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GLOBALSOFT TECHNOLOGIES 
IEEE PROJECTS & SOFTWARE DEVELOPMENTS 
IEEE FINAL YEAR PROJECTS|IEEE ENGINEERING PROJECTS|IEEE STUDENTS PROJECTS|IEEE 
BULK PROJECTS|BE/BTECH/ME/MTECH/MS/MCA PROJECTS|CSE/IT/ECE/EEE PROJECTS 
CELL: +91 98495 39085, +91 99662 35788, +91 98495 57908, +91 97014 40401 
Visit: www.finalyearprojects.org Mail to:ieeefinalsemprojects@gmai l.com 
Snapshot and Continuous Data Collection in Probabilistic 
Wireless Sensor Networks 
Abstract 
Data collection is a common operation of Wireless Sensor Networks (WSNs), of which the 
performance can be measured by its achievable network capacity. Most existing works 
studying the network capacity issue are based on the unpractical model called deterministic 
network model. In this paper, a more reasonable model, probabilistic network model, is 
considered. For snapshot data collection, we propose a novel Cell-based Path Scheduling 
(CPS) algorithm that achieves capacity of Ω(1/ 5ω ln n·W) in the sense of the worst case 
and order-optimal capacity in the sense of expectation, where n is the number of sensor 
nodes, ω is a constant, and W is the data transmitting rate. For continuous data collection, 
we propose a Zone-based Pipeline Scheduling (ZPS) algorithm. ZPS significantly speeds 
up the continuous data collection process by forming a data transmission pipeline, and 
achieves a capacity gain of N√n/√(log n) ln n or n/log n ln n times better than the optimal 
capacity of the snapshot data collection scenario in order in the sense of the worst case, 
where N is the number of snapshots in a continuous data collection task. The simulation 
results also validate that the proposed algorithms significantly improve network capacity 
compared with the existing works. 
Existing system 
Data collection is a common operation of Wireless Sensor Networks (WSNs), of which the 
performance can be measured by its achievable network capacity. Most existing works 
studying the network capacity issue are based on the unpractical model called deterministic 
network model.
Proposed system 
In this paper, a more reasonable model, probabilistic network model, is considered. For 
snapshot data collection, we propose a novel Cell-based Path Scheduling (CPS) algorithm 
that achieves capacity of Ω(1/ 5ω ln n·W) in the sense of the worst case and order-optimal 
capacity in the sense of expectation, where n is the number of sensor nodes, ω is a 
constant, and W is the data transmitting rate. For continuous data collection, we propose a 
Zone-based Pipeline Scheduling (ZPS) algorithm. ZPS significantly speeds up the 
continuous data collection process by forming a data transmission pipeline, and achieves a 
capacity gain of N√n/√(log n) ln n or n/log n ln n times better than the optimal capacity of the 
snapshot data collection scenario in order in the sense of the worst case, where N is the 
number of snapshots in a continuous data collection task. The simulation results also 
validate that the proposed algorithms significantly improve network capacity compared with 
the existing works. 
SYSTEM CONFIGURATION:- 
HARDWARE CONFIGURATION:- 
 Processor - Pentium –IV 
 Speed - 1.1 Ghz 
 RAM - 256 MB(min) 
 Hard Disk - 20 GB 
 Key Board - Standard Windows Keyboard 
 Mouse - Two or Three Button Mouse 
 Monitor - SVGA 
SOFTWARE CONFIGURATION:- 
 Operating System : Windows XP
 Programming Language : JAVA 
 Java Version : JDK 1.6 & above.

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IEEE 2014 JAVA NETWORKING PROJECTS Snapshot and continuous data collection in probabilistic wireless sensor networks

  • 1. GLOBALSOFT TECHNOLOGIES IEEE PROJECTS & SOFTWARE DEVELOPMENTS IEEE FINAL YEAR PROJECTS|IEEE ENGINEERING PROJECTS|IEEE STUDENTS PROJECTS|IEEE BULK PROJECTS|BE/BTECH/ME/MTECH/MS/MCA PROJECTS|CSE/IT/ECE/EEE PROJECTS CELL: +91 98495 39085, +91 99662 35788, +91 98495 57908, +91 97014 40401 Visit: www.finalyearprojects.org Mail to:ieeefinalsemprojects@gmai l.com Snapshot and Continuous Data Collection in Probabilistic Wireless Sensor Networks Abstract Data collection is a common operation of Wireless Sensor Networks (WSNs), of which the performance can be measured by its achievable network capacity. Most existing works studying the network capacity issue are based on the unpractical model called deterministic network model. In this paper, a more reasonable model, probabilistic network model, is considered. For snapshot data collection, we propose a novel Cell-based Path Scheduling (CPS) algorithm that achieves capacity of Ω(1/ 5ω ln n·W) in the sense of the worst case and order-optimal capacity in the sense of expectation, where n is the number of sensor nodes, ω is a constant, and W is the data transmitting rate. For continuous data collection, we propose a Zone-based Pipeline Scheduling (ZPS) algorithm. ZPS significantly speeds up the continuous data collection process by forming a data transmission pipeline, and achieves a capacity gain of N√n/√(log n) ln n or n/log n ln n times better than the optimal capacity of the snapshot data collection scenario in order in the sense of the worst case, where N is the number of snapshots in a continuous data collection task. The simulation results also validate that the proposed algorithms significantly improve network capacity compared with the existing works. Existing system Data collection is a common operation of Wireless Sensor Networks (WSNs), of which the performance can be measured by its achievable network capacity. Most existing works studying the network capacity issue are based on the unpractical model called deterministic network model.
  • 2. Proposed system In this paper, a more reasonable model, probabilistic network model, is considered. For snapshot data collection, we propose a novel Cell-based Path Scheduling (CPS) algorithm that achieves capacity of Ω(1/ 5ω ln n·W) in the sense of the worst case and order-optimal capacity in the sense of expectation, where n is the number of sensor nodes, ω is a constant, and W is the data transmitting rate. For continuous data collection, we propose a Zone-based Pipeline Scheduling (ZPS) algorithm. ZPS significantly speeds up the continuous data collection process by forming a data transmission pipeline, and achieves a capacity gain of N√n/√(log n) ln n or n/log n ln n times better than the optimal capacity of the snapshot data collection scenario in order in the sense of the worst case, where N is the number of snapshots in a continuous data collection task. The simulation results also validate that the proposed algorithms significantly improve network capacity compared with the existing works. SYSTEM CONFIGURATION:- HARDWARE CONFIGURATION:-  Processor - Pentium –IV  Speed - 1.1 Ghz  RAM - 256 MB(min)  Hard Disk - 20 GB  Key Board - Standard Windows Keyboard  Mouse - Two or Three Button Mouse  Monitor - SVGA SOFTWARE CONFIGURATION:-  Operating System : Windows XP
  • 3.  Programming Language : JAVA  Java Version : JDK 1.6 & above.