A Decision-Centered Survey of Machine Learning for Routing in Ad Hoc Networks: MANETs, VANETs, and FANETs

Abstract

Machine learning is rapidly transforming routing research across mobile, vehicular, and flying ad hoc networks (MANETs, VANETs, and FANETs). However, cross-study comparison remains difficult because individual works focus on disparate objectives within inconsistent simulation environments. The existing reviews are often organized by algorithmic families, which include Q-learning, deep Q-networks, convolutional and graph networks, and multi-agent reinforcement learning. Consequently, such reviews clearly discuss the model used in a paper yet blur the object of study: the same algorithm may act at many points of the routing decision or replace a protocol such as AODV or GPSR. However, two natural questions arise: how does a design change the routing decision and which learning method implements the change? This survey takes the routing decision itself as the main focus: it unifies vehicular, UAV-swarm, and MANET studies in one decision-centered framework. Under this framework, we survey 92 papers on learning-based routing along five dimensions: (1) the learner’s role and decision authority, (2) the network-state representation it consumes, (3) its information horizon, (4) its temporal horizon, present versus predicted future, and (5) its policy organization and coordination. We provide an evolutionary map, a classification of all 92 papers in the corpus, a mechanism-oriented comparison of trade-offs, and an evaluation audit of a focused 37-paper analytical core. Within this core, the reported results are based on fragmented simulation environments and self-selected baselines: mechanisms can improve performance, but the magnitude of these improvements remains uncertain. We conclude this survey with open challenges that reframe learning-based routing around generalization, prediction reliability, security, reproducible evaluation, and deployability rather than incremental packet-delivery gains.