Mesh Networks for Multi-Drone Operations

Aerial access network (AAN) architecture for cellular networks that enables efficient and scalable deployment of aerial base stations to provide coverage and capacity in dynamic environments. The AAN has multiple control, forwarding, and access layer units that dynamically manage aerial base stations and UAVs as network nodes. It allows seamless integration of aerial base stations into existing terrestrial networks, inter-aerial base station communication, and UAV-to-ground device communication. The architecture enables aerial base stations to provide fast, ubiquitous connectivity in mobile scenarios while avoiding network congestion and interference.

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Dynamic path selection in multi-hop wireless networks to improve transmission efficiency in congested environments. Nodes collect link qualities between themselves and select a relay node based on the collected qualities. When a node receives a signal addressed to another node, it requests link qualities from the destination node. If the destination is further away, the node relays the request. Nodes receiving requests select a relay based on link qualities. This allows efficient dynamic path selection that adapts to changing link qualities.

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Mesh-based communication systems with wireless nodes that use specialized antennas to reduce interference and simplify installation. The nodes have reflectarray antennas with multiple elements fed by a single RF chain. Each element applies phase shifts to the signal. This allows pTP links with directional beams and pTMP links with wider coverage. The reflectarray reduces interference compared to separate pTP antennas. The single-chain simplifies installation vs multiple pTP radios.

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A self-sustaining aerial data link system for UAVs and ground stations that enables real-time communication without line-of-sight requirements. The system comprises an unmanned vehicle equipped with a data link transceiver, and deployable data link transceivers that can be automatically deployed or reconfigured to maintain continuous communication. This autonomous deployment capability allows the system to maintain data link connectivity even when the UAV is in areas with obstacles such as terrain features or buildings, eliminating the need for traditional line-of-sight communication. The deployable transceivers can be automatically reconfigured to adapt to changing environmental conditions, enabling continuous communication over varying distances.

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Dynamic channel management for unmanned vehicles through intelligent channel switching and coordination. The system continuously monitors channel occupancy and dynamically switches between channels to optimize communication efficiency. When a channel is occupied, the system automatically switches to an alternative channel to avoid interference. The system also enables coordinated channel changes through preemption messages, allowing multiple vehicles to select the best available channel. This approach enables efficient channel utilization while maintaining reliable communication between vehicles.

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Efficiently managing channel resources in a synchronous wireless distributed communication system like drones, to prevent collisions and interference. The method involves terminals periodically broadcasting their allocated channel resources. Other terminals receive these broadcasts and use the information to select and allocate new resources that avoid conflicts. This allows terminals to proactively choose channels with less interference and collision potential, especially as their positions change. Reallocation occurs if conflicts are detected.

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Abstract: - Conventional tracking systems relying on GPS and cellular infrastructure are ineffective in environments with little or no connectivitysuch as remote wilderness, mountainous regions, disaster-affected areas. This paper introduces a hybrid architecture that integrates satellite communication, mesh networking, Radio Tomographic Imaging (RTI), signal jumping, drone-assisted relays, Low-Power Wide-Area Networks (LPWAN). The proposed system addresses loss challenges by utilizing AI-driven prediction autonomous drones to extend coverage improve real-time traceability. Performance is evaluated through simulations real-world case studies based key metrics including coverage, latency, energy efficiency, reliability. approach demonstrates strong potential for critical applications search rescue, defense operations, systems, contributing toward the development of resilient off-grid communication technologies. Keywords: Remote Tracking, Dead Zones, Mesh Networks, Satellite Communication, Drone Relays, RTI, LPWAN, Signal Prediction, Off-Grid Search Rescue.

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Routing optimization for networks with mobile nodes like satellites and drones. The optimization involves pre-calculating routing tables for networks with moving nodes, taking into account their movement patterns and communication environments. The optimization is done by dividing the routing generation period into time slots, generating network models for each slot based on node locations, evaluating the models, and selecting the best slot. The selected slot's routing table is then sent to the nodes. This allows pre-computing optimal routes for the dynamic network.

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Dynamic grouping of semi-autonomous drones to efficiently perform tasks like surveillance, delivery, mapping, etc. The drones can self-organize into teams based on capability matching to execute complex operations. A server determines tasks and assigns drones based on capabilities. Lead drones decompose tasks and create plans. Followers execute tasks. If a leader fails, another drone takes over. This allows flexible and scalable drone swarms without centralized control.

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Building an ad-hoc network with moving nodes that reduces energy consumption while maintaining high throughput. Each node collects state info on neighbor distances/angles. Nodes learn actions (e.g., changing transmission range) based on rewards (energy vs throughput) from state changes. Nodes generate policies to maximize cumulative reward. This allows nodes to adapt and coordinate optimally as positions change, without centralized planning.

