Data Aggregation In WSN Using Ant Colony Algorithm For Energy Efficient Wildlife Monitoring

[2012]

ABSTRACT

Wildlife management has today become a very important topic in terms of protection and

preserving our wildlife system and overall balancing the ecosystem and preserving the core

of the nature. And being an important area of which requires a lot of work to be done, and has

become a priority for many countries and organizations around the world. One of the very

basic idea surrounding wildlife management and most importantly for endangered species is a

good understanding of a species’ behavior and habitat. With Nepal being a country with

various diverse ecosystem, Nepal do possess various rare and endangered animals and those

require constant monitoring so that their habitat can be learned to help them survive the harsh

and unpredictable environment.

With the advent of technological field, the way the management is done has seen a great deal

of changes and with the help of Wireless Sensor Network (WSN), wildlife management

system can be designed in such a way that there is minimum invading in their habitat so as to

protect their habitat from external interfering. Since a full-fledged WSN requires lot of nodes

to work as a wholesome network, and since the wildlife management process can be for

longer period there is requirement for energy efficient WSN so that the research process can

be carried out for longer period with full maximum utilization of the resources available.

Data aggregation is important in energy constraint wireless sensor networks which exploits

correlated sensing data and aggregates at the intermediate nodes to reduce the number of

messages exchanged network. An ant colony algorithm for data aggregation in wireless

sensor networks provides an opportunity for energy efficiency. Artificial simulated ants will

be used to simulate the real time activities that ant perform while searching for source of food

or the path using pheromone trail and during this process every ant will explore all possible

paths from the source node to the sink node. The data aggregation tree is constructed by the

accumulated pheromone resulting in finding the shortest path between their nest and the food

sources.

ii | P a g e

Department of CSE, Kathmandu University, Kavre


[2012]

INDEX

TABLE OF CONTENTS Page No.

Cover page i

Abstract ii

List of Tables iv

List of Figures v

Abbreviations and symbols vi

CHAPTER No. Title Page No.

1. INTRODUCTION 1-8

1.1 Background 11.2 Problem System 31.3 Objectives 31.4 Literature Review 41.5 Significance 61.6 Limitation 71.7 Organization of Study 7

2. METHODOLOGY 9-15

2.1 Universe of Study 92.2 Data Collection and Sampling 102.3 Testing and Simulation 12

3. CONCLUSION AND DISCUSSION 16-17

3.1 Conclusion 163.2 Discussion 173.3 Future Works 17

BIBLIOGRAPHY 18

Appendix

iii | P a g e



[2012]

LIST OF TABLES

S. No. Table No. Caption of the Table Page No.

1 Table 1 Wildlife monitoring in Protective Areas of Nepal 10

2 Table 2 Pert Chart 14

3 Table 3 Division of work 15

iv | P a g e



[2012]

LIST OF FIGURES

S. No. Figure Number Caption of the Figure Page No.

1 Figure 1 Ant Colony Optimization shortest path selection 12

sample

2 Figure 2 ACO algorithm 13

3 Figure 3 Work Division of the Project 15

v | P a g e



[2012]

ABBREVIATIONS AND SYMBOLS

ACO Ant Colony Optimization

APU Anti Poaching Units

BNP Bardiya National Park

CNP Chitawan National Park

CO Colony Optimization

DNPWC Department of National Parks and Wildlife Conservation

EIA Environment Investigation Agency

GPS Global Positioning System

GPRS General Packet Radio Service

OS Operating System

VHF Very High Frequency

WSN Wireless Sensor Network

WWF World Wildlife Federation

vi | P a g e



[2012]

CHAPTER 1

1. INTRODUCTION

1.1) Background

Various project work have been done in co-ordination with the various organization

involved in the work and has been solely concentrated on upgrading the information

system currently available but not in an extended format. So to determine the behavior

and location of wild animals, they need to be monitored in some way. So to track the

animals the main idea is to use a WSN to track their movements and follow their

tracks and try to protect their natural habitat and also help researchers to take the data

collected and use the data to further help in their quest to provide a better protection to

the animals and the natural landscape and environment of the habitat present.

