Abstract
Data
sensing and retrieval in wireless sensor systems have a widespread application
in areas such as security and surveillance monitoring, and command and control
in battlefields. In query-based wireless sensor systems, a user would issue a
query and expect a response to be returned within the deadline. While the use
of fault tolerance mechanisms through redundancy improves query reliability in
the presence of unreliable wireless communication and sensor faults, it could
cause the energy of the system to be quickly depleted. Therefore, there is an
inherent tradeoff between query reliability vs. energy consumption in
query-based wireless sensor systems. In this paper, we develop adaptive fault tolerant
quality of service (QoS) control algorithms based on hop-by-hop data delivery
utilizing “source” and “path” redundancy, with the goal to satisfy application
QoS requirements while prolonging the lifetime of the sensor system. We develop
a mathematical model for the lifetime of the sensor system as a function of
system parameters including the “source” and “path” redundancy levels utilized.
We discover that there exists optimal “source” and “path” redundancy under
which the lifetime of the system is maximized while satisfying application QoS
requirements. Numerical data are presented and validated through extensive
simulation, with physical interpretations given, to demonstrate the feasibility
of our algorithm design.
Architecture
Architecture of WSN
Algorithm
1. Adaptive fault tolerant QoS control
(AFTQC) algorithm:
Algorithm developed in this
paper takes two forms of redundancy. The first form is path redundancy. That
is, instead of using a single path to connect a source cluster to the
processing center, mp disjoint paths may be used. The second is source redundancy.
That is, instead of having one sensor node in a source cluster return requested
sensor data, ms sensor nodes may be used to return readings to cope with
data transmission and/or sensor faults. The above architecture illustrates a
scenario in which mp = 2 (two paths going from the CH to the processing
center) and ms = 5 (five SNs returning sensor readings to the CH).
2. Clustering Algorithm:
A
clustering algorithm that aims to fairly rotate SNs to take the role of CHs has
been used to organize sensors into clusters for energy conservation purposes.
The function of a CH is to manage the network within the cluster, gather sensor
reading data from the SNs within the cluster, and relay data in response to a
query. clustering algorithm is executed during the system lifetime.
• Aggregation
of readings
• Each
cluster has a CH
• Users
issue queries through any CH.
• CH
that receives the query is called the Processing Center (PC)
• Each
non-CH node selects the CH candidate with the highest residual energy, sends it
a cluster join message (includes the non-CH node’s location). The CH will acknowledge this message.
• Randomly
rotates role of CH among nodes -> nodes consume their energy evenly
Existing System:
Existing
research efforts related to applying redundancy to satisfy QoS requirements in
query-based WSNs fall into three categories: traditional end-to-end QoS,
reliability assurance, and application specific QoS . Traditional end-to-end
QoS solutions are based on the concept of end-to-end QoS requirements. The
problem is that it may not be feasible to implement end-to-end QoS in WSNs due
to the complexity and high cost of the protocols for resource constrained
sensors.
This method does not
consider the reliability issue.
Disadvantages:
1. Complexity and high cost
of the protocols for resource constrained sensors
2. Does not consider the
reliability issue.
3. Does not consider energy
issues.
4. Data delivery such as
reliability and timelines are not considered.
Proposed System:
In this paper, we develop adaptive
fault tolerant quality of service (QoS) control algorithms based on hop-by-hop
data delivery utilizing “source” and “path” redundancy, with the goal to
satisfy application QoS requirements while prolonging the lifetime of the
sensor system. We develop a mathematical model for the lifetime of the sensor
system as a function of system parameters including the “source” and “path”
redundancy levels utilized. We discover that there exists optimal “source” and
“path” redundancy under which the lifetime of the system is maximized while
satisfying application QoS requirements.
Advantages:
1. To
applying redundancy to satisfy application specified reliability and timeliness
requirements for query-based WSNs.
2. We
develop the notion of “path” and “source” level redundancy
3. Lifetime
of the system is maximized.
4. Timeliness,
Multiple data delivery speed options.
5. Reliability,
Multi-path forwarding.
Modules:
1. General Approach
In this paper we are also
interested in applying redundancy to satisfy application specified reliability
and timeliness requirements for query-based WSNs. Moreover, we aim to determine
the optimal redundancy level that could satisfy QoS requirements while
prolonging the lifetime of the WSN. Specifically, we develop the notion of
“path” and “source” level redundancy. When given QoS requirements of a query,
we identify optimal path and source redundancy such that not only QoS requirements
are satisfied, but also the lifetime of the system is maximized. We develop adaptive
fault tolerant QoS control (AFTQC) algorithms based on hop-by-hop data delivery
to achieve the desired level of redundancy and to eliminate energy expended for
maintaining routing paths in the WSN.
2.
Software Fault
For source redundancy, ms
SNs are used for returning sensor readings. If we consider both hardware and
software failures of SNs, the system will fail if the majority of SNs does not
return sensor readings (due to hardware failure), or if the majority of SNs
returns sensor readings incorrectly (due to software failure). Assume that all
SNs have the same software failure probability, denoted by qs. Also
assume that all sensors that sense a given event make the same measurements. The
probability that the majority of ms SNs failing to return sensor
readings due to hardware failure, and the second expression is the probability
that the majority of ms SNs returning sensor readings but no majority of
them agrees on the same sensor reading as the output because of software
failure.
3. Data Aggregation
The analysis performed
thus far assumes that a source CH does not aggregate data. The CH may receive
up to ms redundant sensor readings due to source redundancy but will just
forward the first one received to the PC. Thus, the data packet size is the
same. For more sophisticated scenarios, conceivably the CH could also aggregate
data for query processing and the size of the aggregate packet may be larger
than the average data packet size. We extend the analysis to deal with data aggregation
in two ways. The first is to set a larger size for the aggregated packet that
would be transmitted from a source CH to the PC. This will have the effect of
favoring the use of a smaller number of redundant paths (i.e., mp)
because more energy would be expended to transmit aggregate packets from the
source CH to the PC. The second is for the CH to collect a majority of sensor readings
from its sensors before data are aggregated and transmitted to the PC.
4. Forward Traffic
The analysis performed in
the paper considers only the reserve traffic for response propagation from SNs
to the PC but neglects the forward traffic for query dissemination from the
sink to the CH and SNs. The reliability and energy consumption of the forward
traffic due to hop-by-hop query delivery can be calculated by following a
similar analysis as for the reverse traffic. The success probability (Rq)
would be adjusted by considering the forward traffic and reverse traffic together
as a series system. The energy consumption of a query (Eq) would be used
to calculate the maximum number of queries the system can possibly process.
HARDWARE & SOFTWARE REQUIREMENTS:
HARDWARE REQUIREMENTS:
·
System :
Pentium IV 2.4 GHz.
·
Hard Disk :
40 GB.
·
Floppy Drive :
1.44 Mb.
·
Monitor :
15 VGA Color.
·
Mouse :
Logitech.
·
Ram :
512 MB.
SOFTWARE REQUIREMENTS:
·
Operating system : Windows
XP Professional.
·
Coding Language :
C#.NET
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