DOI: 10.5281/zenodo.16995332
title: UAV Underwater Drones network traffic statistics

description: Dataset: UAV and Underwater Drone Network Anomaly Dataset (NTNU and SINTEF)

Overview

This dataset contains real time operational telemetry and network traces from the Trondheim Fjord region in Norway. Data originates from long running campaigns conducted by the Marine Cybernetics Laboratory at the Norwegian University of Science and Technology and the SINTEF Ocean Research Center. Platforms include autonomous underwater vehicles, remotely operated vehicles, unmanned aerial vehicle relays, coastal buoys, and a shoreside gateway. The collection spans February 2022 to March 2025 and reflects real world marine communication where acoustic, radio frequency, and satellite links operate together.

The public release is anonymized and standardized for research in anomaly detection, graph and temporal modeling, and privacy aware federated learning. Sensitive identifiers were removed in line with Norwegian maritime governance and European Union GDPR requirements.

What is included

File format





Comma separated values file with header




One row per client per timestamp




Time zone naive timestamps in ISO 8601 format



Temporal coverage





Start: 2022 02 01




End: 2025 03 31




Sampling interval: thirty minutes in this release





Note: the file name may reference ten minute cadence from earlier internal builds. The current artifact uses thirty minute cadence.





Participants and roles





Ten nodes total across the fleet configuration: three autonomous underwater vehicles, two remotely operated vehicles, two unmanned aerial vehicles, two coastal buoys, one gateway.



Column count





Total columns: 113





General identifiers: 4




Feature columns: 97




Multi label targets: 12





Schema and feature groups

Field names match column headers exactly.

General





timestamp, client_id, role, mission_id



Network and transport





pkt_tx, pkt_rx, pkt_drop, pkt_retx




throughput_up_kbps, throughput_down_kbps




latency_avg_ms, latency_p95_ms, latency_jitter_ms




rtt_avg_ms, rto_events




syn_cnt, fin_cnt, rst_cnt




udp_flows, tcp_flows, flow_churn_rate




payload_entropy, header_anomaly_score




routing_updates, route_changes, ttl_avg




queue_len_avg, queue_drop_rate



Link layer and physical channel





mac_tx_fail_rate, collision_rate




snr_db, ber, bler




tx_power_dbm, rx_power_dbm




channel_utilization_percent, cs_busy_time_ms




freq_shift_hz, multipath_delay_ms



Underwater environment





sound_speed_mps, absorption_coef_dbkm




ambient_noise_db, shipping_noise_db, sea_state




thermocline_depth_m, water_temp_c, salinity_psu, turbidity_ntu



UAV relay and backhaul





uav_link_rssi_dbm, uav_link_snr_db, uav_link_latency_ms




handover_events, uav_backhaul_type




uav_altitude_m, uav_speed_ms, uav_heading_deg




uav_relay_duty_percent, uav_link_uptime_percent



Kinematics and platform health





depth_m, pitch_deg, roll_deg, yaw_deg




speed_ms, accel_rms, turn_rate_dps




prop_rpm, thruster_load_percent




imu_health_flag, nav_mode



Energy and thermal





battery_soc_percent, battery_voltage_v, battery_temp_c, energy_draw_w




cpu_util_percent, mem_util_percent, gpu_util_percent




board_temp_c, thermal_throttle_events



Security telemetry





port_scan_hits, syn_flood_score, frag_anomaly_score




dns_req_rate, dns_nxdom_rate




arp_spoof_score, mac_spoof_score




auth_failures, cert_errors, ids_alerts_count, ml_ids_score



Topology and context





neighbors_count, link_changes, gw_hops




path_stability_score, link_distance_m, uav_distance_m, mission_phase



Derived temporal indicators





latency_trend, throughput_cv, pkt_drop_trend




snr_min_last5, rtt_ema, anomaly_ewma



Labels

Multi label targets for twelve conditions. A value of one indicates presence at the timestamp, otherwise zero.





Normal, Latency_Anomaly, PacketLoss_Anomaly, Throughput_Degradation, Jitter_Anomaly, Jamming_Attack, Spoofing_Attack, Replay_Attack, Routing_Blackhole, Channel_Distortion, Sensor_Fault, Energy_Fault