hydrogen
Plant
Wetness
CO2
Photosynthesis
Source: Wireless sensor networks for agriculture: The state-of-the-art in practice and future challenges.
Tamoghna Ojha, Sudip Misra a, Narendra Singh Raghuwanshi; Soil and Crop Sensing. Institute of Agriculture
and Natural Resources. http://cropwatch.unl.edu/ssm/sensing
Water-level Sensors
This report primarily focuses to survey the liquid- and water-level sensors used in agriculture
to contribute to the successful design and implementation of the feasibility study, which is
discussed later in the report. For water level monitoring and measurement, commonly used
sensors can be categorized by four main measuring features: pressure, supersonic waves, heat,
and image20. The type of water-level sensors is further divided by the sensor’s contact or non -
contact characteristic with water. The contact type sensors include pressure type, capacitance
type, shaft encoders (also known as rotary encoder), and bubblers (hydrostatic pressure type),
and the non-contact type includes ultrasonic, radar, and mobile canal control sensors21.
Table 3. Water-level Sensors by Measuring Features
Measuring
Description Pros Cons Sensor Types
Features
Measure quantify of water
Pressure
and directly translate to Need to be calibrated and
sensors,
water level through force replaced frequently due to
hydrostatic
Pressure per unit area represented Easy to use potential breakdown of
pressure
by product of mass and sensors by continuous
sensors, staff
acceleration of gravity of water pressure
gages
water
Measure the time of travel Free from water Inaccurate returning
Ultrasonic,
of supersonic wave pulse pressure; Lifespan values when temperature
radar,
Supersonic from emitter to receiver is temporary as it fluctuates between high
electromagnetic
reflected by the water does not contact and low, or when water
sensors
surface water directly fluctuates rapidly
Measure the amount of Need to be well insulated
temperature drop of a and the temperature of
solid, which is proportional the solid should be
to its contact area with Lifespan is controlled near the
Heat
fluid, and translate the temporary measurement area; heat
temperature change to the type sensors are used for
depth information of the restricted applications
solid in water such as the measurement
6
of cooling water level in
nuclear plants
Costly compared to other
types; Need a large and
secured space to install
Measured data
Provide the surrounding data, and capacity to
can be confirmed
information around the transmit large data size; Mobile canal
Image by the
sensor as well as the water Measurement accuracy is control
surrounding
level affected by lighting
information
conditions and minimum
light is required during
night time
Source: Yu and Hahn, Remote Detection and Monitoring of a Water Level Using Narrow Band Channel, 2010
& Nakka, Sensors for Agriculture and Water Use Efficiency, http://www.slideshare.net/saibhaskar/sensors-
for-agriculture-and-water-use-efficiency, 2016
There is an extensive range of experiments, pilot projects and initiatives that have been
conducted in developing in-situ sensors in paddy fields. The available academic literature
confirms the effectiveness of sensor deployment in enhancing rice cultivation by allowing more
precise measurement of critical factors in paddy fields such as water level, water salinity and soil
moisture. While some projects have specific project objective, others have deployed a more
comprehensive network system to improve rice crop monitoring. However, the current state of
research remains largely experimental or pilot-level, which calls for scaling up of proved sensor
technologies. Also, many of these projects deployed sensors in controlled settings. Below table
4 is the list of available academic literature on sensor application in rice fields.
Table 4. Selected List of Academic Research for Water-level Sensors Use in Rice Fields
Projects Objective Sensor Description
Developed a wireless sensor network
To develop an automatic salinity comprised of low-cost electrical
Smart on-line water salinity
sensor system in response to conductivity (EC) measurement nodes
measurement network to
increasing salinity and excessive and an autonomous power supply based
manage and protect rice
usage of irrigation water caused on energy harvesting, that is capable of
fields (SMART-PADDY) by
using coastal groundwater for rice transmitting readings of water salinity in
European Commission22
farming paddy fields in real-time to a central
server
7
For on-farm water monitoring the
ultrasonic sensors was used with RBC
Pilot initiative of To obtain real time information and Flume for water inflows and outflows;
WALAMTARI by ClimaAdapt establish decision support system For measuring the water in the fields
project23 to improve water use efficiency and ultrasonic sensors fitted to Bowmen
water productivity in rice fields water tube are used; Other parameters
measured are temperature and relative
humidity24
To design and develop a paddy field
moisture content and water depth
Integrated soil moisture and
sensor, calibrate the sensor with
water depth sensor for Wireless, frequency domain soil
various soil samples, and test the
paddy fields (Xiao et al, moisture and water depth sensors for
performance of WFDSS in terms of
2013)25 paddy field
measurement range, stability,
system error, energy consumption
and transmission distance
research various areas related to
Paddy Field Sensor, Initiative sensor application in paddy fields
Water level and temperature sensors
of Japanese Government26 such as measurement,
(sensor type not specified)
communication, installation and
cost analysis
Development and Electro-mechanical sensor to measure
To resolve the soil acidity of the
Deployment of Wireless water level, in which the mechanical part
paddy fields in Kuttanad, India, and
Sensor Network in Paddy floats on water and the electrical part
thus to improve rice production in
Fields of Kuttanad27 (Simon produces signals based on the portion of
the region
and Jacob, 2012) the floating device
Monitoring the Paddy Crop
Humidity, pH sensor, temperature, and
Field Using ZigBee Network28 To test fully automated rice field
pressure sensors
(Sriharsha, Rao, Pravin and monitoring
(sensor type not specified)
Rajasekhar, 2012)
Sensors for (1) prediction of paddy
growth stages, (2) water level control
A Fully Automated Water
corresponding to growth stages, (3)
Management System for To develop a system for an
estimation of mean water level, (4)
Large Rice Paddies29 automatic water management for
prediction of water consumption, and (5)
(Sekozawa) large-scale paddy fields
optimal water allocation for minimizing
of damage
(sensor type not specified)
To develop a system that
Application of Sensor Sensor nodes for Barometric Pressure,
guarantees a low-cost, high
Networks for Monitoring of Ambient Light, Relative Humidity,
performance and flexible
Rice Plants: A Case Study30 Temperature
distributed monitoring system with
(Kumar et al.)
an increased functionality in rice
(sensor type not specified)
fields
A floating sensing system to To develop a sensing system that
Floating soil sensor (FloSSy) as
evaluate soil and crop can float, acquire and process
electromagnetic induction technique
variability within flooded detailed geo-referenced soil
does not require physical contact with
paddy rice fields31 (Islam et information within flooded rice
the soil
al., 2011) paddy fields
8
Basic Concepts on Feasibility Study
The feasibility study is concurrently conducted in two pilot sites, Vietnam and India, to
deploy low-cost water-level sensors to promote the use of alternate wetting and drying (AWD)
irrigation method in rice fields to increase irrigation efficiency. AWD is a proven technique to
optimize irrigation water use in rice, which entails frequent drying of the paddy fields and
irrigating once the water level reaches a prescribed limit, depending on the stage of plant growth
as depicted in the Figure 1. To provide details on how AWD irrigation method works, it requires
farmers to re-flood the field to a depth of 5cm a few days after the disappearance of the ponded
water when the water level drops to 15cm below the soil surface. From one week before to a
week after flowering, the field should be kept flooded, topping up to a depth of 5cm as needed.
