Low Latency Routing Protocols for Flying Ad Hoc Network

Apparatus and method for guiding an air mobility's flight route in real-time, using a server that receives flight environment information from a preceding air mobility and calculates optimal route costs based on wind, airspeed, and navigation system availability. The server determines the optimal route by minimizing the sum of costs between nodes, with costs updated in real-time as the flight environment changes.

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A D2D scheduling method and apparatus for a UAV-based IoT network that enables scheduling of transmission links without channel state information (CSI). The method uses a reinforcement learning-based approach that leverages a geographical map of the network coverage area to extract features, which are then input to actor and critic networks to determine scheduling decisions. The scheduling decisions are transmitted to the D2D network and reward is calculated based on the achievable transmission rate.

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A lightweight and fast networking mechanism for highly dynamic networks based on beaconless forwarding technology. The mechanism reduces network overhead and transmission delays by optimizing routing updates and data forwarding interactions. It also simplifies network establishment and new node access processes through structured message formats that minimize topology updates.

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A proactive routing protocol for mobile ad-hoc networks that enables efficient and resilient data transmission between sensor nodes and target homeport nodes. The protocol, called Target Homing Optimized Routing (THOR), establishes and maintains multiple route chains between nodes through a proactive propagation mechanism, allowing for reliable data transmission even in the presence of link failures. THOR enables sensor nodes to transmit data to target homeport nodes through a primary route chain, while maintaining alternative paths through passive collection at intermediate nodes.

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An adaptive routing joint prediction method for flying Ad Hoc networks that uses multi-metric prediction and decision-making to enhance transmission link reliability by predicting mobility and data traffic. The method employs a long short-term memory (LSTM) based joint prediction module to forecast drone movement and data flow, and an entropy weight-based multi-metric (EWMM) method to determine the suitability of routing paths. The EWMM method normalizes decision factors and calculates their metric entropy to evaluate the availability of each node, enabling fast and joint routing decisions.

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Unmanned aerial vehicle (UAV) system for over-the-air computing that enables efficient data transmission through a relay network. The system optimizes UAV flight trajectories to maximize coverage while maintaining reliable communication links between the UAV and ground sensors. The system employs a relay-based architecture where the UAV acts as a relay node, forwarding data between the UAV and ground sensors. The trajectory optimization problem is solved through a combination of convex optimization techniques and iterative algorithms to balance coverage and reliability.

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A distributed collaborative evolution method for UAV networks that enables autonomous routing through deep reinforcement learning and blockchain-based co-evolution. The method integrates a decentralized DQN-based routing algorithm with a blockchain-based collaborative evolution mechanism, where each UAV trains its own DQN model independently and collaborates with other UAVs to converge on optimal routing parameters. The blockchain-based system enables regular model updates and consensus-driven parameter broadcasting, ensuring rapid convergence of the DQN model while maintaining network adaptability.

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Using drones and balloons as part of a communications system with satellites to provide global coverage. The drones and balloons are non-orbiting aerial nodes that can be used in local systems or in combination with satellites for wider area communications. They are deployed at altitudes of at least 10 miles to avoid interfering with commercial aviation. The drones can be lighter-than-air or heavier-than-air, and can be lift-assisted. This allows using drones closer to the ground to increase signal strength. The drones can transition to higher altitude satellite nodes for longer distance routes. The system uses multiple layers of aerial nodes at different altitudes for routing data and calls over long distances.

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A method for routing data packets in a mobile ad-hoc network (MANET) that achieves efficient packet flooding and passive clustering without dedicated clustering state data. The method uses a broadcast address comprising a single address of a total address space of a data packet to enable nodes to determine their clustering status and forward packets accordingly. The method also enables nodes to compile spatial awareness information and utilize it to route packets along a shortest line from a source node to a destination.

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A mesh network for aircraft enables reliable data communication between multiple aircraft through a self-healing network of nodes that adapt to changing communication conditions. The network is formed by multiple computing devices on each aircraft, which generate nodes that communicate with each other and adapt their transmission pathways based on feedback from other nodes. The network can operate over multiple frequencies and includes encryption for secure communication.

