GawaliVikram*
PandeMilind1
SayyadMunir1
BhadadeRaghunath1
-
(School of Electronics and Communication Engineering, Dr. Vishwanath Karad MIT World
Peace University, Pune 411038, India )
Copyright © The Institute of Electronics and Information Engineers(IEIE)
Keywords
Vision-based monitoring, Sensor-based system, Closed drainage systems, Blockages, Smart cities, Underground drainage system
1. Introduction
The Indian government started the smart city initiative to raise living standards
in major cities. As various networks (e.g., LAN, WLAN, and 3GPP) are becoming increasingly
interconnected to support the smart city infrastructure, proper underground drainage
systems must be implemented, which is a major goal of the initiative. Cities must
be clean, and the spread of waterborne diseases must be stopped. Many cities in India
have weak systems for monitoring underground sewage, which has led to problems, such
as the contamination of drinking water and the spread of disease. The use of open-channel
drainage systems, which is common in rural areas and urban areas with less development,
has particular problems because of the need for regular maintenance and the possibility
of blockages in the systems. They also presented a health risk to the nearby population
because they serve as a breeding ground for parasites and mosquitoes that spread disease.
On the other hand, closed drainage systems are safer and more hygienic when used in
urban and metropolitan areas, but they present maintenance and cleaning challenges.
Cleaning closed drainage systems is laborious and time-consuming, and workers are
exposed to serious health risks because of the need to enter access chambers that
collect poisonous gases. Furthermore, unanticipated obstructions in drainage lanes
can result in the flooding of drainage water on roads, contaminating clean water and
causing the spread of dangerous waterborne diseases.
This paper suggests the development of smart underground infrastructure with sensor
networks to detect various drainage parameters, including the precise location of
blockages and images of blockages, to address these issues. The proposed system requires
a crusher system to clear obstructions and a crane system to lift the trash into the
access chamber. In addition, various sensor networks are required to detect toxic
gas detection systems, access chamber status indicators, and water flow, viscosity,
and depth. Therefore, improving the upkeep and cleaning of underground drainage systems
in metropolitan and urban cities requires the development of an automated drainage
monitoring system. An automated drainage monitoring system is required to maintain
and clean urban underground drainage systems.
2. Related Work
WSNs are a cost-effective way of monitoring the environment. WSNs are battery-powered
sensor nodes with processors, communication devices, and sensors. These nodes in the
monitored area communicate to gather and process data, which is sent to a base station
in the center for analysis. WSNs are used for environmental, precision agriculture,
structure health, and underground monitoring. This literature review focuses on underground
wireless sensor network opportunities and challenges.
Prasad Reddy et al. proposed a magnetic induction-based wireless underground sensor
network [1]. The authors described an underground magnetic induction-based communication system.
The 25-centimeter-range device has a transmitting and receiving unit. The results
show that the proposed underground communication system is reliable and efficient.
Kim et al. proposed a wireless subterranean sensor network using magnetic core antennas
and multiple surface acoustic wave sensor modules [2]. The authors described a system with many surface acoustic wave sensor modules for
sensing and magnetic core antennas for communication. The system under consideration
in a laboratory can detect and categorize diverse entities. Wang et al. [3] proposed a dynamically uncertain and wave-disturbed AUV path planning and following
control system. The AUVs were planned and controlled using models in uncertain and
disturbed environments. The simulations tested the system. Despite the uncertainties
and disturbances, the system planned and controlled paths accurately in the experiment.
Stewart et al.'s dynamic modeling system [4] has improved AAUV-specific passive drainage.
Manzanilla et al. suggested an autonomous navigation system for unmanned underwater
vehicles (UUVs) [5]. The authors proposed a vision-based UUV navigation and control system. The simulations
suggested that the system under consideration could accurately navigate and manage
unmanned underwater vehicles (UUVs). Wang et al. [6] developed an organization that could detect multiple drainage pipeline blockages
by combining support vector machine features with VMD characteristics. The authors'
drainage pipeline obstruction detection and removal system used machine learning.
