Mesh Networks for Multi-Drone Operations

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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Optimizing mesh network connections between devices to reduce unreliability and latency. The optimization involves coordinating midpoint nodes to improve bandwidth. Devices monitor triggers to initiate optimization when conditions warrant. When triggered, the initiating device identifies an optimal midpoint node and sends coordination info to the other device. Both devices then release the current connection and establish a new one using the optimized midpoint. This allows coordinated use of better nodes to improve reliability.

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Extending network coverage in dynamic RF environments using a swarm of mobile aerial nodes with directional antennas operating in the millimeter wave spectrum. The nodes form a mesh network that can adaptively route data around interference sources by steering antenna beams and selecting paths. This allows nodes to mitigate interference and extend range compared to omnidirectional antennas. The nodes can also adjust beam patterns to null out interferers. The millimeter wave frequencies offer higher bandwidth and smaller antennas for compact drone nodes.

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Optimizing the topology and power allocation for large-scale drone swarm networks to improve capacity in the presence of interference. The method involves a two-step optimization procedure. First, given a fixed network topology, an interior point method is used to find the optimal power levels for each drone to maximize network capacity. Then, a network topology optimization is performed by simultaneously finding the best connections between drones and their power levels to further increase capacity subject to constraints. The method handles interference from jammers and internal drone interference by introducing redundant variables and converting non-convex constraints into easier forms.

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Ad hoc wireless mesh network system for robust, flexible, and scalable communication in challenging environments. The system uses a mesh network topology where nodes forward data packets. Nodes can be gateways, repeaters, or terminals. The gateway connects to other networks, the repeater extends range, and terminals access services. The mesh network self-configures when nodes move or fail. It provides coverage extension, redundancy, and resilience compared to point-to-point links. The nodes have features like channel selection, video compression, error correction, and interference mitigation.

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Heterogeneous network communication method for unmanned aerial vehicles (UAVs) and ground vehicles in dynamic clusters. The method uses a hierarchical opportunistic routing protocol that combines the energy efficiency of opportunistic routing with the low latency of geographic routing. Ground vehicles use opportunistic routing and UAVs use geographic routing. Clustering based on signal strength and obstructions helps deploy UAVs at critical locations. This avoids routing holes and improves connectivity.

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Network topology control for large-scale drone clusters to improve communication efficiency, fault tolerance, and robustness. The method involves estimating dynamic path changes based on drone flight information, adaptively adjusting communication overhead, calculating node lifespans, reconfiguring important nodes, and reconstructing the topology. This allows autonomous optimization and adaptation of the drone network structure in response to environmental changes and task requirements.

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Self-organizing communication network topology for collaborative drone operations that allows drones to autonomously form adaptive networks for collaborative missions. The network structure allows drones to connect and communicate with each other as well as the ground station. The network dynamically adjusts when nodes join, leave, or fail to maintain functionality. It enables drones to coordinate and share information without relying on centralized control. This allows extended range, depth, and resilience for collaborative drone missions compared to centralized or distributed architectures.

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Dual-frequency heterogeneous ad hoc network data link for reliable long-distance communication between platforms like military unmanned vehicles. The system uses a central star-shaped link over a long-range frequency like L band, and a non-central mesh link over a short-range frequency like U band. The central star link has a central node and child nodes, and the mesh link connects child nodes when their central link quality is poor or broken. This provides fallback and redundancy through the mesh link while leveraging the central star's real-time capability. The dual frequencies mitigate interference.

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Method and system for reliable networking of unmanned aerial vehicles (UAVs) using adaptive multi-hop transmission to improve connectivity and stability. The method involves prioritizing transmission in the same link and then hopping to adjacent links when congestion exceeds 95%. This prevents disconnections from isolating UAVs. The system has multiple UAVs forming transmission packets with at least one link per UAV. Long-range links are repaired instead of adding nodes, while short-range links use multiple nodes to maintain distance.

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UAV network routing method that improves stability and reliability of data transmission in spectrum interference environments. The method combines a table-driven routing protocol with a radio environment map to model the network topology as a weighted graph. Nodes allocate weights based on neighbor count, then use interference area info from the map to quickly detect faulty nodes and recalculate routes that bypass interference areas. This avoids packet loss and combines network and physical layers for stable routing in spectrum interference.

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Self-organizing network for unmanned aerial vehicles (UAVs) that allows UAVs to communicate with each other and ground stations without relying on fixed infrastructure. The network uses satellite links and mesh networking to connect a command center, portable satellite station, UAV swarm, ground mobile base, and individual soldiers. The UAV swarm can operate autonomously with direct links between UAVs and ground stations. The satellite station provides connectivity to the UAV swarm when out of range of ground stations. The command center coordinates the network and has satellite links to the satellite station and ground stations. This allows UAVs to communicate with each other and ground stations without relying on fixed infrastructure.

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Constructing a dynamic network topology for swarms of unmanned aerial vehicles (UAVs) that adapts to changing network conditions. The network has two levels: a first level with fixed cluster heads formed by medium/large UAVs, and a second level with temporary cluster heads formed by small UAVs. The small UAVs communicate with the fixed cluster heads through their temporary cluster heads. This allows flexible network organization that can quickly adapt to node mobility and topology changes.

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