PEG 2003 Design and Implementation Cory Sharp UC Berkeley NEST Retreat, June 2004, Santa Cruz, CA.

Slides:



Advertisements
Similar presentations
Dynamic Source Routing (DSR) algorithm is simple and best suited for high mobility nodes in wireless ad hoc networks. Due to high mobility in ad-hoc network,
Advertisements

Telos Fourth Generation WSN Platform
Topic 7 Local Area Networks (LAN)
Decentralized Reactive Clustering in Sensor Networks Yingyue Xu April 26, 2015.
Wireless Sensor Networks Craig Ulmer. Background: Sensor Networks n Array of Sensor Probes ( ) n Collect In-Situ Data about Environment n Wireless.
CSE 5392By Dr. Donggang Liu1 CSE 5392 Sensor Network Security Introduction to Sensor Networks.
A Survey on Tracking Methods for a Wireless Sensor Network Taylor Flagg, Beau Hollis & Francisco J. Garcia-Ascanio.
Impala: A Middleware System for Managing Autonomic, Parallel Sensor Systems Ting Liu and Margaret Martonosi Princeton University.
Topology Control Presenter: Ajit Warrier With Dr. Sangjoon Park (ETRI, South Korea), Jeongki Min and Dr. Injong Rhee (advisor) North Carolina State University.
PERFORMANCE MEASUREMENTS OF WIRELESS SENSOR NETWORKS Gizem ERDOĞAN.
Monday, June 01, 2015 ARRIVE: Algorithm for Robust Routing in Volatile Environments 1 NEST Retreat, Lake Tahoe, June
Extreme Scaling … and friends Presented by Cory Sharp UC Berkeley.
TOSSIM A simulator for TinyOS Presented at SenSys 2003 Presented by : Bhavana Presented by : Bhavana 16 th March, 2005.
The Mote Revolution: Low Power Wireless Sensor Network Devices
An Energy-Efficient MAC Protocol for Wireless Sensor Networks
Crossbow Open Mote Developments Crossbow Technology.
An Energy-Efficient MAC Protocol for Wireless Sensor Networks Wei Ye, John Heidemann, Deborah Estrin -- Adapted the authors’ Infocom 2002 talk.
PEDS September 18, 2006 Power Efficient System for Sensor Networks1 S. Coleri, A. Puri and P. Varaiya UC Berkeley Eighth IEEE International Symposium on.
Magnetometer calibration and detection Robert Szewczyk, Alec Woo Nest Retreat June 17, 2002.
Mica: A Wireless Platform for Deeply Embedded Networks Jason Hill and David Culler Presented by Arsalan Tavakoli.
More routing protocols Alec Woo June 18 th, 2002.
Smart Dust Mote Core Architecture Brett Warneke, Sunil Bhave CS252 Spring 2000.
ExScal Extreme Scaling presented by Cory Sharp UC Berkeley NEST Retreat, June 2004, Santa Cruz, CA.
Generic Sensor Platform for Networked Sensors Haywood Ho.
A New Household Security Robot System Based on Wireless Sensor Network Reporter :Wei-Qin Du.
The Mote Revolution: Low Power Wireless Sensor Network Devices
Radio Stack Iteration How to improve the CC1000 Joe Polastre January 15, 2004 NEST Retreat.
1 Deluge: Data Dissemination for Network Programming at Scale Jonathan Hui UC Berkeley NEST Retreat June 3, 2004.
8/22/20061 Maintaining a Linked Network Chain Utilizing Decentralized Mobility Control AIAA GNC Conference & Exhibit Aug. 21, 2006 Cory Dixon and Eric.
PEG Breakout Mike, Sarah, Thomas, Rob S., Joe, Paul, Luca, Bruno, Alec.
Distributed Algorithms for Guiding Navigation across a Sensor Network Qun Li, Michael DeRosa, and Daniela Rus Dartmouth College MOBICOM 2003.
Pursuit Evasion Games (PEGs) Using a Sensor Network Luca Schenato, Bruno Sinopoli Robotics and Intelligent Machines Laboratory UC Berkeley
Dr. Shankar Sastry, Chair Electrical Engineering & Computer Sciences University of California, Berkeley.
Wireless Sensor Network Deployment Lessons Learned Steven Lanzisera Environmental Energy Technologies Division, LBNL 21 January 2011.
CC2420 Channel and RSSI Evaluation Nov/22/2006 Dept. of EECS, UC Berkeley C O nnect vityLab i.
A Transmission Control Scheme for Media Access in Sensor Networks Alec Woo, David Culler (University of California, Berkeley) Special thanks to Wei Ye.