The proposed system successfully identified drainage pipeline obstructions using real-world
data. Nielsen et al. [7] described the use of genetic programming to model an autonomous underwater vehicle
fitted with a plastic grabber. The main aim of the experiment was to recover plastic
items from the ocean. The researchers presented a methodology using an autonomous
underwater vehicle simulation to retrieve plastic materials. Jawhar et al. in reference
[8] proposed an architecture for using autonomous underwater vehicles in wireless sensor
networks. The authors described an architecture for wireless sensor networks for pipeline
monitoring that uses autonomous underwater vehicles as mobile sensor nodes. The proposed
system was tested in a simulation environment. The results showed that it is capable
of precisely monitoring underwater conditions.
Soung-YueLiew and ShenKhangTeoh [9] proposed a data store-and-delivery approach for air-ground collaborative wireless
sensor networks. They developed a data storage and delivery framework that can deliver
ground-based sensor data to an unmanned aerial vehicle (UAV) or a ground station while
storing it. They conducted simulation experiments to assess their suggested strategy.
Haswani and Deore [10] proposed a Web-based real-time underground drainage or sewage monitoring system using
WSNs. They used a ZigBee-based WSN for gathering and transmitting real-time data from
the underground drainage system to the control center. They used both simulation and
in-person tests to assess their suggested system.
In wireless underground sensor networks (WUSNs), Hoang ThiHuyenTrang, Le The Dung,
and SeongOun Hwang [11] examined the connectivity of underground sensors. They used mathematical modeling
and simulations to assess the connectivity of various WUSN topologies. They also suggested
a hybrid topology that could enhance the WUSN connectivity. Sahana et al. [12] reviewed underwater wireless sensor network (UWSN) routing protocols and their difficulties.
They discussed various UWSN routing protocols and identified problems that needed
to be solved to boost UWSN performance. Choudhary et al. [13] reviewed autonomous underwater vehicles (AUVs) underwater navigation systems. They
identified the issues and future directions for research in this area while discussing
various AUV navigation systems.
Moridia et al. [14] developed wireless sensor networks for monitoring and communication systems underground.
They used a ZigBee-based WSN to monitor underground environments, such as mines and
tunnels. They conducted experiments to gauge the effectiveness of their suggested
system. Cao et al. [15] developed an unmanned surface vehicle (USV) to monitor water quality. Data on water
quality was gathered using a multi-sensor system and sent to the command center. They
conducted actual experiments to gauge the effectiveness of their suggested system.
Wu et al. [16] proposed a long-range wide area network (LoRaWAN) for agricultural wireless underground
sensor networks (WUSNs). They improved the coverage of WUSNs in agricultural fields
and increased their communication range using LoRaWAN. They used simulations to assess
their suggested system.
Mishra et al. proposed an underground coal mine IoT-based multimode sensing platform
[17]. A multimode sensing platform measured the pressure, humidity, gas concentration,
and temperature. Their proposed platform was tested using simulations. \"{U}nsal et
al. proposed WSN power management for underground mining [18]. Dynamic voltage scaling reduced the underground mining WSN power consumption. They
tested their plan with simulations. Akkas reviewed WUSNs for mines and miners’ safety
[19]. Jalaja and Jacob [20] proposed a mobile-element-based adaptive data collection scheme for sparse underwater
sensor networks (UWSNs). They discussed WUSN applications in mines and miners’ safety,
challenges, and future research. Mobile elements with acoustic communication tools
collected data from sparse network sensor nodes. Avoiding redundant data transmission
would improve network coverage and save energy. The simulation showed that the proposed
scheme outperformed other schemes in coverage and energy efficiency. Zhu et al. [21] suggested using the Gaussian mixture model (GMM) clustering and complete ensemble
empirical mode decomposition (CEEMD) to detect underwater drainage pipe blockages.
The system was designed to improve submerged drainage pipe blockage detection. The
authors reported that their system outperformed others in accuracy and false alarm
rates.
See et al. [22] suggested a Zigbee-based WSN for sewerage monitoring. The proposed system monitored
sewerage conditions in real time and reduced maintenance costs by detecting blockages
and leaks early. The experimental evaluation showed that their system was more reliable
and energy efficient than others. Kennedy and Bedford studied underground wireless
networking standards for tunneling and mining [23]. The authors evaluated and compared the throughput, dependability, and signal attenuation
of the Ultra-Wideband (UWB), Wi-Fi, and Zigbee standards. Zigbee had the best signal
attenuation and reliability.
