Main Reference PaperReliable Data Aggregation using Ant Colony Optimization with State Transition Ant Rule in Sensor Network, International Journal of Computer Applications, June 2014.
  • Data Aggregation Ant Colony Algorithms (DAACA) computes better energy and the quantity in choosing the next hop, however fails to forward packets with the sensed nodes causing inefficiency in data aggregation. Ant Colony Optimization with State Transition Ant Rule (ACO-STAR) is developed in this project which works as per the foraging movement of ants analyzing state transition rules. ACO- STAR algorithm provides a significant way of identifying the search space for obtaining optimal data aggregation in WSN. The solution ofACO-STAR steadily attains the global optimal solution through effective forwarding of packets in terms of adjusting the clustering effectbased on quantities of foraging movement of ants.

+ Description
  • Data Aggregation Ant Colony Algorithms (DAACA) computes better energy and the quantity in choosing the next hop, however fails to forward packets with the sensed nodes causing inefficiency in data aggregation. Ant Colony Optimization with State Transition Ant Rule (ACO-STAR) is developed in this project which works as per the foraging movement of ants analyzing state transition rules. ACO- STAR algorithm provides a significant way of identifying the search space for obtaining optimal data aggregation in WSN. The solution ofACO-STAR steadily attains the global optimal solution through effective forwarding of packets in terms of adjusting the clustering effectbased on quantities of foraging movement of ants.

  • To improve data aggregation report with the identification of higher computational problem using Transition Ant Rule

  • To delay measurement is reduced using the ACO-STAR data aggregation

+ Aim & Objectives
  • To improve data aggregation report with the identification of higher computational problem using Transition Ant Rule

  • To delay measurement is reduced using the ACO-STAR data aggregation

  • Each node calculate the weight. Node weight = Node Energy + Node Mobility + Node Degree + Neighboring Nodes positions + Data Rate + Target Revisit Rate + distance to the base station. The weight factor can be adjusted. The node with the maximum weight is considered as the best candidate for becoming a cluster head.

+ Contribution
  • Each node calculate the weight. Node weight = Node Energy + Node Mobility + Node Degree + Neighboring Nodes positions + Data Rate + Target Revisit Rate + distance to the base station. The weight factor can be adjusted. The node with the maximum weight is considered as the best candidate for becoming a cluster head.

  • OS : Window 7 (Cygwin) / Ubuntu 12.04 LTS 64bit.

  • Simulator: NS 2.35, Language : TCL and AWK script, (C++)

+ Software Tools & Technologies
  • OS : Window 7 (Cygwin) / Ubuntu 12.04 LTS 64bit.

  • Simulator: NS 2.35, Language : TCL and AWK script, (C++)

  • B.E / B.Tech / M.E / M.Tech

+ Project Recommended For
  • B.E / B.Tech / M.E / M.Tech

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