INT598 Sensor Networks Silvia Nittel Spatial Information Science & Engineering University of Maine...

INT598Sensor Networks

Silvia NittelSpatial Information Science & Engineering

University of MaineFall 2006

INT598: IGERT in Sensor Science, Engineering and Informatics 2

© Dr. Silvia Nittel, NCGIA, University of Maine, 2006

IGERT



Overview

Motivation & Applications Platforms, Operating Systems,

Power Networking

Protocols, naming, routing Data Collection and Aggregation



Motivation

Trends: Developments of new sensor materials Miniaturization of microelectronics Wireless communication

Consequences: Embedding devices into almost any man-made and

some natural devices, and connecting the device to an infinite network of other

devices, to perform tasks, without human intervention. Information technology becomes omnipresent.

”Pervasive Computing”: The idea that technology is to move beyond the personal computer to everyday devices with embedded technology and connectivity as computing devices become progressively smaller and more powerful.



Embedded Networked Sensing Potential• Micro-sensors, on-

board processing, and wireless interfaces all feasible at very small scale– can monitor

phenomena “up close” in non-intrusive way

• Will enable spatially and temporally dense environmental monitoring

• Embedded & Networked Sensing will reveal previously unobservable phenomena

Habitat Monitoring

Storm petrels on Maine’s Great Duck Island

Contaminant Transport

Marine Microorganisms Vehicle Detection



Multiscale Observation and Fusion: Example, Regional (or greater) scale to local scale

images from Susan Ustin, UC Davis

Satellite, airborne remote sensing data sets at regular time intervals

coupled to regional-scale “backbone” sensor network for ground-based observations

fusion, interpolation tools based on large-scale computational models

Small-scaleSensor network



Overview


Power Networking


In-network data aggregation



Emergence of WiSeNets 1994 Pottie and Kaiser propose Low Power Wireless Integrated

Microsensors DARPA Sensit Program (Sensor Information Technology) Late 97-98 handhelds emerge

Palm platform ITSY, BWRC PicoRadio, etc. Matchbox PCs Bluetooth promised

Berkeley SmartDust 1999 WeC mote offshoot

2000 Mote/TinyOS platforms WINS finally appears in Linux for Darpa’s Sensit 2002 Mica NEST OEP creates de facto platform 2003 Bluetooth revival 2004 Telos, lowest power mote, supports IEEE 802.15.4



Abbreviations

Sensit Darpa’s Program “Sensor Information

Technology” WINS

Wireless Integrated Network Sensor Platforms Developed by Sensoria Corporation for Darpa’s

Sensit program NEST

Network Embedded Systems OEP

Open Experimental Platform (a middleware for sensor networks)



Sensor Network

• “Sensor Node”:• Tiny vanilla computer with operating system, on-

board sensor(s) and wireless communication (“PC on a pin tip”)

• Trend towards low-cost, micro-sized sensors• Use of wireless low range RF communication• Batteries as energy resource

• “Sensor Network”• Massive numbers of “sensors” in the environment

that measure and monitor physical phenomena • Local interaction and collaboration of sensors• Global monitoring• Tightly coupled to the physical world to sense and

influence it



UC Berkeley Family of Motes



Mica2 and Mica2Dot Processor:

ATmega128 CPU RAM/Storage:

Chipcon CC1000 Manchester

encoding Tunable frequency Byte spooling

Power usage scales with range

1 inch



Basic Sensor Board

Light (Photo) Temperature Prototyping

space for new hardware designs



Mica Sensor Board Light (Photo) Temperature Acceleration

2 axis Resolution:

±2mg Magnetometer

Resolution: 134G

Microphone Tone Detector Sounder

4.5kHz



Mica Weather Board

Total Solar Radiation Photosynthetically

Active Radiation Resolution: 0.3A/W

Relative Humidity Accuracy: ±2%

Barometric Pressure Accuracy: ±1.5mbar

Temperature Accuracy: ±0.01oC

Acceleration 2 axis Resolution: ±2mg

Designed by UCB w/ Crossbow and UCLA

Revision 1.5

Revision 1.0



Telos: New OEP Mote Single board philosophy

Robustness, Ease of use, Lower Cost Integrated Humidity & Temperature sensor

First platform to use 802.15.4 CC2420 radio, 2.4 GHz, 250 kbps (12x mica2) 3x RX power consumption of CC1000, 1/3 turn on time Same TX power as CC1000

Motorola HCS08 processor Lower power consumption, 1.8V operation,

faster wakeup time 40 MHz CPU clock, 4K RAM

Package Integrated onboard antenna +3dBi gain Removed 51-pin connector Everything USB & Ethernet based 2/3 A or 2 AA batteries Weatherproof packaging

Support in upcoming TinyOS 1.1.3 Release Co-designed by UC Berkeley and Intel Research Available from Moteiv (moteiv.com)



COTS-BOTS (UCB)Commercial Off-The-Shelf roBOTS

5” x 2.5” x 3” size <$250 total 2-axis

accelerometer



Robomote (USC) Less than 0.000047m3

$150 each Platform to test algorithms for adaptive wireless networks

with autonomous robots



A Network

S. Madden, UBerkeley



Wireless Sensor Networks

They present a range of computer systems challenges because they are closely coupled to the physical world with all its unpredictable variation, noise, and

asynchrony; they involve many energy-constrained,

resource-limited devices operating in concert;

they must be largely self-organizing and self-maintaining; and

they must be robust despite significant noise, loss, and failure.



