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Reducing Power Consumption of Weather Stations for Landslide Monitoring

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Among different natural disasters, landslides are widespread in hilly areas. For landslide monitoring, one needs to collect data via weather stations about the prevailing weather and soil properties at remote locations that are prone to landslides. Due to the non-availability of grid-power, one may need to depend upon large-sized solar panels and batteries if the weather station's power requirements are high. The primary objective of this paper is to apply software and hardware methods to reduce the power requirements of a weather station for landslide monitoring. In an experiment, three different microcontroller implementations were used as part of a weather station for evaluating weather and soil properties: ATmega2560 (Mega), ATmega328p (Uno), and ATmega328p (low-power). The Mega and Uno microcontrollers exist as part of the popular Arduino open-source electronic prototyping platform. In the low-power microcontroller, we simplified the power circuit and closed pins on the microcontroller that were not being used at different times. All three microcontrollers were connected to an identical set of sensors in identical weather stations and run at the same voltage setting of 5V at the same time. Results revealed that Mega, while awake, consumed 140mA current. In sleep mode, it consumed 40mA current. Similarly, the Uno, while awake, consumed 50mA current. In sleep mode, it consumed 40mA current. However, the low-power microcontroller consumed only 10mA when awake and less than 0.25mA when sleeping. We highlight the implications of this research for developing low-cost and low-power landslide monitoring solutions in the world.
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Reducing Power Consumption of Weather Stations for
Landslide Monitoring
Ankush Pathania1, Praveen Kumar1, Jyoti Kesri1, Priyanka1, Shubham Agarwal1,
Naresh Mali3, Ravinder Singh4, Pratik Chaturvedi2, K V Uday3, Varun Dutt1
1 Applied Cognitive Science Lab, Indian Institute of Technology Mandi, Himachal Pradesh,
India
2 Defence Terrain Research Laboratory, Deference Research and Development Organization,
New Delhi, India
3 Geohazard Studies Laboratory, Indian Institute of Technology Mandi, Himachal Pradesh,
India
4 National Disaster Management Authority, New Delhi, India
Abstract. Among different natural disasters, landslides are widespread in hilly areas. For
landslide monitoring, one needs to collect data via weather stations about the prevailing
weather and soil properties at remote locations that are prone to landslides. Due to the non-
availability of grid-power, one may need to depend upon large-sized solar panels and batteries
if the weather station’s power requirements are high. The primary objective of this paper is to
apply software and hardware methods to reduce the power requirements of a weather station for
landslide monitoring. In an experiment, three different microcontroller implementations were
used as part of a weather station for evaluating weather and soil properties: ATmega2560
(Mega), ATmega328p (Uno), and ATmega328p (low-power). The Mega and Uno
microcontrollers exist as part of the popular Arduino open-source electronic prototyping
platform. In the low-power microcontroller, we simplified the power circuit and closed pins on
the microcontroller that were not being used at different times. All three microcontrollers were
connected to an identical set of sensors in identical weather stations and run at the same voltage
setting of 5V at the same time. Results revealed that Mega, while awake, consumed 140mA
current. In sleep mode, it consumed 40mA current. Similarly, the Uno, while awake, consumed
50mA current. In sleep mode, it consumed 40mA current. However, the low-power
microcontroller consumed only 10mA when awake and less than 0.25mA when sleeping. We
highlight the implications of this research for developing low-cost and low-power landslide
monitoring solutions in the world.
Keywords: Weather station, landslides, landslide monitoring, current consumption, AT-
mega328p.
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1 Introduction
Natural calamities like flash floods, cloudburst, landslides, and earthquakes affect
different parts of the world [1]. Among these disasters, landslides are widespread in
hilly areas [2]. The occurrence of landslides is influenced by the prevailing weather
(rain) as well as the susceptibility of the area (soil) to landslides. Thus, it is necessary
to monitor the weather parameters and soil properties for the monitoring of the land-
slides. Monitoring of these parameters can be done with the help of sensor-based
weather stations [3]. For running weather stations in hilly areas there is a basic re-
quirement of power supply. However, remote deployment locations in hilly areas may
not provide grid-connected power [4]. Thus, in the absence of grid power, one needs
to switch to the renewable sources of energy for powering sensor-based systems. One
source of renewable energy is solar photovoltaic (PV), where this form of energy is
noiselessness with simple operation and maintenance [5]. However, to harness the
solar energy one needs to use solar panels and batteries, where the cost of solar panels
and batteries is high. Overall, to keep the costs low, there is a need to reduce the elec-
trical power consumption of sensor-based systems for landslide monitoring. The pri-
mary objective of this paper is to develop a low-power weather monitoring system for
landslide monitoring in remote hilly areas.
