1

PRE-DISSERTATION

ON

IMPROVE DELAY USING POINT OF INTEREST IN MOBILE COVERAGE WIRELESS

SENSOR NETWORK

Submitted in partial fulfillment of the

Requirement for the award of the degree

Of

MASTER OF TECHNOLOGY

IN

ECE

Wireless Communication

By

Adusumalli mallikharjuna Rao

Under The Esteemed guidance of

MR. Koushik Barman

School of Electrical & Electronics Engineering

Lovely Professional University

Punjab

APRIL 2015

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CERTIFICATE

This is to certify that the Thesis titled “IMPROVE DELAY USING POINT OF INTEREST IN

MOBILE COVERAGE WIRELESS SENSOR NETWORK ” that is being submitted by

“ADUSUMALLI MALLIKARJUNA RAO” is in partial fulfillment of the requirements for the award

of MASTER OF TECHNOLOGY DEGREE (WIRELESS COMMUNICATION ), is a record of

bona fied work done under my/our guidance. The content of this thesis, in full or in parts, have neither

taken from any other source nor have been submitted to any other Institute or university for award of and

degree or diploma and the same is certified.

MR. Koushik Barman

ASSISTANT PROFESSOR

(LOVELY PROFESSIONAL UNIVERSITY)

Objective of the Thesis is satisfactory /unsatisfactory

EXAMINER I EXAMINER II

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ACKNOWLEDGEMENT

I would like to thank LOVELY PROFESSIONAL UNIVERSITY for giving me opportunity to use

their resource and work in such a challenging environment. I am grateful to the individuals whom

contributed their valuable time towards my thesis.

I wish to express my sincere and heart full gratitude to my guide “MR. Koushik Barman” Assistant

professor, who guides me to take up this thesis in sync with global trends in scientific approach.

I would also like to extend my gratitude to my friends and family who always encouraged and

supported me in this thesis work.

Last but not the least; I would like to thank all the staff members of department of Electronics &

Electrical Engineering who have been very patient and co-operative with us.

ADUSUMALLI MALLIKHARJUNA RAO

Reg. No. 11407829

4

CANDIDATES DECLARATION

I, ADUSUMALLI MALLIKHARJUNA RAO student of MASTER OF TECHNOLOGY

(WIRELESS COMMUNICATION) under Department of ELECTRONIC & COMMUNICATION

ENGINEERING of LOVELY PROFESSIONAL UNIVERSITY, Punjab, hereby declare that all the

information furnished in this pre dissertation reports based on my own intensive research and is genuine.

This pre dissertation does, to the best of my knowledge, contain part of my work which has been

submitted for the award of my degree either of this university without proper citation.

Date:

ADUSUMALLI MALLIKHARJUA RAO

Registration No. 11410097

5

TABLE OF CONTENTS

1.1 SENSOR NODE 5

1.2 PRINCIPLE OF WIRELESS SENSOR NETWORK 5

CHAPTER 2 WIRELESS SENSOR NETWORKS WORK 07

2.1 HOW WIRELESS SENSOR NETWORKS WORKS 07

2.2 TYPES OF SENSOR NETWORKS 08

2.3 BASE STATION DESIGN 10

2.4.1 HARDWARE 11

2.4.2 SOFTWARE 11

2.5 COMMUNICATION 12

2.6.1 SPECIAL FEATURES OF WIRELESS SENSOR NETWORKs 13

2.6.2 DIFFRENT TYPES OF NODES 13

2.6.3 WIRELESS SENSOR NETWORK COVERAGE

2.6.4. COVERAGE TYPE 13

2.6.5 DEPLOYMENT 14

2.6.6 Next Generation Wireless Sensor Nodes 16

2.6.7 Point of interest 16

2.6.8 POI COLLECTIONS 17

2.6.9 APPLICATIONS 17

6

2.7 WIRELESS SENSOR NETWORK APPLICTION 18

2.8 DESIGN ISSUES & CHALLENGS 19

2.9 CHARACTERISTIC REQUIREMENTS FOR WSNC WSNs 19

2.10 FOURTH GENERATION WIRELESS TECHNOLOGIES 20

2.10.1 THE COMMUNICATION PUZZLE 20

2.10.2 THE SCOPE OF VARIOUS WIRELESS TECHNOLOGIES 21

CHAPTER 3 TITLE AND ITS SPECIFICANCE 22

CHAPTER 4 REVIEW OF LITERATURE 23

CHAPTER 5 RESEARCH OBJECTIVE 28

CONCLUSION 29

CHAPTER 6 REFERENCES 30

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LIST OF FIGURES

Figure No Name of the figure Page no.

1.1 Accessing wsn through inter net 2

1.2 Architecture of wireless sensor network 3

2.1 Sensor node system 7

2.3 Base station design 10

2.4 Model Component Flow 12

2.10 FOURTH GENERATION WIRELESS TECHNOLOGIES 20

2.10.2 THE SCOPE OF VARIOUS WIRELESS TECHNOLOGIES 21

ABSTRACT

8

Wireless sensor networks have been used for many delay-sensitive applications, e.g., emergency

response and plant automation. In such networks, delay measurement is important for a number

of reasons, e.g., real-time control of the networked system, and abnormal delay detection

We prove the sniffer placement problem develop two algorithms to solve it. Using a combination

of small-scale tested experiments and large scale simulation, we demonstrate that our

architecture leads to accurate delay monitoring and is effective in detecting abnormal delays, and

furthermore, the number of sniffers required by algorithms is close to the minimum required

value

In some mobile wireless sensor network applications, it is not necessary to

monitor the entire field all the time, and only a number of critical points or points of

interest need to be monitored periodically. In this paper, we address the improving-

delay POIs coverage in mobile wireless sensor network. problem with cost restriction,

which is how to schedule the limited number of mobile sensors monitoring to improving

the service delay of POIs. Study two scenarios of the problem. In the first scenario, the

start positions of mobile sensors are determined in advance, we propose the SSR

algorithm to address this problem. In the second scenario, without pre-defined start

positions, we propose two algorithms, the TSP-S and the SSNOR. By the

comprehensive simulations, we evaluate the performance of the proposed algorithms.

