Algorithm
Input: no of nodes Output: delay constraint 1. Node creation 2. Cluster formation 3. Select the cluster head among the nodes
4. #finds the number of packets in the simulation If (packet_id>highest_packet_id#SUM++
5. Packet duration = end - start
If packet duration>0#receivenum++
6. Base station gives higher priority to nearby CH
7. Delay = sum/recvnum*1000; END
IV. Simulation Performance In order to evaluate the performance of the proposed DCHS algorithm, we compare MILP with the energy consumption and delay constrained routing protocol. Fig 1: varying no of nodes with delay(s)
No of nodes 10 20 30 40 50
MILP 0.8sec 1.3sec 1.6sec 1.9sec 2.2sec
DCHS 0.5sec 0.9sec 1.3sec 1.6sec 1.9sec
Table 1: Delay Constraint
In the fig1 represents the number of nodes varying with respect to the delay as compared with MILP optimal formulation. It explained our proposed algorithm is better than the MILP formulation. Fig 2: Varying no of nodes with energy consumption (J)
In the fig 2 represents the minimizing the energy consumption as compared with MILP formulation.
No of nodes 10 20 30 40 50
MILP 0.5J 0.8J 1.3J 76J 2.4J
DCHS 0.3J 0.6J 0.9J 1.5J 2.9J
Table 2: Energy Consumption
V. Conclusion
*…show more content…*

The objective of the topology control algorithm is to adjust the transmission power to minimize interference, which is contradictory to the requirement of delay constraint. When transmission power is increased to reduce the delay, which increases the number of neighbors covered by the transmission range and causes more interference from other active nodes in the network. In order to reduce packet loss and improve the throughput by adaptive-FEC technique can be incorporated with DCHS

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