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A Survey on UAV-Aided Maritime Communications: Deployment Considerations, Applications, and Future Challenges

Nikolaos Nomikos, Panagiotis K. Gkonis, Petros S. Bithas, Panagiotis Trakadas

arXiv:2209.09605v1cs.NIeess.SP

TL;DR

Emerging maritime IoT applications require broadband, low-delay, and reliable connectivity beyond what shore-based stations and satellite links provide. This survey synthesizes UAV-aided maritime communications across architectures, technologies, optimization methods, deployments, and 6G-related challenges. It concludes that UAVs provide a flexible aerial complement for maritime connectivity and associated services.

  • Problem

    Existing shore-based and satellite deployments provide limited coverage, low data rates, and high latency for emerging maritime IoT applications requiring broadband and reliable connectivity.

  • Method

    The survey categorizes UAV-aided maritime architectures and conventional or machine-learning solutions across physical-layer, resource-management, computing, caching, trajectory, experimental, and open-issue studies.

  • Results

    The survey presents UAVs as an intermediate aerial layer complementing shore and satellite deployments and supporting maritime IoT and broadband use cases.

  • Takeaways & Limitations

    UAV integration offers deployment flexibility and dynamic resource provisioning for maritime communications within emerging 6G-oriented network architectures.

Abstract

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Maritime activities represent a major domain of economic growth with several emerging maritime Internet of Things use cases, such as smart ports, autonomous navigation, and ocean monitoring systems. The major enabler for this exciting ecosystem is the provision of broadband, low-delay, and reliable wireless coverage to the ever-increasing number of vessels, buoys, platforms, sensors, and actuators. Towards this end, the integration of unmanned aerial vehicles (UAVs) in maritime communications introduces an aerial dimension to wireless connectivity going above and beyond current deployments, which are mainly relying on shore-based base stations with limited coverage and satellite links with high latency. Considering the potential of UAV-aided wireless communications, this survey presents the state-of-the-art in UAV-aided maritime communications, which, in general, are based on both conventional optimization and machine-learning-aided approaches. More specifically, relevant UAV-based network architectures are discussed together with the role of their building blocks. Then, physical-layer, resource management, and cloud/edge computing and caching UAV-aided solutions in maritime environments are discussed and grouped based on their performance targets. Moreover, as UAVs are characterized by flexible deployment with high re-positioning capabilities, studies on UAV trajectory optimization for maritime applications are thoroughly discussed. In addition, aiming at shedding light on the current status of real-world deployments, experimental studies on UAV-aided maritime communications are presented and implementation details are given. Finally, several important open issues in the area of UAV-aided maritime communications are given, related to the integration of sixth generation (6G) advancements.

I. INTRODUCTION

Maritime communications must support heterogeneous applications and devices, but existing shore-based and satellite infrastructure is limited by coverage, data rate, delay, and reliability. The survey examines UAV integration as a flexible aerial complement and organizes solutions by architecture, network layer, performance target, deployment, and 6G-related open issues.

  • I. INTRODUCTION: Maritime IoT supports trade, ocean exploration, pollution monitoring, tourism, and search-and-rescue across vessels, buoys, platforms, vehicles, sensors, and actuators.Service requirements range from broadband connectivity and real-time video to ultra-reliable, ultra-low-latency communications.
  • I. INTRODUCTION: Shore-based base stations and satellites cannot adequately support emerging maritime applications because of limited coverage, low data rates, high delays, and unreliable connectivity.
  • I. INTRODUCTION: UAV-aided maritime networks provide a flexible aerial layer that complements terrestrial and satellite segments while reducing path loss and delay.UAVs can act as relays for ground-to-vessel connectivity, support underwater data collection with USVs and UUVs, and provide high-capacity links during search-and-rescue operations.
  • I. INTRODUCTION: The survey reviews maritime network architectures and categorizes physical-layer, resource-management, cloud/edge, caching, and trajectory-optimization solutions by network layer and performance target.
  • I. INTRODUCTION: The survey also presents experimental UAV-aided deployments and identifies open issues involving 6G technologies such as IRS, NOMA, swarm intelligence, and wireless power transfer.

D. Structure

The survey is organized from architectures and communication technologies through performance targets, computing, trajectories, experiments, open issues, and conclusions. Its maritime architecture comprises underwater and surface activities supported by shore, aerial, and space segments.

