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UNIVERSITY GRANTS COMMISSION
BAHADUR SHAH ZAFAR MARG
NEW DELHI – 110 002
FINAL REPORT OF THE WORK DONE of
Minor Research Project On
“Optimization of QoS of Wireless Sensor Network in Large
Multigrain Storage Monitoring”
For the period
(24 March 2017 to 23 March 2019)
Submitted By
Mr. Shelar Dipak Shivaji
Principal Investigator
Ahmednagar Jilha Maratha Vidya Prasarak Samaj’s
New Arts, Commerce and Science College, Ahmednagar (NAAC Accredited ‘A++’ Grade College)
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Table of Contents
Sr. No. Content Page No.
1. Introduction 3
2. Literature Survey 6
3. Simulation Study of Wireless Sensor Network 10
4. Development of Wireless Sensor Node 30
5. Wireless Sensor Network Data Monitoring Using LabVIEW 48
6. Result and Discussion 52
7. Conclusion 53
8. Acknowledgement 54
9. List of Publications 55
10. References 56
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1. Introduction
1.1 Origin of the Research Problem
Agriculture plays a vital role in India’s economy. 54.6% of the population is
engaged in agriculture and allied activities and it contributes 17% to the country’s Gross
Value Added. During 2016-17, food grain production was 275.68 million tones. The government
buys food grains from the farmers but does not have enough space to store it. Food grains undergo
a series of operations such as harvesting, threshing, winnowing, bagging, transportation, storage,
and processing before they reach the consumer, and there are appreciable losses in crop output at
all these stages. After harvesting and drying, grain and seed should be stored in clean, insect-free,
weather-proof storage places from which nearby sources of insect infestation have been
eliminated. The sources of insect infestations in stored grains vary with crop and region. The post-
harvest losses occurs due lack of sufficient storage infrastructure at farm level. Quantity losses
occur when insects, rodents, mites, birds and microorganisms, consume the grain. Infestation
causes reduced seed germination, increase in moisture, free fatty acid levels, and decrease in pH
and protein contents etc. resulting in total quality loss. Quality losses affect the economic value of
the food grains fetching low prices to farmers.
Grains are the biggest source of foods in most of the countries. In many countries, grains
are harvested once a year or seasonally. Therefore, to provide food to the population, produced
food grains are mostly stocked in foodgrain warehouses. Foodgrains such as rice, maize, wheat,
sorghum and millets are stored for few months to years and this storage plays a crucial role in the
economic system of developed and developing countries [2]. Foodgrain warehouses are intended
for the storage and physical protection of bagged grain. In India, excess food grains are stored by
State Warehousing Corporation (SWC), Food Corporation of India (FCI) and Central
Warehousing Corporation (CWC) [3].
Considerable losses both in quality and quantity of food-grains take place in storage due to
a number of factors. Organisms directly responsible for causing loss in stored products are insects,
mites, rodents, fungi and bacteria. Among them, insects and mites are the most important hazards
to the safe storage of grains. The insects that attack stored grains are rather general feeders, but
some of them prefer certain grains. It is estimated that 5-10 percent of the stored grain are lost
every year due to insect damage in India.
About 57,676 tons of foodgrain stored in Food Corporation of India (FCI) godowns have
got damaged and become useless for human consumption in the past five years owing to pest
attack, leakage in godowns, exposure to rain and floods, procurement of poor quality stock etc.
This amount was sufficient feed more than 1.15 crore people for a month, according to a report by
the Ministry of Consumer Affairs. Also some amount of foodgrain also gets wasted during
transportation in trains and trucks.
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Table 1 Foodgrain wastage
Grain temperature and moisture content are the primary factors that affect the grain storage.
As temperature and moisture content increases, grain will deteriorate faster. The most important
thing that the manager needs to do is to master the grain storage condition in time, and then to take
effective measures, such as aeration and drying, to prevent the grain spoiling. Monitoring grain
temperature during storage provides a good management tool for quality control. Temperature is
useful for determining aeration needs to control excess moisture which causes microbial growth,
sprouting and germination. It is also useful to determine optimal aeration times to control insect
population growth or to achieve insect mortality. . The grains are stored in conventional
warehouses as shown in figure 1.
Figure 1 Storage of foodgrain bags inside storage
The wireless sensor network can be used inside Foodgrain Warehouse for monitoring
temperature, humidity and CO2 gas. In wireless sensor networks, sensor nodes are distributed in
an application area to monitor as well as control different parameters cooperatively. They are
useful in various applications such as environmental monitoring, home monitoring and control,
Foodgrain Wastage
Year Quantity
(in tons)
2013-14 24695.5
2014-15 18847.2
2015-16 3115.7
2016-17 8775.6
2017-18 2244.74
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medical, precision agriculture and industry. Each application of WSN consists of several sensor
nodes and one base station called sink node. All sensor nodes sense the parameters as per
application requirement and send it to the sink. Sensor node data reaches the destination in one
hop or multi-hops. While considering the data transfer from source to sink we have to consider the
parameters like delay, reliability, congestion status, and node lifetime. All these parameters are
important for maintaining the quality of service (QoS) of WSN [1].
The research is focused on performance analysis of QoS parameters packet delivery ratio,
throughput, end to end delay and energy consumption. When the event occurred, it can be detected
and transmit rapidly in the WSN with low-energy consumption. The QoS mechanism should
improve the efficiency and reduce the energy consumption of sensor nodes to increase the
network’s lifetime [4].
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2. Literature Survey
Grain issue has respect to the issue of national economy and people’s livelihood, which is
valued all through the ages, while the current available grain storage systems are wired. The wired
method has inherent flaws, such as wiring complexity, suffering lightning strike and destroying
easily, high cost on installation and maintenance. Food grain storage capacity is planned in India
to meet the storage requirement for buffer and operational stocks, public distribution system and
farm level storage. Planning of additional capacity is made on the basis of assessment of additional
storage requirement on macro-level as also to take care of regional imbalances and the needs of
people on micro-level basis. There is need to monitor the environmental conditions inside a
storages using wireless sensor network for avoiding more food grain spoilages. In wireless sensor
networks, sensor nodes are usually deployed randomly in sensor field, therefore, coverage problem
is one of basic problem, and the problem has some influence on monitoring and tracking object.
Wireless Sensor networks (WSNs) have become one of the most interesting areas of
research in the past few years. A WSN is composed of a number of wireless sensor nodes which
form a sensor field and a sink. These large numbers of nodes, having the abilities to sense their
Surroundings, perform limited computation and communicate wirelessly form the WSNs. Recent
advances in wireless and electronic technologies have enabled a wide range of applications of
WSNs in military, traffic surveillance, target tracking, environment monitoring, precision
agriculture, healthcare monitoring, and so on. There are many new challenges that have surfaced
for the designers of WSNs, in order to meet the requirements of various applications like sensed
quantities, size of nodes, and nodes’ autonomy. Therefore, improvements in the current
technologies and better solutions to these challenges are required [18].
As the technologies for wireless nodes improve, the requirements for networking are
increasing. That enables possibilities for new applications. To reduce costs and time of the
deployment process, simulation of the network is a preferred task before testing with real
hardware. Modeling task includes real time, energy efficiency and routing protocol simulation and
accurate radio modeling with 3D definition of the indoor or outdoor environment. The simulator
should allow dynamic environment changes with moving sensor nodes or obstacles. Different
simulation tools available are NS-2, OMNeT++, Prowler, TOSSIM, OPNET, J-Sim, Castalia,
QualNet and WSN Planner tool.
In 2009 Qinglian Ren, Chunbo Chang developed a grain storage monitoring system based
on wireless sensor network. A complete system of monitoring grain condition monitors various
indicators which contains the temperature, humidity, pest density, oxygen content and phosphate
content in grain depot. In 2009, Jian-guang et al. has proposed a novel scheme of the monitor
system for grain depots is proposed based on wireless sensor network (WSN). Huiling Zhou, et
al. has developed A Real-time Monitoring and Controlling System for Grain Storage with Zigbee
Sensor Network in 2009. In 2010 Yawei Zhao, Yang Yu, Mingbo Yuan designed Grain Depot
Temperature Measurement System’s Research Based on Wireless Sensor Networks. In 2010, Xiao
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dong et al. has designed an embedded environmental monitoring system for grain storage based
on ARM technology
This section gives the details of NS-2 based simulation studies carried out for WSN.
Fenghui Zhang et al. [4] used simulation software NS-2 for simulation study of wireless sensor
network with 50 nodes. Packet delivery ratio, node density and average end-to-end delay were
used for sensor network performance evaluation using MAC layer protocol IEEE 802.15.4. Genita
Gautam et al. [5] implemented wireless sensor network in NS2 and presented simulation details
for creating a wireless sensor network. The packet delay and average energy consumption of the
sensor network are evaluated using MAC protocol 802.11.
Prashant Panse et al. [7] work was focused on performance evaluation of VANET. The
work carried out using SUMO and NS2 was based on data delivery rate, average end-to-end delay,
residual energy and routing overhead. Shumin Xu et al. [6] designed wireless network model using
four nodes, throughput and average end to end delay were measured and results analysis was
performed using NAM and gnuplot tools.
