SWARMCONTROL: Toward Elastic, Programmable, Optimized Swarm Networking

Abstract

The main goal of this project has been to design, develop and prototype an automated and self-organizing control framework for Unmanned Aerial Vehicles (UAVs). We have successfully achieved our goal by combining softwarization and abstraction principles, optimization and Artificial Intelligence (AI), which allowed us to develop a prototype that is capable of understanding network operator goals and adapting networking parameters and functionalities to respond to changing environment conditions and guarantee high performance.

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Document Details

Document Type
Technical Report
Publication Date
Mar 10, 2022
Accession Number
AD1163064

Entities

People

  • Tommaso Melodia

Organizations

  • Northeastern University

Tags

Communities of Interest

  • Air Platforms
  • Autonomy

DTIC Thesaurus Topics

  • Air Force
  • Air Force Research Laboratories
  • Aircrafts
  • Artificial Intelligence
  • Computer Communications
  • Computer Networks
  • Computer Programming
  • Computers
  • Control Systems
  • Directives
  • Multiple Access
  • Network Protocols
  • Neural Networks
  • Operating Systems
  • Reliability
  • Software Defined Radio
  • Throughput
  • Transport Protocols
  • Unmanned Aerial Vehicles
  • Wireless Networks

Fields of Study

  • Computer science

Readers

  • Aerial Unmanned Vehicle Swarm Micro Periodontal Dentistry.
  • Enterprise Information Systems Architecture and Joint Command Capability Interoperability Support.
  • Neural Network Machine Learning.

Technology Areas

  • AI & ML
  • AI & ML - Autonomous Systems
  • AI & ML - DoD AI Strategy
  • AI & ML - Machine Learning Algorithms
  • Autonomy
  • Autonomy - Autonomous System Control