After flowering, during grain filling and ripening, the water level can be allowed to drop again to
15cm below the soil surface before re-irrigation32. The AWD can be implemented by using a ‘field
water tube’ in Figure 2. It is estimated that AWD can save water usage up to 30% and reduce
greenhouse gas (GHG) emissions, particularly methane by up to 48% while maintaining yields33.
Figure 1. Graphics for AWD Schedule
Figure 2. Images of AWD Field Water Tubes
9
The successful deployment of AWD method requires accurate estimation of water-level in
the ground so farmers use that to make irrigation decision making. However, manual
measurement of water, often using measuring tapes, is challenging, inaccurate, and labor-
intensive. Against this background, the feasibility study aims to develop sensor-based
measurement to increase the potential utilization of AWD, and therefore contribute to significant
water-saving efforts. The proposed model will use (a) water-level sensors to measure standing
water-level in rice crop and subsequently to determine irrigation schedule; (b) mobile-based
pump control system and a decision support system to implement irrigation scheduling and
monitor the pump operation remotely; and (c) monitoring system for the volume of water
extracted along with energy consumption.
At the time of this report, the information on the sensors and IoT system design of the
feasibility study taking place in India became available. To fully capture the benefits enabled by
the optimization of water use, such as saving ground water, lowering carbon footprint and
methane emission, and reducing irrigation costs, various sensors are considered for the pilot
study: soil moisture sensors, standing water level sensors, rain gauge, water flow meter, ground
water measurement sensors, and temperature & humidity sensors. The application purpose of
each sensor category is explained below with brief descriptions of different technical types
considered for the feasibility study as shown in Table 5.
• Soil moisture sensors measure the moisture level of soil during AWD
• Standing water-level sensors measure the water level in the field during ponding period
and alternate wetting and drying period
• Rain gauge measures the quantity of rainfall
• Water flow meter measures the volume of water pumped out
• Ground-water measurement sensors measure the distance of water presence in
borewell from the ground level
• Temperature and humidity sensors measure the temperature and humidity of
ambience/atmosphere in the field
Table 5. Summary of Sensors used and considered in Feasibility Study in India
Sensor Types Considered Major Comparable Characteristics
Soil Moisture Capacitive Corrosion free
Resistive Corrosive
Standing Water-level Ultrasonic water-level sensor Contactless; More expansive than pressure-
based sensors
Hydrostatic Pressure sensors Can be clogged by muddy water in the fields
and may not give accurate values
Rain Gauge Tipping bucket Simple, reliable, and cost-efficient
Water Flow Meter Electromechanical Has a magnetic drive and dry dial for visual
readings in the field
10
Water flow sensor Suitable for small diameter medium duty
pipelines
Clamp-on type water meter Portable and easy to install but expensive;
More suitable for one time measurement;
Requires calibration every time when the
pipe dimensions change
Ground water Barometric Sensor Challenges faced when Installing the sensors
measurement directly inside pumping tube wells; Acoustic
contactless ground water-level sensors will
be considered in the future
Temperature & Electromechanical Gives ambient temperature, humidity, and
Humidity atmospheric pressure directly; Can be
integrated to a controller or data logging
device directly; gives decent accuracy; needs
installation inside a weather shield for
outdoor applications
Note: Yellow cells indicate the type of sensor selected for the pilot study
Basics of Internet of Things (IoT)
Thanks to the recent decline in the cost of sensors based on the advances in computing
capability and the rapid expansion of the global mobile broadband penetration over the years,
IoT can benefit the farmers in developing regions alike. For example, sensors can be deployed –
on the ground or on unmanned aerial vehicles (UAV) like drones – to collect data on target inputs
such as soil moisture, fertilizer level, and crop health. The data are logged and stored to a gateway,
which integrates all connected sensors and devices, to a server or cloud system wirelessly. Then,
farmers access the information gathered by the sensors with mobile apps or analytical software
in any end-devices such as mobile phones, tablets, and computers (See Figure 3 for the flow of
11
the process). Depending on the context, farmers can choose to manually control connected
devices or to automate processes for any required actions.
Figure 3. Overview of IoT Architecture (source: https://www.embitel.com/blog/embedded-blog/how-
iot-works-an-overview-of-the-technology-architecture-2)
When you take a deeper look into how IoT works, as in the above scenario, sensors and
embedded devices explain only one part of the equation. Indeed, Internet Society (ISOC)
recognizes ubiquitous connectivity, widespread adoption of IP-based networking, computing
economics, miniaturization, advanced in data analytics, and the rise of cloud computing as the
technologies that enable the wide adoption of IoT in real life 34 . Similarly, ITU identifies that
connectivity, object identification, real-time information, smart devices, and advances in
miniaturization as key technologies that need to be further developed and deployed at affordable
costs. Therefore, comprehending the underlying technical concepts behind IoT is useful to think
about the ways in which the global development can benefit from IoT. Specifically, this part of
the report focuses on the IoT network connectivity to envisage another enabling ingredient, the
expansion of the global mobile broadband coverage, and to introduce opportunities and
challenges ahead.
Fundamentals of IoT Network Connectivity: Design Space Approach
Wireless communication and wireless networks are important element of IoT as they are the
“glue” that holds Internet of Things systems together35. Without connectivity of IoT networks,
data cannot be collected nor transmitted to end users—or farmers in the context of IoT
application in agriculture. As ITU states, the basic idea behind the IoT is the connection of
anything, at any time, from any place, as indicated in Figure 4. Recalling the definition of precision
agriculture mentioned in the introduction, the goal behind IoT shares much similarity with the
“right thing, right time, right place, right way” principles of precision agriculture,
12
Figure 4. Basic Idea behind the IoT: any place, any time, and any thing
In the IoT network design space Figure 5), there are three axes in the graph: battery life, data
rate (“duty cycle”), and range between device and gateway. Battery life indicates power
consumption of device. Depending on the settings, you may opt to use powered devices or the
devices that have years of battery lifetime. The second axis is the device’s data rate, which is the
speed at which data is transferred within a computer or between computing devices measured
in bytes per second 36 . Simply said, it is the number of bytes per day that you would like to
communicate between your devices and the servers on the Internet. Your IoT system may use as
little as a few bytes every day or as many as gigabytes (GB) per day depending on the applications.