Considerable research studies have been carried out in protected areas of Nepal over

last three decades. Often, these studies are species oriented on selected endangered

mammals and do not deliver conservation actions potentially to serve management

needs. Comprehensive, multidisciplinary research approach and long term monitoring

is scant at present and is of little conservation significance. Research policies,

guidelines, prioritization and evaluation criteria are important facets for sound

protected area management. Persistent concerted efforts are needed to make research

and monitoring as an integral part of protected area management.

With various research being conducted still the available resources which have been

widely used in various monitoring activities in other countries have not been widely

adopted in regards to monitoring in Nepal and still lag a long way behind in terms of

research works being carried out. Use of Wireless Sensor Networks can be applied in

the field of wildlife monitoring with WSNs being composed of huge number of sensor

nodes which can monitor the environment by collecting, processing as well as

transmitting collected data to the remote sink node through direct or multihop

transmission.

1 | P a g e



[2012]

With motes being powered with GPRS, Bluetooth, GPS modules, sensor boards,

TinyOS and many more, there is a lot of possibilities which can be worked, with these

features of the motes which goes in long way to carrying out the monitoring activities.

The main concern regarding the sensor nodes is the fact that it is resource constrained

and that is where data aggregation using ant colony optimization comes in since the

tiny sensor nodes are powered by limited battery resources, energy efficiency is one

of the primary challenges to the successful application of WSNs. Usually energy is

consumed during three processes which are sensing, processing and communication

process.

In ACO (Ant colony optimization) a colony of artificial ants is used to construct

solutions guided by the pheromone trails and heuristic information. ACO was inspired

by the foraging behavior of real ants. This behavior enables ants to find shortest paths

between food sources and their nest. Initially, ants explore the area surrounding their

nest in a random manner. As soon as an ant finds a source of food, it evaluates

quantity and quality of the food and carries some of this food to the nest. During the

return trip, the ant deposits a pheromone trail on the ground. The quantity of

pheromone deposited, which may depend on the quantity and quality of the food, will

guide other ants to the food source. The indirect communication between the ants via

the pheromone trails allows them to find the shortest path between their nest and food

sources. This functionality of real ant colonies is exploited in artificial ant colonies in

order to solve Optimization problems. In ACO algorithms the pheromone trails are

simulated via a parameterized probabilistic model that is called the pheromone model.

The pheromone model consists of a set of model parameters whose values are called

the pheromone values. The basic ingredient of ACO algorithm is a constructive

heuristic that is used for probabilistically constructing solutions using the pheromone

values. In general, the ACO approach attempts to solve a CO problem by iterating the

following two steps:-

1. Solutions are constructed using a pheromone model, that is, a parameterized proba-

bility distribution over the solution space.

2 | P a g e



[2012]

2. The solutions that were constructed in earlier iterations are used to modify the

pheromone values in a way that is deemed to bias the search toward high quality so-

lutions.

1.2) Problem Statement

Natural habitats are vital for assuring sustainable development. Wild habitats are the

repositories of biological diversity (biodiversity) which are the raw material for

natural selection and adaptation. They provide myriad services that enrich and sustain

human life with both tangible and intangible economic and social value. Biodiversity

is especially important to South Asia which is home to 13 – 15% of the world's

biodiversity and hosts some of the most charismatic and endangered species on Earth.

Habitats across Bangladesh, Bhutan, India and Nepal are home to over 65% of the

3,000 or so remaining wild tigers and the Himalayas is the last redoubt of the

critically endangered snow leopard, whose numbers are unknown. India is classified

as a mega-diverse country and the Himalayas. In Nepal basically various hunting,

poaching and illegal trading of animals parts which are endangered are being

conducted under a racket making it difficult for activists and those involved in

protection a very hard time, so using the latest technology available a well deployed

system can be used to make it better for the habitat monitoring of such animals.