Currently, there exists several sensor-based architectures for weather monitoring
[6-9]. For example, some researchers have used a Raspberry Pi computer for building
weather stations [6]. Similarly, reference [7] used an 8266 NodeMCU for building
weather stations [7]. Also, some researchers have used two ubiquitous boards, AT-
mega2560 (Mega) and ATmega328p (Uno), for developing weather stations [9-11].
These boards are popular, easy to use, and there are variety of codes available online
for different kind of weather-related sensors that could be interfaced with these boards
[19]. One major problem using these boards is that they consume a lot of electrical
current [18]. For example, the ATmega328p (Uno) board is equipped with various
resistors and regulators, which causes this board to consume a large amount of elec-
tric current. In contrast, the ATmega328p microcontroller on the ATmega328p (Uno)
board is a pico-powered microcontroller using electric current in the Pico (10-12) am-
pere range. Thus, the microcontroller uses very little current independent of its Uno
board with resistors and regulators.
Building upon this idea, in this paper, we applied software and hardware methods
to reduce the power consumption of an ATmega328p-based weather station with var-
ious sensors for measuring weather and soil parameters. Specifically, in this paper, we
used three different microcontroller implementations as part of the same weather sta-
tion for evaluating soil and weather properties at a landslide site: ATmega2560
(Mega), ATmega328p (Uno), and ATmega328p (low-power). The ATmega328p
(low-power) implementation is the optimized low-power implementation that uses the
ATmega328p microcontroller. The ATmega328p (low-power) is compared to existing
ATmega328p (Mega) and ATmega328p (Uno) implementations.
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2 Background
There are lot of architectures available to implement weather station using ATmega
microcontrollers. For example, reference [9] used ATmega 2560 (Mega) for the
weather station, which was powered by an AC supply [9]. In this station, these authors
used a DHT 22 sensor and other sensors for the weather monitoring. Reference [10]
also used ATmega 2560 (Mega) in their weather station [10]. In this weather station,
DHT 11 and BMP 180 sensors were used to measure the temperature, humidity, and
atmospheric pressure. Reference [11] used ATmega328p (Uno) as the microcontroller
for their weather station [11]. In this weather station, wind vane, wind turbine, and
rain gauge were integrated with the microcontroller board and board was powered by
AC supply. This weather station had the capability of measuring wind direction, wind
speed, rain, temperature, and humidity.
Reference [6] used Raspberry Pi for their weather station [6]. Raspberry Pi is a
minicomputer whose operating system is based on Linux and sensors can be integrat-
ed via GPIO pins. These GPIO pins can be easily controlled via simple programming
in the Raspberry Pi. This minicomputer can directly be interfaced with the Ethernet
LAN and data is sent to cloud server very easily. Reference [7] used an 8266
NodeMCU for their weather station [7]. The 8266 NodeMCU is a Wi-Fi module with
digital pins with which sensors were interfaced. Reference [8] used the NodeMCU
along with couple of sensors for the weather monitoring. In this architecture, they
used an 8266 NodeMCU, DHT11, BMP 180, and rain-gauge module. These all sen-
sors were used to monitor the weather parameters such as temperature, humidity,
barometric pressure, and rain, respectively.
Although there are a wide variety of microcontroller architectures available for
weather stations, most of them are AC powered and consume a lot of power (due to a
large current flow). For example, the ATmega 2560 (Mega) and ATmega328p (Uno)
are power hungry microcontroller boards. Thus, if these microcontroller boards are
used for weather applications, then they will require a high-power source with large
battery backup. Overall, lesser attention has been given to reducing the power con-
sumption of weather stations based upon commercially available prototyping boards.
In this paper, we overcome this problem by developing a low-powered weather sta-
tion for the landslide monitoring. The low-powered weather station uses the AT-
mega328p microcontroller with a customized printed circuit board. Due to its low-
power requirements, the ATmega328p (low-power) board uses a small-sized solar-
panel and batteries. We applied software and hardware techniques to reduce the pow-
er consumption of ATmega328p (low-power) board.