Majid Ahmad Charoo et al. [8] introduced Multi-region Pre-routing (MRPR) scheme for
large scale wireless ad hoc network. The pre-routing scheme used to have a good improvement in
average energy consumption as compared to AODV protocol. In addition the average routing time
also improved. Ankit Bhavsar et al. [9] presented a model based on IEEE802.15.4 standard for
performance investigation of WSN in animal health monitoring application. Their simulation
results reported better packet delivery ratio and less delay.
Chuan Zhu et al. [13] describes two fundamental issues related WSNs such as Coverage
and connectivity. These two parameters have a great impact on QoS of wireless sensor networks.
They also discussed existing problems, challenges and summarized typical issues of coverage and
connectivity in WSNs. Omar said [15] constructed a simulation environment for WSN using the
NS-2 simulator and proposed system for guaranteeing WSN QoS. Wireless sensor network was
divided into different groups where every group comprises of a number of nodes. Performance
parameters viz., packet loss, throughput, latency and sensor power consumption were measured.
Mohammad Asif et al. [16] reviewed QoS routing protocols used in wireless sensor network. The
work was focused on QoS satisfaction, challenges and requirements at each layer. QoS aware
protocols for WSN are discussed comprehensively and computational intelligence techniques for
QoS management are described.
Wireless sensor networks (WSN) are generally set up for gathering records from insecure
environment. Nearly all security protocols for WSN believe that the opponent can achieve entirely
control over a sensor node by way of direct physical access. The appearance of sensor networks
as one of the main technology in the future has posed various challenges to researchers. Wireless
sensor networks are composed of large number of tiny sensor nodes, running separately, and in
various cases, with none access to renewable energy resources. In addition, security being
fundamental to the acceptance and employ of sensor networks for numerous applications; also
different set of challenges in sensor networks are existed.
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In a wireless sensor network, the size of radio nodes has direct relation to the cost of total
wireless sensor networks, and at the same time, the problem is closely connected to wireless sensor
networks performance, such as robust, fault-tolerance, and furthermore, it is considered at first as
wireless sensor networks are designed. Sensor localization is a fundamental and crucial issue for
network management and operation. In many of the real world scenarios, the sensors are deployed
without knowing their positions in advance and also there is no supporting infrastructure available
to locate and manage them once they are deployed.
In Department of Electronic Science, University of Pune work on prototypes Crossbow
Wireless sensor network, development of wireless sensor node using PIC 18F4520
microcontroller, Design and implementation on Java based managerial control interface,
localization of problem source, Energy overheads and energy saving issues in WSN with directed
connectivity has been carried out.
It has been observed that due to humid conditions, the grains start germinating [33]. This
process is non reversible and hence destructive. Reports state that tones of grains are wasted due
to this problem. Neha Deshpande and A. D. Shaligram have perform the work with the help of
sensor nodes and determine that germination has started in the storage and suggest for corrective
action. This study is useful for determination of germination conditions of grains in the large food
grain warehouses. The sensor along with humidity and temperature sensors can provide detailed
information of the indoor conditions of the warehouse. Further when each sensor node is provided
with a wireless transmission facility, the acquired data can be transmitted to a remote base station.
A novel scheme of monitor system designed for grain depots is proposed by Jianguang Jia,
et al. based on wireless sensor network (WSN). System experiments are conducted in a house-
mode granary, which compose an autonomous network, to collect environmental data of grain
storage and then transmit them to remote control center by means of wireless and multi-hop
communication. Due to using small, low cost sensor nodes and wireless communication, several
problems in traditional monitor system are solved [34].
Neha Deshpande et al. presents the application of E-nose system along with smart
embedded sensor system to study the deterioration of food grains under different stress
(temperature, humidity, insects etc.) and room environmental conditions. The food grain
conditions are artificially generated and the effects are studied with α- Fox 2000 e-nose system.
The grains were tested at different conditions viz. cold, at room temperature, pest infected, grain
in sac, at high humidity level. During the experiments temperature and humidity conditions are
monitored. In order to analyze the data of rice, millet, wheat, jawar under different stress
conditions, they performed different analysis viz, Principal Component Analysis & Discriminant
Factorial Analysis on the acquired E-nose data [35].
Meeting the requirement and development of large granaries, an embedded environmental
monitoring system for grain storage based on ARM technology was designed by Xiaodong Zhang
et al. [36]. The proposed system architecture of the embedded monitoring system for grain storage
consists of Lower computer terminals of Samsung S3C2410X microprocessor, with grain data
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acquisition, signal processing and communication functions, Host computers located in control
room, consist of database servers, data manipulation servers, clients, printers, and wireless network
modules. Lower computer terminals are connected to the host computers via the Ethernet or the
GPRS network, using client-server distributed application architecture. The system was tested
using an industrial model machine in the laboratory and had a very satisfactory performance.
Qinglian Ren and Chunbo Chang designed grain storage monitoring system which consist
of two level network structure, tree-network structure and star network structure. The grain storage
monitoring system based on WSN in low power consumption and collecting data mode. Grain
storage monitoring system overcomes flaws of traditional wired system and shortcomings of other
wireless methods [37].
A Smart Sensor System is proposed by Santosh kumar et al. to monitor grains in storage
depots. The sensor system monitors the parameters like temperature, humidity and light which
influence the storage of grains. The alarm is sent to the observing station if the value received at
the observing station is above the threshold value. This helps to monitor the grain depot and help
to prevent the damage done to the stored grains in depots [38].
Manoj Kumar Tyagi et al. propose spot and Continuous monitoring to perform study of
Wireless sensor networks for warehouse monitoring and management. Temperature, air pressure,
humidity, and presence of animals in the warehouse are monitored. The wireless sensor network
works on battery and solar energy also [39].
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3. Simulation Study of Wireless Sensor
Network 3.1 Simulation Softwares:-
Wireless Sensor Network consist of number of sensor nodes for monitoring environmental
parameters. A sensor node consist of sensor, microcontroller, transceiver and power supply.
Wireless Sensor Nodes can be used for measuring physical parameters inside foodgrain
warehouse. Humidity, temperature and CO2 are three important parameters which can be measured
with the help of sensors. The information received from sensors can be processed by
microcontroller. The transceiver module perform the communication between different sensor
nodes. The number of sensor nodes can be used for monitoring purpose and the information
collected by sensor node will be send to coordinator or Base Station in single hop or multiple hops.
Coordinator perform the further processing on data and information is available to end user, based
on that it can take the decision. This process will controls the losses of foodgrains occurs in future.
As deployment of sensor nodes on the field is much expensive. Therefore before placing the
hardware devices on the field we can perform simulation study using different simulators available
in market. We have used WSN Planner tool and NS-2 simulators.
3.1.1 WSN planner tool:-
WSN planner deals with various arrangements of wireless nodes. WSN planner deals with
placements of wireless nodes in the field or area to be monitored. These wireless nodes can sense
certain environmental parameters such as humidity, temperature etc. This information is useful for
further processing and monitoring of entire area and further helps user to take necessary action.
Purpose of this tool box is to provide facility for user which assists him to find best arrangement
of nodes with minimum nodes to cover maximum area. This application simulates connectivity
pattern between these wireless nodes for given parameters and calculates coverage area on the
basis of inputs such as range, area under consideration, and method of placements. WSN planner
tool is studied and simulated following different Wireless Sensor Network arrangements:-
A) Manual:-
1) Import
2) Guided Placement
B) Tool Generated:-
1) Random
2) Cartesian
3) Radial
4) Hexagonal
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3.1.2 NS-2 Simulator
Network Simulator (Version 2), widely known as NS2, is an event- driven simulation tool
that is useful in studying the dynamic nature of communication networks. Simulation of wired as
well as wireless network functions and protocols (e.g., routing algorithms, TCP, UDP) can be done
using NS2. In general, NS2 provides users with a way of specifying such network protocols and
simulating their corresponding behaviors. Due to its flexibility and modular nature, NS2 has gained
constant popularity in the networking research community.
NS-2 has many and expanding uses including:
o To evaluate the performance of existing network protocols.
o To evaluate new network protocols before use.
o To run large scale experiments not possible in real experiments.
o To simulate a variety of IP networks.
NS2 provides users with executable command ns which takes on input argument, the name
of a Tcl simulation scripting file. Users are feeding the name of a Tcl simulation script (which sets
up a simulation) as an input argument of an NS2 executable command ns. In most cases, a
simulation trace file is created, and is used to plot graph and/or to create animation. NS2 consists
of two key languages: C++ and Object-oriented Tool Command Language (OTcl). While the C++
defines the internal mechanism (i.e., a backend) of the simulation objects, the OTcl sets up
simulation by assembling and configuring the objects as well as scheduling discrete events (i.e., a
frontend). The C++ and the OTcl are linked together using TclCL. Network animator (Nam) is a
Tcl/TK based animation tool for viewing network simulation traces and real world packet traces.
It is mainly intended as a companion animator to the ns simulator. After the trace file is created
Scripting languages such as AWK (Aho Weinberger Kernighan) script and PERL script can be
used to calculate the performance metrics.