Poaching has become so intense that tigers have disappeared from many parks

throughout Asia. Nowhere has the impact been greater than in India and Nepal which

remain the bastions of tiger conservation. Nepal has emerged as the transit hub for the

trade in illegal wildlife commodities destined for consumption in East China which is

a very serious issue and have to be addressed very soon which can be done with the

help of the monitoring system that can be well devised to pertain to the needs of such

habitat in need of monitoring.

1.3) Objectives

The main objective of the research undertaken is to: -

To create an energy efficient wireless sensor network for wildlife monitoring

3 | P a g e



[2012]

To determine optimal in-network data aggregation points in sensor network

using ACO algorithm

To test the equipment and various application in co-ordination with the na-

tional park in small scale and then try a full-fledged project

Keep track of the animals’ travels that are under study and, with immediate

learning if they have fell prey to any mishaps naturally or artificially.

Develop a fully functional, highly mobile, energy efficient sensing system that

determines accurate positional data and can propagate it through the network.

To design an optimized system to incorporate as maximum elements as possi-

ble for the tracking and usability of the information system as a whole

1.4) Literature Review

Scientists have used a variety of methods to track animal species. Before the 1960s,

it was common practice to capture a small number of subjects, attach identifiers (i.e.,

tags or bands) to them, and release them back into the wild so that they could be

tracked. Eventually, the animals were recaptured, or their remains were found, and

the identifiers were retrieved. Generally speaking, this approach is cheap and can

provide baseline information about the observed wildlife; however, the recovery rate

is low and the accuracy is poor due to inevitable observation errors.

The first breakthrough in tracking technology occurred with the advent of radio

telemetry. A radio telemeter, which consists of a Very High Frequency (VHF)

transmitter, an antenna, and a power cell, is attached to the subject by a harness, a

collar, glue, subcutaneous prongs, or surgical implants. Signals emitted by VHF

transmitters are detected by using receivers with homing techniques or by applying

triangulation-based techniques. Although these approaches can collect much more

accurate information than previous methods, the radio telemeter system causes two

additional problems: 1) the maximal lifespan of the system is limited by the battery

capacity, which is also a common problem in the methods discussed below; and 2)

the tracking range is limited by the radio range, which makes wildlife tracking

extremely labor-intensive.

4 | P a g e



[2012]

Currently, the US/French Argos system is the only functional satellite telemeter

system, and it has been widely applied by many wildlife tracking projects. However,

to transmit signals to satellites, the Argos system consumes much more power than

radio telemeter based systems and thus has a shorter lifespan. Moreover, the

operating cost is quite high - about USD 500 per animal per month.

The Great Duck Island (GDI) project addressed the requirements for habitat

monitoring in general. It’s proposed the architecture for monitoring seabird nesting

environment and behavior. The deployed network consisted of 32 nodes on a small

island off the coast of Maine streaming useful live data onto the web. The application

driven design exercise served to identify important areas of further work in data

sampling, communications, network re-tasking, and health monitoring.

The Princeton’s ZebraNet Project is an inter‐disciplinary effort with thrusts in both

Biology and Computer Systems to track zebra behavior at Mpala Research Centre,

Kenya. ZebraNet team finally developed a fully functional, highly mobile, energy

efficient sensing system that determines accurate positional data and can propagate it

through the network. Several system level energy management techniques were

deployed to reduce energy consumption. The hardware utilized a solar array to

recharge the batteries. The system was first tested on January 12, 2004 where seven

zebras, six females and one male were collared. Position logs were generated for

these zebras. The area covered was around 36 square kms.

Jurdak et al. in their paper related to energy efficient localization for virtual fencing

have specified that addresses the tradeoff energy consumption and localization

performance in a mobile sensor network application. It focused on combining GPS

location with more energy-efficient location sensors to bound position estimate

uncertainty in order to prolong node lifetime. The focus was on an outdoor location

monitoring application for tracking cattle using smart collars and using empirically-

derived models to explore duty cycling strategies for maintaining position

uncertainty, within specified bounds.