The below Table 6 is help you understand the approximate volume of data required for each
selected application example 37 . Additionally, it is important to note that these axes are not
independent, which makes the IoT connectivity design space even more complicated38.
Table 6. Data volume required for selected applications
(source: https://www.computerhope.com/issues/chspace.htm)
Data volume Comparable Scale Example
Bit Binary: 1 or 0 -
Byte 8 bits 1 character, e.g. “a”
Kilobyte (KB) 1,024 bytes 2 or 3 paragraphs of text
Megabyte (MB) 1,024 Kilobytes 4 books (200 pages)
Gigabyte (GB) 1,024 Megabytes 341 digital pictures (with 3MB average file size)
13
Figure 5. IoT Network Design Space (Source:
https://6s062.github.io/6MOB/2017/materials/lec5-IOTx-WirelessNetworkConnectivity.pdf)
Wireless Connectivity Technologies
With the IoT network connectivity design in mind, there are many wireless connectivity
technologies used for different purposes and applications. An easy way to categorize different
wireless networks is by the network range of the connectivity technologies. A network’s range is
typically classified into four classes: personal area networks (PANs), local area networks (LANs),
neighborhood area networks (NANs) and wide area networks (WANs)39.
Figure 6. Different ranges and applications for PANs, LANs, NANs and WANs
14
PANs are usually wireless and cover a range of about 10m. Wireless PAN (WPAN) devices
usually have low radio transmission power and run on small batteries —for example, a
smartphone connected over Bluetooth to a smart watch or fitness device40. Common WPANs
include ZigBee, Bluetooth, and RFID. LANs are either wired or wireless, and Wireless LANs
(WLANs) usually cover a range up to 100m. A home Wi-Fi network providing internet access to
computers, smartphones, and IoT-enabled home appliances is a common example of WLAN.
NANs are usually wireless and can reach more than 25km. Lastly, WANs cover a very large area,
as big as the entire globe41. The internet is considered a WAN and comprises a complex mix of
wired and wireless connections42. In addition, WWANs include 2G, 3G, 4G/LTE, and low-power
wide-area networks (LPWANs) like LoRA 43 . The summary of the characteristics of different
wireless telemetric options is available in the Annex 1.
Figure 7. Comparing IoT Connectivity Technologies (Source: ITU and Cisco)
One thing to note here is that many of the wireless connectivity technologies the report
introduces are operating in the industrial, scientific and medical (ISM) band where spectrum use
is free or “unlicensed.” However, the licensed frequency band, such as cellular communication,
can be also used as one method to connect devices in IoT systems. As we have covered the basic
concepts of wireless connectivity and the considerations for designing IoT networks, below are
the predominant wireless connectivity technologies in more detail (non-exhaustive).
15
Wireless Personal Area Networks (WPAN)
ZigBee
ZigBee is an open global standard and is the only standards-based wireless technology 44 designed
specifically to be used in machine-to-machine (M2M) networks 45 . It is designed to provide a whole
connectivity solution for device interoperability and cloud connectivity46. The technology is inexpensive
to run and doesn’t require a lot of power, making it an ideal solution for many industrial applications.
Zigbee’s module cost ranges US$1-10 and its data transmission range typically falls between 10-100
meters, power output and environmental characteristics47. Moreover, the technology has a low latency,
and a low duty cycle, allowing products to maximize battery life. Specifically, Zigbee has data throughput48
rates of 250kbps, 40kbps, and 20kbps49. Devices using Zigbee are usually low data rate emitting sensors
and actuators and vastly minimizing air time and therefore power consumption based on the low-power
performance of the underlying 802.15.4 link layer protocol 50 . The technology is also based on mesh
networks, which allow nodes to be connected together through multiple pathways.
One of Zigbee’s major benefits is that it provides a complete solution that enables true device
interoperability between different manufacturers51. The Zigbee protocol suite incorporates the Zigbee
cluster library, which refers to a standard library of devices types, data models and behaviors built by
original equipment manufacturers (OEMs) operating in different domains and proven in actual
deployments for many years52. Zigbee’s ability to connect multiple devices together simultaneously and
seamlessly makes it ideal for a connected home environment such as smart locks, lights, robots and
thermostats to talk to one another 53 . Service providers like Comcast, Deutsche Telekom and Alibaba
endorsed Zigbee as the protocol of choice when introducing their home automation services to consumers,
and lighting manufacturers like Philips and Osram have a whole line of Zigbee-based wireless LED
products54.
Bluetooth
Bluetooth is a wireless technology primarily used for short-range communication. The Bluetooth became
successful in mobile phones, and is frequently used in small devices, such as fitness accessories or speaker
systems, that connect users to smartphones and tablets. The technology operates in the 2.4GHz ISM band
based on point-to-point communication design, also known as start network topology55. The Bluetooth
technology is fairly low-power and devices typically use small rechargeable batteries or two alkaline
batteries 56 . It supports data throughput up to 2Mbps, with up to eight connected devices. If a Wi-Fi
connection is available, as the case in a mobile device, Bluetooth can operate over Wi-Fi, known as
Bluetooth High Speed, and provide transfer rates of up to 25Mbps.
Bluetooth Low Energy (BLE), or Bluetooth Smart, is a more recent addition to the Bluetooth specification
that is more suitable for IoT connectivity application. While BLE uses less power than the classic Bluetooth
with the trade-off of lower data throughput57, it enables lengthy operation using coin-cell batteries58. BLE
also uses 2.4GHz ISM band but is not compatible with Classic Bluetooth 59 . The current standard
specification, Bluetooth 5, offers improved the data transmission range of 200m (quadruples the previous
specification Bluetooth 4.2), doubles the speed up to 2Mbps, and provide an eightfold increase in data
broadcasting capacity by increasing the advertising data length60. The increases in range and data rates
make BLE increasingly attractive in the growing segments such as industrial data loggers or smart energy
meters61.
Bluetooth and BLE has the advantage of built-in compatibility with mobile devices. Its mobile device
connectivity offers an excellent avenue for data display and retrieval, Internet connectivity, and initial
16
provisioning and configuration 62 . Another strength of the Classic Bluetooth standard is that includes
application profiles such as for audio/video remote control and heart-rate monitors 63 . The recorded
profiles help to understand how applications exchange information to achieve specific tasks and include
comprehensive Bluetooth Special Interest Group (SIG)-defined certification program to ensure
interoperability in the market64. BLE also offers similar functionality by offering generic attribute (GATT)
profiles. GATT provides a structured list that defines the services, characteristics, and attributes of a given
node, enabling BLE to quickly adapt to new applications65.