  • D. Structure: The survey proceeds through architecture designs, physical-layer issues, resource management, cloud/edge computing and caching, trajectory design, experiments, open issues, and conclusions.
  • D. Structure: The maritime network architecture contains maritime, shore, aerial, and space segments supporting heterogeneous nodes and communication roles.The maritime segment covers underwater and surface activities, while the other segments provide terrestrial, aerial, and satellite connectivity.
  • D. Structure: Edge nodes acquire surface and underwater data, cooperatively relay traffic through multiple communication modes, and perform edge-computing tasks.
  • D. Structure: Underwater nodes use acoustic signals for longer-range communication because electromagnetic waves experience high attenuation in seawater.
  • D. Structure: Surface ships, USVs, and buoys support intelligent transportation, environmental observation, underwater data relaying, and maritime search and rescue.

2) Shore segment:

The shore segment provides terrestrial coverage and connects the maritime network to the space segment, while UAV-aided communications address coverage, broadband, latency, and reliability limitations.

  • 2) Shore segment:: Shore base stations provide cellular coverage to nearby maritime nodes and UAVs, while ground stations connect the network to satellites.
  • 2) Shore segment:: Existing shore base stations and satellites offer broad maritime coverage but cannot sufficiently support broadband or ultra-reliable low-latency services.
  • 2) Shore segment:: UAV-aided maritime networks dynamically provision radio resources to remote areas, provide lower latency than satellite links, and improve reliability through multi-hop and diverse wireless paths.
  • 2) Shore segment:: The space segment includes GEO systems such as INMARSAT and LEO constellations such as Starlink, providing backup coverage and backhaul or fronthaul.
  • 2) Shore segment:: Maritime networks use heterogeneous communication technologies because their segments experience different environmental and propagation characteristics.The survey focuses on wireless technologies, although some underwater topologies use wired connections.

2) Free space optical (FSO) communications:

Maritime communications combine optical, visible-light, acoustic, and RF technologies to meet bandwidth, latency, coverage, and energy objectives under heterogeneous propagation conditions. These objectives introduce tradeoffs that can be addressed through optimization and machine learning.

  • 2) Free space optical (FSO) communications:: FSO communication offers large bandwidth, unlicensed spectrum, high data rate, fast deployment, and reduced power consumption under line-of-sight conditions.Atmospheric absorption, scattering, and turbulence can affect FSO transmission performance.
  • 3) Visible-light communications (VLC):: VLC can support underwater links among UUVs and between UUVs and ships, USVs, or UAVs acting as data sinks.Efficient VLC must account for maritime-node mobility and maintain accurate transceiver pointing.
  • 4) Underwater communications:: Underwater acoustic communication enables long-range transmission but suffers from poor, highly dynamic channels, lower capacity than RF, and larger propagation delay.Hybrid acoustic, RF, and optical schemes have been proposed to address attenuation.
  • C. Performance targets: Maritime network design balances spectral efficiency, energy efficiency, network lifetime, delay, reliability, and implementation complexity.These targets may conflict, motivating conventional optimization and machine-learning algorithms.
  • 2) Energy efficiency/network lifetime maximization:: Energy-efficient techniques and wireless power transfer are used to address the growing deployment of battery-dependent UUVs, USVs, and UAVs.Energy efficiency is measured in bits/joule, while power control and routing support network lifetime.

4) Task and data offloading:

This section surveys maritime UAV communication research spanning channel modelling, physical-layer optimization, and underwater or surface data-transfer schemes. It emphasizes models and algorithms that account for mobility, environmental effects, connectivity, data rates, and computational efficiency.

  • Channel modelling for maritime environments: Maritime channel models must account for direct paths, sea volatility, extreme weather, and propagation conditions that differ substantially from terrestrial environments.The survey discusses FDTD, non-stationary multi-mobility, and ray-tracing approaches for representing these effects.
  • Channel modelling for maritime environments: The non-stationary UAV-to-ship model combines line-of-sight, sea-surface single-bounce, and waveguide-induced multi-bounce components while supporting arbitrary motion of vessels, UAVs, and clusters.This addresses multimobility in maritime links.
  • Physical-layer solutions: Physical-layer studies optimize maritime links through machine-learning channel estimation, distributed beamforming, FSO deployment, and transceiver design under environmental constraints.Reported evaluations include improved channel-estimation accuracy and downlink rates, weather-dependent FSO degradation, and normalized beamforming gains in convergence and spectral efficiency.
  • Coverage and data-rate optimization: Hybrid satellite-UAV-terrestrial coverage maximizes the minimum vessel rate while constraining leakage interference to satellite users through user-centric clustering and edge-based control.The architecture assigns each vessel a group of UAVs and terrestrial base stations.
  • Underwater data transfer: Underwater UAV-assisted schemes connect sensors, sink nodes, and UAVs through acoustic and wireless links, deriving connectivity models that incorporate trajectories, antenna characteristics, sink stability, and beamwidth.The surveyed approaches also include edge computing for underwater data acquisition and beamwidth selection under power or attenuation constraints.