Regarding the energy efficiency of the WSNs various works have been carried out in

5 | P a g e



[2012]

this field which includes Directed diffusion (DD) (Intanagonwiwat et al., 2003),

which is a typical data-centric routing paradigm for sensor networks. It consists of

four basic elements: interests, data messages, gradients, and reinforcements. In DD, a

task, which is a list of attribute-value pairs, is flooded into the whole network as an

interest for named data. A gradient is a direction state created in each node that

receives an interest. Events start flowing toward the originator of interest along the

established shortest path. Data from different sources are opportunistically

aggregated. However, opportunistic aggregation on a low-latency tree is not efficient

because data may not be aggregated on nodes near the sources. In-network data

aggregation is an important in energy constraint sensor network which exploits

correlated sensing data and aggregates at the intermediate nodes reducing the number

of messages exchanged network. In data gathering application large amount of

communication is reduced by in-network aggregation achieving maximum lifetime

of network.

Optimal aggregation tree problem is NP-hard (Al-Karaki et al., 2004) which is

equivalent to Steiner tree (Krishnamachari et al., 2002), weighted set cover

(Intanagonwiwat et al., 2002) problems. Many researchers have made efforts on data

aggregation in wireless sensor networks (Bhattacharjee and Das, 2007;

Intanagonwiwat et al., 2002; Krishnamachari et al., 2002; Li et al., 2006; Misra and

Mandal, 2006; Motegi et al., 2006).

In Li et al. (2006), their greedy algorithm constructs a multicast tree by iteratively

adding source nodes to the existing tree until all the source nodes and the sink node

are included. Initially, the tree includes only the sink node. Each time the algorithm

finds a source node among the remaining source nodes, which is closest to the

existing tree, it adds the shortest path between that source node and the existing tree

to the tree. This process continues until all the source nodes have been included in

the tree. In addition, in order to further reduce the computational complexity and

improve the quality of the output solution, they design another heuristic

approximation algorithm based on minimum spanning tree to construct the data

aggregation tree.

6 | P a g e



[2012]

1.5) Significance

In Nepal, the poaching and illicit trade of wildlife, particularly Asian big cats, is a

major concern. In response, WWF is working with the Nepalese government to close

down trade routes and transit markets for illegal wildlife through improved

monitoring and enforcement. Each year, hundreds of millions of plants and animals

are caught or harvested from the wild and then sold as food, pets, ornamental plants,

tourist curios and medicine. While a great deal of this trade is legal and is not harming

wild populations, a worryingly large proportion is illegal and threatens the survival of

many endangered species. With the help of such monitoring system a fast measure can

be taken in terms of protecting the endangered species. With such technological

advent in recent times, the monitoring system can be moved on from the previously

uncanny monitoring system that prevailed in Nepal can be replaced with simple and

effective system for monitoring the wildlife and using energy efficient WSNs is a

major step towards achieving what has till now been a difficult process regarding the

wildlife monitoring.

1.6) Limitation

The main limitation associated with the study is the fact that the permission of the na-

tional park is the very core component that defines the entire project and the study it-

self that has been proposed. The other main limitation of the study which may influ-

ence the study during its progress is the technological aspect of the study, since the

study itself is being carried out in the wild there may be some interference regarding

the transmission of the signal and maybe need to be complied to its suitableness to the

environment of the national park where the study will be conducted .The battery that

will be used in the nodes will only have some specific lifetime and since it is that spe-

cific area which we are trying to concentrate on improving and hence the lifetime of

the device thus used will be directly in consideration with the study being conducted.

Secure access to the data must be maintained so as to avoid snooping and various

other activities which the poachers or other people may use to their advantage and in-

formation thus gathered but it will be a long way before that can be achieved and the

security part will have to be worked out from the beginning of the study being con-

ducted.

7 | P a g e



[2012]

1.7) Organization of Study

The introduction introduces briefly the various aspects of the research that will be

carried out and covers the problem that the research is going to address along with

relevant information about why this is an important problem along with background

information, and the purpose of the study. The methodology and further topic covers

around with the details of various sources of data and methods used to design the

project with theoretical definition regarding various aspects of the project.