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A method for configuring a network topology of unmanned aerial vehicles (UAVs) that predicts future positions and link quality based on current location, movement direction, speed, and sensor information, including weather data, to enable optimal routing and communication in dynamic FANET environments.

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Self-organizing, autonomous network of unmanned aerial vehicles (UAVs) that can provide high-bandwidth wireless backhaul connectivity over long distances beyond line-of-sight. The network uses a high bandwidth mmWave wireless mesh backhaul between the UAVs to enable applications like LTE coverage in disaster areas, wide-area search and rescue, and autonomous surveillance in inaccessible areas. The UAVs jointly optimize position, yaw, and traffic routing to efficiently configure the network. A migration process determines the optimal configuration in the least time to reconfigure the network dynamically in response to events.

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Aerial networks that enable simultaneous use of wireless communication links, including free-space optical communications, in a tactical communications environment. The network employs a hybrid architecture that combines heterogeneous wireless links, including FSO and RF, to provide independent routing and QoS control. This enables the simultaneous use of multiple communication chains, including FSO, while maintaining optimal link utilization and network efficiency. The network achieves this through load-balanced routing, QoS signaling, and predictive traffic load balancing across multiple wireless links.

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Approximately synchronous broadcast method for aircraft collaboration, where each aircraft broadcasts its current and predicted states at variable intervals based on task requirements and state accuracy thresholds, enabling adaptive and efficient information sharing in dynamic environments.

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A relay point generation method for unmanned aerial vehicles (UAVs) that simplifies path planning by predicting the target's next state and determining a single relay point instead of planning a continuous path segment. The method involves establishing a search range around the target based on its predicted state, sampling multiple location points within the range, and selecting the point with the highest score as the relay point. This approach reduces computational complexity and latency compared to traditional path planning methods.

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A communication system for unmanned aerial vehicles (UAVs) that enables reliable and efficient communication between a controller and multiple UAVs over long distances. The system employs a mesh network architecture, where a mesh device acts as a relay node between the controller and UAVs, allowing for indirect communication through multiple hops. This architecture enables reduced transmission power requirements for the controller, increased communication distances, and improved reliability in the presence of obstacles. The mesh device can be integrated into the system as a separate node or can be implemented using existing devices such as RTK base stations or repeaters.

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Energy-efficient data transmission in drone networks through adaptive hop selection. The method analyzes data volume, drone energy consumption, relay drone availability, and target drone position to determine the optimal hop path. It then evaluates the hop transmission conditions using a data-driven approach to determine whether the current path is energy-efficient. If not, the method selects a new path based on factors like data volume, drone energy constraints, and relay drone availability. This approach enables dynamic optimization of data transmission in drone networks to minimize energy consumption while maintaining network connectivity.

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A routing method for cluster drone communication that enables efficient and reliable communication in large-scale mobile nodes. The method uses a hierarchical routing structure with weighted clustering, where cluster heads are selected based on energy, connectivity, mobility, and distance. Reactive routing lookups are performed between cluster heads, discarding duplicate packets from the same node. This approach improves network scalability and reduces packet loss in dynamic drone networks.

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Method for packet transmission of data between at least two terminal devices via at least one flying object, comprising: transmitting a data packet from a transmitting terminal device to a transmitting flying object; determining whether the receiving terminal device is in communication with a flying object associated with a cluster registration flying object; and relaying the data packet from the transmitting flying object to a receiving flying object associated with the cluster registration flying object.

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Constructing UAV network routes by dynamically selecting relay drones based on transmission channel characteristics, such as signal-to-interference ratios and channel capacities. The method optimizes relay selection by evaluating drone performance through metrics like distance to the base station, signal-to-interference ratios, and channel capacity improvement. The relay selection process balances factors like distance, interference, and capacity to ensure optimal transmission coverage while maintaining network reliability. The method integrates this relay selection with power control and transmission optimization to achieve efficient UAV network routing.

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