Congestion Prediction and Adaptive Rate Adjustment Technique for Wireless Sensor Networks by Mrs. U. Urathal alias Sri Swathiga

Author:Mrs. U. Urathal alias Sri Swathiga
Language: eng
Format: epub
Tags: wireless sensor networks (wsns), congestion prediction, adaptive rate adjustment technique, ijorcs, ijorcs.org
Publisher: White Globe Publications
Published: 2013-07-04T16:00:00+00:00

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Sender

Nodes

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Receiver

Upstream nodes

Figure 2: Congestion notification message is sent to upstream nodes

Algorithm-1

1. Let S be the set of sensor nodes S= (S lt S 2 , S3... S„j

2. Consider Al, A2 and A3 as the level of congestion that represents less, medium and high congestion respectively

3. Si estimates node degree (N), arrival rate (A) and queue length (Q)

4. N, A and Q are made pass through the fuzzy system and congestion level is obtained

4.1 If (congestion level — Al) Then

4.1.1 The flow does not require any data rate regulation

4.2 Else if (congestion level — A2 && A3) Then

4.2.1 Then it requires data rate regulation

4.2.2 Rate reduction mechanism is triggered

5. End if

6. The sender forwards rate regulation notification message to upstream nodes

7. Upstream nodes estimate their congestion level

7.1 If (estimated congestion level — A2 && A3) Then

7.1.1 Measures it new data sending rate (as per equation 6)

8. New data sending rate is forwarded to the sender

9. The sender regulates its data sending rate according to the received new data rate

10. End if

IV. SIMULATION RESULTS

We use NS-2 [17] to simulate our proposed technique. In our simulation, the channel capacity of mobile hosts is set to the same value: 2 Mbps. We use the distributed coordination function (DCF) of IEEE 802.11 for wireless LANs as the MAC layer protocol. It has the functionality to notify the network layer about link breakage.

In our simulation the nodes are moved in a 1000 meter x 1000 meter region for 50 seconds simulation time. We assume each node moves independently with the same average speed. All nodes have the same transmission range of 250 meters. In our simulation, the transmission rate is varied from 100 to 500kb. The simulated traffic is Constant Bit Rate (CBR).

Our simulation settings and parameters are summarized in table 2

Table2. Simulation Parameters

A. Performance Metrics

We compare our CPARA technique with the HCCP [14] technique. We evaluate mainly the performance according to the following metrics.

Average end-to-end delay: The end-to-end-delay is averaged over all surviving data packets from the sources to the destinations.

Average Packet Delivery Ratio: It is the ratio of number of packets received successfully to the total number of packets sent.

Drop: It is the number of packets dropped during the data transmission.

Received Bandwidth: It is the number of bits transmitted per second.

B. Results

1. Based on Flow

In our first experiment we vary the number of flows as 2, 4, 6 and 8.

www.ijorcs.org

Mrs. U. Urathal alias Sri Swathiga, Dr.C. Chandrasekar

Figure 3: Flows vs Received Bandwidth

Figure 4: Flows vs Delivery Ratio

Flows Vs Delay

20 -i

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