The simulation results show the efficiency of our algorithms. this techniques applying

delay improving life time .one sensor to anther sensors connoting passing data

without delay

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CHAPTER 1

INTRODUCTION

A wireless sensor network is a collection of nodes organized into a cooperative network. Each

node consists of processing capability may contain multiple types of memory have an RF

transceiver, have a power source (e.g., batteries and solar cells), and accommodate various

sensors.

Typically, a wireless sensor node (or simply sensor node) consists of sensing, computing,

communication, actuation, and power components. These components are integrated on a single

or multiple boards, and packaged in a few cubic inches. With state-of-the-art, low-power circuit

and networking technologies, a sensor node powered by 2 AA batteries can last for up to three

years with a 1% low duty cycle working mode. A WSN usually consists of tens to thousands of

such nodes that communicate through wireless channels for information sharing and cooperative

processing. WSNs can be deployed on a global scale for environmental monitoring and habitat

study, over a battle filed for military surveillance and reconnaissance, in emergent environments

for search and rescue, in factories for condition based maintenance, in buildings for

infrastructure health monitoring, in homes to realize smart homes, or even in bodies for patient

monitoring

After the initial deployment, sensor nodes are re-possible for self-organizing an appropriate

network infrastructure, often with multi-hop connections between sensor nodes. The onboard

sensors then start collecting acoustic, seismic, infrared or magnetic information about the

environment, using either continuous or event driven working modes. Location and positioning

information can also be obtained through the global positioning system (GPS) or local

positioning algorithms. This information can be gathered from across the network and

appropriately processed to construct a global view of the monitoring phenomena or objects. The

basic philosophy behind WSNs is that, while the capability of each individual sensor node is

limited, the aggregate power of the entire network is scent for the required mission.

In a typical scenario, users can retrieve information of interest from a WSN by injecting queries

and gathering results from the so-called base stations (or sink nodes), which behave as an

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interface between users and the network. In this way, WSNs can be considered as a distributed

data base. It is also envisioned that sensor networks will ultimately be connected to the Internet,

through which global information sharing becomes feasible.

Emerging field of wireless sensor networks combines sensing, computation, and communication

into a single tiny device. Through advanced mesh networking protocols, these devices form a sea

of connectivity that extends the reach of cyberspace out into the physical world. As water flows

to fill every room of a submerged ship, the mesh networking connectivity will seek out and

exploit any possible communication path by hopping data from node to node in search of its

destination. While the capabilities of any single device are minimal, the composition of hundreds

of devices offers radical new technological possibilities.

The power of wireless sensor networks lies in the ability to deploy large numbers of tiny nodes

that assemble and configure themselves. Usage scenarios for these devices range from real-time

tracking, to monitoring of environmental conditions, to ubiquitous computing environments, to

in situ monitoring of the health of structures or equipment. While often referred to as wireless

sensor networks, they can also control actuators that extend control from cyberspace into the

physical world.

The most straightforward application of wireless sensor network technology is to monitor remote

environments for low frequency data trends. For example, a chemical plant could be easily

monitored for leaks by hundreds of sensors that automatically form a wireless interconnection

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network and immediately report the detection of any chemical leaks. Unlike traditional wired

systems, deployment costs would be minimal. Instead of having to deploy thousands of feet of

wire routed through protective conduit, installers

Simply have to place quarter-sized device, such as the one pictured in Figure 1-1, at each sensing

point. The network could be incrementally extended by simply adding more devices – no rework

or complex configuration. With the devices presented in this thesis, the system would be capable

of monitoring for anomalies for several years on a single set of batteries.

In addition to drastically reducing the installation costs, wireless sensor networks have the ability

to dynamically adapt to changing environments. Adaptation mechanisms can respond to changes

in network topologies or can cause the network to shift between drastically different modes of

operation. For example, the same embedded network performing leak monitoring in a chemical

factory might be reconfigured into a network designed to localize the source of a leak and track

the diffusion of poisonous gases. The network could then direct workers to the safest path for

emergency evacuation. Current wireless systems only scratch the surface of possibilities

emerging from the integration of low-power communication, sensing, energy storage, and

computation. Unlike traditional wireless devices, wireless sensor nodes do not need to

communicate directly with the nearest high-power control tower or base station, but only with

their local peers. Instead, of relying on a pre-deployed infrastructure, each individual sensor or

actuator becomes part of the overall infrastructure. Peer-to-peer networking protocols provide a

mesh-like interconnect to shuttle data between the thousands of tiny embedded devices in a

multi-hop fashion. The flexible mesh architectures envisioned dynamically adapt to support

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introduction of new nodes or expand to cover a larger geographic region. Additionally, the

system can automatically adapt to compensate for node failures.

The vision of mesh networking is based on strength in numbers. Unlike cell phone systems that

deny service when too many phones are active in a small area, the interconnection of a wireless

sensor network only grows stronger as nodes are added. As long as there is sufficient density, a

single network of nodes can grow to cover limitless area. With each node having a

communication range of 50 meters and costing less that $1 a sensor network that encircled the

equator of the earth will cost less than $1MAn example network is shown in Figure 1-2. It

depicts a precision agriculture deployment an active area of application research. Hundreds of

node scattered throughout a field assemble together, establish a routing topology, and transmit

data back to a collection point. The application demands for robust, scalable, low-cost and easy

to deploy networks are perfectly met by a wireless sensor network. If one of the nodes should

fail, a new topology would be selected and the overall network would continue to deliver data. If

more nodes are placed in the field, they only create more potential routing opportunities.