3 System for monitoring weather parameter at landslide
locations
Figure 1 shows the hardware scheme used for all three microcontrollers configura-
tions, ATmega2560 (Mega), ATmega328p (Uno), and ATmega328p (low-power).
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The system shown in Figure 1 is connected in the same fashion on all micro-
controller boards. All microcontrollers have some digital pins, some analog pins, and
some interrupt pins for receiving external interrupts. All sensors connected to differ-
ent microcontroller boards require a voltage, a ground, and an additional digital or
analog pin for data reading. In the system, a soil moisture sensor, measuring volumet-
ric moisture in soil, uses an analog pin. The system uses a common I2C protocol [26,
17] for the light intensity sensor (BH 1750) and the barometric pressure sensor (BMP
180) as well as a voltage and ground for each of these sensors. Rain gauge is connect-
ed to the microcontrollers via an external interrupt. We carefully designed the circuit
in such a way that the sensors are only active when they are read and are inactive
(sleeping) otherwise. Every sensor is read in a serial manner every 15-minutes. To
make sensors active for reading and inactive for a 15-minutes duration, we give the
input supply to different sensors via digital pins. These digital pins can easily be
turned on and off in different microcontroller boards.
Fig. 1. Hardware scheme used in all three microcontrollers configurations, AT-
mega2560 (Mega), ATmega328p (Uno), and ATmega328p (low-power)
ATmega 2560 (Mega) and ATmega 328p (Uno) boards provides female connec-
tions to digital pins where one can easily connect different sensors to these boards. In
the ATmega 328P (low power) implementation, the microcontroller was connected to
sensors via a custom printed circuit board. Although the ATmega 2560 (Mega) and
ATmega 328p (Uno) boards consume current in milliamps, the ATmega 328P (low
power) board consumes current in micro-amps. We reduce the current consumption in
the ATmega 328P (low power) board by burning a new bootloader [20] in its micro-
controller, where the new bootloader turns-off certain fuses in the microcontroller.
Turning off these fuses in the microcontroller switches-off the overvoltage protection
(called brown-out-detection) in the microcontroller where this protection is always
active, and it consumes a lot of power. The ATmega 328P (low power) board does not
need overvoltage protection as we do not supply it with more than the recommended
voltage of 5V. In addition, we removed the need of regulator circuits on the ATmega
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328P (low power) board. These regulator circuits perform different DC voltage con-
versions between different components and the microcontroller and dissipate electri-
cal energy as heat. These regulator circuits are present on the ATmega 2560 (Mega)
and ATmega 328p (Uno) boards and they increase the power consumption of these
boards. The description of different sensors and software used across all microcon-
troller implementations are detailed below.
4 Sensors (Hardware):
DHT 22: This sensor is used for measuring temperature (in degree centigrade) and
relative humidity (in percentage) in the air. DHT 22 utilizes exclusive digital-signal-
collecting-technique and humidity sensing technology, assuring its reliability and
stability [20]. Its operating range is 0-100% for humidity and -40 to 125o Celsius for
temperature. It has the accuracy of +/-2% for humidity and +/-0.5o Celsius for tem-
perature.
BMP 180: This sensor is used for measuring barometric pressure (in millibars (mb).
Barometric pressure (also known as atmospheric pressure), is the pressure caused by
the weight of air pressing down on the Earth [16]. The pressure sensing range for
BMP180 is 300-1100 hPa (9000m to -500m above sea level). Its operational tempra-
ture range is -40 to +85°C with the accuracy of +-2o Celsius.
BH 1750: The BH 1750 is a light intensity sensor with a 16-bit analog-to-digital con-
verter built-in, which can directly output a digital signal [25]. In this sensor, the light
intensity is measured in Lux (Lx). The operation range of this sensor is between 0
Lux (night) to 65535 Lux (day).
SKU: SEN0193: The SKU: SEN0193 is a capacitive soil-moisture sensor and it
works on the principle of change in capacitance due to changes in the dielectric of the
soil in contact with the sensor [26]. The sensor measures the resonant frequency of an
RC circuit and this frequency value is linearly calibrated to volumetric water content
of soil in percentage. The sensor’s range is between 0-100% moisture by volume, its
accuracy is +/-5%, and its sensitivity is +/-1% [26].
Rain Gauge: The rain-gauge is a tipping-bucket gauge, which collects the amount of
rain in its collector. A seesaw below the collector can collect up to 2.25 ml of water
before tipping from one side to the other side [27]. Each time the seesaw tips, a mag-
netic switch in the sensor can count the tipping and the tipping count allows one to
measure the rain in mm.
