1

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)

2

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

3

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.

4

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

5

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].

6

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

7

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.

8

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

9

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].

10

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

11

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

12

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

13

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.

14

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

15

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

16

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

17

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

18

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

19

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

46

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.

51

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.

52

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.

53

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.

54

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.

55

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

References

1. Vivek S. Deshpande: Handling the Energy in Wireless Sensor Networks. Wireless Sensor Networks from

Theory to Applications, CRC Press (2014) 369-387.

2. D.L. Proctor: Grain storage techniques Evolution and trends in developing countries. Food and Agriculture

Organization of the United Nations (FAO) Rome (1994).

3. M. Esther Magdalene Sharon, C.V. Kavitha Abirami and K. Alagusundaram: Grain Storage Management in

India. Journal of Postharvest Technology, (2014) 12-24.

4. Fenghui Zhang,Huiling Zhou, Xiaoguang Zhou and Qizheng liao: Simulation of Temperature Measurement

System for Grain Storage Based on Zigbee Technology. IEEE (2009).

5. Genita Gautam and Biswaraj Sen: Design and Simulation of Wireless Sensor Network in NS2. International

Journal of Computer Applications, March 2015.

6. Shumin Xu and Yatao Yang: Protocols Simulation and Performance Analysis in Wireless Network Based on

NS2. IEEE (2011).

7. Prashant Panse, Tarun Shrimali and Meenu Dave: Performance Evaluation of Vehicular Ad Hoc Network

Using SUMO and NS2. Proceedings of the International Congress on Information and Communication

Technology, Advances in Intelligent Systems and Computing, 127-135.

8. Majid Ahmad Charoo and Durgesh Kumar Mishra: Multi-region Pre-routing in Large Scale Mobile Ad Hoc

Networks (MRPR). Proceedings of the International Congress on Information and Communication

Technology, Advances in Intelligent Systems and Computing, 637-645.

9. Sonu Gehlaut, Jayasudha Koti and Kavita Sakhardande: To Improve the Lifetime of Wireless Sensor

Network.IEEE (2016).

10. Smitasri Chattopadhyay and G.Vijayalakshmi: Improving the Lifetime of Wireless Sensor Network through

Energy Conservation. International Journal of Computer Science and Information Technologies, Vol. 5

(2),(2014) 2345-2347.

11. 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, Vol. 6, Issue 6, (2017).

12. Teerawat Issariyakul and Ekram Hossain: Introduction to Network Simulator NS2. Second Edition, Springer

(2012).

13. Chuan Zhu, Chunlin Zheng, Lei Shu and Guangjie Han, A survey on coverage and connectivity issues in

wireless sensor networks. Journal of Network and Computer Applications (Elsevier).

14. Amitabha Ghosh and Sajal K. Das: Coverage and connectivity issues in wireless sensor networks: A survey.

Pervasive and Mobile Computing (2008) 303–334.

15. Omar Said: Performance evaluation of WSN management system for QoS guarantee. EURASIP Journal on

Wireless Communications and Networking (2015).

16. Muhammad Asif, Shafiullah Khan, Rashid Ahmad, Muhammad Sohail and Dhananjay Singh: Quality of

Service of Routing Protocols in Wireless Sensor Networks: A Review. IEEE Access (2017) 1846-1871.

17. Ian F. Akyildiz and Mehmet Can Vuran : Wireless Sensor Networks, A John Wiley and Sons, Ltd,

Publication, 2010.

18. Neha Deshpande, A. D. Shaligram, B. A. Botre, Satish Bindal and S.S . Sadistap : Embedded E-nose

Application to Sense the Food Grain Storage Condition (2010).

19. Naregalkar Akshay, M.Praveen Kumar, B.Harish and Sujata Dhanorkar : An Efficient Approach for Sensor

Deployments in Wireless Sensor Network, 2010 IEEE.

20. S. N. Merchant, B. N. Jain and Uday B. Desai : Wireless Sensor Networks: Technology Roadmap(2007).

21. Dr. K. T. Chandy : Agricultural & Environmental Education.

57

22. Fu Lin, DengYi Zhang and Wen Hai Li : Research on Quality of Service in Wireless Sensor Networks (2011).

23. Wang, Chun-Chi Hu, and Yu-Chee Tseng, Senior Member, IEEE : Efficient Placement and Dispatch of

Sensors in a Wireless Sensor Network (2008).

24. Qinglian Ren and Chunbo Chang :A grain storage monitoring system based on wireless sensor

network.’(2009)

25. Yuling Lei, Yan Zhang and Yanjuan Zhao: The research of coverage problems in wireless sensor

network,(2009).

26. Vivek Katiyar, Narottam Chand, Naveen Chauhan, “Recent advances and future trends in Wireless Sensor

Networks”(2010).

27. Marcos Augusto M. Vieira, Luiz Filipe M. Vieira and Linnyer B. Ruiz : Scheduling Nodes in Wireless Sensor

networks(2003).

28. Peng Yu, Luo Qinghua and Peng Xiyuan : The Design of Low-power Wireless Sensor Node (2010).

29. Akos Ledeczi and Miklos Maroti : Wireless Sensor Node Localization(2008).

30. Marko Korkalainen, Mikko Sallinen, Niilo Karkkainen and Pirkka Tukeva: Survey of Wireless Sensor

Networks Simulation Tools for Demanding Applications(2009).

31. Mrs. Poonam Chhimwal, Dhajvir Singh Rai and Deepesh Rawat : Comparison between Different Wireless

Sensor Simulation Tools (2013).

32. Jian-guang, JIAZun-wen,HE Jing-ming and KUANG Hong-bo YAN : Application of Wireless Sensor

Network in Monitor System for Grain Depots’(2009).

33. Neha R.Deshpande and A.D.Shaligram : Determination of Germination Status of Food Grains In Warehouses

Using CO2 Sensor Data Acquisition System.

34. Jianguang Jia, Jingming Kuang, Zunwen He and Yuhang Mu : Design of Monitor System for Grain Depots

Based on Wireless Sensor Network, Proceedings of International Conference on Mechatronics and

Automation,2009, IEEE.

35. Neha Deshpande, A. D. Shaligram, B. A. Botre, Satish Bindal and S. S. Sadistap : Embedded E-nose

Application to Sense the Food Grain Storage Condition, 2010, IEEE.

36. Xiaodong Zhang et al. “Design and Implementation of Embedded Monitoring System for Grain Storage”

2010, IEEE.

37. Qinglian Ren and Chunbo Chang : A Grain Storage Monitoring System on Wireless Sensor Networks,

Proceedings of ICCTA 2009.

38. Santoshkumar et al. :Smart Sensor Network System based on Zigbee Technology to Monitor Grain.

39. Manoj Kumar Tyagi et al : Warehouse Monitoring and Management with the help of WSNs & Solar Energy”

IJESAT, 2012.

40. Holger Karl and Andreas Willig, “Protocols and Architectures for Wireless Sensor Networks”, John Wiley

& Sons Ltd, 2005.