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EENG 460a / CPSC 436 / ENAS 960Networked Embedded Systems &
Sensor Networks
Lecture 1Andreas Savvides
[email protected]: AKW 212
Tel 432-1275Course Website
http://www.eng.yale.edu/enalab/courses/2005f/eeng460a
Welcome to EENG 460a!
Course Overview• Embedded Systems• Sensor Networks & Applications
Course details• Requirements & Grading• Logistics• Lecture format• Topics covered
Why take this course?
Learn the basics of embedded systems design Learn about sensor networks and emerging
technologies Undergraduates
• Good opportunity to exercise many of the things you learned in your previous classes
• Learn things that will help you with your senior design projects• Get ready for graduate school or industry
Graduate students• Good breadth topic, good chance to jump-start your research project• Get some hands-on experience on tools and platforms to support your
research
Why networked embedded systems?
Technology is reaching a point where it can significantly impact our everyday lives• Low power processors and radios, MEMs and other sensors
Enable “orthogonal spikes of progress” in many other fields• Medical applications, understanding nature & more• Intelligent environments, smart offices, optimized assembly
lines etc• Many opportunities with existing technologies, many things
up to your imagination An interface to many other disciplines
Applications in All Aspects of Life
Slide from Intel Presentation
More Examples...
Signal processing systems• radar, sonar, real-time video, set-top boxes, DVD players,
medical equipment, residential gateways Mission critical systems
• avionics, space-craft control, nuclear plant control Distributed control
• network routers & switches, mass transit systems, elevators in large buildings
“Small” systems• cellular phones, pagers, home appliances, toys, smart cards,
MP3 players, PDAs, digital cameras and camcorders, sensors, smart badges
Typical Characteristics of Embedded Systems
Part of a larger system• not a “computer with keyboard, display, etc.”
HW & SW do application-specific function – not G.P.• application is known a priori• but definition and development concurrent
Some degree of re-programmability is essential • flexibility in upgrading, bug fixing, product differentiation, product
customization Interact (sense, manipulate, communicate) with the external
world Never terminate (ideally) Operation is time constrained: latency, throughput Other constraints: power, size, weight, heat, reliability etc. Increasingly high-performance (DSP) & networked
“Traditional” Software Embedded Systems = CPU + RTOS
Modern Embedded Systems?
Embedded systems employ a combination of• application-specific h/w (boards, ASICs, FPGAs etc.)
o performance, low power
• s/w on prog. processors: DSPs, controllers etc.o flexibility, complexity
• mechanical transducers and actuators
Application Specific Gates
Processor Cores
Analog I/O
Memory
DSP Code
Course Goals
Learn the basics of sensor networks• Learn about distributed computing on a set of small
platforms• Find out about new technologies that are out there• Learn to use distributed embedded systems and solve
some problems in this domain This knowledge allow you
• Design and implement complex systems to support your research or industry career
• An opportunity to utilize the knowledge you acquired in previous engineering courses
What about Sensor Networks?
Networks of small devices equipped with sensors Embedded systems become more powerful when
they are networked! From a networking and computing perspective:
• Device-to-device communication instead of person-to-device
Want to have massive distributed systems of low-cost collaborative devices to achieve large tasks• Such as?
What is the big deal about SN?
Availability of sensor measurements from the physical world
Changes the assumption of how communication is done• More event driven, greatly influenced by the
happenings in the environment New needs for distributed processing, and
interpretation of data
Multidisciplinary Nature
Networked embedded systems create opportunities to utilize, blend and create knowledge from other disciplines• Statistical Signal Processing• Information Theory• Communication Theory• Operating Systems and Languages• Databases• VLSI systems and MEMS • Many more…
Traffic/Load/Event Models: Dimensions
Frequency (spatial, temporal)• Commonality of events in time and space
Locality (spatial, temporal)• Dispersed vs. clustered/patterned
Mobility• Rate and pattern• Diversity
Example early adopter applications: CENS Systems under design/construction
Biology/Ecosystems• Microclimate monitoring• Triggered image capture• Canopy-net (Wind River
Canopy Crane Site)
Contaminant Transport• County of Los Angeles
Sanitation Districts (CLASD) wastewater recycling project, Palmdale, CA
Seismic monitoring• 50 node ad hoc, wireless,
multi-hop seismic network• Structure response in USGS-
instrumented Factor Building w/ augmented wireless sensors
Event Detection
Localization &Time Synchronization Calibration
Programming Model
In Network Processing
Systems: Challenges and Services
Resource constrained nodes (energy, comm, storage, cpu)
Irregular deployment and environment
Dynamic network topology Hand configuration will fail
• Scale, variability, maintenance
Routing and transport in a Tiered architecture Channel/connectivity characterization Time synchronization and Localization
services In Network Processing Programming model
Wide Variety of Sensors
• Passive elements: seismic, acoustic, infrared, strain,
salinity, humidity, temperature, etc.