1.1 SENSOR NODE COMPONENTS

• Processing Unit

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• Transceiver Unit

• Power Unit

• Sensing Unit

• Location Finding System (optional)

• Power Generator (optional)

• Mobilizes (optional)

1.2 PRINCIPLE OF WIRELESS SENSOR NETWORK

Wireless sensor networks are a subset of wireless networking applications, which focus on

enabling connectivity without, the need, generally, of wires to connect to the sensors and

actuators (Gutierrez et al. 2004). Due to the length of the name „wireless sensor and actuator

networks‟‟ or „„wireless sensor and control networks‟‟, most people have adopted the shorter

„„wireless sensor networks‟‟ instead. In any case, it is important to remember that the design of

this type of network is meant to collect information from wireless sensors and send control

commands to actuators attached to the wireless network.

Sensor and actuator networks have existed for decades. Computer based control systems are a

typical hardwired sensor and actuator network. As shown in Fig. 2.1, sensors and actuators are

connected with a central computer or control terminal via a data bus system or other networks

and implement control and monitoring functions. This type of hardwired sensor network is

simple and reliable, and often seen in industrial control such as process control and

manufacturing production control. Because of the involvement of large amount of cabling in the

installation, wired sensor networks are hard to extend. The installation cost of hardwired sensor

networks is high, which takes in the form of cabling, labor, material, testing, and verification.

Furthermore, cables require connectors that can become loose, lost, misconnected, or even break.

This problem is commonly known as the last meter connectivity problem and is called this due to

the analogous problem in a wide area network.

The use of large number of hardwired sensors networked to a system brings considerable

complexity to the system, including cabling deployment, power supply, and configuration,

making it impossible in many cases such as forest monitoring and battlefield surveillance. Recent

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Integrated Circuit (IC) and Micro Electro Mechanical System (MEMS) have matured to the point

where they enable the integration of wireless communications, sensors and signal processing

together in a single low-cost package, named as a

sensor node Such a sensor node is equipped with data processing and communication

capabilities. A set of such sensor nodes forms a wireless sensor network. It is now feasible to

deploy ultra-small sensor nodes in many kinds of areas to collect information. The sensing

circuitry measures ambient condition related to the environment around the sensor and

transforms them into measurable signals. After necessary processing, the signals are sent to a

pre-defined destination via a radio transmitter. All of these operations are powered by batteries to

ease deployment, since a traditional power supply (i.e. mains power) may not be available. This

type of wireless solutions for sensor networks combines flexible connectivity with ease of

installation. The scope of sensors determines the range of Applications of wireless sensor

networks. There are many types of wireless sensors depending upon the type of sensing required.

CHAPTER 2

WIRELESS SENSOR NETWORKS WORK

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2.1 HOW WIRELESS SENSOR NETWORKS WORKS

For better understanding of sensor network it is important to know about all the components of

sensor node. Common sensor node architecture is shown in Figure2. The architecture of a

generic wireless sensor node consists of four subsystems [5]. A computing subsystem consisting

of a microprocessor, ALU and memory, a communication subsystem consisting of a short range

radio for wireless communication, sensing subsystem that links the node to the physical world

and consists of a group of sensors and actuators, and a power supply subsystem, which houses

the battery and the (optional) DC-DC converter, and powers the rest of the node. Each subsystem

plays an main role in the sensor node.

2.2TYPES OF SENSOR NETWORKS:

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Wireless sensor networks are deployed on land, underground, and underwater. A sensor network

faces different challenges and constraints according to the environment in the sensor network

deployed. There are five types of the wireless sensor network as discussed by Jennifer et at.[4].

Terrestrial Wireless sensor network.

Underground Wireless sensor network.

Underwater Wireless sensor network.

Multi-media Wireless sensor network.

Mobile Wireless sensor network.

an ad hoc or in a pre-planned manner. In ad hoc deployment, sensor nodes can be dropped from a

plane and randomly placed into the target area. In pre-planned deployment, there is grid

placement, optimal placement, 2-d and 3-d placement models. In a terrestrial WSN, reliable

communication in a dense environment is very essential. Sensor nodes must be able to

successfully communicate with the base station in terrestrial

WSN, while battery power is a limited. In any case, it is essential for sensor nodes to

conserve energy. Energy of sensor nodes can be conserved with multi-hop optimal routing, short

transmission range, in-network data aggregation, reducing data redundancy, minimizing delay

and using low duty-cycle operations in terrestrial WSN .Underground WSNs in which sensor

node covered underground, basically it used for detect used to monitor underground situation.

And sink node are used for transmit information to the sensor node to the base station. This

wireless senor network is more costly as compare to terrestrial WSN in terms of equipment,

deployment, and maintenance. Underground sensor nodes are expensive because proper

components must be used for reliable communication through soil, rocks, water, and other

mineral contents. The underground environment makes wireless communication a challenge due

to signal losses and high levels of attenuation. An underground WSN requires careful planning

energy and cost considerations during deployment to increase network lifetime. Underwater

WSNs consist of a number of sensor nodes and vehicles deployed underwater. Unlike terrestrial

WSNs, underwater sensor nodes are more costly and less dense. Independent underwater

vehicles are used for searching or gathering data from sensor nodes. Sensor nodes communicate

via acoustic waves in underwater WSN. Acoustic communication is a challenge in underwater

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due to limited bandwidth, long propagation delay, and signal fading problem. Underwater WSNs

consist of a number of sensor nodes and vehicles deployed underwater. Unlike terrestrial WSNs,

underwater sensor nodes are more costly and less dense. Independent underwater vehicles are

used for searching or gathering data from sensor nodes. Sensor nodes communicate via acoustic

waves in underwater WSN. Acoustic communication is a challenge in underwater due to limited

bandwidth, long propagation delay, and signals fading problem. And also node failure. Sensor

node in wireless sensor network has a capability to take a harsh ocean environment condition.

