Data collection in multi application sharing

1. Data Collection in Multi-Application Sharing
Wireless Sensor Networks
ABSTRACT
 Data sharing for data collection among multiple applications is an
efficient way to reduce communication cost for Wireless Sensor
Networks (WSNs). This paper is the first work to introduce the
interval data sharing problem which is to investigate how to
transmit as less data as possible over the network, and meanwhile
the transmitted data satisfies the requirements of all the
applications.
 Different from current studies where each application requires a
single data sampling during each task, we study the problem
where each application requires a continuous interval of data
sampling in each task. The proposed problem is a nonlinear
nonconvex optimization problem.
 In order to lower the high complexity for solving a nonlinear
nonconvex optimization problem in resource restricted WSNs, a 2-
factor approximation algorithm whose time complexity is O(n2)
and memory complexity is O(n) is provided. A special instance of
this problem is also analyzed. This special instance can be solved
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2. with a dynamic programming algorithm in polynomial time, which
gives an optimal result in O(n2) time complexity and O(n) memory
complexity.
 Three online algorithms are provided to process the continually
coming tasks. Both the theoretical analysis and simulation results
demonstrate the effectiveness of the proposed algorithms.
 EXISTING SYSTEM
• Once a network is deployed, it is expected to run for a long time
without any human interruption. Therefore, it is inefficient to carry
out only one application in a network.
• Sharing a network for multiple applications can significantly
improve network utilization efficiency . Currently, it is popular for
multiple applications to share a WSN.
• Task T1 is for the first application, and Task T2 is for the second
one. T1 and T2 may overlap on the time axis, and both of them
need to sample data once. A naive method is to sample data
independently, e.g. s1 is sampled by T1 and s2 is sampled by T2
resulting in two pieces of data s1 and s2.
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3. • T1 is for the first application, and T2 is for the second one. Both
tasks need to continuously sample data for an interval s.
PROPOSED SYSTEM
• proposed a data sampling algorithm for each node, such that the sampled
data can be shared by as many applications as possible.
• A greedy approximation algorithm is proposed to solve the problem so as
to reduce the cost of solving the nonlinear nonconvex optimization
problem at resource restricted sensor nodes.
• The proposed algorithm is proved to be a 2-factor approximation
algorithm. The time complexity of this algorithm is O(n2), and the
memory complexity is O(n).
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4. • We evaluate the effectiveness of the proposed algorithms through
simulations. The simulations are implemented with TOSSIM which is a
widely used simulation tool for WSNs.
PROPOSED SYSTEM ALGORITHMS
 Greedy approximation algorithm have been Reduce the communication
cost for Data Collection in Multi-Application Sharing Wireless Sensor
Networks
ADVANTAGES
Reach the maximum Nodes .
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5. System Architecture
ALGORITHM:
 Interval data sharing problem
 O(n^2) quick short worst case
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6.  Find the path for Send source to node
 Greedy Algorithm
 Merge the nodes
MODULE DESCRIPTION
 Any cast link cost
 Transmission-count
Any cast link cost:
We must first generalize the notion of link cost to account for any cast rather than
unicast forwarding. We define the any cast link cost (ALC) as the cost to send a
packet from any node in the. Similarly to standard unicast link costs, choosing any
cast link cost is a modeling decision that depends on the cost criterion of our
network. Note that for any path routing to be worthwhile, it must be used with any
cast link costs that decrease when the candidate set is enlarged; otherwise there is
no advantage to having more than one candidate relay, and any path routing will
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7. end up computing least-cost single-path routes. Any ALC must have two simple
properties.
Transmission-count:
• We can generalize the expected transmission count metric for unicast
transmission. This metric counts the expected number of transmissions to
successfully deliver a packet across an unreliable unicast link. With link-
layer any cast, the expected number of transmissions until any node in J
receives the packet. Its expression is Of course, the above definition assumes
spatial independence, such that transmission is received independently by
nodes.
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8. Css
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SYSTEM SPECIFICATION
Hardware Requirements:
 System : Pentium IV 2.4 GHz.
 Hard Disk : 40 GB.
 Floppy Drive : 1.44 Mb.
 Monitor : 14’ Colour Monitor.
 Mouse : Optical Mouse.
 Ram : 512 Mb.
Software Requirements:
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9.  Operating system : Windows 7 Ultimate.
 Coding Language : ASP.Net with C#
 Front-End : Visual Studio 2010 Professional.
 Data Base : SQL Server 2008.
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10.  Operating system : Windows 7 Ultimate.
 Coding Language : ASP.Net with C#
 Front-End : Visual Studio 2010 Professional.
 Data Base : SQL Server 2008.
Head office: 3nd
floor, Krishna Reddy Buildings, OPP: ICICI ATM, Ramalingapuram, Nellore
www.pvrtechnology.com, E-Mail: pvrieeeprojects@gmail.com, Ph: 81432 71457