EENG 460a / CPSC 436 / ENAS 960Networked Embedded Systems &

Sensor Networks

Lecture 1Andreas Savvides

andreas.savvides@yale.eduOffice: 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

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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

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K V

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K V

K VK V

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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 (dimitrios.lymberopoulos@yale.edu)

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/