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Deep learning-based route reconfigurability for intelligent vehicle networks to improve power-constrained using energy-efficient geographic routing protocol - 2024

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Research Paper On Deep learning-based route reconfigurability for intelligent vehicle networks to improve power-constrained using energy-efficient geographic routing protocol

Research Area:  Machine Learning

Abstract:

Transferring data in the mobile ad hoc network can be enabled to analyze data transferring and the network that manages the data and route them into the VPN-based routing. Here is the process of maintaining the gateway for the analysis. The main problem here is the routing of the data packets, and the analysis of the nodes in the form of packages is the main issue in this study. To fix this, troubleshooting problems can be enabled for the packets which reach the destinations and the echo response. The primary technique used in this study is energy efficient geographic routing protocol and reward-based intelligent Ad hoc routing is used for the analysis. The energy-efficient geographic routing protocol enables the EGRPM method to reduce the sensor nodes and the WSN. This allows gathering the data and the nodes to maintain the geographic way. Reward-based intelligent Ad hoc routing is used in automatic decision-making, and the analysis of the system to produce the selection action for the research is reinforcement learning. This results from the study of the configuration and the analysis of the data in the ad hoc network. This enables the formation of learning about the routing protocol and facilitates the current data transfer to the research done in the ad hoc networks. This data analysis in the mobile network helps analyze the system and the entire data management.

Keywords:  

Author(s) Name:  Liyakathunisa Syed, P.Sathyaprakash, A.Shobanadevi, Ha Huy Cuong Nguyen, Mohammad Alauthman, M.Vedaraj, R.Premalatha

Journal name:  Wireless Networks

Conferrence name:  

Publisher name:  Springer

DOI:  10.1007/s11276-023-03525-z

Volume Information:  Volume 30, pages 939-960, (2024)