Aeronautical Ad Hoc Networks Research Articles

Civil Aeronautical Ad Hoc Network Zero-Overhead Clustering Algorithm Based on Realtime Position Information of the Aircraft

Clustering is an important means to solve the poor scalability of aeronautical ad hoc networks (AANET). To improve the stability and performance of AANET and avoid unnecessary waste of resources caused by civil aircraft in communication, we proposed a zero-overhead clustering algorithm according to the real-time position of the aircraft based on the known trajectory. Firstly, the route and trajectory models are used to obtain geographical coordinates by the aircraft positioning algorithm. On this basis, the geographical cluster and cluster head region are divided in order to complete the cluster setting. Considering the aircraft maintenance cluster generation time updates, we use the communication sub-cluster generation algorithm to control the size of the cluster, and also, the flexibility of cluster hops is guaranteed by the subsidiary cluster members. The continuity of communication and the scalability of the cluster are maintained by the gateway node, thereby forming a network structure and increasing the stability of clusters. Finally, the actual route data are used to simulate the performance of the algorithm. The experimental and analytical results show that clustering and maintenance of the algorithm have zero overhead. Additionally, compared with the traditional algorithm, our proposed method can maintain a reasonable number of clusters, reduce the frequency of cluster head replacement, reduce the number of cluster members entering and leaving the cluster and avoid the loss of control of cluster heads to cluster members. So, it has important application value in the field of civil aviation.

A Combined Topology Formation and Rate Allocation Algorithm for Aeronautical Ad Hoc Networks

This paper aims to address the problem of providing internet connectivity to aircraft flying above the ocean without using satellite connectivity given that there is no ground network infrastructure in the ocean. Is it possible to guarantee a minimum flow rate to each aircraft flying over an ocean by forming an aeronautical ad hoc network and connecting that network to internet via a set of limited number of ground base stations at the coast as anchor points? We formulated the problem as mixed-integer-linear programming (MILP) to maximize the number of aircraft with flow data rate above a certain threshold. Since this is a multi-commodity flow problem and at least NP-complete, we propose a two-phase heuristic algorithm to efficiently form topology and assign flows to each aircraft by maximizing the minimum flow. The performance of the heuristic algorithm is evaluated over the North Atlantic Corridor, heuristic performs only 8% less than the optimal result with low densities. In high network densities, the connectivity percentage changes from 70% to 40% under 75 Mbps data rate threshold. Furthermore, the connectivity percentage is investigated for different network parameters such as altitude and compared with upper and lower bounds and a baseline algorithm.

AI-enabled cluster head selection through modified density based clustering in Aeronautical Ad Hoc Networks

Offering in-flight connectivity has emerged as an essential demand for everyday flights. Such aircraft’s high speed and dynamic characteristics make this task challenging, particularly in distant areas with no ground-to-air link. Aeronautical Ad Hoc Network (AANET), a network of commercial airplanes with air-to-air connectivity, is a viable solution to this problem. However, the instability of air-to-air links results in poor performance of such ad hoc networks. A cluster-based topology formation is an initial step to boost the performance and connectivity in such a network. Selecting a well-connected cluster head is the next stage in enhancing connection and stability. This study presents a new cluster head selection technique for AANETs that calculates the neighbor nodes within a given distance of each node and selects the node with the most connections as the new cluster head. A gateway node is chosen to facilitate connections with other clusters. According to simulations, the proposed method increases packet delivery ratio by 3%, end-to-end delay by 9%, and throughput by up to 10%. In addition, the proposed method reduces cluster head replacements by 17% and increases cluster head longevity by 8%.