Figure 2 Basic Architecture of NS
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After simulation, NS2 outputs either text-based or animation-based simulation results. To interpret
these results graphically and interactively, tools such as NAM (Network AniMator) and XGraph
are used. To analyze a particular behavior of the network, users can extract a relevant subset of
text-based data and transform it to a more conceivable presentation.
Transport Control Protocol
The architecture of computer and communication networks is often structured in layers: physical,
data link, network (or internetworking), transport, and other higher layers, including session,
presentation, and application. Each lower layer acts as a service provider to its immediate upper
layer, which is a service user. Interactions between neighboring layers occur through service access
points (SAPs). For example, the data link layer provides link services to the network layer, which
is immediately above the link layer. The network layer provides addressing and routing services
to the transport layer above it, which in turn provides message transportation service to the layers
above it.
The transport layer provides end-to-end segment transportation, where messages are
segmented into a chain of segments at the source and are reassembled back into the original
message at the destination nodes. The transport layer does not concern itself with the underlying
protocol structures for delivery and/or with the mechanisms used to deliver the segments to the
destination nodes. Examples of transport protocols are the transport control protocol (TCP), the
user datagram protocol (UDP), the sequenced packet exchange protocol (SPX), and NWLink
(Microsoft’s approach to implementing IPX/SPX). TCP and UDP are commonly deployed in the
Internet. The transport protocol can incorporate QoS considerations into flow and congestion
control.
TCP is the commonly used connection-oriented transport control protocol for the Internet.
Some applications, such as FTP and HTTP, reside on the TCP layer. TCP uses network services
provided by IP layer, with the objective of offering reliable, orderly, controllable, and elastic
transmission. TCP mechanisms allow flexible flow and congestion control.
TCP operation consists of three phases:
1. Connection establishment
2. Data transmission
3. Disconnect
User Datagram Protocol:-
UDP is a connectionless transport protocol. It exchanges datagrams without a sequence number,
and if information is lost in the process of exchange between the transmitter and the receiver, this
protocol does not have the mechanisms to recover it. Since it does not offer a sequence number in
the datagrams it therefore does not guarantee orderly transmission. It also does not offer
capabilities for congestion or flow control. In circumstances where both TCP and UDP are present,
since UDP does not perform congestion or flow control, it may turn out that it outperforms TCP.
In recent years a TCP-friendly rate control (TFRC) has been proposed for UDP to implement a
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certain level of control in this protocol. The basic idea behind TFRC is to provide almost identical
throughput to both TCP and UDP when they are present on a connection.
Routing Protocols
In this section, we briefly describe the key features of the AODV and DSDV protocols studied in
our simulations.
Ad-hoc On-demand Distance Vector (AODV):- AODV is a combination of on-demand and
distance vector, i.e. hop-to-hop routing methodology. When a node needs to know a route to a
specific destination, it creates a ROUTE REQUEST. Next the route request is forwarded by
intermediate nodes, which also create a reverse route for itself for the destination. When the request
reaches a node with a route to destination it creates again a REPLY which contains the number of
hops that are required to reach the destination. All nodes that participate in forwarding this reply
to the source node create a forward route to destination. This route created for each node from
source to destination is a hop-by-hop state and not the entire route as in source routing.
Destination Sequenced Distance Vector (DSDV):- DSDV is a hop-by-hop distance vector
routing protocol requiring each node to periodically broadcast routing updates based on the idea
of classical Bellman-Ford Routing algorithm. Each node maintains a routing table listing the “next
hop” for each reachable destination, number of hops to reach the destination and the sequence
number assigned by the destination node. The sequence number is used to distinguish stale routes
from new ones and thus avoid loop formation. The stations periodically transmit their routing
tables to their immediate neighbors. A station also transmits its routing table if a significant change
has occurred in its table since the last update sent. So, the update is both time-driven and event-
driven. The routing table updates can be sent in two ways: a “full dump” or an “incremental”
update.
3.2 Performance Metrics: - The performance metrics help to characterize the network that is
substantially affected by the routing algorithm to achieve the required Quality of Service (QoS).
In this work, the following metrics are considered.
3.2.1. Packet Delivery Ratio [%] (PDR): PDR is defined as the ratio of the number of data
packets successfully delivered to the destination nodes and the number of data packets produced
by source nodes.
3.2.2. Average Throughput: Throughput refers to the amount of data transfer from source node
to destination in a specified amount of time.
3.2.3. End-to-End Delay: The average time interval between the generation of a packet in a source
node and the successful delivery of the packet at the destination node.
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3.3 Simulation Study
Maintenance and monitoring of grain storage warehouses, is a difficult task which has to be
carried out meticulously to ensure safe grain storage over long time periods. Wireless sensor
networks can be effectively used for automating the process.
3.3.1 Design and Simulation of Wireless Ad Hoc Network Using NSG2
NS-2 can be used to study both wire and wireless scenario. Wireless ad hoc network is
simulated using NS2 simulator version 2.35 which install on Ubuntu 14.04. For creating a tcl script
file, script generator tool NSG2 is used. NS2 Scenarios Generator 2(NSG2) is a JAVA based ns2
scenarios generator. Since NSG2 is written by JAVA language, it can run NSG on any platform.
NSG2 is capable of generating both wired and wireless tcl scripts. Some major functions of NSG2
are listed above:
1. Creating wired and wireless nodes
2. Creating connection between nodes
3. Creating links (Duplex-Link and Simplex-Link)
4. Creating agents (TCP and UDP)
5. Creating applications (CBR and FTP)
Figure 3 NS2 Scenarios Generator 2
Table 2 Simulation Parameters
Network
Parameters
Values
Number nodes 2, 3
Connection Type tcp, udp
Source traffic ftp,cbr
Packet size 500,1000,1500
Routing Protocol AODV
Simulation Time 10 ms
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Simulation Experiments:-
Basically three types of experiments were carried out
1. One source and one destination
2. Two Source and One destination
3.4 Performance Evaluation:-
NS2 visual trace analyzer provides an easy way to fulfill this exhaustive task allowing users
to trace graphics, filter packets, visualize nodes position, calculate node and traffic statistics, and
so on. This is a standalone application, with a user friendly interface. The performance parameters
that can be obtained through Visual trace analyzer are
1. Throughput
2. End to end delay
3. Jitter
4. Packet
NS2 visual trace analyzer also provides information related to number of packets generated,
number of packets dropped, sent packets etc.
Figure 4 Connectivity Information in Visual Trace Analyzer
Figure 5 Visual trace analyzer shows connections, number of packets, delay and throughput
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3.5 Performance Analysis
We evaluate the performance of a wireless sensor network for monitoring of food grain
storages. Indoor environment of foodgrain storages is different as compared to other applications
of WSN which affects the performance of the sensor network. Deployment of sensor nodes inside
foodgrain storages is an important issue, which decides coverage and connectivity of the sensor
network. We used WSN planer Tool software for arrangement of sensors nodes in the network.
The major issues that affect the design and performance of a wireless sensor network are as
follows: Hardware and Operating System for WSN, Wireless Radio Communication
Characteristics, Medium Access Schemes, Deployment, Localization, Synchronization,
Calibration, Network Layer, Transport Layer, Data Aggregation and Data Dissemination,
Database Centric and Querying, Architecture, Programming Models for Sensor Networks,
Middleware and Quality of Service.
The significance of WSN can be determined using QOS parameters like Reliability, Energy,
Throughput, Congestion, Fairness and Delay. Congestion causes packet loss and leads to excessive
Figure 8 Throughput vs. simulation time
Figure 7 Jitter Vs. No. of Packets Figure 6 Delay Vs. No. of Packets
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energy consumption with increased delay. Congestion can occur at two levels. One is node-level
congestion and second is a link-level congestion.
In WSNs reliability is a very important parameter which used as a measure to show how
reliable the sensed information can be reported to the destination i.e. sink. The reliability can be
controlled at different levels like MAC layer, transport layer, routing layer and physical layer.
There two types of reliability. First one is desired reliability and second is observed reliability.
Desired reliability is nothing but the reliability that application should achieve and observed
reliability is the reliability currently achieved by application. Likewise reliability, energy is also a
most important factor. There is always a trade-off in between energy and packet delivery ratio i.e.
reliability. The nodes of wireless sensor network have finite amount energy and it is hard to replace
the battery of sensor nodes. So, the energy efficiency is an important and crucial parameter in
wireless sensor network.
3.5.1. Simulation Experiment 1
The simulation software WSN Planner tool is used for planning the wireless sensor network inside
the large foodgrain warehouse. In input window of WSN Planner tool area, position of base station,
number of nodes, communication range and sensing range are entered. Output section shows result
of arrangements and experiments in the form of number of nodes connected, area covered and
sensed area. We have performed experiment using random arrangement and Cartesian
arrangement using 12 sensor nodes and one coordinator. Figure 9 shows Cartesian (left) and
random (right) deployment of 12 sensor nodes and 1 coordinator with coverage area marked.
