Improving reliability & performance of wsn via routing errors

International Journal of Current Trends in Engineering & Technology
ISSN: 2395-3152
Volume: 02, Issue: 02 (MAR-APR, 2016)
272
Improving Reliability & Performance of WSN via Routing Errors
Anjila Daheriya, Rishi Sing kushwah, Kailash Patidar
Computer science & engineering Department
Sri Satya Sai Institute of Science and Technology, Sehore, India
Abstract- Wireless sensor networks have recently received
increased attention for a broad array of applications such as
surveillance, environment monitoring, medical diagnostics,
and industrial control. A wireless sensor network (WSN) is
a self-organized network that consists of a large number of
low-cost and low powered sensor devices, which can be
deployed on the ground, in the air, in vehicles, on bodies,
under water, and inside buildings. The reliability of
individual links’ performance is crucial in these
applications, e.g., in a surveillance network, the
transmissions must be reliable to avoid false alarms and
missed detections. We propose a scheme which
automatically detect faulty link and discover secure shortest
path for data transmission. The key is used for secure data
transmission. In large-scale wireless sensor networks,
damaged link detection plays a critical role in network
diagnosis and management. The backup route cache is
fetched from the backup node to check link damage failure.
This message is used by backup node to replace the
contents of data packet. This packet is used to inform all the
nodes about route changes in the network. After getting the
message source node S directs the packets with new and
secured node. Our protocol ensures that multicast data is
delivered from the source to the members of the multicast
group, even in the presence of link failure, as long as the
group members are reachable through non-adversarial path.
We proposed Trust key computing, secure node
authentication, and secure route discovery across the node
to detect and secure data transmission. Experimentally
result showed that our scheme is well suited for better data
transmission.
Keywords- Faulty Link, Routing, Route Discovery,
Security, Wireless Sensor Network.
I. INTRODUCTION
Wireless ad hoc network does not have any combined
server but is a distributed, self-governing of any pre-
established substructure network.A wireless sensor
network (WSN)[1] consists of many tiny sized sensor
nodes that have computation power, communication
capability, and sensing functions. Each sensor node[1]
can sense physical phenomena, like temperature,
vibration, light, electromagnetic strength, humidity, and so
on, and transmit the sensed data to the sink node through a
chain of multiple intermediate nodes that help forward
the data. Wireless sensor networks (WSNs) have received
significant attention in recent years due to their potential
applications in military sensing, wildlife tracking, traffic
surveillance, health care, environment monitoring, building
structures monitoring, etc. The most significant benefit of
sensor networks is that they extend the computation
capability to physical environment where human beings
cannot reach. They can operate for prolonged periods in
habitats that are hostile, challenging or ecologically too
sensitive for human visitation, Moreover it has the potential
to provide wealth of data about the environment in which
they are deployed and send their results across the network
to the end-users. Wireless Sensor Networks (WSNs) have
been the preferred choice for the design and deployment of
next generation monitoring and control systems. It has now
become feasible to deploy massive amounts of low-cost
sensors to monitor large regions over ground surface,
underwater, or atmosphere. WSNs can be treated as a
special family of wireless ad hoc networks. A WSN is a
self-organized network [2] that consists of a large number
of low-cost and low powered sensor devices, which can be
deployed on the ground, in the air, in vehicles, on bodies,
under water, and inside buildings. Each sensor node is
equipped with a sensing unit, which is used to capture
events of interest, and a wireless transceiver, which is used
to transform the captured events back to the base station,
called sink node. Sensor nodes collaborate with each other
to perform tasks of data sensing, data communication, and
data processing. Compared to the wired networks, it seems
much more essential to detect link faults [2] rather than
node faults in WSNs. A wireless link itself virtually exists,
which means we can’t directly observe and assess whether
it performs well or not. It proves difficult to localize [3] the
faulty links under a dynamic mal-condition in the wild, for
the link quality will be significantly impacted by the natural
environment like trees in the forest and flow in the ocean.
Faulty link detection becomes more difficult in the multi-
hop networks due to topology features. Faulty link detection
plays an important role in network failure detection and
network management. Link failure is one of the problems
against wireless sensor networks and can affect the whole
sensor network communication. The variety of technique
against fault link detection is overwhelming. Because of the
challenges of designing of routing protocols of wireless
sensor network we have many constraints. WSN have

ISSN: 2395-3152
273
limitations due to resources. WSN have low storage
capacity, low bandwidth. The other limitations are low
central processing unit and limited battery energy.The rest
of the paper is organized as follows. Section 2 concentrates
on the literature survey. Section 3 provides the proposed
work and proposed algorithm. Section 4 provides the
implementation and result analysis of proposed work.
