135 Dr. V. Bhoopathy, Dr.P.Sivakumar International Journal of Innovations & Advancement in Computer Science IJIACS ISSN 2347 – 8616 Volume 5, Issue 6 June 2016 Trailing Mobile Sinks to Data Coverage Protocol for Wireless Sensor Networks 1 Dr. V. Bhoopathy, 2 Dr.P.Sivakumar, 1 Sr. Asst. Professor, Department of CSE, Madanapalle Institute of Technology & Science(Autonomous),Madanapalle, Andhra Pradesh. 2 Associate Professor, Department of CSSE, Sree Vidyanikethan Engineering College (Autonomous) ,Tirupati, Andhra Pradesh Abstract—Wireless Sensor Networks (WSNs), leveraging data sinks’ mobility for data gathering has drawn substantial interests in recent years. Our planning is to identify a mobile sink’s moving space in advance to achieve optimized network performance, or target at collecting a small portion of sensed data in the network. To perform a data reporting process we divided number clusters total sensors and which sensors to be performed we can identified the current sensors. We check the prediction automatically using our concepts prediction that has divided into two ways. One is Prediction enable, the rest is Prediction disable identified the Prediction enable and Prediction disable when Prediction Process is performed. The data Reporting investigations contain as header, Member, Prediction enable/disable. The proposed protocols feature low-complexity and reduced control overheads. Two unique aspects distinguish our approach from previous ones: 1) We allow sufficient flexibility in the movement of mobile sinks to dynamically adapt to various terrestrial changes; and 2) Without requirements of GPS devices or predefined landmarks, Sink-Trail establishes a logical coordinate system for routing and forwarding data packets, making it suitable for diverse application scenarios. We systematically analyze the impact of several design factors in the proposed algorithms. Both theoretical analysis and simulation results demonstrate that the proposed algorithms reduce control overheads and yield satisfactory performance in finding shorter routing paths. Index terms: WSN, Clusters, Sink trail, Prediction enable and disable, GPS. I INTRODUCTION Networks (WSNs) have enabled a widespectrum of applications through networked low-cost low-power sensor nodes, e.g., habitat monitoring precision agriculture [and forest fire detection Inthese applications, the sensor network will operate underfew human interventions either because of the hostileenvironment or high management complexity for manualmaintenance. Since sensor nodes have limited battery life,energy saving is of paramount importance in the design ofsensor network protocols. Recent research on data collection reveals that, ratherthan reporting data through long, multi-hop, and error-prone routes to a static sink using tree or cluster networkstructure, allowing and leveraging sink mobility is morepromising for energy efficient data gathering. Mobilesinks, such as animals or vehicles equipped with radiodevices, are sent into a field and communicate directly withsensor nodes, resulting in shorter data transmission pathsand reduced energy consumption.However, data gathering using mobile sinks introducesnew challenges to sensor network applications. To betterbenefit from the sink’s mobility, many research efforts havebeen focused on studying or scheduling movement patternsof a mobile sink to visit some special places in a deployedarea, in order to minimize data gathering time. In suchapproaches a mobile sink moves to predetermined sojournpoints and query each sensor node individually. Althoughseveral Mobile Elements Scheduling (MES) protocols havebeen proposed to achieve efficient data collection viacontrolled sink mobility determining anoptimal moving trajectory for a mobile sink is itself anNP- hard problem and may not be able to adapt toconstrained access areas and changing field situations. Takethe precision agriculture application as an example, asshown in Fig. 1, where mobile sinks collecting data mainlyfollow trails or field boundaries in order not to damagecrops, and change trajectories dynamically according tofarmland situations. Typically, without scheduling the trajectory for a mobile-sink in advance, a data gathering protocol using mobile-sinks suggests that a mobile sink announce its locationinformation frequently throughout the network. Many Sink- Oriented Data Dissemination (SODD) protocols use suchapproach, e.g., Directed Diffusion, Declarative RoutingProtocol (DRP) and GRAB [whereas differentaggregation methods may be adopted. This
8
Embed
Trailing Mobile Sinks to Data Coverage Protocol for …academicscience.co.in/admin/resources/project/paper/f...The TTDD protocol, constructed a two-tier data dissemination structure
This document is posted to help you gain knowledge. Please leave a comment to let me know what you think about it! Share it to your friends and learn new things together.