Digital Twin Evolution for Hard-to-Follow Aeronautical Ad-Hoc Networks in Beyond 5G

The aircrafts were top of the places that disrupted the seamless connectivity requirement of 5G and beyond. The Aeronautical Ad-hoc Networks (AANETs) take the attention of both industry and academia to satisfy this connectivity requirement with the low cost, easy deployment, and continuous coverage features. On the other hand, the ultra-dynamic characteristics of AANET with unstructured topology make its environment hard-to-follow. Here, Artificial Intelligence (AI)-based methodologies have an essential role in handling the management complexity of this hard-to-follow environment. However, these methodologies increase the computational complexity of aircraft due to the continuous update, convergence time, and scalability issues. At that point, we propose the utilization of the Digital Twin (DT) technology to handle the management complexity of AANET while solving the main issues of AI-based methodologies on it. The DT can virtually replicate the physical AANET components through closed-loop feedback in real-time. Therefore, this work introduces the utilization of DT technology for the AANET orchestration and, accordingly, proposes a DT-enabled AANET (DT-AANET) topology management framework. This framework consists of the Physical AANET Twin and Controller, including Digital AANET Twin with Operational Module. Here, the Digital AANET Twin virtually represents the physical environment while the operational module executes the AI-based computations on them through unsupervised learning-based training or supervised learning-based prediction. Finally, we present a case study based on Learning Vector Quantization (LVQ) to show the usability of the proposed framework and support this through evaluation results.

An aeronautical data communication demand model for the North Atlantic oceanic airspace

Information exchange via aeronautical data communication is of increasing importance for the communication between pilots and air traffic control, providing the basis for surveillance of aircraft in oceanic or remote airspaces, as well as enabling the communication between an airlines’ fleet and its operational control. The aeronautical data communication that is being transmitted via data link encompasses, among others, surveillance-related aircraft position updates, clearances for flight path change requests, maintenance-related status reports, estimated arrival times and weather information in accordance with the required performance, that is set by technical standards. Aeronautical data communication events are driven by an aircraft’s flight phase, the current airspace or may occur in a randomized manner throughout the flight. As the usage of aeronautical data communication is expected to grow in future, ample data link technologies are being evaluated and developed. The usability and operational value of new data link technologies for aeronautical applications can be evaluated by applying suitable models of the respective data link communication pattern combined with operational simulations. Current models for aeronautical data communication demand support the design and evaluation of direct aircraft-to-ground communication networks. The geographical location of the data communication demand is secondary for these models, since coverage areas can be defined, where direct communication links are available. New data link technologies offer the opportunity of direct data transfer between aircraft and forwarding of messages from sending aircraft to a ground entity via ad-hoc communication networks between aircraft. This is of special interest for the North Atlantic oceanic airspace, an airspace with high traffic density and little ground infrastructure, where communication currently relies mostly on satellite-based systems. For these airborne ad-hoc networks the definition of coverage areas around ground or space-based entities is not possible. To assess the new data link technology a model for aeronautical data link communication demand is needed, that accounts for operationally derived communication events such as handover procedures at boundaries of oceanic control areas or status reports at route-specific waypoints, which cause an accumulation in certain geographical regions that pose a challenge to the dynamic connectivity coverage of aeronautical ad-hoc networks. Addressing this issue, we present a new modelling approach for air traffic service communication that considers the operational context of the simulated airspace and provides a geospatial data communication demand distribution, which is derived from air traffic management procedures, airspace geometries and events inherent to each flight path. The air traffic service communication is then validated based on 2019 air traffic and performance monitoring data provided by ICAO supplemented by an existing model for communication related to airline operational control. In a next step, the communication demand per area in the North Atlantic Oceanic airspace is being assessed. The aeronautical data traffic model shows deviations of less than one message per aircraft and airspace when compared to recorded data traffic from 2019 for two key services in the most frequented North Atlantic oceanic control areas. Therefore, it is assumed to be suitable for the evaluation of network-based data link technology and operational impact assessments.