The summary, conclusion part of the report discusses about the overall work to be

carried out the research and the testing carried out and also about the future work with

respect to the project.

8 | P a g e



[2012]

CHAPTER 2

2. METHODOLOGY

2.1) Universe of Study

International trade of wildlife species and products is estimated to be worth USD 20

billion (Chungyalpa 1998). Despite strict legislation ensuring a poacher/trader 15

years' imprisonment or a fine of up to USD 1,300 (or both), trade has continued to

flourish (Chungyalpa 1998). In 1991, WWF Nepal and the Department of National

Parks and Wildlife Conservation (DNPWC) sought to identify deterrents to tiger and

rhino poaching in the national parks. As a result, antipoaching units (APUs) were

formed in Chitawan National Park (CNP) and Bardiya National Park (BNP). Although

APUs were set up to reduce the level of poaching of tigers and rhinos, they quickly

also became involved in monitoring the trafficking network of wildlife species and

their products. Concerns have focused on the increasing trend of Nepal being used as

a transit route by well-coordinated and well-financed organized groups with

international links. The Environment Investigation Agency’s (EIA) recent report The

Tiger Skin Trail described Kathmandu as a ‘staging point’ for illegal skins brought in

from India to be sent to Tibet

Responding to the overwhelming concern of the global community and recent tiger

conservation issues in India, WWF and TRAFFIC met to strategize on the emerging

threats to endangered wildlife due to illegal trade and consumption. A 5-year action

plan was drawn up to address issues at various sites in the trade chain. Nepal has been

identified as a key player in the transit process. This proposal thus draws upon the

action plan as well as on the national level priorities that have been identified as part

of WWF Nepal’s experience of wildlife trade issues in Nepal.

Nepal is a signatory and party to CITES and had in place all legal and institutional

instruments to address wildlife trade issues. However, the illegal wildlife trade has

recently become more organized, demand has increased and the traders have a more

sophisticated system for transporting consignments. Therefore, it is now also

9 | P a g e



[2012]

necessary for Nepal to address external drivers, like international demand and

enforcement at cross-border levels, as well as regional level advocacy, policy analysis

and feedback, working with non-conventional stakeholders such as transport

companies.

There have also been various study regarding the protection of endangered and other

in danger in Nepal but most of them have been non-technological methods and have

been conducted with help of manual labor and below is the table showing various

work regarding the recent survey that has been overtaken:-

Species Year Place Methods Results Collaboration

Rhino 2008 Chitawan and

Bardiya NP

Total direct

count

430 DNPWC/CNP/BNP/

NA/WWF

Nepal/NTNC/ZSL/Darwin

Initiative

Gaur 2008 Parsa WR Total direct

count

37 DNPWC/PWR/NA/WWF

Nepal-TAL

Tiger 2008 Suklaphanta WR Photographic-

capture recapture

sampling method

7 DNPWC/WWF Nepal-

TAL/NTNC-SCP

Gharial 2008 Koshi,Narayani,Rap

ti,Babai,Karnali

rivers

Total direct

count

81 DNPWC/WWF Nepal

Swamp

Deer

2008 Suklaphanta WR Total direct

count

1674 SWR/WWF

Nepal-TAL/NTNC-SCP

Table 1: - Wildlife monitoring in Protective Areas of Nepal

2.2) Data Collection and Sampling

Since this is an exploratory and descriptive research and its methodology will consist

of various conceptual frameworks, study area, source of information, methods and

techniques. The study will be done in the Bardiya National Park area, Parsa wildlife

reserve and Chitwan National Park to achieve the objectives that has been undertaken

for the completion of the research .The reason behind the selection is because the

demand and need of information system is very high in wildlife conservation area

10 | P a g e



[2012]

with lots of large organization and with lots of individual working towards its

conservation and protection. So to cope with the current requirement and need in

terms of protection it is perfect for data collection and analysis, the areas that has been

selected for the purpose .A set of questionnaire and pre-testing the questionnaire and

field survey and various different methods and techniques of data analysis will be

used during the course of this study regarding the information about habitat and the

number of animals that will be under investigation. With due concern for the

economical completion of the research study, the design in such studies must be rigid

and not flexible and must focus attention on the following: -

1. Formulating the objective of the study

2. Designing the methods of data collection

3. Selecting the sample

4. Collecting the data

5. Processing and analyzing the data

6. Reporting the findings.

Primary Data / Secondary Data

The being an exploratory and descriptive type, various sources and techniques

will be used in gathering the information. Both primary and secondary data

relevant to the study will be collected and analyzed during the study.