Finally, Section5 provides concluding remarks, limitation
discussion.
II. LITERATURE SURVEY
A wireless sensor network is which organized itself
according to the situation. It is a collection of nodes. The
nodes are low cost and low battery power sensor devices.
WSN can be positioned on the ground, in the air. It can be
positioned in vehicles, on bodies of the human or animals.
It can be deployed under water, and inside the houses. The
main components of wireless sensor networks are sensing
unit, and a wireless transceiver. The function of sensing unit
is capture events of attention. The main function of wireless
transceiver is transforming the captured events back to the
base station. The base station is called sink node. Sensor
nodes cooperate with every other to achieve tasks of data
identifying, data communication, and data processing. In
WSNs serious incident data collected [4] by the sensor
nodes necessity to be reliably delivered to the sink for
successful monitoring of an environment. The greatest
noteworthy advantage of sensor networks is that they
increased the computation ability to physical atmosphere
where human beings cannot reach. They can work for
lengthy periods in locales that are antagonistic, challenging
or environmentally too sensitive for human examination.
Moreover it has the probable to send prosperity of data
about the setting in which they are organized and send their
outcomes across the network to the users. A sensor node is
a tiny component that is proficient of computation, sensing
and communication competences. Sensor node is the main
component of WSN. Sensor nodes can be used to sense
moisture and temperature. It is also used to sense
temperature and light. Since a single sensor transports only
limited information; a system of these devices is used to
achieve huge surroundings. The communication component
in sensor nodes is used to transfer information. It has now
become possible to install enormous quantities of low cost
sensors to monitor big regions over ground underwater,
surface, or atmosphere. WSNs have received noteworthy
consideration in current years due to their potential
applications in wildlife tracking, armed sensing, traffic
investigation, fitness care, atmosphere monitoring, building
constructions monitoring, etc. Nodes in WSNs are disposed
to letdown due to hardware letdown, energy reduction,
communication link faults, mischievous attack, and so on.
The main WSN objectives are low node cost, small node
size, low power consumption, scalability, self-
configurability, better channel utilization, fault tolerance,
adaptability, Qos [5] support and security. A wireless link
itself virtually exists, which means we can’t directly
observe and assess whether it performs well or not. It
proves difficult to localize the faulty links under a dynamic
mal-condition in the wild, for the link quality will be
significantly impacted by the natural environment like trees
in the forest and flow in the ocean. Multi-hop networks
suffer more harm than single- hop networks due to link
failures. For example, a critical link may cause a large area
of partition, or significantly interfere with routing protocol
among the nodes, producing problems such as routing cycle
and even network partition. Accordingly, compared to
single-hop networks, faulty link detection becomes more
difficult in the multi-hop networks due to topology features.
A packet has to traverse multiple links to the sink, it is for
this reason that exactly localizing a faulty link becomes
really hard if only on the basis of whether the packet arrives
at the sink or not. Therefore, faulty link detection becomes
one of the most critical issues in multi-hop network
diagnosis. According to the status of a link, packet loss
failure, routing failure, partition can be found easily.
Notably, link performance actually reflects a network’s
reliability and bottleneck if exist. Routing in wireless
network is different from simple adhoc network. Wireless
sensor network is infrastructure less. Wireless links are not
reliable. All the routing protocols of wireless sensor
network require good energy. Wireless sensor node may fail
because of infrastructure. The wireless sensor network
protocols are location based protocols, hierarchical
protocols, data centric protocols, multipath based protocols,
QoS based protocols, mobility based protocols, and
heterogeneity based protocol. Although single link failures
are more common, multiple link failures occur due to
shared risks such as failure of a link while another link is
under maintenance, or natural disasters that cause links
traversing a region to fail. In [6], the authors use monitoring
paths and cycles to localize single link and Shared Risk
Link Group (SRLG) failures. They also prove that (k+2) -
edge connectivity was necessary and sufficient to uniquely
localize all SRLG failures involving up to k links with one
monitor. In practice, however, not all sensor networks can
satisfy this strict condition, especially in the cases we
spread the sensor nodes randomly in the area of interest. In
addition, in most cases we are not allowed to set any more
monitors after the deployment. What we expect is to utilize
the rule-free probes (i.e., without computing the exact
probing paths) to achieve link scan. One of the most
peculiar routing characteristics of WSN is routing

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274
dynamics. It is not surprising that a sensor node frequently
changes its parent to forward packets. Unfortunately, many
existing approaches just aim to detect the faulty links which
had been behaving badly, but fail to offer an inspection on
other unused ones, thus have no guidance to reroute when
the current routing strategy is less than satisfactory. To
solve the above problems, in this work we propose Link
Scanner (LS) [1], a passive and rule-free detection approach
for discovering faulty links in sensor networks. The object
of LS is to provide a blacklist containing all possible faulty
links. With such a blacklist, further analysis and recovery
processes become possible, including (i) exploring the root
causes of observed symptoms in the network, (ii) adjusting
routing strategy for the related nodes, (iii) offering the spare
list of links for every node. As a result, we not only achieve
the goal of diagnosis, but also take a big picture of wholly
link performance. To maintain a sensor network running in
a normal condition, many applications in flooding manner
are necessary, such as time synchronization,
reprogramming, protocol update, etc. In the flooding
process, each node is expected to receive multiple probe
messages through different paths. By embedding
lightweight data into the flooding packet, LS passively
collects hop counts of received probe messages at sensor
nodes. Since faulty links may cause probes dropped, there
must be mismatches between the received hop counts in
sensor nodes and our expectations according to the
topology. With a probabilistic and heuristics based
inference model, LS analyzes the mismatches and deduces
the faulty links. A wireless network often contains a large
number of links which virtually exist in the air, but can
never directly observe whether they perform well or not.
