Search

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 cost-effectively. 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., Comment: 30 pages, 9 figures, 8 tables

Current networking protocols deem inefficient in accommodating the two key challenges of Unmanned Aerial Vehicle (UAV) networks, namely the network connectivity loss and energy limitations. One approach to solve these issues is using learning-based routing protocols to make close-to-optimal local decisions by the network nodes, and Q-routing is a bold example of such protocols. However, the performance of the current implementations of Q-routing algorithms is not yet satisfactory, mainly due to the lack of adaptability to continued topology changes. In this paper, we propose a full-echo Q-routing algorithm with a self-adaptive learning rate that utilizes Simulated Annealing (SA) optimization to control the exploration rate of the algorithm through the temperature decline rate, which in turn is regulated by the experienced variation rate of the Q-values. Our results show that our method adapts to the network dynamicity without the need for manual re-initialization at transition points (abrupt network topology changes). Our method exhibits a reduction in the energy consumption ranging from 7% up to 82%, as well as a 2.6 fold gain in successful packet delivery rate}, compared to the state of the art Q-routing protocols, Comment: 13 pages, 7 figures, 5 tables

Unmanned aerial vehicles (UAVs), commonly known as drones, are becoming increasingly popular for various applications. Freely flying drones create highly dynamic environments, where conventional routing algorithms which rely on stationary network contact graphs fail to perform efficiently. Also, link establishment through exploring optimal paths using hello messages (as is used in AODV algorithm) deems extremely inefficient and costly for rapidly changing network topologies. In this paper, we present a distance-based greedy routing algorithm for UAV networks solely based on UAVs' local observations of their surrounding subnetwork. Thereby, neither a central decision maker nor a time-consuming route setup and maintenance mechanism are required. To evaluate the proposed method, we derive an analytical bound for the expected number of hops that a packet traverses. Also, we find the expected end-to-end distance traveled by each packet as well as the probability of successful delivery. The simulation results verify the accuracy of the developed analytical expressions and show considerable improvement compared to centralized shortest path routing algorithms., Comment: 7 pages, 5 figures, accepted in IEEE International Conference on Sensing, Communication and Networking (SECON), Workshop on Communications, Data Processing and Control for Unmanned Autonomous Systems (CPC-UAV)