Multiple-Objective Packet Routing Optimization for Aeronautical Ad-Hoc Networks

Providing Internet service above the clouds is of ever-increasing interest and in this context aeronautical <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">ad-hoc</i> networking (AANET) constitutes a promising solution. However, the optimization of packet routing in large ad hoc networks is quite challenging. In this article, we develop a discrete <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\epsilon$</tex-math></inline-formula> multi-objective genetic algorithm ( <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\epsilon$</tex-math></inline-formula> -DMOGA) for jointly optimizing the end-to-end latency, the end-to-end spectral efficiency (SE), and the path expiration time (PET) that specifies how long the routing path can be relied on without re-optimizing the path. More specifically, a distance-based adaptive coding and modulation (ACM) scheme specifically designed for aeronautical communications is exploited for quantifying each link's achievable SE. Furthermore, the queueing delay at each node is also incorporated into the multiple-objective optimization metric. Our <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\epsilon$</tex-math></inline-formula> -DMOGA assisted multiple-objective routing optimization is validated by real historical flight data collected over the Australian airspace on two selected representative dates.

Q-Learning Driven Routing for Aeronautical Ad-Hoc Networks

The aeronautical ad-hoc network (AANET) is one of the critical methodologies to satisfy the Internet connectivity requirement of airplanes during their flights. However, the ultra-dynamic topology and unstable air-to-air link characteristics increase the need for AANETs to a particular routing algorithm compared to the terrestrial networks. This need is mainly because these AANET-specific characteristics increase the delays, packet losses, and network load with accuracy reduction by continuously changing topology and breaking air-to-air links during routing. The works in the literature do not satisfy the ultra-dynamic topology and unstable air-to-air link characteristics of AANETs during routing. On the other hand, the routing algorithm can adapt to the dynamic conditions of AANETs by utilizing Artificial Intelligence (AI) based methodologies. For adaptation to this dynamic environment, we aim to let the airplanes find their routing path through exploration and exploitation by mapping the AANET environment to QLR. Clearly, this article proposes an updated Layered Hidden Markov Model (updated-LHMM) estimation-based Q-learning (QLR) routing for AANETs to solve the delay, packet loss, network load, and accuracy problems. For this aim, the Bellman Equation is adapted to the AANET environment by proposing different methodologies for its related QLR components. Results reveal that the proposed strategy mainly reduces the routing delay and packet losses by 30% and 33% compared to the methods in the literature.

Dynamic Virtual Topology Aided Networking and Routing for Aeronautical Ad-Hoc Networks

Aeronautical Ad-hoc Networks (AANETs) have been proposed as the promising complement to terrestrial networks for promoting the global interconnection to provide in-flight network service, emergency communication, vessel traffic service, etc. However, the large network scale of AANETs may induce severe synchronization overhead when the traditional topology-based networking algorithms are adopted. Moreover, the high-dynamic topology and changeable flight routes make the existing position-based routing algorithms suffer loop routing and forwarding failure. Motivated by these problems, this paper aims at developing efficient and low-cost networking and routing algorithms relying on the concept of dynamic virtual topology which organizes the disordered topology of AANETs into a structural and simplified one. The basic idea is that each connected aircraft is assigned with a unique and sequentially increased Virtual Identifier (VID) and thus all the connected aircrafts are organized into a virtual cluster consisting of one trunk and several branches. An event-driven synchronization mechanism is leveraged for maintaining the virtual topology as well as relieving the communication burden imposed by periodical broadcasting. By jointly considering the geographic locations and the virtual locations of aircrafts, we formulate the routing problem in AANETs as a weighted distance minimization problem, and further propose a novel routing algorithm, namely Trunk-Branch Cooperation aided Routing (TBCR) algorithm. Specifically, TBCR employs the geographic greedy forwarding strategy for enhancing its flexibility and boosts the routing efficiency by adopting the loop-free virtual topology based local forwarding. For extending the networking and routing algorithms to the global range, a multi-domain routing solution is also provided. Extensive experimental results show that the proposed Virtual Topology based Networking (VTN) cooperated with TBCR can reduce at least 30% average end-to-end transmission delay in large-scale AANETs and provide more than 90% lower synchronization overhead than the existing solutions.

AI-Driven Aeronautical Ad Hoc Networks for 6G Wireless: Challenges, Opportunities, and the Road Ahead.

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.

