Delay-Tolerant Communication using Aerial Mobile Robotic Helper Nodes recuv.colorado.edu Delay-Tolerant Communication using Aerial Mobile Robotic Helper Nodes Daniel Henkel April 4, 2008

Overview DTN Test Bed Direct, Relay, Ferry Models Relay Optimization Choosing Optimal Mode Sensor Data Collection We have developed a test bed, which is a challenged network – motivation for exploring DTN Looking at theoretical limits of communication in models of D, R, and F Modeling such that upper bounds can be seen Find optimum operation parameters for RELAY networks along a communication path; T-R separation distance, environment (PL-exponent) In Ferrying Mode we optimize the path a ferry takes through the network Question: Which mode yields highest t-put and lowest delay? Practical application is sensor data collection as hinted at in the paper.

University of Colorado Location

Unmanned Aerial Vehicles (UAVs) Small (10kg) Low-Cost (<$10k) UAVs 60-100km/h, 1hr endurance 5HP gas engine Built in-house

Ad hoc UAV-Ground Networks Scenario 1: increase ground node connectivity. NOC Scenario 2: increase UAV mission range.

Applications Military Scientific Civil / Commercial Intelligence, Surveillance, Reconnaissance (ISR) Border Patrol Scientific Atmospheric Research (NIST, NCAR) Tornado/Hurricane & Arctic Research Civil / Commercial Disaster Communication & Intelligence (Fire) Sensor Data Collection NIST - National Institute for Standards and Technology (like PTB) NOAA – NCAR – National Center for Atmospheric Research

AUGNet Ad Hoc UAV Ground Network recuv.colorado.edu 241cm Group 1 16cm Group 2 241cm Our research started with a real-life test bed. Wanted to know how MANETs behave in real world. Simulations have severe limitations and don’t accurately reflect what’s going on. - Explain the test bed elements Ad Hoc UAV Ground Network recuv.colorado.edu

AUGNET as Delay Tolerant / Challenged Network Plane banking Simultaneous end-to-end paths might not exist Antenna configuration Links might be very lossy Unmanned planes Nodes might move at high speeds Links might have extremely long delays Links might be intermittently up or down From these networks come new challenges for protocols!

Using node mobility control to enhance network performance Research Goal Using node mobility control to enhance network performance UAV2 Ferrying Relay Direct UAV1 UAV3 GS1 - Now: question becomes more theoretical. What combination of these three modes optimizes performance? - explain the modes - explain controlled mobility of our planes - GS2

Assumptions Controllable helper nodes Known communication demands z y Controllable helper nodes Known communication demands Single link perspective Theoretical rather than implementation x λ - The trade-offs of these modes will be explained with theoretical models. Not implemented yet. - Assumptions might be unrealistic, but give insights into capacity of modes and trade-offs S R

Direct Communication Shannon capacity law Signal strength Thermal noise (normalized) - This might look simplistic, but Gaussian Channel is often used and Rappaport has verified (SINR) models. - Prediction models of SINR are common in cellular systems; planning is done based on these models. Can be determined for given, fixed environment, e.g., a city. - We are only using P(d) for ease of computation. Could switch model to be more accurate. - R(SINR) is any monotonically decreasing function. Again, Rappaport has concrete models, but we stick to Shannon formula. - Data rate

Direct transmission (zero relays) Relay Network d dk S R Direct transmission (zero relays) End-to-end data rate: RR Packet delay: τ = L/RR Relay transmission

“Single Tx” Relay Model a.k.a., the noise-limited case t=0 S dk R

“Parallel Tx” Relay Model a.k.a., the interference limited case > Optimal distance between transmissions? t=0 t=0 t=0 ρ S R

Conveyor Belt Ferrying Model A F B

Optimizing “Single Tx” Where is the trade-off? dk vs. # of transmissions Optimal number of relays: Initially: rate increase higher than ‘relaying cost’ (put graph R over #relays here) Then: additional relay decreases R with

Optimizing “Parallel Tx” Where is the trade-off? interference vs. # parallel transports Use Matlab! R k ρ link reuse factor

“Triple Play” 2km 4km 8km 16km eps=5 PN=10-15 W

Distance—Rate Phase Plot

Delay—Rate Phase Plot

Delay—Rate Animation

Sensor Data Collection Challenges: No end-to-end connection Intermittent connectivity Sensors and SMS unknown SMS-3 Gateway-2 CDMA Sensor-1 SMS-1 Sensor-2 SMS-2 Gateway-1 Sensor-3 Have sensors all on left side Highlight ferrying to the right side SMS locations Sparsely distributed sensors Limited radio range, power Multiple monitoring stations

Hardware Implementation RTT 40ms, 15hrs sustained operation Soekris SBC, embedded Gentoo Linux Atheros miniPCI, Madwifi-ng driver

Functional Evaluation FS2 83 on & off 84 85 FS1 82 81

Functional Evaluation II

Next Steps Ferry route planning with Reinforcement Learning Multi UAV operations/hybrid with MAVs UAV Swarming Phased array antenna WiMAX trial

Research and Engineering Center for Unmanned Vehicles (RECUV) Daniel Henkel, henk@gmx.com recuv.colorado.edu