Swarm UAV Communication Networks

Dual-mode radar and communications devices for use in swarms of autonomous drones. The devices combine radar sensing and high-speed data communication capabilities in a single module with low size, weight, power, and cost. The modules leverage integrated circuits, phased arrays, and software defined radio techniques to provide radar sensing at medium range and data communication at high rates. This enables swarms of drones to autonomously navigate, inspect objects, and share sensor data using a mesh network with focused radar beams to reduce interference.

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Determining an optimal communication structure to link a set of delivery drones with a base station in a way that maximizes the number of drones that can stay connected during long distance delivery missions. The structure involves positioning a subset of relay drones at static locations along the delivery routes. This allows the relay drones to form communication regions that the delivery drones can access when they pass through. By strategically selecting the relay drone locations, it minimizes the number of unsupported delivery drones. This balances the tradeoff between the relay drone count and the maximum number of hops from a delivery drone to the base.

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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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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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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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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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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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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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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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Wireless perception system energy and information transmission method for UAV swarms that enables efficient and reliable wireless perception in large-scale swarms. The method involves jointly optimizing power allocation, beamforming, and time division to maximize the total throughput of sensors in the swarm while ensuring data throughput in the UAV swarm. The optimization is done using a golden section search algorithm to find the optimal values for power allocation, beamforming, and time division.

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Method and device for enabling fast and reliable communication between drones in a swarm without fixed infrastructure like cell towers. The method involves establishing a separate coordinate system for each drone using UWB positioning. Drones share their coordinates in real-time. They also determine the overall swarm coordinates. Drones use this swarm coordinate data along with the drone's own time slot to route messages efficiently through the swarm. The drones dynamically update routing tables. This allows rapid construction of a dynamic ad-hoc network without relying on base stations.

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Method for constructing a drone swarm relay network without relying on absolute positioning. The method involves having the drones determine their relative positions by measuring distances between them using received signal strength indicator (RSSI). This allows them to build the network by flying and hovering at specific positions without needing absolute positioning systems like GPS. The drones can also calculate connectivity between themselves using relative positioning for network optimization.

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An unmanned aerial vehicle (UAV) system enabling swarm flight through coordinated communication between multiple UAVs. The system employs 5G wireless communication technology to facilitate real-time data exchange between UAVs, enabling autonomous formation and coordinated flight operations. The system includes a UAV controller that manages the swarm flight operations, and a 5G network that provides reliable and low-latency communication between the UAVs and the controller. The system also includes a UAV traffic management (UTM) system that provides route planning and collision avoidance services for the swarm.

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Secure communication method and device for multi-hop ad hoc networks using drones to improve reliability and performance compared to ground-based networks. The method involves using drones as relay nodes to create a distributed network. Communication between the drones and a central command center is done over MESH links. The drones collect data, process it locally, and if needed, collaboratively calculate with nearby drones. If still unable to complete processing, it's sent to the command center for cloud computing. This allows distributed computing to overcome limitations of limited drone resources. The method leverages the mobility and coverage of drones to provide secure, flexible, and resilient networking in areas without fixed infrastructure.

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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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Unmanned aerial vehicle (UAV) communication system that uses a mesh network to enable better UAV connectivity and improve battery life of controllers. The system has a mesh device that acts as a relay between controllers and UAVs. The mesh network allows UAVs, controllers, and mesh devices to communicate indirectly through the mesh. This provides flexibility, scalability, and redundancy for UAV operations. The mesh network can bypass obstacles, extend range, and avoid line-of-sight issues compared to direct controller-UAV links.

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Avoiding collisions in wireless communication systems with multiple modules in close proximity by shifting the timing of monitoring total transmission time. Each module monitors its own transmission time during a fixed interval, and a central control determines different start times for the intervals. This distributes the concentrated transmission periods between modules, preventing simultaneous transmissions from colliding.

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Cooperative multi-UAV clusters have been widely applied in complex mission scenarios due to their flexible task allocation and efficient real-time coordination capabilities. The Air Command Aircraft (ACA), as the core node within UAV cluster, is responsible for coordinating managing various tasks cluster. When ACA undergoes fault recovery, a handover operation required, during which must re-authenticate its identity with cluster re-establish secure communication. However, traditional, centralized authentication mechanisms face security risks such single points of failure man-in-the-middle attacks. In highly dynamic network environments, single-chain blockchain architectures also suffer from throughput bottlenecks, leading reduced efficiency increased latency. To address these challenges, this paper proposes mathematically structured dual-chain framework that utilizes distributed ledger decouple management information. We formalize process using cryptographic primitives accumulator functions validate through BAN logic. Furthermore, we conduct quantitative analyses key performance metr... Read More