Sensor node have limited battery power which cannot be recharges and replace. For energy

conservation, underwater WSNs involve developing efficient underwater communication and

networking techniques. Multi-media WSNs are used to monitoring and tracking of events in the

form of multimedia. Multi-media WSNs consist of a number of low cost sensor nodes equipped

with cameras and microphones. These sensor nodes communicate with each other for data

retrieval, process, correlation, and compression over a wireless connection. Multimedia sensor

nodes are deployed in a pre-planned manner into the atmosphere for coverage guarantee. High

bandwidth demand, high energy consumption, quality of service (QoS) condition, data

processing and compressing techniques and cross-layer design are challenges in multi-media

WSNs. Multimedia content such as video stream needs high bandwidth in order to content to be

delivered. Therefore, energy consumption is high for high data rate. High bandwidth and low

energy consumption transmission techniques have to be developed. QoS is difficult to preserve

in a multi-media WSNs due to variable

delay and variable channel capacity. It is essential to get a certain level of QoS for reliable

content delivery. In-network processing, filtering, and compression of contents can significantly

improve network performance by filtering and extracting redundant information and merging

contents. Similarly, cross-layer interaction between the layers can improve the processing and

the delivery process. Mobile WSNs is a collection of sensor nodes that can move on their own

and interact with the physical environment. Mobile nodes have the ability of sensing, computing,

and communication like static nodes. A key difference is mobile nodes have the ability to change

the position and organize itself in the network. A mobile WSNs can start with some initial

deployment and nodes can then spread out to gather information. A mobile node can

communicate to another mobile node when they are within the range of each other and transfer

gathered information. Another key difference is data distribution. In mobile WSNs, data can be

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distributed using dynamic routing while fixed routing or flooding is used in static WSNs. Sensor

nodes deployment, localization, self-organization, navigation and control, coverage, energy,

maintenance, and data process are challenges in mobile WSNs. Mobile WSNs applications

include environment monitoring, target tracking, search and rescue, and real-time monitoring of

hazardous material etc. Mobile sensor nodes can achieve a higher degree of coverage and

connectivity compared to static sensor nodes.

2.3 BASE STATION DESIGN

Base stations are often thought of as just a central component that is used to gather data from

distributed nodes. This approach allows advanced users to control their crop, it has a very steep

learning curve. We take a different approach that focuses on creating a system that is easy to

learn, easy to use, and makes it easy to generate actionable that can be used to improve growing

conditions. This system also allows for configuring irrigation at the node level from a centralized

interface

2.4.1 HARDWARE

In our system, the base station consists of two main components, the computer and the radio. The

computer is typically an inexpensive laptop that runs Ubuntu Linux. It is important to note that

19

while most systems are using laptops with relatively small memory requirement the easiest way

to improve system performance is by upgrading to a faster computer. This system supports

several radio modules including the Digi DigiMesh 900 MHz and 2.4 GHz modules and the Digi

XSC radio operating at 900MHz. For most cases the Digi XSC radio is utilized for its simplicity

as well as for the 900 MHz operating frequency which has better range and better foliage

penetration characteristics. The other radios provide features such as multi hop networking and

alternate operating frequencies. It should be noted that prior work (Valada et al. 2011) has

developed and implemented a multi-hop networking algorithm optimized for WSN‟s that can be

used with the Digi XSC radios.

2.4.2 SOFTWARE

The base station software system has four of miner components: the database, the base module,

the user interface, and the Grower Tools module. The glue for the entire system is the SQLite3

database. SQLite3 was chosen for the fast read times associated with a file based database.

Database efficiency is very important since users need to query large amounts of data (for

example to create a chart). Many database optimization methods are not effective for this

application. All of the data is stored in its raw form and conversions are applied as users access

the data.

The base module is responsible for all node communications. This module gets data from the

nodes and inserts it into the database followed by sending any newly modified node parameters

to the node. The base module is written in Perl which provides a full scripting language to help

parse data from the nodes.

The next component is the user interface. The user interface is designed using the Ruby on Rails

(RoR) web framework. This RoR framework provides direct database access and allows for an

object oriented approach to the data. For example to get data from the database a call such as

“Data .last. convert” will return the most recent data entry and convert it from raw units to proper

units. Another benefit to RoR is that the entire interface is a web site and is commonly

configured for remote access.

The last module is for running Grower Tools. Grower Tools are derived data products that

require processing; some of these tools include Vapor Pressure Deficit, Dew Point, and water use

predictions made using plant specific physiological models. The Growing Tools are handled by a

Ruby utility called Rake. A conventional RoR application is event driven and responds to user

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actions such as mouse clicks and keyboard commands. Rake allows for scripting while having

full access to the methods defined in the RoR application. The Growing Tool application runs

every five minutes to check if any Growing Tool needs to be run as each tool has its own run

frequency. The reason for checking this every five minutes is to reduce the load on the database.

The Growing Tool interface is modular and allows for the easy introduction of new Growing

Tools. Each tool consists of a database record with the input and output units of the model

defined and the directory where the model script

FIGURE 2.4 Model Component Flow

When a user creates a model instance the Growing Tool system automatically generates an input

XML file based on the units that the model wants from the nodes that are selected, runs the

model script, and reads in the output file The output file is then loaded into the database where it

can be plotted in a chart or used to control irrigation.

2.5COMMUNICATION

A node to base station communication protocol was developed with Decagon Devices to provide

secure and reliable operations. In order to keep everything in a 64KB window for radio

transmission efficiency and to create a more secure system, all of the data is transmitted in binary

and not using ASCII characters. CRC checksums are used to verify packet integrity, and a

confirm delivery protocol ensures that each message is received by the base station from the

nodes. Certain commands such as manual irrigation commands have a twofold confirmation

procedure where the base station first sends the request, then the node confirms it, then the base

confirms the nodes confirmation in order to ensure reliability. If there are node parameters that

need to be updated the base will include a flag in the confirmation packet that alerts the node to

not go to sleep and wait for the configuration updates. This method helps to conserve battery

power by minimizing the awake time of the node/radio. The protocol has successfully been

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implemented and tested on both commercially available Decagon wireless data loggers and

CMU research WSN nodes.

