A Decision-Centered Survey of Machine Learning for Routing in Ad Hoc Networks: MANETs, VANETs, and FANETs
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Liu, Yue
Li, Xue Jun
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MDPI AG
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.
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40 Engineering, 4009 Electronics, Sensors and Digital Hardware, Behavioral and Social Science, Machine Learning and Artificial Intelligence, Basic Behavioral and Social Science, 0906 Electrical and Electronic Engineering, 4009 Electronics, sensors and digital hardware, machine learning, reinforcement learning, deep reinforcement learning, routing protocols, mobile ad hoc networks (MANETs), vehicular ad hoc networks (VANETs), flying ad hoc networks (FANETs), multi-agent reinforcement learning, survey, taxonomy
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Electronics, ISSN: 1450-5843 (Print); 2079-9292 (Online), MDPI AG, 15(19), 4424-4424. doi: 10.3390/electronics15194424
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© 2026 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license.
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Except where otherwise noted, this item's license is described as Creative Commons Attribution (CC BY) license.