Figure 9 Cartesian (left) and Random (right) arrangement of Sensor nodes using WSN planner tool
NS2 simulator with IEEE 802.15.4 standard is used to study performance characteristics of the
Wireless Sensor Network. We have conducted experiments for finding the best position of
coordinator and to study the effect of varying packet size and number of connections with two-ray
ground and shadowing propagation models using NS2. Cartesian arrangement is chosen for
experiments and simulations were performed using Network Simulator 2 (NS2) software (NS-
2.35).The multi-hop wireless network is formed with mesh topology consist of 12 nodes and 1.
The simulation is performed with the following parameters mentioned in Table 3. The trace file is
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post processed using Perl scripting language and throughput, packet delivery ratio and throughput
are measured.
Table 3 Simulation Parameters
Experimental result of WSN Planner tool shows Cartesian arrangement gives better connectivity
and coverage as compared to random arrangement. The first experiment performed using NS2
simulator is for checking the best position of the coordinator. We used 12 sensor nodes and 1
coordinator as shown in figure 10.
Figure 10 Arrangement of sensor nodes in NAM
window
Experiments are performed by placing coordinator at center, all corners and at the side
of foodgrain storage namely at center, Left corner_East,Left corner_West, Right corner_East a
Right corner_West. The arrangement of sensor nodes (n0-n11) and coordinator (n12) is shown in
figure 10.
Figure 11 Packet Delivery Ratio vs. position of Coordinator
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Figure 12 Throughput vs. Position of Coordinator Figure 13 Avg. end to end delay vs. Position of
Coordinator
It is well known that position of coordinator is very important for good performance
characteristics. Figure 11 and 12 shows the coordinator position at the center gives good packet
delivery ratio and throughput compared to other positions. End to end delay is less for base station
at the center is shown in figure 13. Therefore, the coordinator at center gives good values of packet
delivery ratio and throughput and less delay.
3.5.2 Simulation Experiment 2
The simulations were performed using Network Simulator 2 (NS-2.35) software. The traffic source
used is constant bit rate (CBR). CBR is configured to generate 20 Byte packets at the rate of 100
bps. The Cartesian arrangement is used for sensor node deployment. Multi hop mesh and grid
topology is used in simulations. There are 35 nodes and 1 central PAN coordinator, which add up
to 36 nodes present in our analysis.
Table 4 simulation parameters
Parameter Parameter Value
Simulator NS 2.35
Channel Wireless Channel
Interface Queue Droptail/PriQueue
Number of Nodes 36
Number of Sources 12
MAC Protocol IEEE 802.15.4
Transport Layer Protocol UDP
Antenna Type Omnidirectional Antenna
Packet Size 20 Bytes
Packet Transmission
Rate
Varied 10 to 100 bits per
second
Propagation Models Two Ray Ground
Routing Protocols AODV, DSDV
Simulation Time 100
20
In simulation using NS-2 simulation software, UDP protocol is used for transport layer and CBR
traffic generator is used as for the application layer. AODV and DSDV protocols are studied for
giving application. Table 4 shows parameter and their values for our setup.
Figure 14 NAM Window of NS-2 simulator
In this experiment the performance analysis for quality of service (QoS), is carried out by
varying the packet transmission rate and measuring the parameters like delivery ratio, throughput
and end to end delay. The Two routing protocols, i.e. AODV and DSDV are considered for the
comparison purpose. The Perl script is used for calculations of packet delivery ratios, throughput
and end to end delay.
Simulation Scenario 1: Performance Analysis by Varying CBR Packet Size. All nodes send
information randomly. The experiments are repeated for 100 seconds with packet rate varying
from 10 to 100 seconds. Results are obtained for AODV and DSDV protocols.
a) Packet Delivery ratio b)Throughput
c) End to End Delay
Figure 15 Performance Analysis of AODV and DSDV Protocols for parallel data transmission
21
Simulation Scenario 2: Periodically, each node sends information. Each node sending information
for 5 seconds and then stop transmission, then its data in a network. The packet transmission rate
is varied from 10 to 100 in step of 10 packets per second.
a) Packet Delivery ratio b)Throughput
c) End to End Delay
Figure 16 Performance Analysis of AODV and DSDV Protocols for periodical data transmission
Figure 15 and Figure 16 Shows variations in packet delivery ratio, throughput and end to
end delay for AODV and DSDV protocols with different packet rates for parallel and periodic data
transmission respectively. Figure 15 (a) and Figure 16 (a) shows AODV protocol has more packet
delivery ratio than DSDV protocol. Figure 15 (b), (c) and Figure 16 (b), (c) shows similar
performance of throughput and end to end delay for both routing protocols, but initially end to end
delay is quite high for DSDV protocol.
Three performance metrics, i.e. PDR, throughput and end-to-end delay for different packet
rates has been evaluated. From the results we concluded that the packet delivery ratio of AODV
protocol is good compare to DSDV protocol, throughput and end to end delay are nearly similar
for parallel data transmission. In periodically data transmission AODV gives constant PDR,
throughput and end to end delay, while DSDV shows variations.
22
Figure 18 Packet delivery ratio vs. Packet size Figure 19 Throughput vs. Packet size
3.5.3 Simulation Experiment 3
Experimental Setup
In Food corporation India (FCI) storage, foodgrain bags are arranged in 12 stacks, in 3X4
matrix. In each stacks the grain bags are stacked one over other up to 22 layers. 12 stacks are
shown in figure 17(left) and actual arrangement of foodgrain bags stack inside a foodgrain
warehouse is in figure 17(right). One sensor node is placed in each stack shown by circle and
coordinator is shown by rectangle at the center.
As studuied in experiment 1 Coordinator at center is the best position for performing
experiment to analyse packet delivery ration, throughput and end to end delay. We have perform
three experiment, in first experiment we have vary packet size, in second experiment we have vary
number of connections and in third experiment we have study the effect of Path loss exponent and
Shadowing Deviation.
A. Effect of Varying Packet size
In this experiment, the network consists of 12 nodes (n0-n11) and 1 coordinator (n12). In
this scenario, all nodes sends constant bit rate (CBR) traffic data toward coordinator. The packet
size is varied from 10 to 100 and packet delivery ratio, throughput and average end to end delay
are measured. Experiment is performed using Two-ray ground and shadowing propagation models.
Figure 17 Arrangement of foodgrain stacks inside foodgrain storage
23
Figure 22 Throughput vs. Number of connections Figure 21 Packet delivery ratio vs. Number of
connections
Figure 20 Avg. End to end delay vs. Packet size
The performance of packet delivery ratio vs. varying packet size is shown in figure 18. It
observed that packet delivery ratio for two-ray ground model is more compare to Shadowing
model. For packet size 80, packet delivery ratio (PDR) and throughput for both propagation
models has highest value. Throughput is increases with increase in packet size is shown in figure
19. Figure 20 shows average end to end delay is maximum for shadowing model compare to Two-
ray ground model.
B. Effect of varying number of Connections
In this experiment coordinator is placed at center and packet size is kept 80 bytes, the
number of connections are varied starting from 1 and incremented on a scale of 1 up to 12 and
performance is analyzed for Two-ray ground and shadowing propagation models. The experiment
is performed using two-ray ground and shadowing models propagation models.
24
The figure 21, 22 and 23 shows the graph of packet delivery ratio, throughput and end to end delay
vs. varying number of connections. The packet delivery ratio is nearly 100% and throughput is
also increases linearly up to 5 connections for both propagation models. Initially average end to
delay is more for two-ray ground model but after 3 connections it remains in between 10 to 25 ms,
but for shadowing model its value more after 3 connections.
C. Effect of Path loss exponent and Shadowing Deviation
Indoor environment inside foodgrain storages is not stable and loading and unloading of grain bags
can cause problems of connectivity, which affects performance of wireless sensor network. The
path loss can vary due to this process and due to random movement of people inside storages. To
study the effect of loading and unloading of grain bags on performance of wireless sensor network
inside a foodgrain storage. The two experiments are performed by changing values of path loss
exponent β and shadowing deviation σdB. For first experiment where source nodes and coordinator
considered in a line of sight, β = 2.0 and σdB = 4.0. If grain bags are between source nodes and
coordinator, β = 2.2 and σdB = 5.0 dB are considered.
Figure 24 shows when increasing values of path loss exponent and shadowing deviation
packet delivery ratio and throughput decreases and average end to end delay increases.
Figure 23 Avg. End to end delay vs. Number of
Connections
25
The performance of Zigbee based wireless sensor network for large foodgrain warehouse
monitoring through simulations carried out using ns2.35 simulator tool. Simulations are carried
out to study the effect of variation in packet size and number of connections on network
performance. From the obtained results it is observed that for multi-hop transmission with 12
nodes, packet delivery ratio and throughput is more for Two-ray ground model than shadowing
propagation model. Throughput increases with increase in packet size and also with increasing
number of connections. Shadowing models shows less packet delivery ratio and throughput and
more delay.
3.5.4 Simulation Experiment 4
Simulation Scenario
To perform simulation study of WSN inside foodgrain warehouse, we have used the NS-2
simulator, which is a discrete event-driven simulation tool and is open source. It is useful to study
the dynamic nature of wire as well as wireless communication networks.