Proposes a passive and low-cost link scanning scheme LS
for faulty link detection. LS infers all links statuses on the
basis of data collection from a prior probe flooding process,
in which leverage hop count to reflect the in/out-going link
performances. In the inference model, use to describe the
inner relationship among the links, and finally output the
optimal fault report with some constraints, which reversely
generates a feedback for DLP’s next computation. The
algorithm through a test bed consisting of 60 TelosB sensor
motes and an extensive simulation study, while a real
outdoor system is deployed to links including those
potential but not used ones in sensor networks. Item
According to the exceptional features of sensor networks
develops an efficient investigation marking scheme that
exposes the innermost dependencies of sensor networks.
Link scanner proposes characterized implication models to
get the multi-level dependences between the network
elements and accomplish great precision. Further introduce
a learning-based inference scheme which increases the
inspection accuracy and is thus scalable for large scale
networks. A field study on a real outdoor deployment is
also presented to verify that LS is practical to surveillance
networks. Network diagnosis has been extensively studied
in recent years. Existing approaches can be broadly divided
into two categories: debugging tools and inference schemes.
This work belongs to the latter category. Authors [7] is used
a notable tool which focuses on debugging sensor nodes at
the source-level, and enables developers to wirelessly
connect to a remote sensor and execute debugging
commands. Declarative Trace points [8] allow the
developers to insert a group of action-associated
checkpoints at runtime, which are programmed in an SQL-
like declarative language. Existing inference-based
diagnosis schemes for WSNs like trust deeply on an add in
procedure that occasionally reports a big amount of network
information from separate sensor nodes to the sink,
announcing enormous overhead to the resource forced and
traffic sensitive sensor network. In order to minimize the
overhead, some researchers propose to establish inference
models by marking the data packets and then parse the
results at the sink to infer the network status, or conduct the
diagnosis process in local areas. [9] Apply Belief Network
with the bipartite graph to represent dependencies among
links and end to end connections, then the root causes can
be deduced by conducting inference on the Belief Network.
[10] Explores the bottleneck nodes in a WSN, and [10]
enhances the network visibility by analyzing the events and
status in history. Besides, most approaches actively design
their probes to fetch desired information for faulty link
detection [9], especially in the managed enterprise WLANs
and wireless mesh networks, where the monitors are easy to
deploy. For each cycle, a node is required to monitor the
cycle’s performance. [7] Develops a non-adaptive fault
diagnosis through a set of probes where all the probes are
employed in advance. The authors in [9] propose a failure
detection scheme, in which monitors are assigned to each
optical multiplexing and transmission section. These
approaches usually compute the probe paths according to
different network symptoms, so as to combine the network
topology to infer the link status. For a large scale sensor
network, however, deploying monitors in the wild not only
increases the cost, but also needs to guarantee sustainable
management. Sniffers can be used to collect the
information.
III. PROPOSED WORK
During flooding processes link scanner passively collects
hop counts of received probe messages at sensor nodes. In
wireless sensor networks, faulty link discovery [4] plays a
serious role in network analysis and organization. The
object of link scanner is to provide a blacklist containing all

ISSN: 2395-3152
275
possible faulty links. Based on the surveillance that
damaged links can result in disparity between received hop
counts and network topology with such a blacklist, further
analysis and recovery processes become possible, including
(a) discovering the root causes of observed indications in
the network, (b) altering routing policy for the related
nodes, (c) contribution the spare list of links for every node.
