A Review of Deep Learning Based Energy Efficient Routing in MANET

Mobile Adhoc Network (MANET) is an Infrastructure less network and it can change the network topology formation in different times. However, the topology changes, mobility of nodes, energy consumption and unreliable communication links significantly considered in analysis of routing effectiveness and efficiency. Conventional routing protocols are emerged for MANET Communication and Transmission but many problems are inherently unsolvable to manage adaptive routing and energy optimization. In recent years, deep learning techniques have shown massive prospective in addressing these boundaries by enabling intelligent, self-adaptive, and data-driven solutions. This paper proposed an analysis of deep learning based algorithms emerged for mobility of network and energy efficient routing in MANET, specifically neural networks, recurrent models such as LSTM and GRU, and deep reinforcement learning algorithms are considered.