Sources of Primary Data

The organizations who are currently involved with fulfilling their services of

providing the work related to wildlife conservation and related field will be the

major sources of the primary data. Similarly internal analysis of the

organization involved and their promotional scheme and data achieving and

striving for the ultimate goal of providing an uncharted information system

which may be able to change the face of the information system regarding

wildlife research. Also apart from these, the information obtained through

observation, discussion and key informant survey will also be given due

consideration.

Sources of Secondary Data

11 | P a g e



[2012]

The sources of secondary data will be the various articles, reports, journals,

books etc. published by various organization and institutes.

2.3) Testing and Simulation

The Ant-Aggregation algorithm will be simulated in MATLAB with a setting of sensor

network of around 50 nodes to support for a single environmental setting with respect

to the Bardiya National Park, Chitawan national park and Parsa Wildlife Reserve indi-

vidually for each setting. The neighborhood is obtained from the random topology. Set

of source nodes 20, 30&40 and a destination will be considered for generating optimal

aggregation tree using Ant-Aggregation. On varying the ACO parameters and weights

of aggregation, shortest distance and correlation, the optimal aggregation tree in sensor

network will be the target to be obtained.

Figure 1: - Ant Colony Optimization shortest path selection sample

12 | P a g e



[2012]

Algorithm: Outline of ant colony optimization metaheuristicSet parameters, initialize pheromone trailsWhile termination criterion not satisfied doConstructAntSolutionApplyLocalSearch /*optional*/Update pheromonesEnd while

Figure (a): - Basic ACO Algorithm

Each ant located at node i hops to node j selected among the neighbors that have not yet been visited according to probability. Probability that ant k in node i will go to node j.α: relative importance of pheromone trialβ : relative importance of the distanceg : neighborhood of current node idij : node potential, gives estimate of early aggregation or shortest route to destina-tion.

Figure (b): - Mathematical algorithm model

13 | P a g e



[2012]

14 | P a g e


Initialize (Variable Phermone, Aggregate_count,source,destination)

Iterate 100 times or Converge to

optimal cost

Permute source and allocate randomly to each Ant For each ant iteratively constructive route by locally selecting

next hop based on probability P(i,j)

If K reached destination?

Select Next hop

Next K (ant)

Find out totalcost

Second Pass: Transverse from destination to source

Until reaches to all source

Find optimal aggregation points from optimal aggregation tree: apply labeling of nodes ants visited once are leaf(src) and aggregation

nodes having Aggregate_count>1

Yes

YesYes

No

No

Figure (c) :- Flowchart representation of ACO

Figure 2 :- ACO algorithm


[2012]

S. No. TasksEstimated time (Weeks)

1 Title selection and Synopsis 2

2 Literature review 4

3 Project rationale 2

4 Design investigation and identify resources available 2

5 Draft a preliminary design to carry out the research 3

6 Finalize target area of conducting experiment 2

7 Identify target group animal and carry out pilot study re-garding the monitoring

4

8 Analyze the data from simulation and real time collec-tion and review pilot study

4

9 Analyze data and produce equivalent analysis 4

10 Conclude and finalize Documentation 6

Table 2 :- Pert Chart

15 | P a g e



[2012]

Project Work Division

Project selection and con-firmation

Designing and overview of the Project

Review and implementa-tion of available similar projects

Coding and review of Codes

Identification of possible area of implementation and future works possible

Testing and Debugging

Documentation

Figure 3:- Work Division of the Project

TASKS STUDENTS

Binit Research

Assistant 1

Research

Assistant 2

Project selection and confirmation X

Designing and overview of the Project

X X X

Review of available similar projects X

Coding and review of Codes X X

Identification of possible area of im-plementation and future works possi-

bleX X

Testing and Debugging X X X

Documentation X X X

Table 3:- Division of work

16 | P a g e



[2012]