2.6.1 SPECIAL FEATURES OF WIRELESS SENSOR NETWORKs

This section discusses some unique features of WSNs, which need to be taken into account when

designing management architectures in WSNs

2.6.2 DIFFRENT TYPES OF NODES

In the wireless sensor network are three types of sensor nodes: the normal nodes is responsible

for collecting information or sensor data. Sensor nodes have resource constraint. That‟s why

sensor node have not storing capability for storing large amount of information or sensor data. It

may take simply data processing if necessary; sink nodes responsible for receiving, storing, and

processing (e.g. aggregation) data from normal nodes; and gateway nodes that connect sink

nodes to external entities called observers. In addition, actuators can also be introduced to control

or actuate on a monitored region in Wireless sensor networks

2.6.3 WIRELESS SENSOR NETWORK COVERAGE

There are several factors that must be considered when developing a plan for coverage in a

sensor networks. Many of these will be dependent upon the particular application that is being

addressed. The capabilities of the sensor nodes that are being used must also be considered. Most

Researchers focus on a single deployment model but there are papers that attempt to develop a

more general algorithm that can be used in many types of deployment.

2.6.4. COVERAGE TYPE

The first step in deploying a wireless sensor network is determining what it is exactly that you

are attempting to monitor. Typically you would monitor an entire area, watch a set of targets, or

look for a breach among a barrier. Coverage of an entire area otherwise known as full or blanket

coverage means that every single point within the field of interest is within the sensing range of

at least one sensor node. Ideally you would like to deploy the minimum number of sensor nodes

within a field in order to achieve blanket coverage. This problem was addressed in where the

author proposes placing the nodes in a construct called an r-strip such that each sensor is located

distance away from the neighboring sensor where r is the radius of the sensing area. The strips

can be then placed in an overlapping formation such that blanket coverage is achieved. The

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biggest problem with this solution is that it is impractical to try to deploy sensors in such a

formation

Target coverage refers to observing a fixed number of targets. This type of coverage has obvious

military applications such as those covered in .The authors in this paper did extensive tests to not

only detect targets, but to classify and track them. The authors in and attempt to maintain target

coverage while conserving energy. The authors in discuss both blanket and target coverage in

terms of energy efficiency.

Barrier coverage refers to the detection of movement across a barrier of sensors. This problem

was defined as the maximal breach path in [9]. The authors in this study quantify the

improvement in coverage when additional sensors are added to a network. Other papers such as

focus on algorithms in barrier coverage. A variation of barrier coverage known as sweep

coverage is also discussed in .Sweep coverage can be thought of as a moving barrier problem.

2.6.5 DEPLOYMENT

A sensor network deployment can usually be categorized as either a dense deployment or a

sparse deployment. A dense deployment has a relatively high number of sensor nodes in the

given field of interest while a sparse deployment would have fewer nodes. The dense

deployment model is used in situations where it is very important for every event to be detected

or when it is important to have multiple sensors cover an area. Sparse deployments may be used

when the cost of the sensors make a dense deployment prohibitive or when you want to achieve

maximum coverage using the bare minimum number of sensors.

In most of the work studying coverage it is assumed that the sensor nodes are static, they stay in

the same place once they are deployed. Newer sensor nodes have the ability to relocate after they

are deployed, these are known as mobile nodes. The algorithm in has each sensor node

determining the location it needs to move to in order to provide maximum coverage. The authors

perform several experiments to determine how well the network covers the area and the

deployment time of the algorithm. The key weakness in this algorithm is that each node must be

within the sensing range of another node in order to determine the optimal location it needs to

move to, if a node is not seen by any other nodes then that node cannot determine its relative

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location. In the deployment algorithm of each node will communicate with its neighbors and tell

them to move away until they are at a distance which maximizes coverage while maintaining

connectivity. The simulations run by the authors show a very high degree of coverage can be

obtained within minutes of deployment. Actual sensors may not perform as well if they are not

able to calculate the distance of their neighbors with the same precision as the nodes in the

simulation. The method introduced in aims to maximize coverage while minimizing sensor

movement. The simulations run by the authors show the method does achieve excellent coverage

with low amounts of movement but it does require a complex algorithm be run which may tax

the sensor nodes. The authors in design three separate deployment protocols that provide a high

level of coverage with minimal movement in a short time. The simulations show that the

protocols hold up with a limited amount of sensors but there are questions about how scalable the

protocols are with larger numbers of sensors.

Sensor network nodes are deployed in an area by either placing them in predetermined locations

or having the nodes randomly located. Dropping sensors from a plane would be an example of

random placement. It is easier to develop a coverage scheme for deterministic placement of

sensor nodes than for random placement. However in many deployments, it is either impractical

or impossible to deploy sensor nodes in a deterministic way. Examples of deterministic and

random placement is of a deterministic placement. A more sophisticated deterministic

deployment method is given in. The authors propose to arrange the sensors in a diamond pattern

which would correspond with a Verona polygon. The pattern achieves four way connectivity

from each of the nodes with full coverage when the communication range divided by the sensing

range is greater than the square root of two. The authors are able to mathematically prove the

validity of their pattern, however the pattern is not practical for actual deployment. It assumes

that the sensing and communication ranges of every node are a perfect circle as well as the

ability to place the sensors in exact locations. Random

Order to relocate to the optimal location. Most research with random deployments of sensor

nodes regards the ability to maintain coverage while minimizing the amount of energy expended.

This will be covered more closely in another part of the paper. Deployments of sensor nodes are

usually dense deployments as well since it is necessary to deploy additional sensors in order to

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achieve coverage if the sensor nodes are stationary. Networks with mobile sensors usually start

out with a random deployment and utilize the mobility property.

2.6.6 Next Generation Wireless Sensor Nodes

In 1996, the Low Power Wireless Integrated Micro sensors (LWIMs) were produced by UCLA and the

Rockwell Science Center. By using commercial, low cost CMOS fabrication, LWIMs demonstrated the

ability to integrate multiple sensors, electronic interfaces, control, and communication on a single device.

LWIM supported over 100 Kbps wireless communications at a range of 10 meters using a 1 mW

transmitter.