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<title>Abstract</title> Trust computation in Flying Ad Hoc Networks (FANETs) is a challenge due to rapid velocity and dynamic nature of Unmanned Aerial Vehicles (UAVs). The growing popularity UAVs increases risk data breaches, which affects the performance security links. As result, different methods have been proposed that are plagued by two problems. First problem insufficient consideration parameters affect nodes FANET. Second relates static assessment threats using CVSS-based techniques. This study evaluates trust through fuzzy logic considering six parameters. presents comparison with traditional emphasize influence on computation. model compared existing models OMNeT++. results show outperforms reducing end-to-end delay 96%, packet loss ratio 78%, increasing detection rate 0.8%.

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Acquiring and utilizing traffic information for unmanned aerial vehicles (UAVs) to improve UAV communication and reduce interference. The method involves acquiring operational data from UAVs and using it to optimize communication parameters like link adaptation, routing, and handoff decisions. A device collects UAV data like position, speed, and congestion, and shares it with the UAVs and base stations. They then determine communication policies based on the traffic information. This enables coordinated UAV mobility management and adaptive communication tailored to the local UAV density.

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Distributing power and data wirelessly between autonomous aerial, ground, and water vehicles to enable long-range, point-to-point power transfer and data communication. The system involves a network of synchronized unmanned vehicles that can form mobile power stations by navigating and maneuvering through the air to establish a beam riding highway. The vehicles can transmit and receive power and data between each other, as well as with land-based, air-based, water-based, and space-based systems, to serve as power and data hubs. This allows extended range wireless power transmission and data communication through a distributed network of autonomous vehicles.

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Enhancing sidelink communication in wireless networks to enable flexible and reliable sidelink communication in 3D spaces like for drones. The method involves transmitting sidelink control and data channels with height information in the hybrid automatic repeat request (HARQ) feedback process. This allows sidelink devices to adapt their sidelink communications based on vertical position changes. The height information in HARQ feedback helps synchronize and retransmit sidelink packets in 3D environments where height matters.

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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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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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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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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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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 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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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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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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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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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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Wireless control system for large networks with improved efficiency and reliability compared to conventional flooding-based methods. The system uses a hybrid network topology combining mesh and star networks. Nodes have separate roles in the mesh (routing) and star (local control) networks. This allows efficient routing around dense node clusters while avoiding collisions. Mesh nodes use multi-hop routing, star nodes have point-to-point links. The topology leverages different protocols with separate frequency bands to enable concurrent communication.

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Communication method for wireless mesh networks using LoRa technology. It involves constructing a network topology with coordinators and terminals, a wireless protocol stack, and a private communication protocol. The coordinators form a mesh network, terminals connect to them. The protocol stack has layers for physical, access, network, application. It uses LoRa for long-range transmission. The private protocol allows terminals to request addresses from coordinators. This method enables multi-hop routing between terminals via coordinators.

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Hybrid wireless sensor network system for field activities that combines long-range and short-range communication to provide reliable, flexible, and battery-efficient communication over long distances. The system has a hybrid network topology with a star network for point-to-point communication between terminals and an MESH network between gateways and relays. The star network uses fixed gateways and terminals for direct communication, while the MESH network with relays provides routing and synchronization for long-range coverage. This allows extending the range beyond terminal limits without needing relays at every hop. The hybrid topology balances range, reliability, and complexity.

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Reducing network load and improving transmission rate in a wireless mesh network by having each relay node determine if it's the root node based on conditions, and having child nodes connect to a specific parent node found through scanning. This ensures each child node has only one parent and one root in its path, reducing network complexity. Data is transmitted directly between parent and child nodes, avoiding multiple hops.

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A method for constructing a self-organizing network that allows unmanned aerial vehicles (UAVs) to cooperate in dynamic environments without infrastructure. The network is composed of multiple, independent subnetworks that interconnect and communicate with each other. UAVs connect to their nearest subnetwork, and ground control stations connect to UAVs' subnetworks. UAVs use one networking mode within their subnetwork, while UAVs and ground stations use a different mode between subnetworks. This allows a self-organizing ad hoc network with robustness and flexibility.

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Maximizing network connectivity using drones as relays when a limited number of drones are available. The method involves having ground nodes self-organize into sub-networks, and then selecting drone deployment locations to connect those sub-networks. The selection is done using a heuristic greedy algorithm that iteratively adds sub-networks until the drone limit is reached, prioritizing sub-networks with the most disconnected nodes. This prevents falling into local optima by connecting distant sub-networks. The algorithm keeps track of the restored connectivity count for comparison if it exceeds the drone limit.

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