Deep-Learning-Aided Packet Routing in Aeronautical Ad Hoc Networks Relying on Real Flight Data: From Single-Objective to Near-Pareto Multiobjective Optimization

Data packet routing in aeronautical ad-hoc networks (AANETs) is challenging due to their high-dynamic topology. In this paper, we invoke deep learning (DL) to assist routing in AANETs. We set out from the single objective of minimizing the end-to-end (E2E) delay. Specifically, a deep neural network (DNN) is conceived for mapping the local geographic information observed by the forwarding node into the information required for determining the optimal next hop. The DNN is trained by exploiting the regular mobility pattern of commercial passenger airplanes from historical flight data. After training, the DNN is stored by each airplane for assisting their routing decisions during flight relying solely on local geographic information. Furthermore, we extend the DL-aided routing algorithm to a multi-objective scenario, where we aim for simultaneously minimizing the delay, maximizing the path capacity, and maximizing the path lifetime. Our simulation results based on real flight data show that the proposed DL-aided routing outperforms existing position-based routing protocols in terms of its E2E delay, path capacity as well as path lifetime, and it is capable of approaching the Pareto front that is obtained using global link information.

Aeronautical Networks for In-Flight Connectivity: A Tutorial of the State-of-the-Art and Survey of Research Challenges

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.

Detection of Spoofing Attacks in Aeronautical Ad-Hoc Networks Using Deep Autoencoders

We consider an aeronautical ad-hoc network relying on aeroplanes operating in the presence of a spoofer. The aggregated signal received by the terrestrial base station is considered as &#x201C;clean&#x201D; or &#x201C;normal&#x201D;, if the legitimate aeroplanes transmit their signals and there is no spoofing attack. By contrast, the received signal is considered as &#x201C;spurious&#x201D; or &#x201C;abnormal&#x201D; in the face of a spoofing signal. An autoencoder (AE) is trained to learn the characteristics/features from a training dataset, which contains only normal samples associated with no spoofing attacks. The AE takes original samples as its input samples and reconstructs them at its output. Based on the trained AE, we define the detection thresholds of our spoofing discovery algorithm. To be more specific, contrasting the output of the AE against its input will provide us with a measure of geometric waveform similarity/dissimilarity in terms of the peaks of curves. To quantify the similarity between <i>unknown</i> testing samples and the <i>given</i> training samples (including normal samples), we first propose a so-called <i>deviation-based algorithm</i>. Furthermore, we estimate the angle of arrival (AoA) from each legitimate aeroplane and propose a so-called <i>AoA-based algorithm</i>. Then based on a sophisticated amalgamation of these two algorithms, we form our final detection algorithm for distinguishing the spurious abnormal samples from normal samples under a strict testing condition. In conclusion, our numerical results show that the AE improves the trade-off between the correct spoofing detection rate and the false alarm rate as long as the detection thresholds are carefully selected.

Three-phased clustered topology formation for Aeronautical Ad-Hoc Networks

In-Flight Internet Connectivity (IFC) has become one of the crucial needs of passengers with technological improvements. The Aeronautical Ad-hoc Networks (AANETs) have been proposed by establishing air-to-air (A2A) links between aircraft to satisfy this need. However, the unstructured aircraft topology caused by their ultra-dynamic characteristics reduces the longevity of A2A links in AANET. This shorter longevity decreases the stability of AANET by accelerating the connection establishment/termination procedures between aircraft. Additionally, the shorter link longevity affects the packet transfer delay and the delivery ratio of AANET. To the best of our knowledge, these three challenges are not investigated simultaneously under one topology management model. This paper proposes a three-phased topology formation model for AANETs to increase the stability and packet delivery ratio in AANET with a shorter packet transfer delay. The first phase corresponds to the aircraft clustering formation, and here, we aim to increase the AANET stability by creating spatially correlated clusters. The second phase consists of the A2A link determination for reducing the packet transfer delay. Finally, the cluster head selection increases the packet delivery ratio in AANET. According to our simulations, we see that the stability and packet delivery ratio of AANET topology are roughly improved 35% and 31% with a 28% delay reduction compared to the methods in the literature.