C. Interference mitigation

The survey presents interference mitigation and energy-efficient resource-management studies for mobile maritime UAV networks. These works address co-channel interference, jamming, handover overhead, energy consumption, and adaptive antenna operation.

  • Interference mitigation: Maritime UAV networks require co-channel interference mitigation because UAV motion makes interference conditions unstable while vessels occupy broad, lane-based geographic areas.The survey links this setting to on-demand UAV placement for maritime coverage.
  • Interference mitigation: A deep reinforcement learning approach trains separate neural networks for power allocation and message transmission, improving BER and reducing overall power consumption under jamming.The method is evaluated specifically for jamming mitigation in UAV-aided maritime communications.
  • Energy-efficient transmissions: Energy-efficient maritime designs maximize component lifetime and reduce outage risk through power control, routing, handover decisions, and joint trajectory-power optimization.The surveyed motivation reflects battery dependence and harsh maritime operating conditions.
  • Energy-efficient transmissions: Reduced-complexity Min-Max approximations converge quickly with moderate computational complexity while achieving performance similar to the optimal solution.The associated evaluation also reports improved energy consumption across different vessel time-slot occupancy cases.
  • Adaptive antenna design: Adaptive cylindrical-patch antenna sub-arrays alter radiation patterns according to supported applications and motivate radar-communication integration for 6G maritime networks.The design uses flexible substrates to connect the antenna sub-arrays.

A. Cooperative UAV data relaying

Cooperative UAV relaying extends maritime connectivity across surface, underwater, satellite, and terrestrial segments. The surveyed studies combine relay placement, mode selection, resource allocation, routing, and security mechanisms to improve coverage, throughput, lifetime, and communication efficiency.

  • Cooperative UAV data relaying: UAVs serve either as active base stations for distant-area coverage or as relays retransmitting signals between terrestrial, surface, and underwater network segments.Ocean monitoring architectures combine underwater sensor-to-sink transmission with radio links from surface sinks toward UAVs.
  • Cooperative UAV data relaying: A buoy communication mode-selection algorithm separately optimizes trajectory, power consumption, and time-slot allocation to reduce computational burden and improve minimum buoy throughput.The approach targets ocean-surface drifting buoys.
  • Cooperative multi-tier relaying: Cooperative LEO-satellite and HAP networks use restricted three-sided matching to manage the higher complexity of matching users, HAPs, and satellites.The approach leverages space-air-ground wireless communications.
  • Multiple access: NOMA shares a resource block among node groups to improve spectral efficiency and multinode connectivity, but requires careful grouping and successive-interference-cancellation reception.The survey identifies inter-user interference as a central grouping concern.
  • Network lifetime maximization: Network-lifetime designs decouple non-convex resource allocation and UAV deployment into UAV-sink delay minimization and underwater-sensor-to-sink lifetime maximization.Simulation results report improved performance compared with other TDMA schemes.
  • Routing and security: An optimized link-state routing protocol using link expiration time and residual energy improves end-to-end delay, packet transmission rate, and routing overhead.The routing design adapts to both link persistence and remaining node energy.
  • Routing and security: A lightweight authentication protocol for 6G-IoT maritime networks is evaluated through formal security assessment and compared with other security mechanisms for its security-to-efficiency trade-off.The work addresses security and privacy concerns arising from integrating diverse maritime communication networks.

V. CLOUD/EDGE COMPUTING AND DATA OFFLOADING

Cloud and edge solutions in UAV-aided maritime communications place computing resources near maritime users and optimize offloading under latency, energy, resource, and computation constraints. The surveyed approaches use conventional optimization, multi-armed bandits, and deep reinforcement learning across heterogeneous architectures.

  • Edge servers mounted on UAVs can be deployed near ship terminals or maritime IoT devices to offload computing tasks.
  • MEC-UAV optimization commonly balances communication and energy resources while accounting for latency and computational complexity constraints.
  • A centralized DRL architecture optimizes a UAV-aided maritime communications network through reward-based actions that maximize a utility function.
  • UAV edge-server selection has been formulated as a budget-constrained multi-armed bandit problem using delay, energy consumption, and other weighted factors.
  • Deep reinforcement learning has addressed joint communication and computation resource allocation, improving resource-utilization efficiency and reducing task-implementation latency.

