Edge-Cloud Drone Monitoring for Low-Latency Detection and Mitigation

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Solution Overview

Problem

Existing drone monitoring systems operate independently and lack scalability, failing to effectively monitor and mitigate unauthorized drone activities due to limited collaboration and data redundancy, leading to public safety and security concerns.

Innovation Solution

A cloud and edge computing-based system that aggregates drone activity data from sensors, implements machine learning and federated learning for analytics, and enables collaborative drone mitigation through intelligent jamming and smart sensor configuration, providing real-time monitoring and unauthorized drone deactivation.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Adaptability or versatility

If traditional independent drone monitoring systems are used, then system simplicity is maintained, but scalability and collaborative capability are insufficient

Engineering Contradiction:

ImprovescalabilityVSAvoidsystem complexity

Core Design Contradiction:

Adaptability or versatilityVSDevice complexity

Solution Approach 1:

The system is divided into multiple independent components: drone sensors for detection, edge servers for local processing, and cloud servers for centralized management. This segmentation allows each component to function independently while contributing to the overall scalable architecture, enabling the system to grow by adding more sensors and servers without requiring complete system redesign

Inventive Principle:

Principle #1Segmentation

Solution Approach 2:

The cloud and edge servers are designed to perform multiple functions including data aggregation, machine learning analytics, federated learning collaborations, and mitigation coordination. This multi-functionality allows a single system architecture to handle diverse drone monitoring tasks across different locations and scenarios, enhancing scalability without proportionally increasing complexity

Inventive Principle:

Principle #6Universality (Multi-functionality)

2Loss of information

If centralized cloud processing is used, then data aggregation capability is improved, but processing latency increases

Engineering Contradiction:

Improvedata aggregation completenessVSAvoidprocessing latency

Core Design Contradiction:

Loss of informationVSLoss of time

Solution Approach 1:

The processing function is segmented between edge servers (local) and cloud servers (centralized). Edge servers perform immediate data aggregation and preliminary processing near the sensors, reducing latency, while cloud servers handle comprehensive data aggregation and long-term analytics. This segmentation allows both low latency and complete data aggregation to coexist

Inventive Principle:

Principle #1Segmentation

Solution Approach 2:

Edge servers act as intermediaries between drone sensors and cloud servers. They aggregate and pre-process data locally before transmitting to the cloud, reducing the amount of data that needs to travel long distances. This intermediary approach maintains data aggregation completeness while significantly reducing processing latency for time-critical operations

Inventive Principle:

Principle #24Intermediary (Mediator)

3Measurement precision

If machine learning and federated learning techniques are implemented, then detection accuracy is improved, but computational requirements increase

Engineering Contradiction:

Improvedetection accuracyVSAvoidcomputational energy consumption

Core Design Contradiction:

Measurement precisionVSUse of energy by moving object

Solution Approach 1:

Computational tasks are segmented and distributed: edge servers perform lightweight local processing and data collection, while cloud servers handle computationally intensive machine learning and federated learning. This segmentation allows high detection accuracy through sophisticated algorithms while distributing the energy consumption across multiple devices rather than concentrating it in one location

Inventive Principle:

Principle #1Segmentation

Solution Approach 2:

The system implements self-service through automated federated learning, where edge servers automatically contribute their local data and models to improve the global model without manual intervention. This automated collaboration enhances detection accuracy across the network while efficiently managing computational resources by only processing data when and where needed

Inventive Principle:

Principle #25Self-service

Data Source

PatentUS12633222B2Systems and methods for drone monitoring, data analytics, and mitigation cloud services using edge computing

Publication Date: 2026.05.19 SKYSAFE INC

AI summary

Systems and methods for providing drone activity cloud services to cloud consumers using cloud and edge computing are provided. The drone monitoring service is rendered by drone sensors detecting and identifying drones, cloud and edge servers aggregating drone activity data from sensors and UAS traffic management systems, and cloud consumers monitoring drone activities using cloud and edge devices to access the cloud. The drone data analytics service reports drone activity statistics, predicted drone activities, and abnormal behaviors to cloud consumers based on the statistics and behavior models obtained by machine learning and federated learning techniques. The drone mitigation service, when initiated by cloud consumers, determines how to optimally configure sensors and collaboratively send signals to deactivate unauthorized drones. Moreover, data processing, artificial intelligence, mobility support, and traffic management functional units empower cloud and edge servers to support these cloud services.