Moridi et al. investigated a Zigbee radio wave attenuation-based underground monitoring
and communication system [24]. The proposed system improved tunneling and underground mining communication. The
authors reported that the system was reliable and low-power compared to other methods.
Girisrinivas and Parthipan proposed an IoT-based DOMS [25]. For safety and health, the proposed system could monitor drainage overflow in real
time. The experiments showed that the authors' system was more accurate and faster.
Manual scavenging-cleaning septic tanks and sewer lines by hand-killed 631 people
in India in the past decade, according to The Hindu [26]. Wireless sensor networks in underground monitoring and communication systems can
reduce manual scavenging and improve worker safety and health in mining and sewerage.
This literature review concludes with a comprehensive review of wireless sensor networks
for underground and underwater monitoring. The review papers covered data collection,
network connectivity, communication technologies, and monitoring systems. The papers
reviewed provide useful information and suggest promising research directions.
3. The Proposed Scheme
The methodology for the automated drainage monitoring system is based on a consideration
of various parameters that need to be monitored to prevent issues that people face
during the rainy season, contamination of drainage water with pure water, accidents
involving drainage cleaning workers, the transmission of infectious diseases by mosquitoes,
and accidents caused by open access chambers. The suggested system uses a sensor network
to identify drainage parameters, such as water level, flow, pressure, and toxic gases.
The system also features a access chamber status indicator.
The suggested model includes using an autonomous underwater vehicle (AUV) to address
obstructions in the drainage system. The AUV can locate obstructions, take pictures
of obstructions, destroy obstructions, and lift the crushed trash to the access chamber.
The proposed smart system consists of the design of sensor nodes to gather various
parameters, the connection of all sensor nodes using GPON with PoE port, the provision
of underwater radio frequency coverage using a Remote Radio Head (RRH) or Remote Radio
Unit (RRU), and the assembly of various sensors and equipment on the AUV to clear
obstructions with a remote command.
Sensor Node (SN), Surface Node, Network Control Center (NCC), and Compilation of various
sensors and equipment on the AUV comprise the four components of the proposed model.
The SN uses different communication protocols to upload the data collected to the
NCC, such as GSM, Wi-Max, Wi-Fi, satellite, and optical fibers (GPON with PoE). The
SN performs various sensing operations, such as pressure, level of savage water, chemical
compositions, and access chamber status indicators.
The NCC gathers information from surface nodes and locates drainage blockages underground.
It issues orders telling the AUV to travel to the location of the obstruction, clear
it, and monitor its operation. After receiving the command from the NCC, the AUV removes
the obstruction, takes pictures with a night vision camera, and sprinkles disinfectant
into the drainage system.
The AUV's RF transceiver, GPS, night vision camera, and sprinkler to clean the drainage
are all factors that were considered when building it. Overall, the automated drainage
monitoring system methodology offers an effective and efficient way to enhance the
upkeep and cleaning of underground drainage systems and stop the spread of waterborne
diseases in India's urban and metropolitan areas.
3.1 ADVMS Architecture
The following steps outline the suggested methodology for developing the Automatic
Drain Vision Management System, or ADVMS:
1.Identification of Parameters: Consider various parameters that must be monitored
to stop issues that people encounter during the rainy season, contamination of drainage
water with pure water, misadventures involving workers who clean the drainage system,
transmission of infectious diseases by mosquitoes, and accidents caused by open access
chambers.
2.Sensor Network: Utilize a sensor network to identify drainage-related factors, such
as water level, flow, pressure, and toxic gases.
3.Autonomous Underwater Vehicle: Employ an autonomous underwater vehicle (AUV) to
clear up clogs in the drainage system. The AUV can locate obstructions, take pictures
of obstructions, destroy obstructions, and lift the crushed trash to the access chamber.
4.Sensor Node: Develop sensor nodes to gather various parameters, such as pressure,
savage water level, chemical compositions, and indicators of access chamber status.