ESP 8266: The ESP8266 is Wi-Fi enabled module developed by Espressif system
[21, 22]. It employs a 32-bit RISC CPU based on the Tensilica Xtensa L106 [22]
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running at 80 MHz (or overclocked to 160 MHz). It has a 64 KB boot ROM, 64 KB
instruction RAM, and 96 KB data RAM. The ESP 8266 module is low cost
standalone wireless transceiver that can be used for end-point IoT developments. The
microcontrollers communicate with the ESP 8266 module using UART having a
specified baud rate [21].
5 Microcontrollers boards (Hardware)
5.1 ATmega 2560 (Mega)
The ATmega 2560 (Mega) is a microcontroller board based on the ATmega 2560
microcontroller (see Figure 2). It has 54 digital input/output pins (of which 15 can
be used as PWM outputs), 16 analog inputs, 4 UARTs (hardware serial ports), a 16
MHz crystal oscillator, a USB connection, a power jack, an ICSP header, and a re-
set button. The board contains everything needed to support the microcontroller,
and it is directly connected it to a computer with a USB cable. One can power the
board using an AC-to-DC adapter or battery [14, 15].
Fig. 2. ATmega 2562 (Mega) board
5.2 ATmega 328p (Uno)
The ATmega 328p (Uno) is a popular microcontroller board based on the AT-
mega328 microcontroller [see Figure 3; 13]. It has 14 digital input/output pins (of
which 6 can be used as PWM outputs), 6 analog inputs, a 16 MHz crystal oscilla-
tor, a USB connection, a power jack, an ICSP header, and a reset button [13]. Over-
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all, the ATmega 328p (Uno) is miniature version of the ATmega 2650 (Mega)
board.
Fig. 3. ATmega 328p (Uno) board
5.3 ATmega 328P (low power)
The ATmega 328P (low power) board is a custom-built board created by the authors
(see Figure 4A, 4B). This board uses the ATmega 328p microcontroller, which has a
modified Harvard architecture 8-bit RISC processor core [13]. The ATmega 328p
microcontroller has 32 KB ISP flash memory with read-while-write capabilities, 1 KB
EEPROM, 2 KB SRAM, 23 general purpose I/O lines, 32 general purpose working
registers, internal and external interrupts, and five software selectable power saving
modes [13]. The ATmega 328P (low power) board operates between 1.8-5.5 volts.
The board achieves a throughput of 1 MIPS per MHz. As shown in Figure 4B, the PCB
of the ATmega 328P (low power) board has terminals for power supply, for mounting
different sensors, and the ESP 8266 module.
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A
B
Fig. 4. ATmega 328p (Low-power) PCB
5.4 Power supply (Hardware):
We used a Li-ion battery (4000mAh, 4.2V) to power different microcontroller
boards. In addition, we used a small solar panel (2Watt, 6V, 330mA) and a small
charge controller (TP 4056; [29]) to charge the battery.
5.5 Platform and programming (Software):
We used the open-source Arduino IDE for the programming of different microcon-
troller boards. The C language was used for programming in the Arduino IDE. The
structure of the program burnt on the ATmega 328p (low-power) board, ATmega
328p (Uno) board, and the ATmega 2560 (Mega) board is presented in a flowchart
in Figure 5.
6 Program's Logic:
As shown in Figure 5, When the program starts, all variables specified in the code are
initialized. After that microcontroller will check the different sensors attached. If all
sensors are available, then the program will proceed to the next step. If any of the
sensors are not available, then the code will report an error and cycle in a loop. Once
all the sensors are available, then the program will initialize the clock, disable all the
digital pins, and put the microcontroller to sleep. When 15-minutes are over, the mi-
crocontroller will get a software interrupt from the internal clock and wake up.
After waking-up, the microcontroller will activate different pins connected to the
sensors and read the values from different sensors serially and store these values in
variables. Once all sensors are read, the stored values are converted into a URL and
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sent to a cloud server via the ESP 8266 Wi-Fi module. If data sending is successful,
then the program will again return to the initial state. However, if data sending is not
successful, then it will try to send data three times and then return to its initial state
and sleep for the next 15-minutes.