• Passive Arrays: imagers (visible, IR), biochemical
• Active sensors: radar, sonar
– High energy, in contrast to passive elements
• Technology trend: use of IC technology for increased
robustness, lower cost, smaller size
– COTS adequate in many of these domains; work remains to be
done in biochemical
What are the challenges?
Sensors are not perfect Sensor measurements are affected by changes in
surrounding conditions and obstacles affect propagation characteristics
Need to understand and combine multipoint measurements
Power consumption always an issue Numerous issues associated with the
programmability and management of sensor devices
How can networked embedded systems scale?
Make them self-configuring• Position and time• Calibrate sensors to a common base
New ways of addressing and administering• Not interested in the temperature reading of sensor X, we
are interested in the temperature of a specific place or room Nodes should autonomously organize themselves into
groups, understand their environments and respond to changes in the environment
Programmability requirements change
Two Main Components
Understanding sensormeasurements and emerging behaviors
Architectural optimizations,
Small form factors, low power
Tiered/Heterogenous/Integrated Sensor Networks
Dependencies on both new algorithms and technological components
Hardware Sensing Platforms
HW Platforms
Shrink the HWExperiment with unknown environments
UC Berkeley’s Spec Node & Smartdust
NIMS Nodes@UCLA
Intelligent Integrated Sensing Network Platforms
Hardware Platform Priorities
HW Platforms
Shrink the HWExperiment with unknown environments
UC Berkeley’s Spec Node & Smartdust
NIMS Nodes@UCLA
Intelligent Integrated Sensing Network Platforms
Power & Cost Reduction Understanding unknownsensing phenomena
Large Diversity in PlatformsC
apabili
tie
s
Size, Power Consumption, Cost
MICA Mote
iBadge
MK - II
StarGate
Design Lineage of Motes
COTS dust prototypes (Kris Pister et al.)
weC Mote (~30 produced) Rene Mote (850+ produced) Dot (1000 produced) Mica node ( 5000+ produced) Mica2 (Current) Spec (Prototype)
Ack: Jason Hill, UC Berkeley
Sensor Node Energy Roadmap
20002000 20022002 20042004
10,0010,0000
1,0001,000
100100
1010
11
.1.1
Ave
rag
e P
ow
er
(mW
)
• Deployed (5W)
• PAC/C Baseline (.5W)
• (50 mW)
(1mW)
Rehosting to Rehosting to Low Power Low Power COTSCOTS (10x)(10x)
-System-On-Chip-System-On-Chip-Adv Power -Adv Power ManagementManagementAlgorithms (50x)Algorithms (50x)
Source: ISI & DARPA PAC/C Program
In Network Processing:Distributed Representation, Storage, Processing
In network interpretation of spatially distributed data• Statistical or model based filtering
• In network “event” detection and reporting
• Direct queries towards nodes with relevant data
• Trigger autonomous behavior based on events
o Expensive operations: high end sensors or sampling
o Robotic sensing, sampling
Support for Pattern-Triggered Data Collection• Multi-resolution data storage and retrieval
o Index data for easy temporal and spatial searching
• Spatial and temporal pattern matching
o Trigger in terms of global statistics (e.g., distribution)
• Exploit tiered architectures
K V
K VK V
K V
K V
K V
K VK V
K V
K VK V
Tim
e
Sample Layered Architecture
Resource constraints call for more tightly integrated layers
Open Question:
Can we define anInternet-like architecture for such application-specific systems??
In-network: Application processing, Data aggregation, Query processing
Adaptive topology, Geo-Routing
MAC, Time, Location
Phy: comm, sensing, actuation, SP
User Queries, External Database
Data dissemination, storage, caching
NIMS Architecture: Robotic, aerial access to full 3-D environment Enable sample acquisition
Coordinated Mobility Enables self-awareness of
Sensing Uncertainty Sensor Diversity
Diversity in sensing resources, locations, perspectives, topologies
Enable reconfiguration to reduce uncertainty and calibrate
NIMS Infrastructure Enables speed, efficiency Low-uncertainty mobility Provides resource transport for
sustainable presence* (Kaiser, Pottie, Estrin, Srivastava,
Sukhatme, Villasenor)
Networked Info Mechanical Systems (NIMS)*
Yale’s XYZ Sensor Node
Sensor node created for experimentation
• Low cost, low power, many peripherals
• Integrated accelerometer, light and temperature sensor
Uses an IEEE 802.15.4 protocol• Chipcon 2420 radio
OKI ARM Thumb Processor• 256KB FLASH, 32KB RAM• Max clock speed 58MHz, scales
down to 2MHz• Multiple power management
functions Powered with 3AA batteries & has
external connectors for attaching peripheral boards
Designed at Yale Enalab and Cogent computer systems, will be used as the main platform for the course
We will be using this for Course assignments and projectswith the SOS operating system
Course Theme for this Semester
View sensor networks as “ambient intelligence” entities that provide services to their users
Examine a class of application requirements Understand the underlying problems Try to come out with some answers as the
course evolves
What you should look out for?