Performance Metrics
1. Packet Delivery Ratio (PDR):- PDR is the ratio of successfully received packets at sink to the
number of packets sent from the source. A higher packet delivery ratio value indicates that the
network performance is good and packet loss is less.
2. Throughput:- The throughput is the maximum rate at which the information is transferred from
source to sink. It is measured as the number of packets arriving at the sink in bits per second (bps).
Throughput =(Number of bytes received∗8)
Time∗1000
3. Average end to end delay: - The measuring of the average time taken by each packet to transfer
the data from the source to sink is called as average end-to-end delay. If it goes higher, the network
suffers congestion.
0
20
40
60
80
100
β=2 and σ =4.0
β=2.2 and σ =5.0
Pac
ket
De
live
ry R
atio
(%)
Pathloss exponent and Shadowing Deviation
Figure 24 Packet Delivery Ratio, Throughput and Avg. end to end delay for different values of path loss
exponent and shadowing deviation
0
0.5
1
1.5
2
β=2 and σ =4.0
β=2.2 and σ =5.0
Thro
ugh
pu
t (k
bp
s)
Pathloss Exponent and Shadowing Deviation
020406080
100120140
β=2 and σ =4.0
β=2.2 and σ =5.0 En
d t
o e
nd
de
lay
(ms)
Pathloss Exponents and shadowing deviation
26
Figure 26 Throughput vs. Distance
4. Energy Consumption: - Amount of energy used by sensor node is referred as energy
consumption. Energy consumption is important parameter that decides the era of sensor network.
Energy consumption can be minimized by putting the sensor nodes in low power modes for most
of the time and active for short duration.
Basic Simulation Experiments: -
Basic experiments are performed using two sensor nodes with a shadowing propagation
model to study the effect of distance variation and packet size. The range for communication of
sensor node is fixed to 40 meters. Node N0 is a source node and node N1 is a sink node. Distance
between two nodes is varied from 10 to 60meters. The experiment is repeated for packet size of
20, 40, 60, 80 and 100. Performance parameters such as Packet Delivery Ratio (PDR), throughput
and average end to end delay are studied.
The PDR is 100% up to 30m distance and more than 90% for the distance between 30 to
40m. Figure 25 shows packet size does not affect the packet delivery ratio. Figure 26 shows, as we
increase the packet size, throughput is increased. Throughput is not much affected by an increased
distance between nodes. As the communication range is 40m, throughput reduces sharply for the
distance greater than 40m. Average end to end delay increases with distance, is independent of
packet size and increases sharply for distance greater than 40m, as shown in figure 27.
Figure 25 PDR vs. Distance
Figure 27 Average end to end delay vs. Distance
27
Study of Energy Consumption
The study of energy consumption is performed by varying the distance between two nodes and
increasing the number of hops. All nodes are powered with 100% energy. The first experiment
was conducted to study energy consumption with increasing distance between two nodes by 10m
step.
Figure 28 Energy Consumption vs. Distance Figure 29 Energy Used vs. Number of hops
Network Scenario for Foodgrain Warehouse Monitoring:- For foodgrain warehouse
monitoring application, 12 sensor nodes are used as a source nodes and one base station is used as
a sink node or base station in the NS-2 simulator. One sensor node is assigned to each foodgrain
stack and base station is placed at the center as shown in figure 30, with the parameters set as
shown in table 5.
Table 5. Simulation Parameters
Sr. No. Parameters Details
1. Number of nodes 13
2. Radio Propagation
Model
Shadowing
3. Antenna Model Antenna/OmniAntenna
4. MAC type 802.15.4
5. Routing Protocol AODV
6. Internet Protocol UDP
7. Traffic Type cbr
8. Packet Size 512 bytes
9. Simulation Area 50 X 30 m
10. Simulation Time 1000 sec
Sensor nodes are deployed using Cartesian arrangement. Figure 30 shows NAM window of the
NS-2 simulator, where sensor nodes send data towards base station.
28
Figure 31 Network performance while all nodes
send data simultaneously
For foodgrain warehouse simulation, we have performed two experiments using NS-2 simulator.
Perl script was used for analysis of trace file. In experiment 1, all nodes send the data towards the
base station simultaneously. Which gives the packet delivery ratio 66.57 %, a throughput of 1.24
kbps and average end to end delay of 9.5 ms which is shown in figure 31.
Figure 32 shows that packet delivery ratio is improved, throughput is reduced and average
end to end delay is increased. But as the foodgrain warehouse monitoring does not require real
time monitoring, average end to end delay can be acceptable. Also, as there is no need to send
sensor data continuously to base station and throughput is acceptable.
Figure 30 NAM Window of NS-2 Simulator
Figure 32 Network performance while all nodes
send data at specific time interval
29
Energy consumption of each node is calculated and graph is plotted. Figure 33 shows
energy consumption of each node for both experiments. Energy consumption of sensor nodes in
experiment 1 is more than experiment 2 where sensor nodes send the data at specific interval which
saves the energy.
Figure 33 Energy Consumption vs. Node
30
4. Development of Wireless Sensor
Node
Building a wireless sensor network requires nodes to be developed for required application as per
specifications. They might have to be small, cheap, or energy efficient, they have to be equipped
with the right sensors, the necessary computation and memory resources, and they need adequate
communication facilities. Sensor Node can be developed using basic components such as sensor,
microcontroller, communication device and battery.
5.1 Hardware components
A basic sensor node comprises five main components:
1. Controller: A controller processes all the relevant data, capable of executing arbitrary code.
2. Memory: Sensor node requires some memory to store programs and intermediate data;
usually, different types of memory are used for programs and data.
3. Sensors and actuators: Sensors have interface to the physical world and converts physical
parameter to electrical quantity. Actuators are the devices that can control physical
parameters of the environment.
4. Communication Device: To form a wireless network requires a device for sending and
receiving information over a wireless channel.
5. Power supply: Batteries provides required energy to sensor node. Sometimes, some form
of recharging by obtaining energy from the environment is available as well (e.g. solar
cells).
Figure 34 Sensor Node Hardware Components
To design a sensor nodes firstly basic components are decided and some basic experiments
are performed.
31
5.1.1. Controller:
The controller is the core of a wireless sensor node. It collects data from the sensors,
processes this data, decides when and where to send it, receives data from other sensor nodes. It
has to execute various programs, ranging from time-critical signal processing and communication
protocols to application programs; it is the Central Processing Unit (CPU) of the node. Simpler
processors specifically geared toward usage in embedded systems are commonly referred as
microcontrollers. Some of the key characteristics why these microcontrollers are particularly
suited to embedded systems are their flexibility in connecting with other devices (like sensors),
their instruction set amenable to time-critical signal processing, and their typically low power
consumption; they are also convenient in that they often have memory built in. In addition, they
are freely programmable and hence very flexible. Microcontrollers are also suitable for WSNs
since they commonly have the possibility to reduce their power consumption by going into sleep
states where only parts of the controller are active; details vary considerably between different
controllers [40].
MSP 430 Launchpad:-
MSP 430 family microcontroller is selected for sensor node development. MSP430 Launchpad
development board is used for because of ultra-low power microcontroller. It has on-chip USB
emulation capability. The Launchpad development kit features an integrated DIP target socket that
supports up to 20 pins, allowing MSP430 Value Line devices to be plugged into the Launchpad
development kit. The MSPEXP430G2 Launchpad development kit comes with an MSP430G2553
MCU by default. The MSP430G2553 MCU has the most memory available of the compatible
Value Line devices. The MSP430G2553 16-bit MCU has 16KB of flash, 512 bytes of RAM, up
to 16-MHz CPU speed, a 10-bit ADC, capacitive-touch enabled I/Os, universal serial
communication interface, and more – plenty to get you started in your development. Free software
development tools are also available: TI's Eclipse-based Code Composer Studio™ IDE (CCS),
IAR Embedded Workbench™ IDE (IAR), and the community-driven Energia open source code
editor. More information about the Launchpad development kit, including documentation and
design files.
Figure 35 MSP 430 LAUNCHPAD
32
Features of MSP 430G2553 Microcontroller:
1) Low Supply-Voltage Range: 1.8 V to 3.6 V
2) Ultra-Low Power Consumption (USCI)
– Active Mode: 230 μA at 1 MHz, 2.2 V
– Standby Mode: 0.5 μA
– Off Mode (RAM Retention): 0.1 μA
3) Five Power-Saving Modes
4) Ultra-Fast Wake-Up From Standby Mode in Less Than 1 μS
5) 16-Bit RISC Architecture, 62.5-ns Instruction Cycle Time
6) Two 16-Bit Timer_A With Three Capture/Compare Registers Interface
7) Up to 24 Capacitive-Touch Enabled I/O Pins
8) Universal Serial Communication Interface (USCI)
9) 10-Bit 200-ksps Analog-to-Digital (A/D) Converter With Internal Reference, Sample and-
Hold and Autoscan
10) Brownout Detector
11) Serial Onboard Programming, No External Programming Voltage Needed On-Chip
Emulation Logic with Spy-Bi-Wire Interface.