Our procedure guarantees that multi cast data is transported
from the source to the associates of the multi cast groups,
even in the presence of link letdown, as long as the group
members are accessible through non adversarial track. In
order to detect faulty link, we proposes a scheme which
help in report to the system. And also find path to secure
data transmission. We evaluate the secure value of each
node to select a protected track for message forwarding to
identify the damaged and malicious nodes which are
supposed to launch connection letdown.
Here authentication framework is used to remove outside
adversaries and guarantee that only approved nodes
accomplish certain operation.
3.1 Proposed Method
Step 1: Apply flooding process in a wireless sensor
network
Step 2: Link scanner infers all links statuses on the basis
of data collection from a prior probe
flooding process.
Step 3: The next step is to count no of hops.
Step 4: If any mismatched in hop count then
Link failure detected and report to
the system
else
select backup node with
authentication
end if
Step 5: Generation of secure key in which node
initialization time is taken as a secure key
After getting the secure key the node is marked
as authenticated node.
Step 6: Check whether node have secure key then
authentication successful for data transmission and node
is marked as secure node.
Step7: The next step is to find route for secure data
transmission. The shortest path is discovered across the
node.
Step 8: Select a node to destination
If selected node is found in route_list then
Selected node is marked as secure
node and route discovery is
successful. Route is confirmed for
data transmission
Else
Select another new node, check
authentication
End if
Step 9: During data transmission any link may fail so
backup node selection mechanism is always used by
system for backup node setup.
Step 10: After successful data transmission the route
record is maintained by system for future use.
Table 1 Simulation parameter
Figure 1Topology generated with secure key generation.
Figure 2 No of nodes and average packet delivery ration
IV. IMPLEMENTATION
We used Network Simulator 2 simulator software for
implementation of our proposed algorithm. The
methodologies we used are improved faulty link detection
and data transmission algorithm to improve the
performance of wireless sensor network. We also used
C/C++ and TCL language for implementation. We

ISSN: 2395-3152
276
performed our experiment in Intel i3 4.0 GHz machine with
2GB RAM. Given Figure shows the average data packet
delivery delay for each level node in a fixed area with fixed
number of nodes using multilevel scheme. As the level
increases, the delay also increases. But since our protocol
tries to minimize the number of levels so the delay is also
reduced as compared to other multihop protocols.
V. CONCLUSION
In large-scale WSNs, damaged link discovery plays a
serious role in network diagnosis and administration.
Secure data transmission regardless of link failure is also
essential condition for huge establishments. The proposed
scheme automatically find damaged link which may break
the communication and determine protected shortest path
for data transmission. We evaluate the trust data of each
node to select a protected path for message forwarding to
find the malicious nodes which are supposed to launch
connection letdown. Our procedure guarantees that
multicast data is delivered from the source to the associates
of the multicast cluster, even in the presence of connection
letdown, as long as the group associates are accessible
through non adversarial path. We proposed Trust value
calculating, protected node verification, and secure route
finding across the node to find and secure data
transmission. Experimentally outcome showed that our
scheme is well suitable for improved and protected data
transmission. We are planning to implement our work with
wireless sensor node localization in surveillance
environment. A direction of future investigation with decent
probable is improving the quality of routing tree. It proves
problematic to localize the damaged links below an active
mal condition in the remote, for the link excellence will be
meaningfully impacted by the natural atmosphere like
plants in the forest and movement in the marine.
REFERENCES
[1]. Qiang Ma, Kebin Liu, Zhichao Cao, Tong Zhu, Yunhao
Liu, Link Scanner: Faulty Link Detection for Wireless
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Communications, Vol. 14, pp. 4428-4438, Aug 2015
[2]. S. S. Ahuja, S. Ramasubramanian, and M. M. Krunz,
“Single-link failure detection in all-optical networks
using monitoring cycles and paths,” IEEE/ACM
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[3]. Q. Cao, T. Abdelzaher, J. Stankovic, K. Whitehouse,
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and application independent debugging system for
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Raleigh, NC, USA, pp. 85–98, 2008.
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[6]. W. Dong, Y. Liu, Y. He, T. Zhu, and C. Chen,
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IEEE/ACM Transaction Network, vol. 22, no. 6, pp.
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[7]. L. Girod et al., “Em-Star: A software environment for
developing and deploying wireless sensor networks,” in
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MA, USA, 2004.
[8]. Y. Hamazumi, M. Koga, K. Kawai, H. Ichino, and K.
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[10]. N. Leone et al., “The DLV system for knowledge
representation and reasoning,” ACM Trans. Computer
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