Aeronautical Ad Hoc network node failure analysis method

Aeronautical Ad Hoc network node failure analysis method

Dynamic Graph Optimization and Performance Evaluation for Delay-Tolerant Aeronautical Ad Hoc Network

With the growing interest in providing internet access and cellular connectivity in the commercial aircraft, the compatibility between Air-to-Air (A2A) communications and current Air-to-Ground (A2G) macro-cellular communications is necessary to form an Aeronautical Ad hoc Network (AANet). Due to the typical features of airborne communications, such as high mobility, long transmission range and three-dimensional macro-cells, AANet is affected by intermittent links, which results in variant delayed data transmissions. To this end, we exploit a dynamic graph approach to model such an AANet with rapidly changing topology under a realistic airborne scenario. Targeting the problem of intermittent transmissions, by employing A2A, an AANet can bear three transmission modes: direct transmission, connected relay transmission and opportunistic transmission. To investigate the potential gain of A2A transmissions in terms of the end-to-end data flow transmission delay and the data traffic amount, we formulate an integer non-linear programming problem that minimizes the average end-to-end delay from Internet Gateways (IGWs) on shore to the aircraft receivers through multiple options of the transmission modes. Through solving this minimization problem based on the realistic dynamic graph model, the tradeoffs between the end-to-end transmission delay and other key network factors related to AANet formation and resource allocation can be obtained without much compromise on the transmission delay. The results show that the average end-to-end delay of the AANet is comparable to the direct transmission delay when having a tradeoff with the data traffic amount, buffer size and the spectrum sharing.

Learning-Vector-Quantization-Based Topology Sustainability for Clustered-AANETs

Aeronautical ad hoc networks (AANETs) dramatically increase the Internet access rates of aircraft by widening the coverage area thanks to the air-to-air links established. However, the mobility and atmospheric effects on AANETs increase air-to-air link breakages, leading to frequent aircraft replacement and reducing link quality. These broken air-to-air links should be transferred to other aircraft to enable the sustainability of the AANET topology. At that point, the wrong and late transfer decisions of broken links make topology sustainability challenging by reducing packet transfer success and increasing end-to-end latency, respectively. Despite these challenges, to the best of our knowledge, air-to-air link transfers between aircraft in AANETs have not been investigated by any study to enable the topology sustainability. This article proposes to utilize learning vector quantization in three phases - winning cluster selection, intra-clus-ter link determination, and attribute update - to enable the sustainability of AANET topology, which is in the form of aircraft clusters. In winning cluster selection, we consider each cluster in the topology as a pattern. Then we aim to find the best matching cluster of an aircraft observing air-to-air link breakage through pattern classification. Then we take airplanes in a cluster pattern as weight vectors with location and queuing delay attributes to determine the intra-cluster links of newly assigned aircraft. Here, the aircraft is modeled according to a G/G/1 queuing system for delay attribute calculations. Finally, according to the freshly established intra-cluster links, we update weight vectors' attributes using the throughput rate as the learning rate. We simulate the proposed system by utilizing OMNET++ and Weka tools with realistic air traffic data obtained from flight radar databases. In these simulations, we can reduce the end-to-end latency of AANET topology by 25 percent and increase the packet transfer rate by 31 percent compared to the methodologies in the literature.

Reinforcement Learning-Based Energy-Efficient Data Access for Airborne Users in Civil Aircrafts-Enabled SAGIN

Airborne users are always dreaming of enjoying a good Internet access experience while in the air. However, due to long propagation delay and limited network coverage, the existing data communication methods utilized in space and ground communications not only fail to ensure the quality-of-service (QoS) of airborne users, but also incur significant energy consumption to process content requests. In this paper, we introduce the aeronautical ad hoc network (AANET) as a new method of network access and design an energy-efficient data access scheme in civil aircrafts-enabled space-air-ground integrated networks (CAE-SAGIN). In order to minimize the energy consumption, we propose a service selection scheme based on reinforcement learning and formulate a joint optimization problem of resource allocation and request distribution. Leveraged by the Lyapunov optimization method, the optimization problem can be solved by the proposed joint optimization algorithm. Extensive simulations are conducted to confirm the stability of the CAE-SAGIN, and demonstrate that the proposed data access scheme can effectively reduce both the energy consumption and the processing delay. Moreover, the advantages of using AANET are becoming more obvious when higher data rate is required.

