Iaetsd quick detection technique to reduce congestion in

Quick Detection Technique to Reduce Congestion in

WSN

M.Manipriya1, B.Arputhamary

2,

1M.Phil scholar, Department of computer science, Bishop Heber college(Autonomous),

Trichirapalli-620 017 2Asst.professor, Department of computer Applications, Bishop Heber college(Autonomous),

Trichirapalli-620017

[email protected]

[email protected]

Abstract - Wireless Sensor Networks (WSNs) are

employed for either continuous monitoring or event

detection in the target area of interest. In event-driven

applications, it is critical to report the detected events in

the area and with sudden bursts of traffic possible due to

spatially-correlated events or multiple events, the data

loss due to congestion will result in information loss or

delayed arrival of the sensed information. Congestion

control techniques detect congestion and attempt to

recover from packet losses due to congestion, but they

cannot eliminate or prevent the occurrence of congestion.

Congestion avoidance techniques employ proactive

measures to alleviate future congestion using parameters

like queue length, hop count, channel conditions, and

priority index. However, maintaining and processing

such information becomes a significant overhead for the

sensor nodes and degrades the performance of the

network. This paper propose a congestion avoidance

quick detection technique (QDT) that uses the queue

buffer length of the sensor nodes to estimate the

congestion and diffuse traffic to provide a congestion-

free routing path towards the base station. This protocol

provides event reporting, packet delivery ratio, by

dynamically diffusing the traffic in the network using

multiple forwarders in addition to backup forwarding.

Results show that our protocol significantly improves

event reporting in terms of packet delivery ratio by

avoiding congestion while diffusing the traffic

effectively.

Keywords: Wireless sensor network, node detection

algorithm, reducing congestion.

I. INTRODUCTION

Wireless sensor network is an emerging

technology in research field; it is used to monitor

health condition, temperature also used in military

application, home applications, etc. Wireless

sensors are also used in forest fire detection,

inventory control, energy management, and so on.

There are thousands of nodes are being

interconnected with one another the control station

collects all data from each node and transmits the

information via one to another node. Nodes are

having limited storage space in terms of bandwidth,

space, battery level, multi hop communication

architecture in sensor networks since nodes send

their data to a sink node for transmitting the

packets. Sensor has been classified into two classes

such as event driven and continuous dissemination.

According to the history of communication, sensor

nodes are constrained in battery level and

bandwidth. Current researches being focused on

sensor networks to maximize the network life time.

The node delay and throughput are common issues

in sensor networks, transmission of data require

end-end delay must be acceptable range to the

users, as delay decreases users meet Quality of

Service (QoS) of the network. During this

transmission tasks some nodes have low energy to

transmit or some nodes are being inactive to send

the packets, so that node can be waste the resources

and also enhanced the congestion between the

nodes which causes high delay and chance to get

packet loss. In order to avoid this issue our

proposed technique solves the congestion control,

minimum delay by detecting inactive nodes during

the transmission.

1.1 Congestion in WSN

Congestion is prejudicial to wireless device

networks as a result of it lowers the out turn of the

network by dropping a lot of packets containing

crucial perceived info and reduces the time period

of the network as a result of weakened energy

efficiency at every device node, particularly for

spatially-correlated events. With the buffer of

the device nodes close to full, there will invariably

be traffic at the node for the information packets, w

hich ends in exaggerated rivalry, exaggerated retran

smissions weakened packet delivery magnitude rela

tions, wireless device networks as a result of it

lowers the outturn of the network by dropping a lot

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of packets containing crucial perceived info and