Transcript
135 Dr. V. Bhoopathy, Dr.P.Sivakumar
International Journal of Innovations & Advancement in Computer Science
IJIACS
ISSN 2347 – 8616
Volume 5, Issue 6
June 2016
Trailing Mobile Sinks to Data Coverage Protocol for Wireless
Sensor Networks
1Dr. V. Bhoopathy,
2Dr.P.Sivakumar,
1Sr. Asst. Professor, Department of CSE,
Madanapalle Institute of Technology & Science(Autonomous),Madanapalle, Andhra Pradesh. 2Associate Professor, Department of CSSE,
Sree Vidyanikethan Engineering College (Autonomous) ,Tirupati, Andhra Pradesh
Abstract—Wireless Sensor Networks (WSNs), leveraging data sinks’ mobility for data gathering has drawn substantial
interests in recent years. Our planning is to identify a mobile sink’s moving space in advance to achieve optimized
network performance, or target at collecting a small portion of sensed data in the network. To perform a data reporting
process we divided number clusters total sensors and which sensors to be performed we can identified the current
sensors. We check the prediction automatically using our concepts prediction that has divided into two ways. One is
Prediction enable, the rest is Prediction disable identified the Prediction enable and Prediction disable when Prediction
Process is performed. The data Reporting investigations contain as header, Member, Prediction enable/disable. The
proposed protocols feature low-complexity and reduced control overheads. Two unique aspects distinguish our approach
from previous ones: 1) We allow sufficient flexibility in the movement of mobile sinks to dynamically adapt to various
terrestrial changes; and 2) Without requirements of GPS devices or predefined landmarks, Sink-Trail establishes a
logical coordinate system for routing and forwarding data packets, making it suitable for diverse application scenarios.
We systematically analyze the impact of several design factors in the proposed algorithms. Both theoretical analysis and
simulation results demonstrate that the proposed algorithms reduce control overheads and yield satisfactory
performance in finding shorter routing paths.
Index terms: WSN, Clusters, Sink trail, Prediction enable and disable, GPS.
I INTRODUCTION
Networks (WSNs) have enabled a widespectrum of
applications through networked low-cost low-power
sensor nodes, e.g., habitat monitoring precision
agriculture [and forest fire detection Inthese
applications, the sensor network will operate
underfew human interventions either because of the
hostileenvironment or high management complexity
for manualmaintenance. Since sensor nodes have
limited battery life,energy saving is of paramount
importance in the design ofsensor network
protocols. Recent research on data collection
reveals that, ratherthan reporting data through long,
multi-hop, and error-prone routes to a static sink
using tree or cluster networkstructure, allowing and
leveraging sink mobility is morepromising for
energy efficient data gathering. Mobilesinks, such
as animals or vehicles equipped with radiodevices,
are sent into a field and communicate directly
withsensor nodes, resulting in shorter data
transmission pathsand reduced energy
consumption.However, data gathering using mobile
sinks introducesnew challenges to sensor network
applications. To betterbenefit from the sink’s
mobility, many research efforts havebeen focused
on studying or scheduling movement patternsof a
mobile sink to visit some special places in a
deployedarea, in order to minimize data gathering
time. In suchapproaches a mobile sink moves to
predetermined sojournpoints and query each sensor
node individually. Althoughseveral Mobile
Elements Scheduling (MES) protocols havebeen
proposed to achieve efficient data collection
viacontrolled sink mobility determining anoptimal
moving trajectory for a mobile sink is itself anNP-