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This paper introduces an Unmanned Aerial Vehicle - enabled content management architecture that is suitable for critical access in communities of users are communication-isolated during diverse types disaster scenarios. The proposed leverages a hybrid network stationary anchor UAVs and mobile Micro-UAVs ubiquitous dissemination. equipped with both vertical lateral communication links, they serve local users, while the micro-ferrying extend coverage across increased mobility. focus on developing dissemination system dynamically learns optimal caching policies to maximize availability. core innovation adaptive framework based distributed Federated Multi-Armed Bandit learning. goal optimize UAV decisions geo-temporal popularity user demand variations. A Selective Caching Algorithm also introduced reduce redundant replication by incorporating inter-UAV information sharing. method strategically preserves uniqueness preferences amalgamating intelligence learning system. approach improves algorithm's ability adapt preferences. Functional verification performance evaluation confirm architect... Read More

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Deployable and reusable networks for power and data distribution across multiple domains. The network integrates multiple autonomous vehicles, including rotor-based aircraft, airships, and spacecraft, to form a hybrid power and data transmission system. The vehicles are equipped with a hybrid propulsion system, including a rotor/propeller and inflatable balloon system, and a docking interface for seamless communication between vehicles. The system enables continuous operations, power beaming, and data transmission across land, air, and space domains.

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This study introduces a novel framework for dynamic reconnaissance operations using Unmanned Aerial Vehicle (UAV) swarms, designed to adapt in real time changes mission parameters and UAV availability. Unlike traditional models that assume static operational conditions, our approach distinguishes between two key categories of change: Type I, related modifications the swarm (e.g., vehicle loss or deployment), II, concerning adjustments configuration area responsibility. These are jointly addressed within unified optimization based on Ant Colony Optimization (ACO), allowing efficient trajectory planning rapid replanning during execution. As part framework, we propose Pheromone Matrix Initialization (PMI) technique accelerate convergence I scenarios by reusing heuristic information from prior optimizations. The effectiveness overall is validated through six realistic scenarios, demonstrating its ability maintain continuity with minimal delay respond efficiently complex sequential changes. Comparative analysis shows consistent superior performance over classical state-of-the-art methods,... Read More

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This paper focuses on algorithms and technologies for unmanned aerial vehicles (UAVs) networking across different fields of applications. Given the limitations UAVs in both computations communications, usually need either low latency or energy efficiency. In addition, coverage problems should be considered to improve UAV deployment many monitoring sensing Hence, this work firstly addresses common applications groups swarms. Communication routing protocols are then reviewed, as they can make capable supporting these Furthermore, control examined ensure operate optimal positions specific purposes. AI-based approaches enhance performance. We provide latest evaluations existing results that suggest suitable solutions practical a comprehensive survey general associated with fields.

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Optimizing communication in swarms of drones to enable efficient and reliable control of large numbers of drones. The optimization involves clustering the drones into groups with a master drone that communicates with a central server, and slave drones that relay messages from the master. This reduces the number of required communication channels compared to each drone directly connecting. Clustering also allows faster area coverage, obstacle avoidance, and resource sharing. If a master fails, another slave can be promoted. This enables robust swarm operation by minimizing communication breakdowns.

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Swarm-based unmanned aerial vehicle (UAV) systems offer enhanced spatial coverage, collaborative intelligence, and mission scalability for various applications, including environmental monitoring emergency response. However, their onboard computing capabilities are often constrained by stringent size, weight, power limitations, posing challenges real-time data processing autonomous decision-making. This paper proposes a comprehensive communication computation framework that integrates cloud-edge-end collaboration with cell-free massive multiple-input multiple-output (CF-mMIMO) technology to support scalable efficient offloading in UAV swarm networks. A lightweight task migration mechanism is developed dynamically allocate workloads between UAVs edge/cloud servers, while CF-mMIMO architecture designed ensure robust, low-latency connectivity under mobility interference. Furthermore, we implement hardware-in-the-loop experimental testbed nine validate the proposed through object detection tasks. Results demonstrate over 30% reduction significant improvements reliability latency, highlig... Read More

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Moving body like a drone that improves alignment accuracy during autonomous movement by determining optimal directions based on network topology and transmission characteristics. The moving body wirelessly communicates with external devices to acquire network connection relationships and transmission path characteristics. It then uses this information to determine directions for movement, rather than relying solely on sensors or GPS. This improves alignment accuracy, especially in environments where visibility or accuracy is poor.

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Updating communication parameters in a mesh network to prevent interruption of data transmission when endpoint addresses or ports change. The method involves periodically synchronizing communication parameters between devices in the mesh network with a central control infrastructure. If a device detects a change in its parameters during communication, it notifies the other devices in the mesh so they can update their parameters to match. This prevents failed transmissions and retransmissions that would occur if devices kept using the old parameters.

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Narrow beam mesh networking with improved reliability, adjustability, and flexibility. The networking involves wireless nodes with adjustable narrow beam antennas that can create point-to-point or point-to-multipoint links. This allows customizable network topologies. The nodes can also have millimeter wave radios for high capacity links. The nodes are financed by customers and leased back to the operator. The nodes can have direct RF-to-optical and optical-to-RF conversion modules to eliminate ADC/DAC. The nodes have digital/network modules for backhaul connectivity. This enables flexible multi-link configurations.

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Method for enabling long-distance end-to-end communication in wireless networks when direct communication between two terminals is not feasible due to distance. The method involves using a relay terminal to convert end-to-end communication into point-to-point communication. If a terminal can't directly communicate with another terminal, it sends a request to the other terminal. If there's no response, the terminal determines indirect communication is needed. It then sends the request to a relay terminal, which relays the message to the destination terminal. This allows end-to-end communication over longer distances by leveraging intermediate relay nodes.

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