Wireless Local Area Networks (WLAN)
Wi-Fi
Wi-Fi is probably the most familiar and common wireless technology. Based on IEEE 802.11 standard, it
was developed as a wireless replacement for the popular wired IEEE 802.3 Ethernet standard and natively
integrated with the TCP/IP-based protocol for Internet connectivity66.
Furthermore, Wi-Fi networks rely on a star topology, where the center node is called an access point (AP)
and the other nodes are called stations 67 . In Wi-Fi networks, therefore, AP functions as the Internet
gateway. In large buildings, more than one AP is often deployed in different locations to increase network
coverage, and some Wi-Fi products have two antennas for network diversity68. Therefore, Wi-Fi would be
a suitable choice for IoT devices located in buildings or homes where Wi-Fi networks are already present.
The most visible advantage of using Wi-Fi is interoperability as Wi-Fi is already integrated into all new
laptops, tablets, smartphones and TVs. Taking advantage of its ubiquity, Wi-Fi is also widely used in IoT
applications that can leverage installed Wi-Fi infrastructures without custom gateways 69 . Despite its
widespread presence, Wi-Fi is complex in its network design and has relatively higher power consumption.
Since most IoT products do not need the maximum data rates that Wi-Fi offers, which has been a major
barrier for IoT developers to choose Wi-Fi as IoT connectivity solution. However, to reduce the average
power consumption, the latest Wi-Fi silicon devices apply advanced sleep protocols and fast on/off times,
enabling Wi-Fi integration into emerging IoT applications and battery-operated devices.
Wireless Wide Area Networks (WWAN)
Cellular Networks
Unlike many of the IoT connectivity technologies, including above-explained ZigBee and Bluetooth,
cellular networks operate on licensed spectrum. The licensed spectrum means that users need to buy a
license from the local regulator to operate a radio transmitter in a designated frequency channel70.
The clear benefit of cellular networks is their widespread coverage across a country and a region. The
ubiquity of mobile cellular coverage is also true in developing countries: GSMA estimated 3G coverage
surpassed almost two thirds of population in developing economies in 201471. However, as witnessed by
the short-range nature of other IoT connectivity solutions, many of the IoT systems do not require the
long-range communication and connection, restraining the adoption of cellular networks as the optimal
connectivity solution. Furthermore, cellular networks are high power-consuming, thus not ideal for many
of IoT applications that need long battery life. Unlike 2G networks, in which the radios didn’t have to be
on all the time waiting for messages, also known as store and forward architecture, 3G and 4G adopt
always-on approach: the radio is always on to get the signal for data reception for the connected device72.
Nevertheless, cellular carriers have been working to standardize several low-power wide area (LPWA)—
LTE-M, Narrowband (NB)-IoT, and EC-GSM—to meet requirements for massive IoT deployment
17
characterized by low-cost devices with low energy consumption and good coverage73. Below is the brief
description 74 for some of the sought-after cellular network connectivity solutions tailored to IoT
applications:
• EC (Extended Coverage)-GSM
o Compatible with existing GSM network, the wireless protocol by which 80% of smart
phone are operated globally
o Could leverage 2G base
• LTE-M
o Compatible with the existing LTE network
o Capping the maximum system bandwidth to 1.4MHz
o Targeting LPWAN applications like smart metering
• NB-IoT
o Doesn’t operate in the LTE bands, thus requiring the new infrastructure to deploy NB-IoT
o Eliminates the need for a gateway, potentially making the less expensive option
Cellular networks could provide other benefits when used as a bridging option 75 , for example, as an
aggregation and routing solution. This capillary network approach allows end devices to utilize varying
access solutions from either the short range or LPWA domain and access the cellular networks via a
gateway choice76. This utilization of existing and new cellular networks for the IoT connectivity solution
introduces the potential uptake of IoT in developing countries alike driven by the rapid expansion of
mobile connectivity across the globe.
Low-Power Wide-Area Networks (LPWAN)
Sigfox
Sigfox is a proprietary LPWAN technology aimed at IoT applications such as remote sensing that transmit
small amounts of data relatively infrequently77. It sets up antennas on towers, like a cell phone company,
and receives data transmissions from devices like parking sensors or water meters78. Compared to cellular,
Sigfox offers significant cost and battery-life advantages to such applications. It has the lowest cost radio
modules under $5, compared to around $10 for LoRa and $12 for NB-IoT79.
The Sigfox network provides a very long-range connectivity, up to 30 km in rural areas between the base
station and the node using a star network topology. The ability to connect such a long range comes with
a very low data transmission rate, up to 100bps using 12 byte packets, and no more than 140 messages
per node per day80. Moreover, Sigfox can be used for uplink only, the function of which is limited to
sending data from sensors to a gateway81, and has the problem of signal interference82.
LoRaWAN
LoRaWAN is an open standard network protocol assembled by LoRa Alliance led by IBM, Actility, Semtech,
and Microchip. It uses underlying chip, LoRa, to implement a full LoRaWAN stack provided by Semtech83.
LoRa’s functionality is similar to SigFox as it is primarily for uplink-only applications with many end-points.
Because LoRaWAN spreads out information on different frequency channels and data rates using coded
messages, there is less likely interference issue, thereby increasing the capacity of the gateway84.
18
Figure 8. Technologies addressing different IoT segments by coverage and performance requirements (Source: Ericsson)
Additionally, understanding different communications models by which IoT devices connect
with one another can further help narrow down connectivity options. According to the Internet
Architecture Boards (IAB) and the Internet Society (ISOC), there are four common communication
models used by IoT devices: device-to-device, device-to-cloud, device-to-gateway, and back-end
data sharing85. The device-to-device communication model indicates the IoT devices that directly
connect and communicate between on another, rather than through an intermediary application
server86. This type of networks allows devices that adhere to a particular communication protocol
to communicate and exchange messages to achieve their function87. These devices often use low-
power radio connectivity technologies such as Bluetooth, Z-Wave, and Zigbee.
Another IoT communication type is a device-to-cloud model, whereby the IoT device
connects directly to an Internet cloud service like an application service provider to exchange
data and control message traffic88. A common example of this communication model would be
popular consumer IoT devices such as the Net Labs Learning Thermostat, in which the device
transmits data to a cloud database where the data can be used to analyzed home energy
consumption 89 . This cloud connection enables the user to obtain remote access to their
thermostat via a smartphone or web interface, and it also supports software updates to the
thermostat90.