B. Data Offloading

Maritime UAV data-offloading studies optimize time-related objectives while modeling vessel mobility, UAV movement, sea propagation, computation collaboration, and trajectory-dependent collection. The surveyed methods include penalty convex-concave procedures and successive convex approximation.

  • Data-offloading studies primarily target reductions in execution, completion, or offloading time across maritime UAV systems.
  • One model jointly considers container-vessel mobility, UAV movements, and wireless propagation over the sea when optimizing maritime data offloading.
  • Incentive-based collaborative computation offloading for USV fleets significantly reduces the overall execution time of computation tasks.
  • Trajectory optimization studies improve connectivity under constraints such as transmission power and minimum data-rate provision.
  • Successive convex approximation jointly optimizes buoy communication scheduling and UAV flight trajectories subject to wind effects.
  • UAV trajectory, transmit power, and transmission scheduling are jointly optimized to maximize energy efficiency in networks containing buoys and underwater sensors.

B. Improving maritime search and rescue

UAV-aided maritime search and rescue research addresses rapid search, tracking, underwater object detection, connectivity, and rescue communications through coordinated UAV, USV, and UUV systems. Studies combine simulation, optimization, channel measurement, and experimental evaluation.

  • Maritime search-and-rescue studies examine UAV deployment, patrolling, recharging, and scheduling while targeting reduced network delay.
  • Simulation results reveal a relationship between UAV height and overall rescue time in rapid maritime search.
  • Collaborative UAV, USV, and UUV strategies divide underwater object detection into search and tracking phases, maximizing search space and minimizing terminal error.
  • Experimental ocean-observing systems use UAVs and USVs to support underwater glider deployment, recovery, battery charging, and communication relay.
  • A UAV relay with directional antennas and PSO-based height control outperformed fixed or random height selection in overall throughput.
  • LTE-based rescue studies evaluate maritime channel models and resource-guaranteed scheduling for UAV-aided emergency communications.

VIII. OPEN ISSUES

Open issues include improving wireless performance with advanced UAV technologies and developing measurement campaigns for 6G carrier frequencies. Existing evaluations have considered frequencies only up to 5 GHz, leaving higher-frequency maritime validation open.

  • The survey identifies UAV-aided maritime communications as an emerging area with open directions concerning coverage, delay, reliability, deployment flexibility, and 6G integration.
  • UAV-carried massive MIMO can improve spatial separation and potentially leverage both spectral and energy efficiency, but increases installation costs and overhead.
  • UAVs carrying intelligent reflecting surfaces can enhance wireless channel quality by phase-shifting relayed signals.
  • Existing performance evaluations have considered frequencies only up to 5 GHz, motivating measurement campaigns for 6G carrier frequencies.

B. UAV-aided non-orthogonal multiple access

UAV-aided maritime networks use flexible positioning, machine learning, edge computing, and federated learning to address scalability, reliability, and resource-management challenges. The survey also identifies security, energy, connectivity, and realistic-topology constraints affecting these approaches.

  • B. UAV-aided non-orthogonal multiple access: UAV repositioning and line-of-sight conditions support flexible maritime connectivity and motivate trajectory optimization for NOMA transmissions.The surveyed NOMA solutions seek to maintain channel asymmetry among co-existing nodes and maximize spectral efficiency.
  • C. Machine learning: Large-scale performance evaluation remains limited because many studies reduce the number of UAVs and service nodes to lower optimization complexity.The survey points to deep reinforcement learning and federated learning as approaches for adapting to broader maritime network conditions.
  • C. Machine learning: Federated learning can combine local model updates across K UAV-aided coverage areas into a global collaborative model for maritime networks.The architecture is presented as an edge-based approach suited to maritime networks spread over wide territories.
  • E. Advanced edge maritime services: UAV-aided edge computing processes maritime data locally, reducing reliance on backhaul links and supporting real-time operation.Joint task allocation and data collection across UAVs, USVs, and UUVs must account for vehicle trajectories, processing capabilities, and energy constraints.
  • F. UAV swarm intelligence: UAV swarms require reliable inter-UAV connectivity and distributed intelligence, but delay, channel degradation, energy limits, and weather can impair coordination.Bandit-based channel prediction and wireless power transfer are identified as possible ways to address outdated channel information and energy limitations.
  • IX. CONCLUSIONS: The survey organizes UAV-aided maritime solutions across physical-layer, resource-management, computing, caching, trajectory-design, and experimental-implementation challenges.It frames these solutions as part of integrating UAVs with shore-based and satellite deployments for maritime communications.
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