CHAPTER 3

3. CONCLUSION AND DISCUSSION

3.1) Conclusion

Use of WSN is proving to be a very powerful tool and critical to the successful

implementation of a project of this scope and complexity. WSN for wildlife

monitoring can provide the means to tracking and monitoring of the wildlife and

especially endangered as that one. WSNs with various sensor nodes and equipment

have provided necessary tools for surveys, navigation, and tracking. Technologies are

being utilized to produce status and monitoring data about the wildlife that were

invaluable in conducting the research that has been carried out and giving an

alternate for resource constrained resource regarding the use of WSNs in tracking

system and was very useful in the final outcome with the system thus proposed for

the power saving property of the prototype system being very useful in getting the

final result which was very effective in the situations and useful for wildlife

management using energy efficient WSNs using ACO.

Patrolling is a basic and the most important function of protected areas. In spite of

patrolling, research and monitoring has remained a low priority activity in most of

the protected areas. A simple monitoring process consists of recording wildlife en-

counters by the staff while on routine patrols, in a standardized format should be

practiced in protected areas. The data so collected over a period of time can provide

insights into the population dynamics and distribution of most species. But the need

today is now to incorporate the use of technological aspect such as WSNs to make it

more easy and efficient for tracking and monitoring purpose.

A review of the use WSNs for wildlife management has indicated that these systems

are predominantly used for analysis of habitat requirements and the prediction of

areas of suitable habitat, and typically consider only relatively short time scales.

These approaches to wildlife management can be applied in the case of Tiger and

17 | P a g e



[2012]

One-horned Rhinos because the issues are ones of ongoing management of data in a

manner that preserves the complex relationships that exist between data items,

allowing them to be analyzed.

3.2) Discussion

The use of energy efficient WSNs will enable to track the position of the animals and

their habitat and in turn help in tracking their daily life activities and their position

with respect to the national park and the reserve and in turn help in nurturing them

and protecting them. Also with respect to information system that has been planned

to be designed, the data available from WSN will be used to display various

attributes to the various animals for eg :- the current position of the animal, their

current location regarding which part of the park they are in so that it can help in

panning out data and reference to helping towards better protection of endangered

animal can be done using GPS enabled motes available. The research and its use has

potential use in various field of the wildlife tracking and then using energy efficient

WSNs to represent in various applications in future with further addition of

components and can do a great deal of tracking and information system that will be

able to provide various information related to wildlife with various pictures, snaps

and the history, origin of the animals and various aspects that will be helpful in better

protection of endangered animals.

3.3) Future Works

In the future there will be more co-ordination and working with biologists and devote

our effort developing adjustable collars that can be remotely controlled and released.

Various plan such as to improve the installation of the WSN motes so that they’re

placed efficiently in the monitored animals’ necks while keeping the antenna on the

top. Moreover, there a plan to package the motes with various sensor into a smaller

case, and perform a large scale field experiment so as to cover a wider area of the

National Park and other sanctuary which can be helpful in long term for the

researches and those working on protecting the habitat of the endangered species of

the animals.

18 | P a g e



[2012]

BIBLIOGRAPHY

[1] A Critique of Wildlife Radio-tracking and its Use in National Parks

http://www.npwrc.usgs.gov/resource/wildlife/radiotrk/wildl.htm.

[2] Argos system. http://www.argos-system.org/.

[3] GPS enabled collars with UHF uploading link.

http://www.lotek.com/gps4400.htm.

[4] GSM. http://www.3gpp.org/.

[5] Helping Santa keep an eye on Donner and Blitzen.

http://www.csiro.au/news/Santas-Reindeers.html.

[6] Pasture yen family. http://www.yenfamily.idv.tw/.