5.Establish a surface node to gather information from sensor nodes.
6.Network Control Center: Gather information from nodes on the surface and locate
drainage blockages underground. Order the AUV to travel to the obstruction's location,
remove the obstruction, and then monitor the vehicle's performance.
7.AUV Compilation: Combine various sensors and equipment on the AUV to clear obstructions
by receiving a remote command. To clean the drainage, consider using an RF transceiver,
a GPS, a night vision camera, and a sprinkler.
Fig. 1 summarizes the ADVMS architecture, which offers a practical and efficient solution
to enhance the upkeep and sanitation of underground drainage systems and stop the
spread of waterborne diseases in India's urban and metropolitan areas. The Network
Control Center can access the data that the Sensor Node uploads to it using communication
protocols like GSM, Wi-Max, Wi-Fi, satellite, and optical fiber (GPON with PoE). The
Sensor Node gathers various drainage system parameters. The Surface Node gathers data
from the Sensor Nodes and uploads it to the Network Control Centre to locate blockages
in the underground drainage system and instruct the autonomous underwater vehicle
(AUV). The AUV uses a night vision camera to take pictures while clearing the obstruction
and sprinkling disinfectant into the drainage system.
Fig. 1. ADVMS Architecture.
3.2 Algorithms of ADVMS System
1) Sensor Node Algorithms:
Four distinct algorithms read and transmit various sensor data to a surface node.
Each algorithm is made to run without interruption until stopped. An explanation of
algorithms is given below:
a) Pressure Sensor: This algorithm waits for the next reading after reading pressure
data from a pressure sensor at a node and sending it to a surface node. It ensures
that the data are continuously gathered and sent to the surface node using a "while
True" loop to run indefinitely.
b) The algorithm employs an infrared (IR) sensor located at a node to retrieve IR
data. Upon transmitting the data to a surface node, the algorithm enters a state of
waiting for the subsequent reading. The "while True" loop ensures continuous data
collection and transmission.
c) The third component of this algorithm uses a camera sensor at a node to take a
picture, send that picture to a node on the surface, and then wait for the next reading.
Utilizing the "while True" loop allows for continuous image transmission to the surface
node, which is a necessary safety precaution.
d) This algorithm uses a surface node to receive gas data from the gas sensor of a
node, which is then sent to a node in the subsystem. Subsequently, the algorithm remains
in a state of readiness until the subsequent reading is obtained. Like other algorithms,
this method utilizes the "while True" loop to guarantee uninterrupted functionality.
Overall, these algorithms show how sensors can gather various types of data, which
can then be transmitted to a central location for additional processing and analysis.
The algorithms can be altered to suit particular requirements and expanded to incorporate
more sensors or types of data.
2) Surface Node Algorithm
The Surface Node Algorithm was developed to gather data from the sensor nodes and
transmit it to the network control center (NCC). The algorithm continuously collects
and sends data while delaying each collection according to the interval_time variable
while running in an infinite loop. The data from each networked sensor node are gathered
by the collect_sensor_data() function. The function calls read_pressure_sensor(),
read_ir_sensor(), capture_camera_ image(), and read_gas_sensor(), which are the specific
sensor reading functions for each type of sensor.
A dictionary called sensor_data contains the combined data. The sensor_data dictionary
is input into the combine_sensor_data(sensor_data) function, which produces a message
with a timestamp from the combined sensor data. This message is forwarded and then
input into the send_data_to_ncc(message) function, which sends the data to the NCC.
The function chooses the best network protocol to send the message based on the available
ones. An error is logged if there are no protocols available. The main loop of the
Surface Node Algorithm calls collect_sensor_data() to gather sensor data and uses
combine_sensor_data(sensor_data) and send_data_to_ ncc(message) to combine it into
a message and send it to the NCC. Using sleep(interval_time), it then waits for the
subsequent data collection interval. This loop keeps running forever, enabling constant
data gathering and transmission to the NCC.
3) NCC (Network Control Center) Algorithm
An algorithm for controlling a system that oversees the maintenance and inspection
of underground drainage systems is called the NCC (Network Control Center) Algorithm.
