AerialDB: A Federated Peer-to-Peer Spatio-temporal Edge Datastore for Drone Fleets

2025-08-09

AI Summary

To address the challenges of limited onboard computing resources and inefficient real-time processing and storage of massive spatiotemporal data (e.g., video/image streams) in UAV swarm-based disaster monitoring, this paper proposes a lightweight, decentralized spatiotemporal data storage and query system. The method integrates a federated peer-to-peer architecture with containerized edge deployment, featuring content-aware replica placement and sharded indexing, and implements a locality-aware distributed query execution engine supporting graceful degradation under edge node failures. Leveraging spatiotemporal data partitioning, distributed query optimization, and cellular-edge cooperative scheduling, the system achieves a 100× improvement in query throughput and a 10× gain in write performance on a large-scale testbed comprising 400 UAVs and 80 edge nodes—significantly outperforming cloud-centric baseline approaches.

Technology Category

• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Data Mining & Knowledge Management: Mining of Spatial, Temporal or Spatio-Temporal Data
• Search and Optimization: Distributed Search

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Data management and stream processing for Web, mobile and wireless applications
• Graph Algorithms and Modeling for the Web: Querying, indexing, and retrieval in Web-related graphs
• Search and Retrieval-Augmented AI: Efficiency and scalability of Web search engines

Abstract

Recent years have seen an unprecedented growth in research that leverages the newest computing paradigm of Internet of Drones, comprising a fleet of connected Unmanned Aerial Vehicles (UAVs) used for a wide range of tasks such as monitoring and analytics in highly mobile and changing environments characteristic of disaster regions. Given that the typical data (i.e., videos and images) collected by the fleet of UAVs deployed in such scenarios can be considerably larger than what the onboard computers can process, the UAVs need to offload their data in real-time to the edge and the cloud for further processing. To that end, we present the design of AerialDB - a lightweight decentralized data storage and query system that can store and process time series data on a multi-UAV system comprising: A) a fleet of hundreds of UAVs fitted with onboard computers, and B) ground-based edge servers connected through a cellular link. Leveraging lightweight techniques for content-based replica placement and indexing of shards, AerialDB has been optimized for efficient processing of different possible combinations of typical spatial and temporal queries performed by real-world disaster management applications. Using containerized deployment spanning up to 400 drones and 80 edges, we demonstrate that AerialDB is able to scale efficiently while providing near real-time performance with different realistic workloads. Further, AerialDB comprises a decentralized and locality-aware distributed execution engine which provides graceful degradation of performance upon edge failures with relatively low latency while processing large spatio-temporal data. AerialDB exhibits comparable insertion performance and 100 times improvement in query performance against state-of-the-art baseline. Moreover, it exhibits a 10 times and 100 times improvement with insertion and query workloads respectively over the cloud baseline.

Problem

• Efficient real-time data offloading for UAV fleets
• Decentralized storage and query of spatio-temporal drone data
• Scalable processing for disaster management applications

Innovation

• Federated peer-to-peer spatio-temporal edge datastore
• Lightweight decentralized data storage and query system
• Locality-aware distributed execution engine for drones