Sensor Network Objectives Several classes of systems:

Mote herds Collaborative processing

arrays (32 bit, 802.11, linux) Networked Info-Mechanical

Systems: Autonomy Achieve longevity/autonomy,

scalability, performance with: heterogeneous systems in-network processing,

triggering, actuation Algorithm/Software challenges

Characterizing sensing uncertainty

Error resiliency, integrity Statistical and information-

theoretic foundations for adaptive sampling, fusion

Programming abstractions, Common services, tools

Data modeling, informatics

lifetime/autonomy

scale

Collaborative processing arrays (imaging, acoustics)

samplingrate

Mote Clusters



Sensor Network Design Topics

Long-lived systems that can be untethered (wireless) and unattended

Communication will be the persistent primary consumer of scarce energy resources (MICA Mote: 720nJ/bit xmit, 4nJ/op)

Autonomy requires robust, adaptive, self-configuring systems

Leverage data processing inside the network Exploit computation near data to reduce communication, achieve

scalability Collaborative signal processing Achieve desired global behavior with localized algorithms

(distributed control)

“The network is the sensor” (Manges&Smith, Oakridge Natl Labs, 10/98)

Requires robust distributed systems of hundreds of physically-embedded, unattended, and often untethered, devices.



Architecture

Data aggregation, Query processing

Adaptive topology, Geo-Routing

MAC, time, location

Phy: comm, sensing, actuation

Data model, Declarative queries

Application: Events, Reactions

Network layer

(temp-spatial)DB layer

Physical layer

Application layer

Source: Deborah Estrin, UCLA



Overview


Power Networking




Communication using Radio

Broadcastingradio signals

Listening &receiving signals



Energy required to transmit signals in distance d Communication is huge battery drain Indoor has lots of other complications

Small energy consumption => short range comm Multihop routing required to achieve distance Routes around obstacles Requires discovery, network topology formation,

maintenance may dominate cost of communication

Energy to receive ~ E*t at short range Dominated by listening time (potential receive) Radio must be OFF most of the time!

PicoRadio and Radio propagation



ISO/OSI Protocol Stack

PhysicalData LinkNetwork

TransportSession

PresentationApplication

7 Layer ISO/OSI Reference Model

The NetworkCard

The InternetProtocols

InternetApplication

The End Computer System View

Transport Control Protocol (TCP)

Internet Protocol

(IP)

*) International Standard Organization's Open System Interconnect



Low-level Networking Physical Layer

Low-range radio broadcast/receive Wireless (wiSeNets)

MAC: Media Access Control Controls when and how each node can transmit in the wireless

channel (“Admission control”) Objectives:

Channel utilization How well is the channel used? (bandwidth utilization)

Latency Delay from sender to receiver; single hop or multi-hop

Throughput Amount of data transferred from sender to receiver per time unit

Fairness Can nodes share the channel equally?



MAC Design Decisions

Energy is primary concern in sensor networks

What causes energy waste? Collisions Control packet overhead Overhearing unnecessary traffic Long idle time

bursty traffic in sensor-net apps Idle listening consumes 50—100% of the power

for receiving (Stemm97, Kasten)

Dominant factor



Networking Network Architecture: Can we adapt the Internet

protocols and the “end to end” architecture to SN? Internet routes data using IP Addresses in Packets

and Lookup tables in routers Many levels of indirection between data name and IP

address, but basically address-oriented routing Works well for the Internet, and for support of Person-to-

Person communication

Embedded, energy-constrained (un-tethered, small-form-factor), unattended systems cannot tolerate communication overhead of indirection

Our sensor network architecture needs Minimal overhead Data centric routing



Data-centric Routing Named-data as a way of tasking motes, expressing data

transport request (data-centric routing) Basically:

“send the request to sensors that can deliver the data, I do not care about their address”

Two initial approaches in literature: Derived from multicast-routing perspective

where you name a logical group of sensor nodes (Diffusion)

Derived from database query language (TinyDB) with stronger semantics on data delivery, timing, sequencing

Commonality is tree-based routing Query sent out from microserver to motes Sink-Tree built to carry data from motes to

microserver



Tree Routing

A

B C

D

FE

Query

Parent Node

Children Nodes



Tree building Queries/Request

What goes in query? Where does query go?

Neighbor selection How does mote select upstream neighbor for

data? Asymmetric links Unidirectional links Route characterization (like ETX)

Multiple microservers What about multiple microservers? How does mote select a microserver?



Tree building

Dynamics How often do you send out a new query? How often do you select a new upstream path

Design Tree building protocol From query source to data producer(s) and back Multihop ad-hoc routing

reliable routing is essential!



Basic Primitives Single Hop packet loss characteristics

Environment, distance, transmit power, temporal correlation, data rate, packet size

Services for High Level Protocols/Applications Link estimation Neighborhood management Reliable multihop routing for data collection



Basic Neighborhood of Devices

Services for High Level Protocols/Applications

Link estimation Neighborhood management Reliable multihop routing for data

collection

Direct Reception Large variation in affinity

Asymmetric links Long, stable high quality links Short bad ones

Link quality varies with traffic load Collisions Distant nodes raise noise floor

Many poor “neighbors”



Neighborhood Management Maintain link estimation statistics and routing

information of each neighboring sensor node How large should this table be?

O(cell density) * meta-data for each neighbor Issue:

Density of nodes can be high but memory of each node is limited

At high density, many links are poor or asymmetric Neighborhood Management

Question: when table becomes full, should we add new neighbor? If so, evict which old neighbor?

Similar to frequency estimation of data streams, or classical cache policy



Reliable Routing 3 core components for Routing

Neighbor table management Link estimation Routing protocol



Routing Protocols

Ad-hoc routing, Geographic routing Topology Formation Directed Diffusion Rumor/Gossip Routing



Overview


Power Networking

Physical layer, MAC, Protocols Routing

Adaptable, Configurable Systems Data Collection and Aggregation

INT598 Sensor Networks Silvia Nittel Spatial Information Science & Engineering University of Maine...

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