This algorithm has been designed to operate perpetually, consistently monitoring the
system and taking appropriate actions as required.
The algorithm starts by gathering information from the surface nodes, which are sensors
positioned throughout the drainage system in various locations. The algorithm then
examines this data to look for systemic obstructions and pinpoint where they are.
The next action is to direct an autonomous underwater vehicle (AUV) to travel to the
location of the obstruction. The algorithm then determines the best route for the
AUV to travel to reach the obstruction, gathering information from the AUV along the
way. The algorithm uses motion planning to direct the crushing blades of the AUV to
the location of the blockage once it has arrived. Throughout the process, the AUV
performance is tracked continuously. The algorithm aggregates and analyzes sensor
data to find any systemic anomalies. The maintenance team is notified if any anomalies
are found so they can respond appropriately. An extensive and automated system for
maintaining and monitoring underground drainage systems is provided by the NCC Algorithm.
4) Autonomous under water Vehicle Algorithm
The algorithm used by autonomous underwater vehicles (AUVs) consists of several functions
that each carry out a specific task.
(1).receive_command (): The network control center issues a command to this function,
which receives it. (2).capture_images (): This function uses a night vision or LIDAR
camera to take pictures of the drainage. (3).remove_blockage (command, images): This
function removes the blockage using the command and the images taken. Images are used
to remove the blockage if the command is "remove_blockage" and they are available.
If not, it removes the obstruction without images. (4).compile_auv () compiles the
AUV based on several factors, including the presence of an RF transceiver, speed,
GPS, night vision camera, LIDAR, and sprinkler.
The algorithm repeatedly receives commands from the network control center, captures
images, removes obstructions based on the commands and images it has received, and
then compiles the AUV based on the specified considerations.
4. Performance Evaluation
The outcomes of the 3D model can offer useful perceptions of how well the drainage
system functions. For example, the simulation might identify places where water is
not moving as effectively as it should or places where it might pool and cause flooding.
These problems can be identified before construction, allowing designers to alter
the drainage system to guarantee it works correctly and efficiently. This can save
time and money by avoiding expensive repairs and ensuring that the drainage system
can handle the anticipated volume of water.
Overall, the 3D model offers an effective tool for designing and optimizing drainage
systems, ensuring they are strong, effective, and resilient in the face of shifting
weather patterns and other environmental factors. A hydrological model was constructed
using the subsequent measurements: The object dimensions in question are 16 feet in
length, two feet in width, and 1.5 feet in height. Access chamber-1, Access chamber-2,
and Access chamber-3 are the three access chambers in the system. Fig. 2 presents the design of the drainage model. The remaining portion of the model was
covered with transparent acrylic sheets to ensure proper drainage system monitoring.
Sensor node access chamber caps, each measuring 2${\times}$2 feet, were installed
on the access chambers. The caps had four sensor node slots to accommodate the various
sensors. The gas sensor, ultrasonic contactless level sensor, and air pressure sensor
were placed in the central slots, while the access chamber status indicator sensor
was set aside for the left slot. The construction of the model made it possible to
monitor the drainage system effectively and provided useful information about how
well it was working.
Fig. 2. 3-D-Modeling of Drainage System.
Fig. 3 presents the actual drainage model. Fig. 4 depicts an MQ-4 gas sensor that finds methane gas (CH$_{4}$) in drainage systems.
The MQ-4 sensor is made to find the primary component of natural gas, CH$_{4}$, in
drainage systems. An analog output can represent the CH$_{4}$ concentration in the
drainage by connecting the MQ-4 sensor to an Arduino Uno microcontroller.
Fig. 3. Prototype of drainage model.
Fig. 4. Simulation of the node for Gas sensing.
Fig. 5. IR Sensor based level sensing setup.