5.1.2. Sensors:-
5.1.2.1 Humidity and Temperature Sensor(DHT 11):-
Figure 36 DHT11 sensor
DHT11 is a composite temperature and humidity sensor contains a calibrated digital signal
output of temperature and humidity. The sensor includes a resistive sense of wet components and
NTC temperature measurement devices, and connected with high performance 8-bit
microcontroller. DHT11 has 16 bit resolution; it works 3.5 to 5.5V DC supply. Its current
consumption is 0.3mA in active mode and 60μA in standby mode. It uses simplified single bus
communication with 40-bit data. 40 bit data includes 16 bit humidity and 16 bit temperature data
and 8 bit parity bit.
5.1.2.2 Temperature sensor (LM35)
LM35 is a precision IC temperature sensor with its output proportional to the temperature
(in oC). The sensor circuitry is sealed and therefore it is not subjected to oxidation and other
processes. With LM35, temperature can be measured more accurately than with a thermistor. It
also possess low self-heating and does not cause more than 0.1oC temperature rise in still air.
33
Figure 37 LM 35
Features
Calibrated Directly in Celsius (Centigrade)
Linear + 10-mV/°C Scale Factor
0.5°C Ensured Accuracy (at 25°C)
Rated for Full −55°C to 150°C Range
Suitable for Remote Applications
Low-Cost Due to Wafer-Level Trimming
Operates From 4 V to 30 V
Less Than 60-μA Current Drain
Low Self-Heating, 0.08°C in Still Air
Low-Impedance Output, 0.1 Ω for 1-mA Load 2
Applications
Power Supplies
Battery Management
HVAC
Appliances
5.1.2.3. Humidity Sensor (HSM20G)
The module of HSM-20G is essential for those applications where the relative humidity can be
converted to standard voltage output.
Figure 38 HSM 20G
34
Specification
• Input voltage range DC 5.0±0.2V
• Output voltage range DC 1.0—3.0 V
• Measurement Accuracy ±5% RH
• Operating Current (Maximum) 2mA
• Storage RH Range 0 to 99% RH
5.1.2.4 Carbon Dioxide Sensor (TGS 4161)
Figure 39 TGS 4161
TGS4161 is a new solid electrolyte CO2 sensor which offers miniaturization and low
power consumption. A range of 350~10,000ppm of carbon dioxide can be detected by TGS4161,
making it ideal for indoor air control applications. The CO2 sensitive element consists of a solid
electrolyte formed between two electrodes, together with a printed heater (RuO2) substrate. By
monitoring the change in electromotive force (EMF) generated between the two electrodes, it is
possible to measure CO2 gas concentration.
The top of the sensor cap contains adsorbent (zeolite) for the purpose of reducing the
influence of interference gases.TGS4161 exhibits a linear relationship between ΔEMF and CO2
gas concentration on a logarithmic scale. The sensor displays good long term stability and shows
excellent durability against the effects of high humidity.
Features of TGS 4161
➢ High selectivity to CO2
➢ Long life and low cost
➢ Low power consumption
Basic measuring circuit of TGS 4161
The TGS4161 sensor requires heater voltage (VH) input. The heater voltage is applied to the
integrated heater in order to maintain the sensing element at a specific temperature which is
optimal for sensing. Electromotive force (EMF) of the sensor should be measured using a high
impedance (>100 GΩ) operational amplifier with bias current < 1pA (e.g. Texas Instruments'
model #TLC271).Since the solid electrolyte type sensor functions as a kind of battery, the EMF
35
value itself would drift using this basic measuring circuit. However, the change of EMF value
(ΔEMF) shows a stable relationship with the change of CO2. Special microprocessor for signal
processing should be used with TGS4161microprocessor.
Figure 40 Basic measuring circuit
Pin Connection:
1. Heater (+)
2. Counter electrode (+)
3. Sensing electrode (-)
4. Heater (-)
5.1.3 Communication Device:
The communication device is used to exchange data between individual nodes. Zigbee wireless
devices are used as a communication device. Zigbee devices are operates on 2.4. GHz frequency
and uses IEEE 802.15.4 standard. This devices provides 250 kbps data rate. Zigbee devices are
used because of low power consumption and good communication range. XBee S2C modules are
used.
XBee S2C Zigbee
The XBee Zigbee RF Modules provide wireless connectivity to end-point devices in Zigbee mesh
networks. Using the Zigbee PRO Feature Set, these modules are inter-operable with other Zigbee
devices, including devices from other vendors. With the XBee, users can have their Zigbee
network up-and-running in a matter of minutes without configuration or additional development.
The XBee Zigbee RF Modules are compatible with other devices that use XBee Zigbee
technology.
36
Figure 41 XBee s2c module
Specifications:
TX Peak Current: 40 mA
RX Current: 40 mA (@3.3 V)
Power-down Current: < 1 μA
Indoor/Urban: up to 133 ft (40 m)
Outdoor line-of-sight: up to 400 ft (120 m)
Transmit Power: 2 mW (3 dBm)
Receiver Sensitivity: -96 dBm
Zigbee is a new standard for wireless sensor and control networks. It has the following
characteristics:
a. Low battery consumption. A Zigbee end device should operate for months or even years
without needing its battery replaced.
b. Low cost.
c. Zigbee can automatically establish its network.
d. Zigbee uses small packets compared with Wi-Fi and Bluetooth.
5.1.4. Power Supply
Power supply provides energy to hardware components used in sensor node. Normally AA
or AAA batteries are used as a power source. Rechargeable and non-rechargeable batteries can be
used for sensor nodes. Uniross Ni-MH AA Rechargeable Battery of 2100 mAh capacity and
SAMSUNG ICR18650 2600mAh Li-Ion Battery are used.
5.1.4.1 Uniross Ni-MH Battery
It is 1.2V 600 mAh AA Cell NiMH Rechargeable Battery.
5.1.4.2. Samsung ICR 18650 Battery
It is 3.7V 2600 mAh Li-ion Rechargeable Battery.
37
5.2 Testing of Hardware Components
5.2.1 Experiments using Zigbee module: - Communication between two XBee devices is tested.
XBee devices are configured using HyperTerminal and XCTU Softwares.
HyperTerminal: - It supports text-based communication through Telnet, SSH, Modem, and Serial
port connections. The software receives data through the connection, and processes the data
through a terminal emulator that is designed to mimic different types of terminal systems.
Following commands are used for to configure Zigbee using Hyper Terminal of PC.
1) +++ For enter into command mode.
2) ATID 16 bit pan id in which it is working.
3) ATMY 16 bit device own id.
4) ATDH 16 bit destination high address.
5) ATDL 16 bit destination low address.
6) ATWR write into non-volatile memory.
7) ATCN exit from command mode.
XCTU software
XCTU is a free multi-platform application designed to enable developers to interact with
Digi RF modules through a simple-to-use graphical interface. It includes new tools that make it
easy to set-up, configure and test XBee RF modules.
XCTU includes all of the tools a developer needs to quickly get up and running with
XBee. Unique features like graphical network view, which graphically represents the XBee
network along with the signal strength of each connection, and the XBee API frame builder,
which intuitively helps to build and interpret API frames for XBees being used in API mode,
combine to make development on the XBee platform easier than ever.
Features:
You can manage and configure multiple RF devices, even remotely (over-the-air)
connected devices.
The firmware update process seamlessly restores your module settings, automatically
handling mode and baud rate changes.
Two specific API and AT consoles, have been designed from scratch to communicate
with your radio devices.
You can now save your console sessions and load them in a different PC running XCTU.
XCTU includes a set of embedded tools that can be executed without having any RF
module connected:
38
o Frames generator: Easily generate any kind of API frame to save its value.
o Frames interpreter: Decode an API frame and see its specific frame values.
o Recovery: Recover radio modules which have damaged firmware or are in
programming mode.
o Load console session: Load a console session saved in any PC running XCTU.
o Range test: Perform a range test between 2 radio modules of the same network.
o Firmware explorer: Navigate through XCTU's firmware library.
An update process allows you to automatically update the application itself and the
radio firmware library without needing to download any extra files.
XCTU contains complete and comprehensive documentation which can be accessed at
any time.
5.2.1.1 Study of QoS parameters using XBee
The Range Test utility, embedded within XCTU, tests the real RF range and link quality between
two radio modules in the same network. To perform a range test, we have connected a local radio
module to PC and added to XCTU, and a remote device in the same network as the local device.
We have performed the experiment by placing XBee nodes inside the lab and other with 1 node
inside and other outside the lab.
Range Test @inside lab
Figure 42 Xbee range testing shows packets send and packets received inside lab
39
Figure 43 RSSI inside lab
RSSI Chart: This chart represents the RSSI values of the local and remote devices during the
range test session.
Packet summary: This control displays the total amount of packets sent, packets received,
transmission errors and packets lost. It also displays the success rate (as a percentage) for sending
and receiving packets during the range test session:
Range Test @outside lab
Figure 44 Xbee Range Testing Shows Packets Send And Packets Received Outside Lab
40
Figure 45 RSSI outside lab
Figure 46 Throughput
Effect of varying the distance:
Figure 47 Effect of Varying distance on RSSI
41
Figure 49 Wireless sensor node using MSP430
Microcontroller
5.3 Development of wireless sensor node using 430 Microcontroller:-
The low power and high performance wireless sensor node is designed. Temperature and
relative humidity were measured and transmitted wirelessly. Basic sensor node consist four main
components: sensor, controller, communication device and battery. The sensor gives actual
interface to the physical world. It is used to observe physical parameters of the environment. A
controller is used to process all the relevant data, capable of executing arbitrary code. Turning
nodes into a network requires a device for sending and receiving information over a wireless
channel, this work is done by communication device. It is used for exchanging of the information
between numbers of sensor nodes present in the network. Batteries provide necessary energy for
working of sensor node.