Deep Reinforcement Learning Aided Packet-Routing for Aeronautical Ad-Hoc Networks Formed by Passenger Planes

Data packet routing in aeronautical ad-hoc networks (AANETs) is challenging due to their high-dynamic topology. In this paper, we invoke deep reinforcement learning for routing in AANETs aiming at minimizing the end-to-end (E2E) delay. Specifically, a deep Q-network (DQN) is conceived for capturing the relationship between the optimal routing decision and the local geographic information observed by the forwarding node. The DQN is trained in an offline manner based on historical flight data and then stored by each airplane for assisting their routing decisions during flight. To boost the learning efficiency and the online adaptability of the proposed DQN-routing, we further exploit the knowledge concerning the system's dynamics by using a deep value network (DVN) conceived with a feedback mechanism. Our simulation results show that both DQN-routing and DVN-routing achieve lower E2E delay than the benchmark protocol, and DVN-routing performs similarly to the optimal routing that relies on perfect global information.

Twin-Component Near-Pareto Routing Optimization for AANETs in the North-Atlantic Region Relying on Real Flight Statistics

Integrated ground-air-space (IGAS) networks intrinsically amalgamate terrestrial and non-terrestrial communication techniques in support of universal connectivity across the globe. Multi-hop routing over the IGAS networks has the potential to provide long-distance highly directional connections in the sky. For meeting the latency and reliability requirements of in-flight connectivity, we formulate a multi-objective multi-hop routing problem in aeronautical ad hoc networks (AANETs) for concurrently optimizing multiple end-to-end performance metrics in terms of the total delay and the throughput. In contrast to single-objective optimization problems that may have a unique optimal solution, the problem formulated is a multi-objective combinatorial optimization problem (MOCOP), which generally has a set of trade-off solutions, called the Pareto optimal set. Due to the discrete structure of the MOCOP formulated, finding the Pareto optimal set becomes excessively complex for large-scale networks. Therefore, we employ a multi-objective evolutionary algorithm (MOEA), namely the classic NSGA-II for generating an approximation of the Pareto optimal set. Explicitly, with the intrinsic parallelism of MOEAs, the MOEA employed starts with a set of candidate solutions for creating and reproducing new solutions via genetic operators. Finally, we evaluate the MOCOP formulated for different networks generated both from simulated data as well as from real historical flight data. Our simulation results demonstrate that the utilized MOEA has the potential of finding the Pareto optimal solutions for small-scale networks, while also finding a set of high-performance nondominated solutions for large-scale networks.

Minimum-Delay Routing for Integrated Aeronautical Ad Hoc Networks Relying on Real Flight Data in the North-Atlantic Region

Relying on multi-hop communication techniques, aeronautical ad hoc networks (AANETs) seamlessly integrate ground base stations (BSs) and satellites into aircraft communications for enhancing the on-demand connectivity of planes in the air. The goal of the paper is to assess the performance of the classic shortest-path routing algorithm in the context of the real flight data collected in the North-Atlantic Region. Specifically, in this integrated AANET context we investigate the shortest-path routing problem with the objective of minimizing the total delay of the in-flight connection from the ground BS subject to certain minimum-rate constraints for all selected links in support of low-latency and high-speed services. Inspired by the best-first search and priority queue concepts, we model the problem formulated by a weighted digraph and find the optimal route based on the shortest-path algorithm. Our simulation results demonstrate that aircraft-aided multi-hop communications are capable of reducing the total delay of satellite communications, when relying on real historical flight data.