The sensor also has a digital output that alerts when methane levels reach or surpasses
a predetermined threshold. This alert can turn on an external LED and notify users
when the CH$_{4}$ gas levels in the environment may be dangerous. The HX710B atmospheric
pressure sensor module was placed in the test using an Arduino microcontroller. The
module has an altitude resolution of 10 cm and is intended for use in altimeters and
variometers. A low-power, 24-bit ADC with internally calibrated coefficients and a
pressure sensor with high linearity are both included in the sensor module. It provides
accurate 24-bit digital pressure and temperature readings, and the user can choose
from various operating modes to optimize conversion efficiency and power usage. Fig. 6 presents the pressure measurement results of the HX710B Atmospheric Pressure Sensor
Module. The sensor module uses a high-precision AD sampling chip and an 0-40KPa air
pressure sensor to detect air pressure and water level. It can be attached to a 2.5mm
hose. The findings show that the module measures the atmospheric pressure with accuracy
and dependability, making it suitable for various applications, including weather
monitoring and altitude tracking. Using a GPON network, the drainage parameters from
various sensor nodes were gathered. A PoE switch was used to connect each sensor node
and supply power. An ultrasonic detector measures the distance of an object using
sound waves.
The node operates by sending out a sound wave at a particular frequency and then waiting
for the wave to return.
The distance between the ultrasonic sensor and the object can be calculated by timing
the emission and reception of the sound wave. The level of water can be determined
using this kind of detector.
Fig. 5 shows the use of an ultrasonic sensor to measure the water level. The sensor sends
out a 40 kHz tone, which the sensor picks up when the signal is reflected back from
the object. The time between the transmission and reception of the sensor is noted,
and the distance is determined using the formula. The distance was calculated as (Time
to Receive Reflected Signal ${\times}$ Sound Speed (340 M/S)) / 2. The formula is
then used to convert the distance to centimeters. Speed of Sound (cm/s) ${\times}$
Time Duration (s/2) equals the distance in centimeters. The drainage water level can
be calculated by deducting the distance from the drainage height. Fig. 7 shows the observed values of various drainage parameters reported by various sensor
nodes. Each container houses four drainage parameters: toxic gas level indicator,
air pressure indicator, drainage water level indicator, and drainage access chamber
status indicator. There are four containers for four sensor nodes. The water level
indicator readings are represented in red when the drainage level exceeds 50\% of
the drainage height.
Fig. 7. Result displayed in the NCC simulation panel.
5. Conclusion
Implementing a 3D model for designing and optimizing drainage systems in smart cities
offers valuable insights into system performance. The model saves time and money while
ensuring efficient drainage by identifying potential issues before construction. Integrating
sensors, such as the MQ-4 gas and HX710B atmospheric pressure sensors, enable real-time
monitoring and data collection. Future prospects include IoT integration, automated
control systems, climate resilience, smart urban planning, public engagement, green
infrastructure, and water recycling. These advances can produce sustainable, resilient,
and efficient smart drainage systems for improved living standards in smart cities.
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Vikram Sadashiv Gawali: School of Electronics and Communication Engineering, Dr.
Vishwanath Karad MIT World Peace University, Pune 411038, India.
Vikram Gawali graduated in Electronics & Telecommunication Engineering at Government
College of Engineering, Jalgaon, Maharashtra, India. He secured a Master of Engineering
in Digital Electronics from Shri Sant Gajanan Maharaj College of Engineering, Shegaon,
Maharashtra, India. Currently pursuing Ph.D. from School of Electronics and Communication
Engineering, Dr. Vishwanath Karad MIT World Peace University, Pune, India. Vikram
Gawali, had joined as Junior Telecom Officer (JTO) in Bharat Sanchar Nigam Limited
(BSNL), Pune Circle, Maharashtra, India in September 2008.
He started his career in BSNL with expansion of second generation (2G) mobile network
in Pune telecom circle, Maharashtra, India. He played a vital role in 2G & 3G radio
frequency planning, installation and commissioning of V-SAT, 2G & 3G Base transceiver
stations (BTSs), Base Station Controllers (BSCs), Microwave stations, Radio frequency
drive & optimization of GSM network & measurement of EMF in Phase-V Mobile network
expansion.
He has been in the teaching profession for more than 08 years. He has presented
and published research papers in National and International conferences and journals
indexed in SCI/SCOPUS. His main area of interest includes Wireless communication,
Wireless Sensor Networks, Optical fiber communication and Internet of Things.