5.3.1 Wireless Sensor Node using DHT 11 Sensor
The hardware components of sensor node includes DHT11 humidity and temperature
sensor, MSP430G2553 Microcontroller, XBee series1 transceiver and four 1.2V Ni-MH
rechargeable batteries is shown in figure 49. The LM 1117 3.3V voltage regulator is used for
providing regulated 3.3V supply voltage to the sensor node. For measurement of environmental
parameters such as humidity and temperature inside a food grain warehouse DHT11 sensor is used.
Figure 48 Basic structure of wireless sensor node
42
Figure 50 Experimental set-up
Measurements of Temperature and Humidity:
DHT11 is a composite temperature and humidity sensor contains a calibrated digital signal output
of temperature and humidity. The sensor includes a resistive sense of wet components and NTC
temperature measurement devices, and connected with high performance 8-bit microcontroller.
DHT11 has 16 bit resolution; it works 3.5 to 5.5V DC supply. Its current consumption is 0.3mA
in active mode and 60μA in standby mode. It uses simplified single bus communication with 40-
bit data. 40 bit data includes 16 bit humidity and 16 bit temperature data and 8 bit parity bit.
Figure 51 XCTU Terminal Window Shows Temperature and Humidity
The Programs have been developed to read the 40 bit data from the DHT 11 sensor and
transmit towards another transceiver. The Code Composer Studio (CCS) 5.3.0 integrated
development environment (IDE) is used for programming the sensor node. The temperature and
humidity data from sensor node is transmitted to other Zigbee module which is connected to PC.
The temperature and humidity readings are observed on X-CTU software’s terminal window as
shown in figure 51. The temperature and humidity was monitored for four days continuously from
43
07:00 PM to 07:00 AM. The sensor node is operated in active mode where it continuously sends
the data towards Zigbee transceiver module which is connected to PC. This data is acquired in PC
using RS232 data logger serial port monitor software. The acquired data is plotted using origin
software to study the variation in humidity and temperature.
Figure 52: variation in temperature and Humidity
A low power consumption wireless sensor node, with MSP430 micro-controller and
Zigbee series 1 radio, is developed. The sensor node was designed using orcad and programming
by CCS in C. The sensor node samples temperature and humidity measurements with the on-board
sensor. The sensor node current consumption is 61 mA is measured for active mode.
Study of Coverage of sensor node:-The sensor node which was designed using MSP 430 is used
to study coverage. The one XBee series module was connected to PC and sensor node was place
at different locations around the food grain bags. But as the number of bags was limited sensor
node sends information regarding humidity continuously to PC.
5.3.2 Development of reconfigurable wireless sensor node:-
Basic sensor node consists four main components: sensor, controller, communication
device and battery. The sensor gives an actual interface to the physical world. It is used to observe
physical executing arbitrary code. Sensor network requires a device for sending and receiving
information over a wireless channel, this work is done with a communication device. It is used for
exchanging the information between numbers of sensor nodes present in the network. Batteries
provide the necessary energy for working on sensor nodes.
44
Temperature
Sensor
LM 35
XBee
Transceiver
Humidity
Sensor
HSM-20G
Gas
Sensor
TGS 4161
MSP 430
Microcontroller
Samsung
Lithium-ion
Battery
Figure 53 Reconfigurable Sensor Node
Schematic and layout of sensor node is designed using Dip trace Software is shown in figure 54 and 55
respectively.
Figure 54 Schematic of Sensor Node
45
Figure 56 Developed of Sensor Node
The reconfigurable sensor node is developed which consists of temperature sensor LM35,
humidity sensor HSM-20G and Gas sensor TGS4161, MSP 430 Launchpad development board,
XBee S2C Zigbee transceiver module and ICR18650 SAMSUNG Li-ion battery. PCB is designed
using Dip Trace software. Energia IDE software is used for programming.
Figure 55 Layout of Sensor Node
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5. Wireless Sensor Network Data
Monitoring Using LabVIEW
Data monitoring system using LabVIEW is a system that could be used for remote
monitoring of physical parameter such as temperature and humidity etc. In foodgrain warehouse
we have to monitor temperature, humidity, light intensity and CO2. We can place number of sensor
nodes inside a foodgrain storage for monitoring this parameters. The sensor data of each node will
indicate the status of location. Due increase in moisture level and the stored grain can be deteriorate
faster. So, we have to control the physical parameters to avoid foodgrain losses. By monitoring
this parameters we can generate a triggered message to the concern authority or caring persons to
take necessary action. The designed system is called a data monitoring system in wireless sensor
network using LabVIEW.
LabVIEW software is used for the graphical representation of receiving data. LabVIEW consist
of front panel for user interface and block diagram programmable logic.
5.1 Block Diagram Data Monitoring System
Sensor Nodes
PC with
LabVIEW
Xbee
S2C
XBee
S2C
Foodgrain Storage
Monitoring Station
Figure 57 Foodgrain Warehouse Data Monitoring System
+
Sensor Nodes are placed inside a foodgrain warehouse. Sensor nodes equipped with
temperature and humidity sensors. At the monitoring station, XBee Co-ordinator is connected PC
with LabVIEW installed. XBee Co-ordinator at monitoring station receives the data from sensor
47
nodes. Wireless communication of XBee modules are tested by using XCTU software. The XBee
coordinator is interfaced with user or consumer computer through the USB cable. Similarly user
can monitor the sensor node data on PC with the help of LabVIEW software and VISA interface.
LabVIEW shows graphical representation of receiving and sending data. It consist of Front
panel for user interface and Block diagram for programming. VISA is a virtual instrument system
architecture. VISA can provide the programming interface between the hardware and
developments such as LabVIEW. VISA is used to serially read this sensor node data and
monitoring this sensor node data in LabVIEW.
5.2. Introduction of LabVIEW
LabVIEW (Laboratory Virtual Instrument Engineering Workbench) is a graphical
programming language that uses icons instead of lines of text to create applications. In contrast to
text-based programming languages that use instructions to determine the order of program
execution, LabVIEW uses dataflow programming. In data flow programming, the flow of data
through the nodes on the block diagram determines the execution order of the VIs and functions.
VIs, or virtual instruments, are LabVIEW programs that imitate physical instruments.
In LabVIEW, we can build a user interface by using a set of tools and objects. The user
interface is known as the front panel. After we build the front panel, we can add code using
graphical representations of functions to control the front panel objects. we add this graphical code,
also known as G code or block diagram code, to the block diagram. The block diagram somewhat
resembles a flowchart. The block diagram, front panel, and graphical representations of code
compose a VI. The following illustration shows a front panel and its corresponding block diagram.
5.2.1 Data flow programming
The programming language used in LabVIEW, also referred to as G, is a dataflow programming
language. Execution is determined by the structure of a graphical block diagram on which the
programmer connects different function-nodes by drawing wires. These wires propagate variables
and any node can execute as soon as all its input data become available. Since this might be the
case for multiple nodes simultaneously, G is inherently capable of parallel execution. Multi-
processing and multithreading hardware is automatically exploited by the built-in scheduler, which
multiplexes multiple OS threads over the nodes ready for execution.
5.2.2 Graphical programming
LabVIEW ties the creation of user interfaces (called front panels) into the development cycle.
LabVIEW programs/subroutines are called virtual instruments (VIs). Each VI has three
components: a block diagram, a front panel, and a connector panel. The last is used to represent
the VI in the block diagrams of other, calling Vs. Controls and indicators on the front panel allow
48
an operator to input data into or extract data from a running virtual instrument. However, the front
panel can also serve as a programmatic interface. Thus a virtual instrument can either be run as a
program, with the front panel serving as a user interface, or, when dropped as a node onto the block
diagram, the front panel defines the inputs and outputs for the given node through the connector
pane. This implies each VI can be easily tested before being embedded as a subroutine into a larger
program.
5.2.3 The LabVIEW Environment
Input mechanisms and supply data to the block diagram of the VI. Indicators simulate
instrument output mechanisms and display data the block diagram acquires or generates.
Select View » Controls Palette to display the Controls palette and then LabVIEW programs
are called Virtual Instruments, or VIs, because their appearance and operation imitate physical
instruments, such as oscilloscopes and multimeters. LabVIEW contains a comprehensive set of
tools for acquiring analyzing, displaying, and storing data, as well as tools to help you troubleshoot
your code.
When opening LabVIEW, you first come to the “Getting Started” window. In order to
create a new VI, select “Blank VI” or in order to create a new LabVIEW project, select “Empty
project”. When you open a blank VI, an untitled front panel window appears. This window displays
the front panel and is one of the two LabVIEW windows you use to build a VI. The other window
contains the block diagram. The sections below describe the front panel and the block diagram.