In 1998, The same team built a second generation sensor node | the Wireless Integrated Network

Sensors (WINS) [11]. Commercial WINS from Rockwell Science Center [197] each consists of a

processor board with an Intel Strong ARM SA1100 32-bit embedded processor (1 MB SRAM and 4 MB

ash memory), a radio board that supports 100 Kbps with adjustable power consumption from 1 to 100

mW, a power supply board, and a sensor board. These boards are packaged in a 3.5"x3.5"x3" enclosure

The processor consumes 200 mW in the active state and 0.8 mW .

(a) The WINS processor board (b) The WINS radio board

2.6.7 Point of interest

A point of interest, or POI, is a specific point location that someone may find useful or

interesting. An example is a point on the Earth representing the location of the Space Needle, or

a point on Mars representing the location of the mountain, Olympus Mons. Most consumers use

the term when referring to hotels, campsites, fuel stations or any other categories used in modern

(automotive) navigation systems. In medical fields such as histology/pathology/histopathology,

points of interest are selected from the general background in a field of view; for example,

among hundreds of normal cells, the pathologist may find 3 or 4 neo plastic cells that stand out

from the others upon staining.

25

A region of interest (ROI) and a volume of interest (VOI) are similar in concept, denoting a

region or a volume (which may contain various individual POIs). The term is widely used in

cartography, especially in electronic variants including GIS, and GPS navigation software. In

this context the synonym waypoint is common. A GPS point of interest specifies, at minimum,

the latitude and longitude of the POI, assuming a certain map datum. A name or description for

the POI is usually included, and other information such as altitude or a telephone number may

also be attached. GPS applications typically use icons to represent different categories of POI on

a map graphically.

2.6.8 POI COLLECTIONS

Digital maps for modern GPS devices typically include a basic selection of POI for the map area

However websites exist that specialize in the collection, verification, management and

distribution of POI which end users can load onto their devices to replace or supplement the

existing POI.[3] While some of these websites are generic, and will collect and categorize POI

for any interest, others are more specialized in a particular category (such as speed cameras) or

GPS device (e.g. Tom-tom/Garmin). End users also have the ability to create their own custom

collections

Commercial POI collections, especially those that ship with digital maps, or that are sold on a

subscription basis are usually protected by copyright. However there are also many websites

from which royalty free POI collections can be obtained

2.6.9 APPLICATIONS

The applications for POI are extensive. As GPS enabled devices as well as software applications

that use digital maps become more available, so too the applications for POI are also expanding.

Newer digital cameras for example can automatically tag a photograph using Exit with the GPS

location where a picture was taken; These pictures can then be overlaid as POI on a digital map

or satellite image such as Google Earth. Geo caching applications are built around POI

collections. In Vehicle tracking systems POIs are used to mark destination points and/or offices

26

to that users of GPS tracking software would easily monitor position of vehicles according to

POIs.

2.7 WIRELESS SENSOR NETWORK APPLICTION

Military Applications

• Monitoring friendly forces, equipment, and ammunition

• Battlefield surveillance

• Reconnaissance of opposing forces and terrain

• Targeting

• Battle damage assessment

• Nuclear, biological, and chemical attack detection

Environmental Applications

• Forest fire detection

• Bio-complexity mapping of environment

• Flood detection

• Precision Agriculture

• Air and water pollution

Health Applications

• Tele monitoring of human physiological data

• Tracking and monitoring doctors and patients inside a hospital

• Drug administration in hospitals

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Home and Office Applications

• Home and office automation

• Smart environment

2.8 DESIGN ISSUES & CHALLENGS

Random deployment autonomous setup & maintenance

Infrastructure-less networks distributed routing

Energy, the major constraint trading off network lifetime for fault tolerance or

accuracy of results

Hardware energy efficiency

Distributed synchronization

Adapting to changes in connectivity

Real-time communication, QoS

Security

2.9 CHARACTERISTIC REQUIREMENTS FOR WSNC WSNs

Scalability

Support large number of nodes

Wide range of densities

Vast or small number of nodes per unit area, very application-dependent

Programmability

Re-programming of nodes in the field might be necessary, improve flexibility

Maintainability

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WSN has to adapt to changes, self-monitoring, adapt operation

Incorporate possible additional resources, e.g., newly deployed nodes

2.10 FOURTH GENERATION WIRELESS TECHNOLOGIES

2.10.1 THE COMMUNICATION PUZZLE

Fourth-generation wireless technologies include

Wireless Personal Area Networks

Wireless PANs or WPANs

Wireless Local Area Networks

Wireless LANs or WLANs

29

Wireless Metropolitan Area Networks

Wireless MANs or WMANs

Wireless Regional Area Networks

Wireless RANs or WRAN

Cellular wide area network

Satellite network

2.10.2 THE SCOPE OF VARIOUS WIRELESS TECHNOLOGIES

30

CHAPTER 3

TITLE AND ITS SPECIFICANCE

My pre dissertation work entitled “IMPROVE DELAY USING POINT OF INTERST

MOBILE COVERAGE IN WIRELESS SENSOR NETWORK”. I chose to work on the

wireless sensor network area as it is has gained worldwide attention in recent years, and there are

some problems related. Interesting research is going on this field and there is a great scope of

improvement.

A wireless sensor network (WSN) consists of a large number of sensor nodes which are

deployed over an area to perform local computations based on information gathered from the

surroundings. Each node in the network is equipped with a battery, but it is almost very difficult

to change or recharge batteries; therefore, the crucial question is: “how to prolong the network

lifetime to such a long time?” Hence, maximizing the lifetime of the network through

minimizing the delay is an important challenge in WSN.

Wireless sensor network is very good area of research because I choosing this area . last 10

years ago communication system very poor foramens .for ex taken voice communication

transmitting very late after then transfer data sum delay .and frequency also rang very less

.present generation fearing without delay and very fast data or voice communication .wireless

sensors network every day different technology implanting .so number of person s interesting

to research wsn .but many scientists applying new thinks delay improve .but not still now not

identifying life delay problems.