reduces the time period of the network as a result

of weakened energy efficiency at every device

node, and exaggerated energy consumption. In

event-driven applications, once there's an abrupt

increase within the traffic, congestion would

degrade the performance of the network by the

loss of the event packets or the delayed arrival of

the packets to the sink. Congestion control is not

solely necessary to enhance the general outturn

however additionally to elongate the network time

period and improve the end-to-end outturn, referred

to as accuracy level, by avoiding the packet loss as

a result of congestion. Congestion being one

among the largest problems for a device network,

needs to be avoided to enhance the Quality of

Service (QoS) in terms of outturn, packet delivery

ratio, latency, and energy efficiency. Congestion

management in WSN has been wide concerning

police investigation the congestion within the

network and dominant the congestion by adjusting

the speed of the input traffic or prioritization of the

info packets or load equalization among the device

nodes. The traffic within the network is adjusted

either hop-by-hop, at every device node, end to end

rate adjustment at the supply nodes wherever the

traffic is generated. While congestion management

concentrates on sanctioning the network to live

through packet loss due to the prevalence of

congestion. Congestion rejection detects early cong

estion or estimates for the congestion within

the network and tries to forestall its prevalence. For

example, in associate event-based approach,

appropriate congestion rejection mechanism might

help to sight the approaching congestion and react

to matters before the particular collapse takes

place. Congestion rejection is that the core thought

for this paper model to proactively determine and

alleviate congestion within the network and change

the network to handle the longer term traffic.

II. RELATED WORK

Chieh-Yih Wan [1] et al. proposed a technique

Congestion detection and avoidance in sensor

networks significantly improves the performance of

data dissemination applications such as directed

diffusion by mitigating hotspots, and reducing the

energy tax with low fidelity penalty on sensing

applications. Sivapuja Srikanth Babu [2] et al.

investigated that jamming avoidance for video

traffic in wireless sensor networks they reduced

packet drops at intermediate node. Further the cost

of retransmission of dropped packets was significa

ntly reduced. Pooja sharma [3] et al. tried to

prolong the lifetime of wireless sensor network by

congestion avoidance techniques. The techniques

included congestion detection and avoidance,

Event-to-Sink Reliable Transport, Pump Slowly,

Fetch Quickly. Akoijam Premita [4] et al. discussed

on power efficient energy aware routing protocol

for wireless sensor networks which occupied less

energy and reduced the end to end delay.

Jayachandran [5] et al. explained fast data

collection with reduced interference and increased

life time they improved packet delivery ratio and

saved the energy of the each node. Abhay Raman

[6] et al. minimized the delay and maximized the

life time of the network by reducing the delay from

source to destination. Hao-Li Wang [7] et al.

enhanced the scheme for quality of service was

used to detect the inactive node bandwidth and

energy. Navneet Kaur [8] elaborated load balancing

technique in sensor network to distribute the

packets it reduces packet loss. M. Vijayalakshmi et

al., [9] proposed Clustering and Prediction

techniques, which use temporal correlation among

the sensor data, provide a chance for reducing the

energy consumption of continuous sensor data

collection. Thus it can achieve stability and

prolongs network lifetime. G.Srinivasan et al., [10]

analyzed in WSN congestion detection and

congestion control is the major research areas. It is

important to design protocols for controlling

congestion.

Parveen Yadav et al.,[11] proposed new cluster

based security architecture which starts from the

initialization of the network. Safe path is not

shortest but next alternative shortest path in mobile

ad hoc network. R.Sumathi et al., [12] surveyed

QoS Based Routing Protocols for Wireless Sensor

Networks. Many QoS issues are focused in their

work. The performance comparison of QoS based

routing protocols such as SAR, MMSPEED,

MCMP, MCBR, and EQSR has also been

analyzed.

III. PROPOSED WORK

Inactive node

Inactive nodes are trying to get many benefits

from the network to occupy battery or bandwidth.

An inactive node might not send the data Packets in

a proper way. An inactive node can do any of the

possible attack in the sensor network.

It turns off the power when it does not

have the communication with other nodes

It may not forward the packets to the exact

destination node from source node

Inactive nodes are sending some packets

and drop other packets.

Techniques to properly cope with inactive

replication allocation approach perform

traditional cooperative technique in terms of

accessibility, cost and minimum delay.



ISBN: 378-26-138420-01

Yes No

Figure 1: Flowchart of QDT function

When data transmitted, it does not forward the

reply request on the reverse route. The proposed

algorithm will detect and solve the problem of

inactive node over wireless sensor network we

develop a quick detection algorithm that considers

partial greediness and new replication allocation.

