Abstract :

Autonomous Drone Swarms Are Increasingly Deployed In Surveillance, Disaster Response, Precision Agriculture, Environmental Monitoring, And Military Operations. However, Traditional Cloud-centric Architectures Suffer From High Communication Latency, Bandwidth Limitations, And Security Vulnerabilities, Making Them Unsuitable For Real-time Swarm Coordination. Edge Computing Offers A Promising Solution By Enabling Computational Intelligence Closer To Drones, Thereby Reducing Latency And Improving Decision-making Capabilities. This Paper Proposes A Secure And Intelligent Edge Computing Architecture For Autonomous Drone Swarm Management That Integrates Edge Intelligence, Federated Learning, Blockchainbased Security, Swarm Coordination Protocols, And Adaptive Task Offloading Mechanisms. The Proposed Architecture Enables Real-time Data Processing, Secure Communication, Decentralized Decision-making, And Resilient Swarm Operations In Dynamic Environments. A Multi-layer Framework Comprising Drone Layer, Edge Layer, Orchestration Layer, And Cloud Layer Is Introduced. Performance Evaluation Demonstrates Significant Improvements In Latency Reduction, Task Completion Efficiency, Energy Utilization, And Communication Reliability Compared With Conventional Cloud-based Approaches. The Proposed Framework Provides A Scalable And Secure Foundation For Next-generation Autonomous Drone Swarm Applications. ([DOI][1]) Keywords: Autonomous Drone Swarm, Edge Computing, Federated Learning, Edge Intelligence, Blockchain Security, Task Offloading, UAV Networks, Artificial Intelligence.

Published:

15-11-2023

Issue:

Vol. 23 No. 11 (2023)

Page Nos:

244 - 252

Section:

Articles

License:

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

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