Affiliations: [a]
Department of Computer Science and Engineering, SNS College of Technology, Coimbatore, Tamil Nadu, India
| [b]
Department of Electrical and Electronics Engineering, SNS College of Technology, Coimbatore, Tamil Nadu, India
Correspondence:
[*]
Corresponding author. R. Lakshmana Kumar, Assistant Professor, Department of Computer Science and Engineering, SNS College of Technology, Coimbatore, Tamil Nadu, India. E-mail: [email protected].
Abstract: Mobile Adhoc Networks (MANET) in modern research have many optimal energy conservation mechanisms that can be deployed easily and in a faster manner. The routing approaches associated with energy consumption play a dominant role in routing the data packets between the mobile sensor nodes within the range of optimization. However, major challenges associated with energy consumption in MANETs include reduced lifetime of sensor nodes, poor coverage, and throughput. Most methods tend to reduce the interference of data while traversing between the sensor nodes and increase the capacity of the network. This results in delays while transmitting the packets across the network, and this may result in failure of packets being transmitted. To resolve this issue, in this paper, we propose an ant colony optimization combined with a flower pollination algorithm for minimal energy consumption and throughput maximisation in MANETs. This hybrid meta-heuristic model resolves the issues, including delays, poor coverage, and reduced network lifetime. This hybrid model uses the estimation of neighbourhood distance among the nodes for optimal placement of nodes for effective location. The estimation of location is found using a flower pollination algorithm with a levy flight mechanism. The estimation is carried out in a hyper sphere model that helps in finding the coverage area of the sensor nodes. Depending upon the estimation of neighbourhood distance among the sensor nodes, the consumption of energy among the sensor nodes in MANETs is reduced. The simulation was conducted between the proposed hybrid approach and conventional soft computing heuristics, where the results show that the proposed model achieves a higher rate of energy conservation and reduces delay than other methods.
Keywords: Mobile adhoc network, flow pollination, neighbourhood distance, ant colony optimization