Another model common to IoT networks is the device-to-gateway model, or more typically,
the device-to-application-layer gateway (ALG) model91. Simply, ALG service indicates that there
19
is application software operating on a local gateway device, which acts as an intermediary
between the device and the cloud service and provides security and other functionality such as
data or protocol translation. A common example of this communication model is when the local
gateway is a smartphone running an app to communicate with a device and relay data to a cloud
service 92 . For example, Fitbit, a popular personal fitness tracker, relies on smartphone app
software to serve as an intermediary gateway to connect the fitness device to the cloud, since it
does not have the native ability to connect directly to a cloud service93. The other common form
of this device-to-gateway model is the emergence of “hub” devices in home automation
applications, that serve as a local gateway between individual IoT devices and a cloud service94.
The last communication model is the back-end data-sharing model. It is a communication
architecture that grants third parties to access the data collected from IoT devices, enabling the
data from multiple sources to be aggregated and analyzed95. This model is an extension of the
earlier mentioned single device-to-cloud communication model, which can lead to data silos
where “IoT devices upload data only to a single application service provider96.” However, ISOC
notes that the back-end data-sharing does not fully eliminate data silos as it cannot overcome
closed system designs, putting emphasis on the interoperability need in the underlying IoT
system designs.
Table 7. Examples of IoT Connectivity Models
(i) Device-to-Device Communication
(ii) Device-to-Cloud Communication
20
(iii) Device-to-Gateway Model
(iv) Back-End Data Sharing Model
Therefore, choosing a suitable IoT network connectivity technology depends on IoT
applications97, based on the IoT network design space and the type of communication models in
consideration. In precision agriculture applications, for example, the farm size in which the
sensors are located, the distance between the sensors and between the nodes and the gateway,
the desired frequency of and data volume required for data transmission, should be understood
in advance before selecting an appropriate IoT connectivity solution. The feasibility study
showcased earlier in the report used public band wireless radio frequencies for the connectivity
between the gateway and sensors while it used cellular-based GSM gateway between the
gateway and the cloud.
21
Mobile Connectivity Expansion
Combined with the cost decline of the sensors and embedded devices, the increase in the
mobile broadband coverage and access to mobile phone is another critical factor behind spurring
the application of IoT in agriculture. In particular, the increasing adoption of smartphone
connections, about 3.8 billion worldwide at the end of 2016 according to GSMA estimate 98, will
further open doors for IoT adoption in agriculture. As one survey indicates, almost half of the
respondents already use mobile devices less than their laptop or desktop computers as a main
monitoring end-device for precision agriculture work99.
Mobile solutions are already highly integrated into an array of agricultural applications in
development context as well, resolving the last-mile service delivery to individuals. Mobile
phones are used for data collection such as mobile surveys, for transactions purpose such as
mobile money payments, and for information exchange such as access to market information
and weather data100. The increase in the use of mobile phones and smartphones in developing
regions can also function as a convenient tool to monitor the data collected by the sensors and
control the devices connected to any IoT systems.
Synthesis of lessons learned from the sensor application
Challenges of Implementing IoT for Agriculture in Developing Countries
Despite the potential role that IoT can serve in agriculture, there are challenges that must be
considered before considering the use of wireless sensor networks. The most predominant issue
is the lack of connectivity in many of the areas where farmers in developing countries are located.
GSMA estimated that 10% of the global population lacked access to basic service and text services,
and about a third lacked access to 3G or 4G mobile broadband Internet. The majority of these
unconnected populations live in the rural regions of Asia and Sub-Saharan Africa, which account
for 3.4 billion of the 4.8 billion people not yet connected to the internet 101. Even though there
are IoT connectivity solutions that operate over unlicensed spectrum, the basic cellular
connectivity is expected an important role of connecting people to access data collected by the
sensors via mobile phones.
When deployed in the context of developing countries, sensors and IoT connectivity can
convey bigger trade-offs between cost, accuracy and precision, frequency and duration, data
retrieval, and power requirements. As implied in IoT network design, major challenges of the
agricultural monitoring systems are power management and network lifetime 102 . Because
batteries lose power over time and require recharging or replacement, power consumption must
be limited to the bare minimum required for effective performance. Managing the sleeping mode
of sensor nodes to enable quick re-activation and appropriate sleep timing are very important.
However, these problems can be resolved by using solar panels in agriculture fields to recharge
the battery and upgrading the power class and antennas to increase the radio range. Existing
agricultural monitoring systems are mostly suitable for a small area.
22
The cost of deploying and commercializing IoT system can be still high for farmers in
developing countries. Depending on the sensor devices and IoT connectivity solutions of choice,
the cost structure and implication can vary. The basic cost elements of building an IoT system
include development costs, manufacturing costs, and operational costs103 as provided in Table 8
and Table 9.
Table 8. Cost Elements of Building IoT System (Source: Link-Labs)
Elements of cost Details Cost
Hardware design and Engineering outsourcing: $10-30,000;
prototyping Prototype Spin: $100-200/each
Industrial design and Engineering outsourcing: $20 – 100,000;
mechanical engineering Mold development: $5-20,000
Firmware development Engineering outsourcing: $10-100,000+
Development cost Software/application
Engineering outsourcing: $10-100,000+
development
Design for manufacturing N/A
Testing N/A
Certification $3,000-$100,000+
Contract manufacturing, lead
Manufacturing times/EOL, unit testing, boxing
Varies
costs and shipping, warehousing,
distribution
Demos and pilots, installations,
support and troubleshooting,
Operational costs Varies
returns and repairs,
connectivity costs
Table 9. IoT connectivity cost (Source: Link-Labs)
Connectivity Option Module cost Connectivity cost Infrastructure
LTE-M $10-15 $3-5/mo for 1MB
NB-IoT $7-12 <$1/mo for 100kb (adequate for
water meter but not for asset
tracker)
Sigfox $5-10 <$1/mo
LoRaWAN Public $9-12 $1-2/mo
LoRaWAN Private $9-12 $0.25/mo $500
If the IoT system in consideration relies on cellular connectivity, the cost can further increase
due to the infrastructure cost and data package plans. This is not just the case for smartphones
application as an extra cost would incur even for the sensor networks opting to use basic phone-
based mobile messaging services. There are two types of messaging services that are widely used
and inherently built within mobile phones: short message service (SMS) and Unstructured
Supplementary Service Data (USSD)104. SMS messages usually have a limit of 160 characters in
23
Latin-based alphabets and enable people to communicate via text between phones, or also from
a computer to phones via an SMS gateway that provides bulk messaging services. USSD
messaging allows phones to communicate directly with their mobile network operator’s
computers. Users can access by dialing a number that starts with an * and ends with a # (e.g.