Step 1: Initiate the process to send the packets

Step 2: Identified the number of nodes and

establish connection with each node

Step 3: Checks for the available route in the path

Step 4: If the route is available forward message to

the other node

{

Select the path which is congestion free

}

Else if (Save message in buffer & initiate

route request)

{

Select the alternative path

}

Else (select the other paths which is

available)

Step 5: If active node becomes as inactive node

find the inactive node and correct the node

error and retransmits the packet again

from buffer itself

Step 6: Repeat the step until selects the path

Step 7: Stop to search

IV. NUMERICAL EXAMPLE

The network is loaded such traffic

converges towards the bottom station from

different directions. Traffic from these

sources are sent at different rates. Table 1 show

with backup forwarder and while not backup

forwarder. Once backup forwarding utilized,

additional packets are received in very shorter

length than while exploitation backup. Though the

packets get diffused through backup forwarders, the

delay is less; as a result of the time taken for the

packets to achieve the bottom station through

backup forwarder is a smaller amount than the wait

time for the potential forwarders once their queues

are full.

Table 1: Backup forwarder

The traffic diffusion approach to

proactively avoiding congestion at the nodes makes

our protocol deliver a lot of packets even with a

high traffic hundreds.

In our proposed method, though every

supply transmits a 100Kbps towards the bottom

station, the speed controller at every node adjusts

the packet loss rate at every hop level and reduces

the particular packets generated.

Figure 2: No. of packets delivered

V. CONCLUSION

This paper introduced quick detection

algorithm aims to reduce the congestion level and

minimum delay to enhance the network

performance. This has been achieved by finding

out inactive node which is present during the

transmission. If there is any error occurred with

inactive node it find and modify the error and send

the data to next node from current node itself. Thus

Without Backup With Backup

No. Of

Packets

Sent

No. Of

Packets

Received

Delay

(Secs)

No. Of

Packets

Received

Delay

(Secs)

1500 1015 0.16 1302 0.14

2250 1555 0.18 1872 0.15

4500 2791 0.20 3171 0.12

0

10000

20000

30000

40000

0 1 2 3 4 5

CTR

QDA

Process Send Request

Checks for the available route

Is route

available?

Forward

Message

Save message in

buffer & initiate

route request

Stop



ISBN: 378-26-138420-01

proposed algorithm can maintain with minimized

congestion level among nodes in a sensor network

and prolong the network life time and reduce the

service disturbance. In wireless sensor network

many algorithms have been used which have lot of

advantages over other algorithms. Since quick

detection algorithm add more nodes of other

algorithm. By this method we can improve the QoS

of WSN and reduce the congestion of the

information and increase the range of sensor nodes.

REFERENCES

[1] Chieh-Yih Wan, Shane B. Eisenman, Andrew T.

Campbell, “Congestion Detection and Avoidance in

Sensor Networks”, Proceedings of SenSys’03,

pp. 266-279, 2003.

[2] Sivapuja Srikanth Babu, R. Konda Reddy, P.

Eswaraiah, Supriya Sivapuja, Srikar Babu S.V,

“Jamming Avoidance for Video Traffic in Wireless

Sensor Networks, International Journal of Emerging

Trends & Technology in Computer Science, volume 2 ,

Issue 2, pp. 293-297, 2013.

[3] Pooja sharma, Deepak tyagi, Pawan bhadana, “A

Study on Prolong the Lifetime of Wireless Sensor

Network by Congestion Avoidance Techniques”,

International Journal of Engineering and Technology,

Volume 2, Issue 9, pp. 4844-4849, 2010.

[4] Akoijam Premita, Mamta Katiyar, “A Review on

Power Efficient Energy- Aware Routing Protocol for

Wireless Sensor Networks”, International Journal of

Engineering Research & Technology, Volume 1, Issue 4,

pp. 1-8, 2012.

[5] Jayachandran. J, Ramalakshmi. R, “Fast Data

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[6] Abhay Raman, Ankit Kr. Singh, Abhishek Rai,

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[8] Navneet Kaur, “Review on Load Balancing in

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[12] R.Sumathi and M.G.Srinivas, “A Survey of QoS

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[14] M. M. Bhuiyan, I. Gondal, and J. Kamruzzaman.

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[15] J. B. Helonde, V.Wadhai, V. Deshpande, and S.

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[16] K. K. Sharma, H. Singh, and R. B. Patel, “A reliable

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[17] P. Sharma, D. Tyagi, and P. Bhadana, “A study on

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