A review of AI-enabled routing protocols for UAV networks: Trends, challenges, and future outlook

Unmanned Aerial Vehicles (UAVs), as a recently emerging technology, enabled a new breed of unprecedented applications in different domains. This technology's ongoing trend is departing from large remotely-controlled drones to networks of small autonomous drones to collectively complete intricate tasks time and costeffectively. An important challenge is developing efficient sensing, communication, and control algorithms that can accommodate the requirements of highly dynamic UAV networks with heterogeneous mobility levels. Recently, the use of Artificial Intelligence (AI) in learning-based networking has gained momentum to harness the learning power of cognizant nodes to make more intelligent networking decisions by integrating computational intelligence into UAV networks. An important example of this trend is developing learning powered routing protocols, where machine learning methods are used to model and predict topology evolution, channel status, traffic mobility, and environmental factors for enhanced routing. This paper reviews AI-enabled routing protocols designed primarily for aerial networks, including topology predictive and self-adaptive learning-based routing algorithms, with an emphasis on accommodating highly dynamic network topology. To this end, we justify the importance and adaptation of AI into UAV network communications. We also address, with an AI emphasis, the closely related topics of mobility and networking models for UAV networks, simulation tools and public datasets, and relations to UAV swarming, which serve to choose the right algorithm for each scenario. We conclude by presenting future trends, and the remaining challenges in AI-based UAV networking, for different aspects of routing, connectivity, topology control, security and privacy, energy efficiency, and spectrum sharing.(1)

Automated summary

This paper reviews the use of AI-enabled routing protocols in aerial networks, focusing on topology prediction and self-adaptive learning-based routing algorithms that can accommodate highly dynamic network topology. The paper justifies the importance and adaptation of AI into UAV network communications and discusses related topics such as mobility and networking models, simulation tools and public datasets, and relations to UAV swarming. The paper also presents future trends and the remaining challenges in AI-based UAV networking.