The aeronautical networks attract the attention of both industry and academia since Internet access during flights turns to the crucial demand from luxury with the evolving technology. This In-Flight Connectivity (IFC) necessity is currently dominated by the satellite connectivity and Air-to-Ground (A2G) network solutions. However, the high installation/equipment cost and latency of the satellite connectivity reduce its efficiency. The A2G networks are utilized through the 4G/5G ground stations deployed on terrestrial areas to solve these satellites’ problems. This terrestrial deployment reduces the coverage area of A2G networks, especially for remote flights over the ocean. The Aeronautical Ad-hoc Networks (AANETs) are designed to provide IFC while solving the primary defects of dominating solutions. The AANET is an entirely novel solution under the vehicular networks since it consists of aircraft with ultra-dynamic and unstructured characteristics. These characteristics separate it from the less dynamic Flying Ad-Hoc Networks (FANETs). Therefore, the environmental and mobility effects cause specific challenges for AANETs. This article presents a holistic review of these open AANET challenges by investigating them in data link, network, and transport layers. Before giving the details of these challenges, this article explores the state-of-the-art literature about satellite and A2G networks for IFC. We then give our specific interest to the AANET by investigating its particular characteristics and open research challenges. The main starting point of this study is that there is a lack of compact research on this exciting topic, although IFC is an inevitable need for the aeronautical industry. Also, the AANET could be underlined by giving all state-of-the-art about the dominating IFC solutions. Therefore, this is the first work exploring the state-of-the-art for all the existing aeronautical networking technologies under a single comprehensive survey by deeply analyzing specific characteristics and open research challenges of AANETs. Additionally, the AANET is a novel topic and should be separately investigated from the FANETs as given in current literature.

Aeronautical ad hoc network (AANET) has been considered a promising candidate to complete the vision of “Internet in the sky” by supporting high-speed broadband connections on airplanes for 6G networks. However, the specific characteristics of AANET restrict the applicability of conventional topology and routing management algorithms. Here, these conventional methodologies reduce the packet delivery success of AANET with higher transfer delay. At that point, the artificial intelligence (AI)-driven solutions have been adapted to AANET to provide intelligent frameworks and architectures to cope with the high complexity. The AI-driven AANET can provide intelligent topology formation, sustainability, and routing management decisions in an automated fashion by considering its specific characteristics during the learning operations. More clearly, AI-driven AANETs support intelligent management architectures, overcoming conventional methodologies’ drawbacks. Although AI-based management approaches are widely used in terrestrial networks, there is a lack of a comprehensive study that supports AI-driven solutions for AANETs. To this end, this article explores the possible utilization of primary AI methodologies on the road to AI-driven AANET. Specifically, the article addresses unsupervised, supervised, and reinforcement learning as primary AI methodologies to enable intelligent AANET topology formation, sustainability, and routing management. Here, we identify the challenges and opportunities of these primary AI methodologies during the execution of AANET management. Furthermore, we discuss the critical issue of security in AANET before providing open issues.

This paper proposes a handover-aware content replication mechanism for Mobile Content Delivery Networks (mCDNs). This mechanism allows us to select the most optimal surrogate servers and assign these to the mobile clients through built-in tables. The optimal surrogate selection process begins by predicting the mobility of clients through Gauss-Markov model. Then, it continues with server modeling based on G/G/1 queuing system for load estimation. We also use the k-order autoregressive model to observe the future values of the estimated loads. The proposed strategy reduces the content replication delays and packet losses by 53% and 42%, respectively.