31

CHAPTER 4

LITERATURE REVIEW

[1] (Michael J. Neely) Delay Analysis for Maximal Scheduling with Flow Control in Wireless

Networks with Bursty Traffic[2009 ieee]

In this paper author ,analyses the delay in wireless network and delay properties of one-hop networks

with general interference constraints and multiple traffic streams with time-correlated arrivals. Authored

treat the case when arrivals are modulated by independent finite state Markov chains. They show that the

well known maximal scheduling algorithm achieves average delay that grows at most logarithmically in

the largest number of interferers at any link. Further, in the important special case when each Markov

process has at most two states (such as burst ON/OFF sources),they prove that average delay is

independent of the number of nodes and links in the network, and hence is order-optimal. They provide

tight delay bounds in terms of the individual auto-correlation parameters of the traffic sources. These are

perhaps the first order-optimal delay results for controlled queuing networks that explicitly account for

such statistical information. Our analysis treats cases both with and without flow control.

[2] (Milan Erdelj) Multiple Point of Interest Discovery and Coverage with Mobile Wireless

Sensors [2012]

A typical scenario in wireless sensor networks is environment monitoring. the sensors have

be deployed and placed on strategic locations to monitor the area of interest. In cases

monitoring the whole area might be not impotence. Because monitoring some points of interest

increases the sensing performance reduce automatically. The deployment new nodes given the

number of sensors monitor the large area can be increased by a given fixed number of sensors.

When sensors have motion capabilities, monitoring only some Poi‟s instead of the whole area

also allows time dependent coverage

Manley control delay novel approach that integrates all the three aspects in multiple Poi coverage

into one simple distributed sensor nodes deployment. It is based on constant variable speed,

mobile sensor movement concentric circular paths that cover the field of interest. By constantly

moving, sensors nodes execute the environment discovery task, and by adjusting the movement

32

velocity, they satisfy the constraints regarding the target coverage and connectivity with the data

sink after than sensor nodes (in terms of reporting the information regarding the Poi‟s). The

algorithm that runs on all the mobile sensors is distributed and represents the simple technique,

of velocity according to the information available from the sensors in one- hop after nodes

sending data different paths.

[3] ( Azad Sharma) Delay Reduction Technique for Dynamic MANET On-demand

Routing Protocol[ IJ 2013]

Delay analysis section a formula average packet delay identifying from the source to the base station is

obtained. During the delay analysis, the processing delays are assumed to be negligible when compared to

transmission delay sensors to nodes to be deployed randomly. And clusters are assumed to have equal

size. Let N be the total number of sensors divided into Kept clusters. The routing within a set is the

situation where the source sensor has base station next-hop, when the source sensor is located at the end

of the set. The average number of hops is considered one sensor in set Packet delay mainly depends on

the number of hops from the source to the base station. On average can be obtained total network failure

occurs, an extra delay is incurred, which is the time consumed by genetic algorithm in sum time s

reconstructing virtual rings. In section (3) virtual ring reconstruction delay is qualified.

[4 (Abhay Raman)]MINIMIZING DELAY AND MAXIMIZING LIFETIME FOR WIRELESS

SENSOR NETWORKS WITH ANYCAST[2013 ]

This paper derives average delay minimizing techniques for one-hop wireless networks that use

scheduling subject data interference constraints. It is known that maximal scheduling algorithms are using

that time implement next automatically can support throughput within a constant factor of optimality.

Our analysis shows that this type of scheduling sums amount of delay. In particular, when arrival

processes are modulated by independent Make processes, we show that average delay. most

logarithmically in the number of nodes in the network. We then obtain an improved delay bound

in the important special case when the individual Make sending data with sensors. Have at most

two states (such as data ON/OFF sources). Average delay in this case is shown to be independent

of the network size, and hence is order-optimal.

[5] (Yawei Li1) On Sweep Coverage with Minimum Mobile Sensors[2010]

33

planning problems sensors nodes finding in order to probability of loss , event and applications,

i.e. finding the minimum velocity for covering a set of Poi‟s with one single sensor. and finding

the minimum number of sensors to be deployed if the velocity of each sensor is fixed. The

authors of study the sweep coverage problem, where a set of Poi‟s need to be monitored

periodically. All of these works assume that mobile sensors can be controlled to follow the

planned for improving the coverage,

[6] (Joohwan Kim) On Maximizing the Lifetime of Delay-Sensitive Wireless Sensor

Networks with Anycast

We are minimizing the delay, and user design maximizing the lifetime delay event-driven

wireless sensor networks, for which events occur infrequently. Optimize the any voice data

forwarding for minimizing the expected packet-delivery and next delays from the sensor nodes

to the sink. Based on this result, we are provide a sum different sensor to the joint control

problem with data flow. how to optimally control the system parameters ,user used sum

different delay minimize techniques .to the scheduling protocol and the any cast packet-

forwarding protocol manly maximize the network lifetime. Subject to constraint on the expected

end to end packet-delivery delay, We develop any cast packet-forwarding scheme to reduce the

event-reporting delay and to prolong the lifetime of wireless sensor networks asynchronous

sleep-wake scheduling. Specifically, we study two optimization problems, First, when the wake-

up of the sensor nodes are given, we develop an efficient and distributed algorithm. to minimize

the expected event-reporting delay from all sensor nodes moving to the sink. Second, using a

specific definition of the network lifetime, we study the lifetime-maximization problem to

optimally control the sleep-wake scheduling nodes in order to maximize the network lifetime

subject to a upper limit on the expected end-to-end delay. Our than results coming the proposed

solution data conversion or moving the nodes data solutions there are obstructions in the

coverage area of the wireless sensor network.

[7](Dong Zhao,)Mobile Sensor Scheduling for Timely Sweep Coverage[2012ieee]

34

Poi is to be timely sweep covered by a scheduling area , covered at least once mobile sensors

scheduled within the sensing delay constraint and the collected data is delivered .to the sink

node within the transmission delay constraint, Scheduling is the mobile sensor movement. In

practice different PoIs may have different delay requirements due to various at different event

locations, and the delay requirement for each PoI should be satisfied under.