6.1 Sleep Modes
All three microcontroller boards were put to sleep during a 15-minutes interval. For
putting to sleep, different microcontroller boards used different sleep libraries. For the
ATmega 328p (Uno), ATmega 328p (low-power), and ATmega 2560 (Mega) boards,
we used a low-power library from Rocketscream [30]. This library allows the micro-
controllers in the different boards to enter a low-power (sleep) state, where the micro-
controller’s brown out detection and analog-to-digital conversion features are turned-
off when the microcontroller is put to sleep. The library makes the microcontroller
sleep for 8s at a time, where the library may be called repeatedly to simulate sleep for
longer durations.
6.2 Interrupts
Across all microcontroller boards, the Interrupt Service Routine (ISR) is called when
the microcontroller receives an interrupt. Overall, when interrupt triggers, the code in
execution is stopped, and the program jumps to the interrupt. While the interrupt is
being executed, any other request is ignored. When the interrupt ends, the execution
returns to the main code, continuing at the same point where it was stopped. The in-
terrupt should be as short and fast as possible, because the main program is blocked
during its execution. The rain gauge was connected to the interrupt pins of different
microcontroller boards and the tipping of the bucket in the rain gauge would cause an
interrupt to wake-up the microcontroller board from its sleep state.
7 Experimental Setup
All three microcontroller boards were connected to an identical set of sensors in iden-
tical weather stations and run at the same voltage setting of 5V at the same time. Also,
we ran an identical software code on all three microcontroller implementations. All
implementations were run for a period of 60-days in the experiment in Kamand, Hi-
machal Pradesh, India. In Kamand, the average solar radiation is for 3-hours in the
day. We measured the current consumed by the different microcontroller boards as
well as the current consumed by different sensors and modules connected to the mi-
crocontroller boards. We also evaluated the battery life in mAh after each day of op-
eration. All microcontroller boards were made to sleep for a 15-minutes period fol-
lowed by data sending in a 1-minute period. Thus, every hour, the microcontroller
boards sent 4 sensor readings to the cloud and they slept for the remaining 56
minutes.
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Fig. 5. Flow chart of the program
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8 Results
Figure 6 shows the average current consumed by different microcontroller boards (in
mA) in the non-sleep (active) and sleep states over a 15-day period. As seen in the
Figure, the ATmega 2560 (Mega) consumed on average around 140mA in non-sleep
mode and 50mA in sleep mode. The ATmega 328p (Uno) consumed on average
around 50mA in non-sleep mode and 40mA in the sleep mode. However, the AT-
mega328p (low-power) board consumed on average around 10 mA in non-sleep mode
and 0.025mA in the sleep mode.
Fig. 6. Average current consumption of different microcontroller boards. The error
bars show the 95% confidence interval around the point estimate.
Table 1 shows the comparison of the average current consumption of different
components used across different microcontroller boards. It can be seen clearly that
most of the current was consumed by the ATmega 2560 (Mega) board. The next
highest current consumed was by the ATmega 328p (Uno) board. The ESP 8266 Wi-
Fi module consumed the third highest current across all microcontroller boards. The
ATmega 328p (low-power) board and other sensors consumed almost negligible
current.
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Table 1. Average current consumption of different components in the microcontroller
boards.
Next, we measured the actual battery life (in mAh) of the 4000 mAh battery con-
nected to different boards over a 60-day period. Each board was connected to a 330
mAh and 6V solar panel and kept in the sun. Figure 7 shows the battery life (in mAh)
of the 4000 mAh battery connected to different boards over a 60-day period. The
ATmega 328p (low-power) board consumed little current and did not discharge the
battery over the 60-day period (the battery maintained a 4000 mAh capacity). Howev-
er, the ATmega 2560 (Mega) board and the ATmega 328p (Uno) board rapidly dis-
charged the battery where the circuit was dead in 6 days and 22 days, respectively.
Fig.7. The battery life (mAh) over a period of 60-days in the different microcon-
troller boards.
Electrical
Components
Average
Current in non-
Sleep mode (mA)
ATmega 2560 (Mega) Board
140
ATmega 328p (Uno) Board
50
ATmega 328p (low-power)
10
DHT 22
1
BMP 180
1
BH 1750
1
SKU: SEN0193
5
ESP 8266
50
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9 Discussion and Conclusions
Landslide monitoring may require running weather stations in hilly areas where grid-
connected power may not be available [17]. In the absence of grid power, one needs to
switch to the renewable sources of energy for powering sensor-based systems. One
source of renewable energy is solar photovoltaic (PV), where this form of energy is
noiselessness with simple operation and maintenance [5]. However, the cost of solar
panels and batteries is high. Thus, to keep the system costs low, there is a need to
reduce the electrical power consumption of weather stations for landslide monitoring.