For each topic discussed• What are really the research challenges?• Can you differentiate the new components and
challenges?• Need to see the “bridge” to other disciplines
Lookout for new ideas and applications you can apply your knowledge to
Game Challenge
Imagine the sensor network as a game Sensor nodes can collect a wide variety of
measurements distributed in space and time The sensor network operates by following a
set of rules and reacts according to the observables
What are the challenges in doing that? Any ideas of what such a game would be?
Course Logistics
Text Wireless Sensor Networks, an Information Processing
Approach by Zhao and Guibas – order online – a copy of the book will be on reserve in the library
Lab: lab and software used for the course available in CO-40.
My office hours Wed 11:00am – 12:00pm & by appointment
TA: Dimitrios Lymberopoulos ([email protected])
Who should take this course?
Senior students • Combine with senior design project• Get some hands-on experience before entering industry or
graduate school• Start early so that you have something to show for when you
start with your applications Graduate students
• Build up background in wireless embedded systems• Use the course to jump-start or support your research
Graduate students will be graded on a different curve and would have slightly different requirements
Requirements and Grading
Class requirements• Attendance is mandatory • Class Discussion & Participation 5%• Homeworks 25%• 2 Midterms 30%• Final Project 40%
Students must have taken EENG 350 or CS 323 or operating systems
Senior or graduate standing Be motivated and be willing to work independently
Course Policies
You cannot reuse the same material from other courses, projects or independent studies for this course
You must turn in assignments at the deadline
Cheating and Plagiarism will not be tolerated
Homeworks and Programming Assignments
Three “basic” programming exercises to get you going with embedded processors
3 homework problems 1 in class presentation in class 2 midterm exams
Course Projects
Opportunity to go deeper in a specific area on your own• Lectures and homework will give you broader coverage, the
project will be more focused Project should have a novelty component
• Does not have to be nobel price but you should add your own flavor to the project
Project proposal due by• Project proposals due on week 3• A list of project suggestions will be handed out next class
Project goals• Pick something that you can realistically do in a semester• Keep focused and aim for high quality
Lecture Organization
At the beginning full lecture will cover new material by me
Later on, some of the lectures will be split in 2• First half will cover new material
• Second half will be one of the followingo Follow-up discussions on embedded system problems
o Topic presentations
o Guest lectures & presentation
Topics and Tentative Lecture Schedule
Week 1: Course Intro
Week 2: The application of embedded systems to sensor networks
Week 3: Embedded Programming
Weeks 4 –5: Localization and time synchronization
Week 6: MAC and Routing Protocols
Week 7-8: Learning in sensor networks
Week 8: Data Aggregation, Storage and Clustering
Week 9: Mobility and Collaborative Control
Week 10: Learning in Sensor Networks
Week 11: Collaborative Signal Processing
Week 12: Security and Data Integrity
Week 13: Misc Topics
Some “neat” Applications
CodeBlue Project at Harvard Networked Cows at Dartmouth & MIT Great Duck Island Habitat Monitoring (initiated by UC
Berkeley) Boundary Estimation at Yale Elder Home Monitoring by Intel Ragobots at UCLA
For more details take a look at the WAMES2005 Program at:http://lcawww.epfl.ch/luo/WAMES%202004%20-%20Program.htm
Undergraduate projects: Acoustic Detection on XYZ(Steven Tully & Nathan Francis)
Prototype status• Can recognize specific
sound signatures
• Continuous sampling and processing of acoustic events up to 40KHz
• Uses a 512-Point FFT that runs in O(1.8ms) on XYZDominant Frequency vs. Time for a Ringed Plover Bird
Chirp on the XYZ
0
500
1000
1500
2000
2500
3000
Time (ms)
Fre
qu
ency
(H
z)
Intelligent Camera ModuleEvan Park (RPI, EE)
Reading for this week
• D. Tennenhouse, “Proactive Computing”• Culler, Estrin and Srivastava, “ Overview of Sensor
Networks”
Articles posted on the course websitehttp://www.eng.yale.edu/enalab/courses/2005f/eeng460a/