Sensor-Unmanned Vehicle Tactical Network Topology (TNT) Experiments: Introduction to DNOC

Center for Network Innovation and Experimentation (CENETIX)Compiled of joint presentations with Dr. Dave Netze

Tactical Networking Testbed Mio Experimentation

Beginning in 2004, a team of Naval Postgraduate School (NPS) researchers together with partners from the Lawrence Livermore National Laboratory (LLNL), started a new interagency experimentation program, which is now collectively known as the TNT MIO Experiments (TNT for Tactical Networking Testbed, MIO for Maritime Interdiction Experiments)

A Conceptual Model for Network Decision Support Systems

We introduce the concept of a network DSS (NWDSS) consisting of fluid, heterogeneous nodes of human and machine agents, connected by wireless technology, which may enter and leave the network at unpredictable times, yet must also cooperate in decision-making activities. We describe distinguishing properties of the NWDSS and propose a 3-tier conceptual model comprised of digital infrastructure, transactive memory systems and emergent collaborative decision-making. We suggest a decision loop of Sense-Analyze-Adapt-Memory leveraging TMS as a starting point for addressing the agile collaborative requirements of emergent decision-making. Several examples of innovative NWDSS services are presented from Naval Postgraduate School field experiments

Hyper-Nodes for Emerging Command and Control Networks: The 8th Layer

11th International Command and Control Research and Technology Symposium (ICCRTS), September 26-28, 2006, Cambridge, U

Tactical Networking and Collaboration on Maritime-Sourced Nuclear Radiation Threat: Tracking, Detection, and Interdiction

Presentation, MIO Experimentation Workshop (March 29-30, 2011)Center for Network Innovation and Experimentation (CENETIX) Publicatio

Testbed for Self-Organizing Networking and Collaboration / Cyber Security Summitt 2009, PowerPoint Presentation

Cyber Security Summitt 2009, PowerPoint PresentationApproved for public release; distribution is unlimited

Expanding Unmanned System Networks for Littoral Operations using Projectile Based Nodes

FY2016 Funded ProposalResearch Proposa

Aerial Delivery System with High Accuracy Touchdown

PatentEmbodiments described herein provide a system and method for persistent high-accuracy payload delivery utilizing a twophase procedure during the terminal descent phase of aerial payload delivery. In the first phase a small parafoil provides aerial delivery of a payload to within a close proximity of an intended touchdown point, e.g., a target. In the second phase a target designator acquires the target and a trajectory to the target is determined. A harpoon launcher deploys a harpoon connected to the payload by an attachment line, such as a rope. A reel mechanism reels up the attachment line causing the payload to be moved to the target thus providing high accuracy touchdown payload delivery

Robustness in Nonorthogonal Multiple Access 5G Networks

The diversity of fifth generation (5G) network use cases, multiple access technologies, and network deployments requires measures of network robustness that complement throughput-centric error rates. In this paper, we investigate robustness in nonorthogonal multiple access (NOMA) 5G networks through temporal network theory. We develop a graph model and analytical framework to characterize time-varying network connectedness as a function of NOMA overloading. We extend our analysis to derive lower bounds and probability expressions for the number of medium access control frames required to achieve pairwise connectivity between all network devices. We support our analytical results through simulation

Minitrack Introduction: Decision Analytics, Machine Learning, and Field Experimentation for Defense and Emergency Response

Proceedings of the 54th Hawaii International Conference on System Sciences 2021The article of record as published may be found at http://http://hdl.handle.net/10125/70746Defense and emergency first responders must make rapid, consequential decisions and machine learning can aid analytics to support these decisions. Machine learning offers enormous promise, yet well publicized struggles reveal the need for better datasets and for opportunities to learn in challenging settings. Field experimentation offers the potential to meet these needs through iterative interactions in complex scenarios. Field experimentation can provide live action to facilitate high fidelity datasets that can support machine learning and artificial/augmented intelligence applications. These experiments may incorporate participants from academia; government agencies; militaries; first responders at all levels; and global industry partners. This minitrack explores the interplay between machine learning, field experimentation, and optimization analytics, whether exploratory, theoretical, experimental, in such critical areas as Defense and Emergency Response