The primary objective of this paper was to develop a low-power weather station for
landslide monitoring, which could run for several days using a small-sized battery and
a solar panel. We developed a low-power weather station using a customized board
consisting of the ATmega 328p microcontroller (ATmega 328p (low-power) board).
Next, we compared this board with popular microcontroller prototyping boards avail-
able in the market, namely the ATmega 2560 (Mega) and ATmega 328p (Uno)
boards. Results revealed that the ATmega 328p (low-power) board consumed very
little power (due to low current consumption) compared to the commercially available
ATmega 2560 (Mega) and ATmega 328p (Uno) boards. In fact, the ATmega 328p
(low-power) board could run a weather station in Kamand, Himachal Pradesh, India
for a 60-day period without discharging the battery, where the ATmega 2560 (Mega)
and ATmega 328p (Uno) boards lasted for 6 days and 22 days, respectively.
Some likely reasons as to why the ATmega 328p (low-power) board consumes less
power compared to the ATmega 2560 (Mega) and ATmega 328p (Uno) boards is due
to the board’s hardware and software design. In the ATmega 328p (low-power) board,
we removed several of the regulator circuits, which are needed for converting voltag-
es between microcontroller and other circuit components. In addition, we also pro-
grammed a new bootloader in the ATmega 328p (low-power) board that switched off
the brown out detection and analog-to-digital conversion features in the microcontrol-
ler. We could disable these features as we were ensured that voltages supplied to the
ATmega 328p (low-power) board did not exceed 5V.
From our results, we also found that the major components consuming power on the
weather stations are not the sensors but the microcontrollers and the modules for
sending data to the Internet. If fact, the ESP 8266 module was the third largest
consumer of current in the active state in our comparison where other sensor
components consumed very little to negligible current. In addition, the ATmega 2560
(Mega) microcontroller consumed the maximum amount of power as this microcon-
troller supports many digital and analog pins, which are far more compared to the
ATmega 328p microcontroller.
Overall, we can conclude that for the implementation of weather station at remote
hill sites for landslide monitoring, it will be best to use the ATmega 328p
microcontroller with a customized regulator-less board and a new bootloader
program. These features enable this microcontroller to provide uninterrupted
operations over several days.
We have a number of ideas to undertake as part of our future research. Recently, the
ESP 32 microcontroller has been introduced in the market [30]. This microcontroller
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offers several new features, including a number of power-saving modes. Thus, as part
of our future research, we plan to extend our current investigation to include the ESP
32 microcontroller board. In addition, we would be interested in extending this
evaluation to sensors used for other applications (e.g., air-pollution monitoring).
Some of these ideas form the immediate next steps in our ongoing program on the
development of low-powered systems for environmental monitoring.
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References
1. Planning commission of India report, planningcommis-
sion.gov.in/plans/stateplan/sdr_hp/sdr_hpch3.pdf, last accessed 2019/04/07.
2. Chaturvedi P, Srivastava S, Kaur B (2016) Landslide Early Warning System
Development using Statistical Analysis of Sensors' Data at Tangni Land-
slide, Uttarakhand, India. 6th International Conference on “Soft Computing
for Problem Solving” (SocProS 2016), Patiala, India, December 2016
3. Chaturvedi, P., Thakur, K. K., Mali, N., Kala, V. U., Kumar, S., Yadav, S.,
& Dutt, V. (2018). A Low-Cost IoT Framework for Landslide Prediction and
Risk Communication. In Internet of Things A to Z: Technologies and Appli-
cations, Edition: First. Wiley-IEEE Press.
4. A. Rajoriya & E. Fernandez (2010) Sustainable energy generation using hy-
brid energy system for remote hilly rural area in India, International Journal
of Sustainable Engineering, 3:3, 219-227, DOI:
10.1080/19397038.2010.484870
5. S.M. Moosavian, N.A Rahim, J Selvaraj,Energy policy to promote photovol-
taic generation, Renewable and Sustainable Energy Reviews, 25 (2013), pp.
44-48
6. Skyfilabs page, weather station, https://www.skyfilabs.com/project-ideas/iot-
based-weather-station, last accessed 2019/04/07.