*1234#). USSD allows callers to navigate and input information through text-based menu
interface. It is often used for messaging that requires multiple responses, as multiple interactions
can take place during one USSD session, which is less costly than sending multiple SMS messages
to obtain the same information. However, USSD services are more complicated than SMS and
require an agreement with a mobile network operator (MNO) or an aggregator, which are third-
party service providers that negotiate agreements directly with the MNOs.
Other challenges are non-technical as some of them require innovative business models,
adequate environmental factors, and human skills development. Karim et al. identifies major
design challenges in agricultural monitoring systems include cost, reliability, readiness of
information system, crop models, the gap between designers and users, and farmer capacity
building105:
• Cost – Cost of sensors is the most important factor. A single framework of machine-to-
machine (M2M) communication network can be used in multiple applications, which
requires densely deployed sensor nodes
• Reliability – In most developing countries, sensor nodes are scarce and climates can be
extreme. Hence, sensor nodes must be protected from outdoor conditions including
moisture and heat. Availability and reliability of telecommunications/wireless
infrastructure in rural areas of developing countries is another challenge.
• Developing a capable and intelligent information system for developing regions is very
important. Otherwise, collecting only raw sensor data would be useless.
• Crop models – simulate the growth of crops in different environmental constraints and
play an important role in designing decision support systems. Two existing crop models
used in agricultural projects, DSSAT and APSIM, consider only the crop growth and yields
and do not consider the impact of pests and diseases and the timing of farming operations.
Moreover, these models do not consider the recent development of environmental
monitoring such as monitoring soil moisture. In such cases, data gathered from sensor-
based MTC devices help to build, design and test more effective crop models. In addition,
as it is very important to be roughly correct rather than precisely wrong, more
environmental factors should be considered in the decision support systems/crop models
that the farmers of developing regions require.
• The failures of information systems projects in developing countries are mainly due to the
gap between the design and actual requirements, that is, the distances between the
designers and users. Any participatory design does not guarantee any success until it
alleviates the gap between the designers and users.
• Another major challenge in technology based agriculture systems in developing countries
is the capability of users (farmers) to understand, use and own the system.
24
Practical Challenges from Local Start-ups in Developing Countries
Due to the nature of context-specific design, it is equally important to identify practical
challenges that emerge in the process of implementing sensor-enabled IoT system in agriculture
in developing countries. The informal interviews were conducted as part of synthesizing relevant
information to the project.
TechAguru
TechAguru is an agriculture technology start-up company based in Philippines and Japan. It
has conducted research on how to help farmers adopt AWD irrigation technology since January
2015 and prototyped water level sensors to be used with the AWD method106. The final prototype
of the sensor was tested with an infrared distance sensor, a SD card module to store data, and a
GSM module to send the distance information to a cloud service. These prototyped sensors are
not made commercially viable.
The challenges that TechAguru faced in developing prototype sensors to be placed in the tube
were multifold (also available in TechAguru’s blog 107 ). Firstly, TechAguru faced technical
challenges – sensor technicality and communication infrastructure – in developing sensor
prototypes for AWD tubes. It began with using ultrasonic sensor but had to develop a new
prototype with infra-red sensor as ultrasonic sensors generated inaccurate data and needed to
be waterproofed and protected from dirt and soils. While there are other sensor types better
suitable for the AWD context, TechAguru narrowed down the menu options to aforementioned
two types considering the cost implications for farmers. It was also noted that the cost of
developing a prototype and manufacturing at a bulk amount will differ. Another technical
challenge that TechAguru experienced was the insufficient level of infrastructure in the region.
The prototypes were GSM-based so they had to be connected to cell-tower and the Internet.
However, not all fields were covered with GSM and this led to a connectivity problem. Moreover,
TechAguru mentioned that WiFi connection is not desirable as it is designed to send bulk amount
of data while the sensor used for the AWD tube does not transmit as much data.
Another set of challenges was associated with the specific farming context. TechAguru
mentioned that the field must be leveled for the water level sensors to be useful. Unless the
sensors are deployed in properly leveled fields, they will generate inaccurate data and the system
would be inefficient. Also, having multiple plots requires more water level sensors, therefore rice
field plots need to be consolidated and leveled to make the use of water level sensors more
efficiently. Consolidating multiple plots may require association or cooperatives of farmers to
collaborate with the use of tractors with leveling attachments108. Moreover, farmers experienced
stealing water from neighboring irrigated rice fields as some farmers closed the irrigation gates
of other farms so they can get more water in their farms. Considering this issue, TechAguru
suggested the introduction of water level sensors and irrigation control may not be feasible in
some irrigated rice paddies.
25
Last set of challenges was related to the farmers’ conservative mindset. TechAguru said many
farmers were hesitant, if not reluctant, to adopting the AWD simply because they were taking
change as a risk.
MimosaTEK
MimosaTEK, a Vietnamese IoT for agriculture start-up, shared its experiences of helping
farmers to have more accurate sense of their crop water demand through the application of IoT
solutions. It has two main business approaches: B2B and B2C. B2B customers include
corporations whose farm sizes range from 300ha to 1,000ha. Its B2C (business-to-customer)
focuses on bringing smallholder farmers to use MimosaTEK’s solutions through 20 pilot projects
across Vietnam. While its experiences are not specifically focused on rice paddy fields nor sensors
for AWD tubes, MimosaTEK introduced and shared its experiences with the current business
chain and model.
MimosaTEK has more than one B2C business approach. It first let farmers try products for
free so that the farmers could test out the effectiveness of the system. Also, MimosaTEK provides
a one-year leasing contract to long-pepper farmers through a cooperative. Under this contract,
each farmer pays $100 deposit for one controller and one soil moisture node and the deposit will
be paid back to the farmers if the products are in good conditions upon return. Besides the
deposit, each farmer has a monthly payment of around $10, 20% subsidized level from the
original fee. It mentioned that this cooperative has incentives to use the IoT solutions because
they can grow pepper in a sustainable manner, which will increase their crop value in export
market, in addition to more visible advantages of precise irrigation and traceability enabled by
sensor devices. In terms of technology and communication infrastructure, MimosaTEK did not
have as many challenges as TechAguru given that Vietnam has a relatively high broadband
penetration rate and that many farmers use smartphones.
Lessons for Future Work and Scale-up
Sensors undoubtedly play a central role of collecting and transmitting data, thereby delivering
valuable information to farmers. However, there is still little information on the cost comparison
of sensors and characteristics of network connectivity solutions tailored to development,
particularly in agricultural applications.