Wei Hong January 16, 2003 Overview of the Generic Sensor Kit (GSK)
Dynamic Clustering for Acoustic Target Tracking in Wireless Sensor Network Wei-Peng Chen, Jennifer C. Hou, Lui Sha Presented by Ray Lam Oct 23, 2004.
Empirical Analysis of Transmission Power Control Algorithms for Wireless Sensor Networks CENTS Retreat – May 26, 2005 Jaein Jeong (1), David Culler (1),
ZIGBEE Compared to BLUETOOTH
1 Energy Efficient Communication in Wireless Sensor Networks Yingyue Xu 8/14/2015.
“SDJS: Efficient Statistics in Wireless Networks” Albert Krohn, Michael Beigl, Sabin Wendhack TecO (Telecooperation Office) Institut für Telematik Universität.
Copyright © Vanderbilt University Dr. Akos Ledeczi Institute for Software Integrated Systems Vanderbilt University Network Embedded Systems Technology.
Energy Saving In Sensor Network Using Specialized Nodes Shahab Salehi EE 695.
A Transmission Control Scheme for Media Access in Sensor Networks Alec Woo and David Culler University of California at Berkeley Intel Research ACM SIGMOBILE.
1 An Adaptive Energy-Efficient MAC Protocol for Wireless Sensor Networks The First ACM Conference on Embedded Networked Sensor Systems (SenSys 2003) November.
Dynamic Clustering for Acoustic Target Tracking in Wireless Sensor Network Wei-Peng Chen, Jennifer C. Hou, Lui Sha.
Introduction to Sensor Networks Rabie A. Ramadan, PhD Cairo University 3.
TinyOS By Morgan Leider CS 411 with Mike Rowe with Mike Rowe.
BMAC - Versatile Low Power Media Access for Wireless Sensor Networks.
Crowd Management System A presentation by Abhinav Golas Mohit Rajani Nilay Vaish Pulkit Gambhir.
Localization using DOT3 Wireless Sensors Design & Implementation Motivation Wireless sensors can be used for locating objects: − Previous works used GPS,
Link Estimation, CTP and MultiHopLQI. Learning Objectives Understand the motivation of link estimation protocols – the time varying nature of a wireless.
Query Processing for Sensor Networks Yong Yao and Johannes Gehrke (Presentation: Anne Denton March 8, 2003)
Example Distributed Sensor Network with TinyOS Motes RPI ECSE – 6965/4694 Daniel Casner 2007 April 13th.
Phong Le (EE) Josh Haley (CPE) Brandon Reeves (EE) Jerard Jose (EE)
Presenter: Abhishek Gupta Dept. of Electrical and Computer Engineering
11/15/20051 ASCENT: Adaptive Self- Configuring sEnsor Networks Topologies Authors: Alberto Cerpa, Deborah Estrin Presented by Suganthie Shanmugam.
Data Collection and Dissemination. Learning Objectives Understand Trickle – an data dissemination protocol for WSNs Understand data collection protocols.
Systems Wireless EmBedded Wireless Sensor Nets Turning the Physical World into Information David Culler Electrical Engineering and Computer Sciences University.
SenProbe: Path Capacity Estimation in Wireless Sensor Networks Tony Sun, Ling-Jyh Chen, Guang Yang M. Y. Sanadidi, Mario Gerla.
KAIS T Medium Access Control with Coordinated Adaptive Sleeping for Wireless Sensor Network Wei Ye, John Heidemann, Deborah Estrin 2003 IEEE/ACM TRANSACTIONS.
0.1 IT 601: Mobile Computing Wireless Sensor Network Prof. Anirudha Sahoo IIT Bombay.
EmStar: A Software Environment for Developing and Deploying Wireless Sensor Networks CENS Research Review October 28, 2005 UCLA CENS EmStar Team.
On Mobile Sink Node for Target Tracking in Wireless Sensor Networks Thanh Hai Trinh and Hee Yong Youn Pervasive Computing and Communications Workshops(PerComW'07)
KAIS T Location-Aided Flooding: An Energy-Efficient Data Dissemination Protocol for Wireless Sensor Networks Harshavardhan Sabbineni and Krishnendu Chakrabarty.
- Pritam Kumat - TE(2) 1.  Introduction  Architecture  Routing Techniques  Node Components  Hardware Specification  Application 2.
Distributing Queries Over Low Power Sensor Networks
Overview: Chapter 3 Networking sensors
Collection Tree Protocol
Presentation transcript:

PEG 2003 Design and Implementation Cory Sharp UC Berkeley NEST Retreat, June 2004, Santa Cruz, CA

PEG Goals Use a lot of sensors –100 nodes In as large field as possible –20m x 20m To help a pursuer –autonomous robot Intercept an evader –human controlled robot Demoed in July 2003

Platform Mica2Dot –8-bit 4 MHz CPU, 128k program, 4k RAM –CC1000 Radio, abput 2 kB/s appl bw Magnetometer Ultrasonic transceiver Robust enclosure Pursuer –266 MHz CPU, 20GB HD, 128MB RAM – wireless radio All-terrain, GPS navigation

Software Design Self-localization –Ultrasonic ToF Vehicle detection –Calibrate, sense –Leader, position estimate –Route to pursuer Pursuer –Filter estimates –Intercept planning –Navigate Management services

PEG Approach Approach of simplicity –Simple Sensor Network –Intelligent Processing on Pursuer Core Services –Vehicle Detection –Routing –Navigation and Control

Vehicle Detection Bandwidth driven design (most precious resrc) –40 packets per second Half for local detection reports Half for system wide behaviors –Assume (design) that one object excites at most 9 nodes Calibration and Sensing –Use 8-bit digital pot with 10-bit ADC to recover a 16- bit magnetic signal –Sample at 20 Hz –Moving average to calibrate static environment Determines a minimum detectible vehicle speed –Physical proximity of radio and magnetometer caused interactions; invalidate readings while TX/RX

Vehicle Detection (2) Local Detection Reports –1-norm magnetometer axes, threshold readings –Individual nodes report at 2 Hz –Put readings into a neighborhood Drove design of Hood Leader Election, Position Estimation –Leader election requires no additional communication –Leader if a node has the max in its neighborhood –A node can report as leader at most 2 Hz, weak epoch of 0.5s –Leader reports immediately in its epoch Maximum detecticle vehicle speed only a fcn of the sensor –Disambiguation is deffered to outside the SN –Position report is 8.8 fixed point (x,y)

Routing Route from many sources to few mobile pursuers –Not many-to-one (base station) routing –Not any-to-any Landmark routing –Split problem into many-to-one and one-to-few –No geographic assumptions –Landmark is a rendezvous point –Spanning tree with crumb trails Many-to-one –Focus on building good trees

Routing (2) Building good trees –Flooding from a beacon node –Select good routes Consider both link quality and hop count Precalibrate RSSI threshold for environment Filter then select lowest hop count parent –Avoid broadcast storm (excess collisions) Adaptive time-delayed backoff

Routing (3) Pursuers build “crumb-trails” Selects a node in its proximity –By overhearing detection events Landmark relays msgs down crumb trail No coupling of pursuer to landmark –Allows for fail-over

Navigation and Control Classic control systems assume periodic readings with zero latency Cleanly separate control system from sensor network Assume reports from SN every few seconds Low-level navigate with GPS Pursuer use of evader position updates is robust to noise and latency

Some Results In the demo, the pursuer caught the evader every time A few noisy nodes Quelled nodes at (4,10) and (4,12)

Deployment Experiences Breakage, “Every touch breaks” –Disassembly, recharge, reprogram, reassembly Packaging –Requirements for deployment versus development –Wish we had external recharge and reprogram –Magnetometer interference Piano wire antenna, battery, metallic base spring Debugging –No logging services, used a big antenna –Ping-like tools to identify failed nodes Reprogram and Reconfig –Wireless reprogramming necessary –Minimize its use with liberal reparameterization

PEG Consequences Some Next Steps Extreme Scaling (ExScal) –10,000 nodes monitoring a 10km long field NEST Final Demo (Capstone) –Berkeley’s baby for next summer Baseline system (Dialtone) –Everything that “proves to be pretty useful”

Dialtone Everything that any deployed application needs, a wish list: Layered Application Retargetting –Config, VMLib, Reprogram Reset, on/off (sleep), ident/ping, scream File system / log to flash Bootloader Service control Self-test (flash, battery, profiling, duty cycle, event log, error log) Health monitoring, watchdog RAM/ROM query (jhill) Multihop Routing Epidemic dissemination (smart flood) TimeSync RAM buffers, message buffers Security

Thanks!