[6] ( Milan Erdelj )Multiple Point of Interest Discovery and Coverage with Mobile

Wireless Sensors[2012]

We refer to the third phase term connectivity, because we investigate both theoretically and by

simulations the impact of devices mobility on connection times. More precisely, we are

interested in evaluating the performance of both of the communication delay between each node

and the base station. After data efforts have been already produced on Delay mobile Networks

when mobility of nodes is taken into transfer anther nodes. In mobility increases connectivity in

k-hop networks, and significantly improves the network lifetime and the power-delay trade off.

the mobility signals follows sensors nodes the moving different paths , specifically the random

walk mobility model non trivial velocity. we assume that nodes can control their mobility

according to surrounding conditions. Even in authors show the positive impact of mobility on

the determination of delay throughput trade off in . Even in this case, nodes‟ mobility is

restricted to movement around nodes‟ own home-points. In the mobility is controlled and nodes

move from the initial configuration towards a spatial distribution that increases the network‟s,

throughput capacity and decreases the mean-service delay of packets. In these works optimal

nodes do not move further facings not connecting write position sensors

[7] Coverage

The coverage and monitoring of a Poi, area of interest whole sensor field . subject or

(data)covered from both the sensor the community by using different approaches and by

focusing on different aspects .but have the possibility to communicate with each other in wireless

communications do not have a basic importance in achieving the coverage.

35

the area survey coverage path planning algorithms for mobile movement network for

improving. network coverage and general where to implement sensors and techniques node

placement respectively. They propose to classify works according to the improve coverage.

Specifically, full coverage aims at cover completely the field by geographically distributing

sensors ,and the entire field according to same technique in order to continuously monitor the

field .coverage calculation in the number of sensor to deploy passing is achieved, fields at all

times and maximize the lifetime. in algorithm of topology control, by varying the radius of the

sensing range in order to maintain the coverage, of the whole field when the network topology

varies. The last techniques work considers a limited mobility of the nodes, in to passing signals

hole area sensors connecting

Authors use mobility of the nodes in a more extensive way, in fact they propose sensor

deployment and target localization, respectively, by using virtual forces. Our approach also

considers a large use of mobile nodes as the last two cited works, but the main difference is in

the algorithms. Movements, we do not use than data transfer so late forces. in order not to limit

the mobility nodes mechanisms. The second group of works one by one sensors passing the

barrier coverage, which is the formation of a through sensors movements to monitor.

Basically, sweep coverage allows nodes to move on the field, because the requested coverage of

Poi is limited in time. In authors propose a theoretical study to prove that the determination of

the minimum number of required sensors is of an algorithm. solves the sweep coverage problem

In last make work that focuses only on the coverage part.

36

CHAPTER 4

RESEARCH OBJECTIVE

4.1 FUTURE SCOPE:

Wsn is very good research area, Wireless sensor network in future day to day update

technology coming, upcoming sensor sensing very long distance and very fast

transmitting data .present or future coming electronics items must and should using sensors

because working very fast.

Future days wireless sensor playing impotent role communication system. Data signals with

out delay transfer data ,and very fast working

4.2 FUTURE WORK:

we propose a measurement architecture using distributed air sniffers, which provides convenient delay

measurement, and requires no clock synchronization or instrumentation at the sensor nodes. One

challenge in deploying this architecture is how to place the sniffers for efficient delay measurement.

develop two algorithms to solve it. Using a combination of small-scale tested experiments and large-scale

simulation, we demonstrate that our architecture leads to accurate delay monitoring and is effective in

detecting abnormal delays

Future days I doing work compare the delay and mobile sensors. , applying algorithm in

mobile sensors I using different algorithms for ex: I applying delay minimize algorithms

basically I using TSP-S algorithm has better performance than the SSNOR algorithm. It is

because SSNOR algorithm only considers local efficiency. TSP-S algorithm considers global

efficiency.

Using coverage TSP algorithm applying time identifying the Minimum Mobile sensor

problem under the sweep delay changing limitation. because the algorithm remove the longer

edge from the TSP ring when generating the trajectory of each mobile sensor which reduces the

sweep distance and time.

37

CONCLUSION

In this report researching “minimize or improving delay using point of interest mobile coverage in wireless

sensor network”. we study how to schedule the given number of mobile sensors to monitor the POIs

such that the delay of the network is minimized. We discuss two scenarios. In the first scenario, with the

determined start positions of mobile sensors, we propose the SSR algorithm. In the SSR, we generate

trajectories by adding the POI which can make the current delay be minimized. This or using two

algorithms this algorithms working the send one to anther nodes moving dada fast in sources to

destination. Algorithm applying time sensor nodes moving write path and and transforming data with

out delay we are using algorithms improving delay.

38

CHAPTER 5

REFERENCES

[1] (Michael J. Neely) Delay Analysis for Maximal Scheduling with Flow Control in Wireless

Networks with Bursty Traffic[2009 ieee]

[2] (Milan Erdelj) Multiple Point of Interest Discovery and Coverage with Mobile Wireless

Sensors [2012]

[3] ( Azad Sharma) Delay Reduction Technique for Dynamic MANET On-demand Routing

Protocol[ IJ 2013]

[4 (Abhay Raman)]minimize delay and maximizing life time wireless sensor network with any

cast[2013]

[5](Yawei Li1) On Sweep Coverage with Minimum Mobile Sensors[2010]

[6] (Joohwan Kim) On Maximizing the Lifetime of Delay-Sensitive Wireless Sensor Networks

with Anycast

[7](Dong Zhao,)Mobile Sensor Scheduling for Timely Sweep Coverage[2012ieee]

[8] ( Milan Erdelj )Multiple Point of Interest Discovery and Coverage with Mobile Wireless

Sensors[2012]

[9] ( Kiran Maraiya) Application based Study on Wireless Sensor Network[International Journal

of Computer Applications 2011]

[10] Principle of Wireless Sensor Networks [ieee]