7. Circuitdigest’s webpage IoT based weather station,
https://circuitdigest.com/microcontroller-projects/esp12-nodemcu-based-iot-
weather-station, last accessed 2019/04/07.
8. R. K. Kodali and S. Mandal, "IoT based weather station," 2016 International
Conference on Control, Instrumentation, Communication and Computation-
al Technologies (ICCICCT), Kumaracoil, 2016, pp. 680-683.
doi: 10.1109/ICCICCT.2016.7988038
9. Arduino’s website project hub page,
https://create.arduino.cc/projecthub/hameau/arduino-weather-station-tft-7-
927131, last accessed 2019/04/07.
10. Electronic-lab’s project webpage, http://www.electronics-
lab.com/project/arduino-weather-station-dht11-bmp180/, last accessed
2019/04/07.
11. Engineersgarage contributions webpage,
https://www.engineersgarage.com/contribution/arduino-based-weather-
station, last accessed 2019/04/07.
12. National Journal of Electronics Communication and Computer Engineering,
7(6), 301.
13. UNO Wi-Fi Rev 2 Board - Arduino | DigiKey.
https://www.digikey.com/en/product-highlight/a/arduino/uno-wi-fi-rev-2-
board, last accessed 2019/04/07.
14. Arduino Mega 2560 R3 (Atmega2560 assembled).
https://www.adafruit.com/product/191, last accessed 2019/04/07.
ICITG2019, 062, v2 (major): ’Reducing Power Consumption of Weather Stations for Lan . . . 15
16
15. The Arduino Mega 2560 is a microcontroller board based on At-Mega 2560
http://www.mantech.co.za/datasheets/products/A000047.pdf, last accessed
2019/04/07.
16. How to Set Up the BMP180 Barometric Pressure Sensor on an Arduino
http://www.circuitbasics.com/set-bmp180-barometric-pressure-sensor-
arduino/, last accessed 2019/04/07.
17. C. Thompson, "Build it. share it. profit. can open source hardware work, "
Wired Magazine, vol. 16, no. 11, pp. 16-11, 2008.
18. Electronza’s webpage, https://electronza.com/power-guzzlers-testing-
arduino-boards/, last accessed 2019/04/07.
19. Gammon’s webpage, http://www.gammon.com.au/power, last accessed
2019/04/07.
20. DHT22 datasheet,
https://www.sparkfun.com/datasheets/Sensors/Temperature/DHT22.pdf, last
accessed 2019/04/07.
21. ESP 8266 Datasheet, https://www.electronicwings.com/sensors-
modules/esp8266-wifi-module, last accessed 2019/04/07.
22. Xtensa’s webpage, https://ip.cadence.com/ipportfolio/tensilica-ip/xtensa-
customizable, last accessed 2019/04/07.
23. Arduino’s website, http://playground.arduino.cc/Interfacing/Processing, last
accessed 2019/04/07.
24. J. Gao, J. Luo, A. Xu and J. Yu, "Light intensity intelligent control system
research and design based on automobile sun visor of BH1750," 2017 29th
Chinese Control And Decision Conference (CCDC), Chongqing, 2017, pp.
3957-3960.
25. BH 1750 Datasheet,
https://www.mouser.in/datasheet/2/348/Rohm_11162017_ROHMS34826-1-
1279292.pdf, last accessed 2019/04/07.
26. SEN0193 datasheet,
https://media.digikey.com/pdf/Data%20Sheets/DFRobot%20PDFs/SEN0193
_Web.pdf, last accessed 2019/04/07.
27. Instructables webpage Rain gauge calibration,
https://www.instructables.com/id/Arduino-Rain-Gauge-Calibration/, last ac-
cessed 2019/04/07.
28. TP 4056 datasheet,
https://dlnmh9ip6v2uc.cloudfront.net/datasheets/Prototyping/TP4056.pdf,
last accessed 2019/04/07.
29. Rocketscream’s Webpage low-power library,
http://www.rocketscream.com/blog/2013/05/22/updated-low-power-library/,
last accessed 2019/04/07.
30. ESP 32 datasheet,
https://www.espressif.com/sites/default/files/documentation/esp32_datasheet
_en.pdf, last accessed 2019/04/07.
16 ICITG2019, 062, v2 (major): ’Reducing Power Consumption of Weather Stations for Lan . . .
ResearchGate has not been able to resolve any citations for this publication.
