29 Palms Fixed/Mobile Experiment

Tracking vehicles with a UAV-delivered sensor network.

UC Berkeley and MLB Co

March 12-14
Marine Corps Air/Ground Combat Center (MCAGCC)
Twentynine Palms, CA


The goals of the experiment were to
  1. Deploy a sensor network onto a road from an unmanned aerial vehicle (UAV).
  2. Establish a time-synchronized multi-hop communication network among the nodes on the ground.
  3. Detect and track vehicles passing through the network.
  4. Transfer vehicle track information from the ground network to the UAV.
  5. Transfer vehicle track information from the UAV to an observer at the base camp.
Details are below, or you can just skip to the results.


People have been talking about combining MEMS sensors with wireless communication for about a decade.  The Smart Dust effort at Berkeley is one of the many projects in this area.  As a part of Smart Dust, we implemented wireless sensor nodes using off-the-shelf components in custom boards.  The nodes, known as macro-motes or just motes, are about a cubic inch in volume and contain sensors, a microprocessor, RF comm., and a battery or solar cell for power.  David Culler and his students got interested in these platforms through our interaction on the Endeavour project, and quickly wrote an operating system called TinyOS.  A new family of modular nodes was designed by the TinyOS/Dust team, and it was good. :)
Shankar Sastry and Kannan Ramchandran felt that there were interesting theoretical issues to be studied in the operation of distributed sensor networks, and wrote a proposal on Sensorwebs which was ultimately funded (under SensIT) after Shankar had already left for DARPA (to run ITO).  Having been cajoled into being a co-PI on Sensorwebs, I was a natural target when Sri Kumar, the SensIT PM, called in June 2000 and asked for "a fixed/mobile demo" from our theoretical team!  Leveraging on the work done in Dust and Endeavour, and with some additional funding from Sri, we pulled it off in 9 months.

Sensor Node Hardware

The sensor nodes consist of a motherboard (Rene), a sensor board, and a power supply board.  The motherboard has a microprocessor, 916.5 MHz OOK radio, and support circuitry.  The power supply board currently sports just a battery connector.  The sensor board has a 2 axis Honeywell HMC1002 magnetometerwith roughly 1mGauss resolution.  All three boards and the lithium battery weigh just under 1 ounce.  The basic nodes can be purchased from Crossbow.
Battery life varies depending on what's powered up.  With everything on, the life is just an hour.  With the magnetometer off, and the radio turned on only once a second to check for messages, the lifetime is many days.
The complete mote  and two motes as attractively packaged for dropping.
The magnetometer board has an amplifier and a software-controlled output nulling feature (to trim out the DC component of the earth's magnetic field so that it doesn't saturate the amplifier/ADC).  Magnetic materials moving near the magnetometer cause a change in the earth's field, and this change is what the motes detect.  The magnetometer signal is sampled at 5 Hz.
The motes are able to detect passenger vehicles at more than 5 meters, and buses and trucks at more than 10 meters.  We didn't do any range experiments in the desert, but had no trouble tracking any of the vehicles at distances of 10 meters and more.  A typical magnetometer signal is shown below.
The green trace is the raw signal, yellow is low-pass filtered signal, and red is high-pass filtered yellow.  The vertical lines mark the start/stop points of the mote's determination that a vehicle is present.


The aircraft is a 5' wingspan fully autonomous GPS controlled pusher-prop built by MLB Co. A custom mote-dropper was built, including an integrated camera to view the motes as they are dropped.  The plane has a color video camera in the nose, and transmits to an auto-tracking ground station from a distance of up to two miles.  The range of the aircraft as configured in the demo was 30 minutes, or 15 miles.  Video range is currently 2 miles. Top speed is 60 mph.

These pictures are low-res versions of photos that were taken by the UAV.
Two views of the basecamp where the UAV was launched, and the VIPs watched the show.
A HMMWV and a Dragon Wagon, two of our typical targets near the intersection.


Closest Point of Approach detection

Vehicle Velocity Estimation

Sensor Location Update

Jason and Steve prepare the mote dropper for a launch.The motes know the order that they will be dropped in, and use that for an initial guess at their relative locations, which they update as they get more track information.


Multi-hop Messaging

Time Synchronization

Sensor Data Acquisition and Filtering

Collaborative Signal Processing

Position Estimate Update

Query Response

And we still had 80 bytes of program memory left!


It worked!

Over the course of the three day experiment, just about everything that could go wrong did go wrong, but ultimately we demonstrated everything that we set out to demonstrate.

Monday March 12

Software re-writes keep the TinyOS team up until 3am.  Mote power consumption drops by a factor of 2 with the new software!

Tuesday March 13

Wednesday March 14

Other photos

Sri Kumar and Steve Morris;  Kris Pister, Sri, and future motes; an LAV and an M1 tank (photos from the summer 00 experiment) ; Rob Szewczyk snoops on the ground network traffic with a couple of Majors.

The future

There is nothing in the current motes that can not be miniaturized.  In three years this demo will be done with a 6" aircraft, and millimeter-scale sensor nodes.

Participants and Sponsors

David Culler, UCB; Co-PI Endeavour (ITO)
Lance Doherty; Sensorwebs/algorithms; ldoherty@eecs.berkeley.edu
Jason Hill; Endeavour/TinyOS software and mote hardware; jhill@eecs.berkeley.edu
Mike Holden, MLB; flight software; mike@spyplanes.com
Charlie Kiers; Marine liaison to DARPA; pmpax@nosc.mil
Sri Kumar; DARPA/ITO; PM SensIT; skumar@darpa.mil
Julius Kusuma; Sensorwebs/algorithms; kusuma@eecs.berkeley.edu
Steve Morris, MLB; PI sub contracts of Smart Dust and Sensorwebs; mlbco@sirius.com
Kris Pister, UCB; PI Smart Dust (MTO); Co-PI, Sensorwebs (ITO); pister@eecs.berkeley.edu
Kannan Ramchandran, UCB; Co-PI, Sensorwebs (ITO); kannanr@eecs.berkeley.edu
Brian Robbins, MLB; aircraft mechanical
Jean Scholtz; DARPA/ITO; PM Ubiquitous computing; jscholtz@darpa.mil
Mike Scott; Sensorwebs/mote hardware; mdscott@eecs.berkeley.edu
Rob Szewczyk; Endeavour/TinyOS; szewczyk@eecs.berkeley.edu
Bill Tang; DARPA/MTO; PM MEMS; wtang@darpa.mil
Alec Woo; Endeavour/TinyOS; awoo@eecs.berkeley.edu