Also, the research shows that the successful deployment and utilization of IoT solutions
requires a comprehensive understanding of inter-linked components that comprise of an overall
IoT system. Smart farming ecosystem enabled by IoT incorporates multifaceted elements to
consider such as enabling technologies, business models, barriers for adoption, knowledge-
sharing109, as shown in Figure 6. In this sense, the application of IoT developments, particularly
to promote precision agriculture as part of smart farming, requires a whole farm management
perspective110.
26
Table 10. Smart Farm and Food Safety Stakeholders
Yet, to fully capture the agricultural opportunities introduced by IoT in development
context, it is equally important to understand existing barriers and think to effectively overcome
them. One of the technical barriers to scaling up IoT in agriculture is based on the decentralized
and fragmented state of IoT technological development, leading to the lack of interoperability
and standardization. This lack of standardized approach is applicable to both hardware and
software sides as many of the IoT platforms are decoupled from one another as they are
developed based off from open source. In addition, proprietary architectures, platforms and
standards could lead to the risks of side-effects such as vendor lock-in and incompatibility with
other systems 111 . Another set of technical challenges associated with IoT deployment in
developing countries is the lack of enabling environment such as the cost affordability of sensors
and network communications infrastructure. There are other technical issues such as the need
for more advanced and flexible data analytics services and data security that are currently
discussed in developed economies112. While these issues convey valid concerns for the future
direction of IoT application in agriculture, their presence is not yet as visible in developing
countries context. The non-technical barriers are also present when applying IoT in agriculture in
development context. These challenges include the sustainability of business models, digital
literacy for farmers to fully reap the benefits of IoT, and government regulations.
27
Annex 1. Comparison of Different Telemetric Options
Telemetry Operating
Specification Suitable for Cons Range Module Cost
Method frequency
Global GSM (global Throughput and delay are
system for mobile variable, and they
2G 35km $1-$15
communications) Wherever there is depend on the number of
bands cellular connection; other users
Periodic monitoring
Cellular
3G Licensed spectrum applications than real- - Up to 100km $35-$50
time; Relieves the
range limitation
4G/LTE Licensed spectrum - Up to 100km $80-$120
Wi-Fi Wherever wireless
(802.11n, 802.11a/g, 2.4 GHz / 5GHz Internet connection is 300m $10+
802.11b, 802.11ac) available
Wireless -
Low power
Sub-1GHz ISM
Wi-Fi consumption and
bands Up to 1000m N/A
(802.11ah113) long-range data
(differs in countries)
transmission
2.4GHz mesh local area
network (LAN) protocol;
Originally designed for
Standards-based wireless
building automation
technology intended to Low data rates (requires
and control – suitable
Zigbee meet the needs of device- 2.4 GHz / 900MHz low specification 10-100m $1-$15
for wireless
to-device hardware)
thermostats and
communications; Uses
lighting systems
small, low-power radios
for data transmission
28
Endnotes
1 "FAO's Director-General on how to Feed the World in 2050." Population and Development Review 35.4 (2009): 837-
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2 International Food Policy Research Institute. "SMALLHOLDER FARMING " http://www.ifpri.org/topic/smallholder-
farming. Web.
3 P.28. Srinivasan, Ancha. Handbook of Precision agriculturePrinciples and Applications . Food Products Press, 2006. Print.
4 P.3. Ibid.
5 P3. Ibid.
6 https://www.automationworld.com/article/topics/cloud-computing/know-difference-between-iot-and-m2m
7 Lesser, Adam. "Declining sensor costs open up new consumer applications." Gigaom. Jan 25 2015. Web.
.
8 International Telecommunication Union (ITU). "ICT Facts & Figures." International Telecommunication Union (ITU). May 2015.
Web. .
9 Ibid.
10 A method of measuring soil moisture by time-domain reflectometry, Ledieu et al., Journal of Hydrology, 1986
11 "Distance Sensors: Ultrasonic ranging sensors." Campbell Scientific. Web. .
12 "Optoelectronic Sensors in Medical Applications." Sept 1, 2003. Web. .
13 http://www.omega.com/temperature/Z/pdf/Z076-080.pdf
14 "Type of Sensor." Web. .
15 P19. ALLIANCE FOR INTERNET OF THINGS INNOVATION. AIOTI WG06 – Smart Farming and Food Safety. Smart Farming and
Food Safety Internet of Things Applications: Challenges for Large Scale Implementations., 2015. Print.
16 P.2. Bogue, Robert, and Robert Bogue. "Sensors Key to Advances in Precision Agriculture." Sensor Review 37.1 (2017): 1-6.
Print.
17 Wireless sensor networks for agriculture: The state-of-the-art in practice and future challenges. Tamoghna Ojha, Sudip Misra
a, Narendra Singh Raghuwanshi
18 A Review of Wireless Sensors and Networks’ Applications in Agriculture , Aqueel-ur-Rehman et al.
19 "Soil and Crop Sensing." University of Nebraska-Lincoln. Web. .
20 Yu and Han, Remote Detection and Monitoring of a Water Level Using Narrow Band Channel, Soongsil University, 2010.
https://pdfs.semanticscholar.org/b29c/c338a845e82da929cdbba5f83896ff13d950.pdf
21 Nakka, Sensors for Agriculture and Water Use Efficiency, http://www.slideshare.net/saibhaskar/sensors-for-agriculture-and-
water-use-efficiency, 2016
22 "Smart on-line water salinity measurement network to manage and protect rice fields." Web.
.
23 "SENSORS FOR WATER MONITORING IN PADDY FIELDS FOR
IMPROVED ON FARM WATER MANAGEMENT." ClimaAdapt. Web.
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24 "SENSORS FOR WATER MONITORING IN PADDY FIELDS FOR IMPROVED ON FARM WATER MANAGEMENT " Web.
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25 Xiao, Deqin, et al. "Integrated Soil Moisture and Water Depth Sensor for Paddy Fields." Computers and Electronics in
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26 http://archive.apan.net/meetings/apan39/Sessions/7/Hiroshi_Shimamura_APAN_Meeting_15.03.04.pdf
27 Poulose Jacob, K., and Simon Santhosh. "Development and Deployment of Wireless Sensor Network in Paddy Fields of
Kuttanad." (2012). Print.
28 Monitoring the Paddy Crop Field Using ZigBee Network. K Sriharsha, et al. International Journal of Computer and Electronics
Research [Volume 1, Issue 4, December 2012]
29 Sekozawa, Teruji. "A Fully Automated Water Management System for Large Rice Paddies". Proceedings of the 14th WSEAS
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and Society (WSEAS), Stevens Point, WI, USA, I. 2010. 325-330. Print.
