Quick Look at Sensor Networks

Elke A. Rundensteiner

Based on material collated bySilvia Nittel, and others.

CS525

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Overview – Sensor Networks

Motivation & Applications Platform & Power Networking Underpinning

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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.

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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

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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

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Overview

Motivation & Applications Platforms and Power Networking

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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

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Mica2 and Mica2Dot Processor:

ATmega128 CPU RAM/Storage:

Chipcon CC1000 Manchester

encoding Tunable frequency Byte spooling

Power usage scales with range

1 inch

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Mica Sensor Board Light (Photo) Temperature Acceleration

2 axis Resolution:

±2mg Magnetometer

Resolution: 134G

Microphone Tone Detector Sounder

4.5kHz

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A Network

S. Madden, UBerkeley

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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.

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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

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Overview

Motivation & Applications Platforms & Power Networking

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Communication using Radio

Broadcastingradio signals

Listening &receiving signals

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

Small energy consumption => short range communication Multi-hop routing required to achieve distance Routes around obstacles Requires discovery, network topology formation, maintenance

may dominate cost of communication

Energy to receive Dominated by listening time (potential receive) Device has a total “lifespan” Radio must be OFF most of the time!

PicoRadio and Radio propagation

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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?

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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

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Networking

Network Architecture: Can we adapt Internet protocols and “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, unattended

system cannot tolerate communication overhead of

indirection sensor network architecture needs

Minimal overhead, and Data centric routing

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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”

Initial approaches in literature: Some form of tree-based routing Query sent out from server to motes Sink-Tree built to carry data from motes to server

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Communication In Sensor Nets

Radio communication has high link-level losses typically about 20%

@ 5m

Ad-hoc neighbor discovery

Tree-based routing

A

B C

D

FE

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Tree Routing

A

B C

D

FE

Query

Parent Node

Children Nodes

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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

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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!

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Basic Primitives Single Hop packet loss characteristics -> link

quality Environment, distance, transmit power, temporal

correlation, data rate, packet siz Services for High Level Protocols/Applications

Link estimation Neighborhood management Reliable multi-hop routing for data collection

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Neighborhood Management Maintain link estimation statistics and routing

information of each neighboring sensor node

Issue: Density of nodes can be high but memory of nodes 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 old neighbor?

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

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Reliable Routing 3 core components for Routing

Neighbor table management Link estimation Routing protocol

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Quick Summary

Motivation & Applications Platforms & Power Networking