5.2.4 Front Panel
The front panel is the user interface of a VI. Generally, you design the front panel first and
then design the block diagram to perform tasks on the inputs and outputs you create on the front
panel. You build the front panel using controls and indicators, which are the interactive input and
output terminals of the VI, respectively. Controls are knobs, push buttons, dials, and other input
mechanisms. Indicators are graphs, LEDs, and other output displays. Controls simulate instrument
select controls and indicators from the Controls
5.2.5 Block Diagram
After you build the front panel, you add code using graphical representations of functions to
control the front panel objects. The block diagram contains this graphical source code, also known
as G code or block diagram code.
5.3 VISA(Virtual Instrument System Architecture)
The Virtual Instrument Software Architecture (VISA) is a standard for configuring,
programming, and troubleshooting instrumentation systems comprising GPIB, VXI, PXI, Serial,
Ethernet, and/or USB interfaces. VISA provides the programming interface between the hardware
49
and development environments such as LabVIEW, Lab Windows/CVI, and Measurement Studio
for Microsoft Visual Studio. NI-VISA is the National Instruments implementation of the VISA
I/O standard. NI-VISA includes software libraries, interactive utilities such as NI I/O Trace and
the VISA Interactive Control, and configuration programs through Measurement & Automation
Explorer for all your development needs. NI-VISA is standard across the National Instruments
product line. With NI-VISA, you can feel confident that your software development will not
become obsolete as your instrumentation interface hardware needs evolve into the future.
5.4 Block Diagram of data monitoring system in LabVIEW
Figure 58 Block Diagram of Monitoring System using LabVIEW
50
5.4.1 Blocks description
1. VISA resource name
The VISA resource name block use in LabVIEW to select input /output port.
2. VISA serial
Right click on Block Diagram and from Instrument I/O choose the VISA pallet. From
the VISA pallet: Place a "VISA Configure serial port". Connect a "Control" to the input.
Default baud rate is 9600 and you may connect a constant to it, or just leave it to use the default
value.
3. Property node
Gets (reads) and/or sets (writes) properties of a reference. ... You also can use the Property
Node to access the private data of a LabVIEW class. The Property Node automatically
adapts to the class of the object that you reference.
4. VISA read
This block is use to continuously read the sensor data serially.
5. Read buffer
This block use to display read data.
6. VISA close
This block use to close visa reading serially.
7. String subset
Replaces one or all instances of a substring with another substring. To include the
multiline? Input and enable advanced regular expression searches, right-click the function and
select Regular Expression. Use this constant to supply a one-character space string to
the block diagram.
8. Fract/Exp string to Number Function
Fract/Exp String to Number Function. Interprets the characters 0 through 9, plus, minus, e,
E, and the decimal point (usually period) in string starting at offset as a floating-
point number in engineering notation, exponential, or fractional format and returns it
in number. ... If FALSE, the decimal separator is a period.
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5.5 GUI of data monitoring system in LabVIEW
Figure 59 GUI on Front panel in LabVIEW
In LabVIEW front panel shows the graphical user interface of the system. Data of each sensor
node is display separately on LabVIEW. At left side Node 1 displays the temperature and humidity
sensor data of sensor node 1. Node 2 display temperature and humidity sensor data of sensor node
2. By observing this values the concern authority can take the correct decision to avoid foodgrain
losses.
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6. Result and Discussion
Operation of wireless sensor network and its design constraints are studied. In this work a
simulation study of WSN to understand the effect of various performance parameters on the QoS
of the network has been performed. Also the performance of Zigbee based wireless sensor network
for foodgrain storage monitoring has been evaluated. Simulation software WSN planer Tool is
used to decide suitable arrangement of sensor nodes in the network and NS2 simulator is used with
IEEE 802.15.4 standard to study performance characteristics of the WSN. The result showed that
the coordinator position in wireless sensor network at the center gives a good packet delivery
ratio and throughput compared to other positions. Also, simulations related to the effect of path
loss exponent and shadowing deviation showed that increase in value of the path loss exponent,
end to end delay increases and Packet Delivery Ratio decreases. It is observed that for multi-hop
transmission with 12 nodes, packet delivery ratio and throughput is more for Two-ray ground
model than shadowing propagation model. Throughput increases with increase in packet size and
also with increasing number of connections. Shadowing models shows less packet delivery ratio
and throughput and more delay.
Also, we have performed the simulation study of QoS parameters of WSN such as a packet
delivery ratio, throughput, average end to end delay and power consumption by increasing the
number of hops and distance between two nodes. Also, the lifetime of wireless sensor network is
optimized. Result shows packet size does not affect the packet delivery ratio. As we increase the
packet size, throughput is increased. Throughput is not much affected by an increased distance
between nodes. As the communication range is 40m, throughput reduces sharply for the distance
greater than 40m. Average end to end delay increases with distance, is independent of packet size
and increases sharply for distance greater than 40m.
Wireless Sensor Network Data monitoring system using LabVIEW successfully designed
and implemented. Two wireless reconfigurable wireless sensor nodes are developed using
temperature sensor LM35 and Humidity sensor HSM20G, MSP430 Launchpad’s and XBee S2C
transceivers. Both sensor nodes work properly and send the data towards monitoring station, where
one XBee S2C module is connected to a PC. Data received from sensor nodes is processed using
LabVIEW software with help of VISA. The developed monitoring System monitors temperature
and humidity sensor data received from two sensor nodes. LabVIEW software shows a graphical
representation of sensor node data. The developed monitoring system using wireless sensor
network for monitoring environmental conditions in the grain storage warehouse is working
properly. Quality of Service parameters of Wireless Sensor Network such as a packet delivery
ratio, throughput, average end to end delay and sensor node power consumption are optimized for
the foodgrain warehouse monitoring application.
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7. Conclusion
Study of Wireless Sensor Network operation and its designed constraints shows operation of a
sensor network is depends on co-operation of nodes used in the network. Design constraints such
as low power consumption, low cost, harsh environmental conditions and limited computing
capability are important while designing a sensor node for required application. Deployment of a
large number of sensor nodes directly inside a foodgrain storages is much costlier. Therefore
simulation study is best way for performance analysis of Wireless Sensor Network before actual
deployment of sensor nodes inside the foodgrain storages.
Simulation study is performed using WSN Planner tool and NS-2 simulators. The WSN planner
tool is the best tool to plan wireless sensor network in a required application area. NS-2 is used for
performance analysis of the sensor network. DSDV and AODV routing protocols are used with
IEEE 802.15.4 standard. For simulation WSN for foodgrain warehouse application shadowing
model is used and the path loss exponent is varied because of number foodgrain bags are stacked.
Some basic simulation experiments are performed with two nodes. The experimental results show
that the packet size does not affect the packet delivery ratio, throughput escalates with an increase
in packet size and average end to end delay remains constant for different packet sizes. The delay
increases with an increase in distance between two nodes as expected. Energy consumption
increases with the distance and decreases as we increase the number of hops.
In a simulation study of foodgrain warehouse monitoring application packet delivery ratio
increases as the duty cycle is reduced. Energy consumption is reduced with periodic data
transmission. The lifetime of wireless sensor network can be improved by reducing the duty cycle
and use of multi-hop sensor network. Energy consumption is a crucial QoS parameter that affects
sensor network lifetime. The present work carried out a performance evaluation of QoS parameters
viz., packet delivery ratio, throughput, average end to end delay and energy consumption using
NS-2 simulator for foodgrain warehouse monitoring.
Future Scope:
1. Wireless Sensor Network can be used to automatically control the environmental parameters
inside the foodgrain storages.
2. The sensor data can be available over the internet and android application can be developed,
so concern higher authority can have easy access to environmental condition inside storages.
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Acknowledgement
I would like to express my profound senses of gratitude to University Grant Commission
for awarding this Minor Research Project which helped to me to do research in field of Wireless
Sensor Networks. I thankful to Authorities of New Arts, Commerce and Science College,
has been kind enough for encouraging me to apply for this Minor Research Project and
also providing all the necessary facilities for the successful completion of the work. I take this
opportunity to express my sincere thanks to the authorities and management of Ahmednagar Jilha
Maratha Vidya Prasarak Samaj, Ahmednagar. Also I thankful to Maharashtra State
Warehousing Corporation for giving permission to work in Kedgaon, Ahmednagar foodgrain
warehouse and providing necessary details.
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List of Publications
1. D. S. Shelar, D. C. Gharpure and A. D. Shaligram: Performance Analysis of ZigBee based
Wireless Sensor Network for Grain Storage Monitoring, International Journal of Advanced
Research in Electrical, Electronics and Instrumentation Engineering (IJAREEIE), Vol. 6, Issue 6,
June 2017, 5027-5035.
2. Dipak Shelar, Arvind Shaligram and Damayanti Gharpure: QoS Optimization of Wireless
Sensor Network for Large Foodgrain Warehouse Monitoring using NS-2, Presented at 3rd
International Conference on Advanced Computing and Intelligent Engineering (ICACIE 2018)
Bhubaneswar, India
56
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