30 Kumar, Suman, et al. "Application of Sensor Networks for Monitoring of Rice Plants: A Case Study." Science (2009): 1-7. Print.
31 Islam, Mohammad Monirul, et al. "A Floating Sensing System to Evaluate Soil and Crop Variability within Flooded Paddy Rice
Fields." Precision agriculture 12.6 (2011): 850. Print
32 "Saving Water with Alternate Wetting Drying (AWD)." Rice Knowledge Bank. Web.
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33 "Overview of AWD." IRRI. March 2016. Web. .
34 Internet Society. The Internet of Things (IoT): An Overview ., 2015
29
35 Hari Balakrishnan. NETWORK CONNECTIVITY FOR IoT., 2017.
36 https://www.pcmag.com/encyclopedia/term/40833/data-rate
37 https://www.computerhope.com/issues/chspace.htm
38 Hari Balakrishnan. NETWORK CONNECTIVITY FOR IoT ., 2017
39 P 5. Texas Instrument. Wireless Connectivity for the Internet of Things: One Size does Not Fit Al l., 2017.
40 Ibid.
41 Ibid.
42 Ibid.
43 Ibid.
44 ZigBee. http://www.zigbee.org/zigbee-for-developers/
45 LinkLabs. https://www.link-labs.com/blog/types-of-wireless-technology
46 P.9. Texas Instrument. Wireless Connectivity for the Internet of Things: One Size does Not Fit Al l., 2017.
47 ZigBee. https://web.archive.org/web/20130627172453/http://www.zigbee.org/Specifications/ZigBee/FAQ.aspx
48 In data transmission, network throughput is the amount of data moved successfully from one place to another in a given time
period, and typically measured in bits per second (bps). See: http://searchnetworking.techtarget.com/definition/throughput
49 Zigbee. http://www.zigbee.org/non-menu-pages/faq/
50 P.10. Texas Instrument. Wireless Connectivity for the Internet of Things: One Size does Not Fit Al l., 2017.
51 Ibid.
52 Ibid.
53 LinkLab, https://www.link-labs.com/blog/types-of-wireless-technology
54 P.10. Texas Instrument. Wireless Connectivity for the Internet of Things: One Size does Not Fit Al l., 2017.
55 Ibid.
56 P 8. Ibid.
57 Ibid.
58 Ibid.
59 Ibid.
60 Ibid.
61 Ibid.
62 Ibid.
63 Ibid.
64 Ibid.
65 P 9. Ibid.
66 P 7. Ibid.
67 P.5. Ibid.
68 P. 7. Ibid.
69 Ibid.
70 P 2. Ibid.
71 GSMA. https://www.gsmaintelligence.com/research/2014/12/mobile-broadband-reach-expanding-globally/453/
72 LinkLabs. https://www.link-labs.com/blog/cellular-iot
73 P 4. Ericsson. Cellular Networks for Massive IoT: Enabling Low Power Wide Area Applications. , 2016.
74 https://www.iotforall.com/cellular-iot-explained-nb-iot-vs-lte-m/
75 P 3. Ericsson. Cellular Networks for Massive IoT: Enabling Low Power Wide Area Applications. , 2016.
76 Ibid.
77 P 13. Ibid.
78 Link-Labs. A Comprehensive Look at Low Power Wide Area Networks.
79 Link-Labs. https://www.link-labs.com/blog/nb-iot-vs-lora-vs-sigfox
80 P. 14. Ericsson. Cellular Networks for Massive IoT: Enabling Low Power Wide Area Applications. , 2016.
81 https://www.leverege.com/blogpost/iot-connectivity-comparison-lora-sigfox-rpma-lpwan-technologies
82 https://www.leverege.com/blogpost/iot-connectivity-comparison-lora-sigfox-rpma-lpwan-technologies
83 https://www.link-labs.com/blog/nb-iot-vs-lora-vs-sigfox.
84 https://www.leverege.com/blogpost/iot-connectivity-comparison-lora-sigfox-rpma-lpwan-technologies
85 http://www.thewhir.com/web-hosting-news/the-four-internet-of-things-connectivity-models-explained
86 P 18. Internet Society. The Internet of Things (IoT): An Overview ., 2015
87 Ibid.
88 P.19. Ibid.
89 Ibid.
90 Ibid.
91 P.20. Ibid.
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92 Ibid.
93 Ibid.
94 Ibid.
95 P.21. Ibid.
96 P.22. Ibid.
97 Hari Balakrishnan. NETWORK CONNECTIVITY FOR IoT ., 2017
98 P.13. GSMA. The Mobile Economy., 2017
99 https://efarmer.mobi/blog/agriculture-technology/
100 USAID and Feed the Future. Digitizing the Agricultural Value Chain.
101 https://www.gsmaintelligence.com/research/?file=53525bcdac7cd801eccef740e001fd92&download
102 P.77. Karim, Lutful, et al. "Sensor-Based M2M Agriculture Monitoring Systems for Developing Countries: State and
Challenges." Network Protocols and Algorithms 5.3 (2013): 68-86. Print.
103 https://www.link-labs.com/thank-you-webinar-costs-in-iot-lte-m-nb-iot-sigfox-lora?submissionGuid=0cd436a2-f931-43b8-
9187-5134bac88d36
104 USAID et al. Integrating Mobiles into Development Projects., 2014. Print
105 P.79. Karim, Lutful, et al. "Sensor-Based M2M Agriculture Monitoring Systems for Developing Countries: State and
Challenges." Network Protocols and Algorithms 5.3 (2013): 68-86. Print.
106 Auxillos, John. "Water Level Sensors for Rice Field Irrigation." TechAguru. Jul 14 2016. Web.
.
107 Ibid.
108 Ibid.
109 ALLIANCE FOR INTERNET OF THINGS INNOVATION. AIOTI WG06 – Smart Farming and Food Safety. Smart Farming and Food
Safety Internet of Things Applications: Challenges for Large Scale Implementations., 2015. Print.
110 P.139. Sundmaeker, Harald, et al. "Internet of Food and Farm 2020." Digitising the Industry-Internet of Things Connecting
Physical, Digital and Virtual Worlds.River Publishers, Gistrup/Delft (2016): 129-51. Print.
111 P.142. Ibid.
112 P.143. Ibid.
113 802.11ah, also known as “the low power WiFi”, is 900 MHz WiFi developed as an IEEE protocol for the wireless Internet
network. However, there is no global standard for 900 MHz as 80% of the world uses 2.4 GHz Wifi. It is expected WiFi AH
products would appear in the next 12 months to 1.5 years. Link-lab mentions that this technology is similar to LPWAN (LoRA).
31