Milind Pande:
Pro-Vice Chancellor,
Dr. Vishwanath Karad MIT World Peace University, Pune 411038, India.
Eminent educationist Prof. Dr. Milind Pande commenced his professional journey
in 2003 as an Assistant Professor at E&TC Department and MAEER’s MIT Pune till 2007.
Later, he embellished as a professor, MBA until 2009.With his vision & excellence
in academics, he was appointed as a Project Director at MIT School of Telecom Management
till 2018. His guidance and perseverance emerged as the strong leadership personality
he possessed. For the year 2018-2019, he was designated R&D head of MIT-WPU, and Pune.
His dedication & endurance have proved an asset to the institution. In 2019 he was
commissioned as a Pro-Vice-Chancellor at MIT WPU Pune.
He has effortlessly worked towards realizing the Dream of the former President
of India, the Late Dr. A. P. J. Abdul Kalam on “Providing Urban Opportunities in Rural
Areas” (PURA). He has co-authored a world-class textbook on Mobile Communications
with a core focus on the Indian Telecom ecosystem. The book titled “Next Generation
Mobile Communications – Mobile, Infra Technology, Management, and Data” was published
by McGraw Hill Education. He serves as a member of TAC (Telecom Advisory Committee),
Ministry of IT & Telecommunication of India, New Delhi, and BSNL Pune.
He has on his credit several National and International Paper Publications and
has visited & attended more than 13 International conferences at several international
universities. He had the privilege of meeting Former presidents of India Dr. A.P.J
Abdul kalam, Smt. Pratibha Tai Patil & Shri. Ramnath Kovind. At MIT School of Telecom,
he has been at the helm of providing MOOCs (Massively Open Online Courses) and TSSC
(Telecom Sector Skills Council) Certifications to the students.
Munir Sayyad: Asst. Vice President Reliance Jio Mumbai, India.
Dr. Munir Sayyad has done Ph.D. in Next Generation Telecom Network Optimization,
currently working in the capacity of Asst. Vice President Reliance Jio Mumbai, India.
Owing 23+ years of techno-rich experience in Research & Development, Project Management,
System Engineering, Training, Technology Validation, Standardization, Network Planning
and Engineering, Competitors Analysis and Network Solutions of Radio Access Network
Elements, Core Network Elements, Data, Transport and Next Generation Network Elements.
Specialized in Next Generation Telecom Network Optimization. Dynamic professional
with expertise in IT, Telecommunication and Electrical TDU Project Management, System
Engineering, Technology Evaluation, Validation, Standardization, Network Planning
and Engineering , Network Quality Audits of Telecom Network and Team Management. Keen
planner with a flair for adopting modern Technologies in compliance with quality standards.
Proficient in swiftly ramping up projects with competent cross-functional skills and
ensuring on time deliverables within cost parameters.
Dr. Munir Sayyad has presented and published numerous research papers in National
and International conferences and journals indexed in SCI/SCOPUS.
Raghunath S. Bhadade:
Associate Professor Department of Electrical and Electronics Engineering Dr. Vishwanath
Karad MIT World Peace University, Pune 411038, India.
Dr. Raghunath S. Bhadade is an accomplished Associate Professor in the Department
of Electrical and Electronics Engineering at Dr.Vishwanath Karad MIT World Peace University,
Pune. He has 23 years of academic teaching experience. His Ph.D degree from the Savitribai
Phule Pune University. His research title is Design and Development of Massive MIMO
Antenna for ISM Band. He hold three patents in his name.
He has presented and published numerous research papers in National and International
conferences and journals indexed in SCI/SCOPUS. He specializes in RF, Microwave and
Antennas, Wireless Communications, Software Defined and Cognitive Radio, and serves
as a faculty member for Vishwashanti CanSat Team.
As a life member of the Indian Society for Technical Education, Dr. Bhadade has
been invited as a resource person in FDP/STTPs in his research areas and as a guest
speaker across India in the field of Electronics and Telecommunication Engineering.
He has organized various social events at his birthplace to aid the overall development
of rural people.
Dr. Bhadade also has a passion for trekking and has actively participated in various
treks across India.