[7] Science and the Environment Bulletin: Wildlife tracking technologies.

http://www.ec.gc.ca/Science/sandejuly99/article1 e.html.

[8] ICIMOD, 2008. Land covers dynamics in Sagarmatha National Park. International

Centre for Integrated Mountain Development, Kathmandu, Nepal

[9] TurtleNet. http://prisms.cs.umass.edu/dome/turtlenet.

[10] UCSD Wireless Topology Discovery Project. http://sysnet.ucsd.edu/wtd/.

[11] Nepal Newsletter, Year 17, No. 12. Department of National Parks and Wildlife

Conservation, Kathmandu, Nepal

[12] The zebranet wildlife tracker. http://www.princeton.edu/∼mrm/zebranet.html.

[13] DNPWC, 2007. Increasing Swamp Deer Population in Suklaphanta Wildlife Re-

serve. Wildlife

[14] M. Birattari, T. Stützle, L. Paquete, and K. Varrentrapp. A racing algorithm for

configuring metaheuristics. In W. B. Langdon et al., editor, Proceedings of the Ge-

netic and Evolutionary Computation Conference,pages 11–18. Morgan Kaufman, San

Francisco, CA, 2002.

[15] A. Chaintreau, P. Hui, J. Crowcroft, C. Diot, R. Gass, and J. Scott. Impact hu-

man mobility on the design of opportunistic forwarding algorithms. In Infocom,

2006.

[16] Arampatzis T, Lygeros J, Manesis S. A survey of applications of wireless and

wireless sensor networks. In:Mediterranean Conference on Control Automation, 2005.

19 | P a g e



[2012]

[17] W. W. Cochran and T. E. Hagen. Construction of collar transmitters for deer.

Technical report, Minnesota Mus. Nat. Hist., 1963.

[18] Bhattacharjee S, Das N. Distributed data gathering scheduling in multihop wire-

less sensor networks for improved

lifetime. In: International Conference on Computing: Theory and Applications (IC-

CTA), 2007.

[19] M. Grossglauser and D. Tse. Mobility increases the capacity of ad-hoc wireless

networks. IEEE/ACM Transactions on Networking, 10(4):477–486, August 2002.

[20] Chen G, Guo T-D, Yang W-G, Zhao T. An improved ant-based routing protocol

in wireless sensor networks. In:

International Conference on Collaborative Computing: Networking, Applications and

Worksharing

(CollaborateCom),2006.

[21] Y.-T. Huang, Y.-C. Chen, J.-H. Huang, L.-J. Chen, and P. Huang. YushanNet: A

Delay-Tolerant Wireless Sensor Network for Hiker Tracking in Yushan National

Park. In The International Conference on Mobile Data Management Systems, Services

and Middleware, 2009.

[22] Akyildiz F, Su W, Sankarasubramaniam Y, Cayirici E. A survey on sensor net-

work. IEEE Commun. Mag.

2002;40(8):102–14.

[23] Al-Karaki JN, Ul-Mustafa R, Kamal AE. Data aggregation in wireless sensor

networks—exact and approximate

algorithms. In: International Workshop on High-Performance Switching and Routing,

2004.

[24] P. Juang, H. Oki, Y. Wang, M. Martonosi, L. S. Peh, and

D. Rubenstein. EnergyEfficient Computing for Wildlife Tracking: Design Tradeoffs

and Early Experiences with ZebraNet. ACM SIGARCH Computer Architecture News,

30(5):96–107, December 2002.

[25] O. Landsiedel, J. gila Bitsch, K. Wehrle, J. Thiele, and H. Mallot. Rat Watch:

Using Sensor Networks for Animal Observation. In ACM REALWSN, 2006.

20 | P a g e


Data Aggregation In WSN Using Ant Colony Algorithm For Energy Efficient Wildlife Monitoring [2012]

APPENDIX

Figure: - Gantt chart of “Data Aggregation In WSN Using Ant Colony Algorithm For Energy Efficient Wildlife Monitoring”

a | P a g e


binit kc proposal computer network wsn wildlife monitoring

Documents