Chapter
Full-text available
A landslide, that is, collapse of a mass of earth or rock from a mountain or cliff, is a common phenomenon in hills. Landslides pose a large threat to life and infrastructure and there is a need to develop low‐cost sensing frameworks that could help in monitoring landslides and alert people before they occur. Certain existing technologies have been used for monitoring landslides (e.g., the use of unmanned aerial vehicle‐based remote sensing). The Internet of Things (IoT) technologies could provide alternate solutions for monitoring landslides. However, existing IoT technologies are diverse and expensive to use. Thus, there is a need for developing low‐cost IoT frameworks for monitoring landslides, especially in developing countries. This chapter proposes a microelectromechanical system (MEMS)‐based IoT framework for sensing landslides at the lab‐scale. The proposed IoT framework offers a promising low‐cost solution for monitoring landslides with a large deployment potential in landslide‐prone areas.
Chapter
Full-text available
Rainfall induced landslides account for over 200 deaths and loss of over Rs.550 crores annually in Himalaya. Literature suggests sensors based site specific Early Warning System (EWS) to be feasible and economic to curtail losses due to landslides for high risk areas. Area selected for current study is Tangni landslide located in Chamoli district of Uttarakhand state, India due to high anticipated risk to the local community residing nearby. For realization of EWS, a near real time instrumentation setup was installed on the slope. The setup measures pore water pressure, sub-surface deformations, and surface displacements along with rainfall. Regression analysis models are developed using antecedent rainfall and deformation data which are further used to find out thresholds for sensors based on z-scores. In future using the results from the sensors installed in the field and laboratory characterizations, numerical analyses will be applied to develop a process based model.
Conference Paper
Full-text available
Rainfall induced landslides account for over 200 deaths and loss of over Rs.550 crores annually in Himalaya. Literature suggests sensors based site specific Early Warning System (EWS) to be feasible and economic to curtail losses due to landslides for high risk areas. Area selected for current study is Tangni landslide located in Chamoli district of Uttarakhand state, India due to high anticipated risk to the local community residing nearby. For realization of EWS, a near real time instrumentation setup was installed on the slope. The setup measures pore water pressure, sub-surface deformations, and surface displacements along with rainfall. Regression analysis models are developed using antecedent rainfall and deformation data which are further used to find out thresholds for sensors based on z-scores. In future using the results from the sensors installed in the field and laboratory characterizations, numerical analyses will be applied to develop a process based model.
Article
Hybrid energy systems are renewable energy system combined in a complementary fashion to ensure dependable power supply at competitive cost. Diesel generators (DGs) are also added here as a back-up source of supply. For remote areas far from a transmission grid, these systems can provide a reliable and cost-effective supply. Addition of DG could instigate environmental pollution in such remote unpolluted areas. In the present work, optimal sizing of hybrid energy system has been attempted for a remote village cluster of Uttarakhand (India) to make available desired power supply at minimum environmental effluence. Hybrid Optimization Model for Electrical Renewable (HOMER) software from National Renewable Energy Laboratory, USA has been employed to attain the objective. The software offered several feasible systems, ranked on the basis of net present cost (NPC). All such systems are further analysed for emissions they have made in the environment. Hence, the optimal system fulfilling the criteria of minimal environmental degradation with sufficiently minimum NPC has been searched for. In the present work, the most appropriate system offered on the basis of NPC is the one which has five wind turbines (10 kW each), one DG (65 kW) and 25 batteries (6 V, 6.94 kW h each). The NPC of the system is $1,252,018, whereas its initial capital cost and levelised cost of energy (COE) are $94,233 and $0.292/kW h, respectively. After further analysis of all the feasible systems on the basis of environmental effluence, the most feasible system explored is the one which has minimal emissions of various pollutants such as carbon dioxide, carbon monoxide, hydrocarbon, particulate matter, sulphur dioxide and nitrous oxide. The system has been obtained on a compromised NPC of $1,270,921 with a capital cost of $148,133 and COE of $0.296/kW h. Components of the system include five wind turbines (10 kW), a 9 kW PV panel and a 65 kW DG along with 30 batteries (6 V, 6.94 kW h each). The system so obtained would prove to be a feasible, optimally sized and sustainable power supply alternative for remote unelectrified hilly rural area.
Build it. share it. profit. can open source hardware work
  • C Thompson
C. Thompson, "Build it. share it. profit. can open source hardware work, " Wired Magazine, vol. 16, no. 11, pp. 16-11, 2008.