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Sensor Networks: intro, overview, example. Jim Kurose* Vic Lesser CMPSCI 791L Sensor Nets Seminar Fall 2003. Some slides used/adapted (with thanks) from D. Estrin, with permission. Today’s class: overview. sensor nets: motivation system design themes themes

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Sensor networks intro overview example

Sensor Networks: intro, overview, example

Jim Kurose*

Vic Lesser

CMPSCI 791L Sensor Nets Seminar

Fall 2003

Some slides used/adapted (with thanks)

from D. Estrin, with permission


Today s class overview

Today’s class: overview

  • sensor nets: motivation

  • system design themes

  • themes

    • time and space: synchronization, location, coverage

    • in-network computation

    • “data is king”

  • illustrative sensor net application, system structure


Embedded networked sensing motivation

Embedded Networked Sensing: Motivation

Imagine:

  • high-rise buildings self-detect structural faults (e.g., weld cracks)

  • schools detect airborn toxins at low concentrations, trace contaminant transport to source

  • buoys alert swimmers to dangerous bacterial levels

  • earthquake-rubbled building infiltrated with robots and sensors: locate survivors, evaluate structural damage

  • ecosystems infused with chemical, physical, acoustic, image sensors to track global change parameters

  • battlefield sprinkled with sensors that identify track friendly/foe air, ground vehicles, personnel


Embedded networked sensing apps

Embedded Networked Sensing Apps

  • Micro-sensors, on-board processing, wireless interfaces feasible at very small scale--can monitor phenomena “up close”

  • Enables spatially and temporally dense environmental monitoring

    Embedded Networked Sensing will reveal previously unobservable phenomena

Seismic Structure response

Contaminant Transport

Ecosystems, Biocomplexity

Marine Microorganisms


Imagine the casa version

Noontime: all clear

DCAS systems monitor 3D winds, 0 to 3 km high

“clear-air” winds provide basis for pollutant monitoring, migratory bird tracking

Imagine (the CASA version)….

Dense network of radars - distributed collaborative adaptive sensing (DCAS)


Imagine

2PM: solar ground heating

wind convergence zones form

DCAS pattern detection algorithms detect convergence

data archiving begins

radars automatically tasked to sample moisture fields around convergence zone

models generate predictions, provided to local emergency managers for planning

Imagine….


Imagine1

3PM: severe weather

Clouds, precipitation develop in convergence several zones

DCAS radars adjust, provide fine-scale measurements, precipitation estimates in critical areas

skies to south clear, but DCAS systems monitoring 3D temperature, moisture to assess potential for future thunderstorms

rotational signatures cause nearby radars to enter tornado tracking mode

location, intensity, projected path provided to community, state organizations, industry. Because of 2PM predictions, officials prepared

spawned tornado destroys two radars, nearby DCAS radars reconfigure

Imagine….


Imagine2

5PM: storms move south to Houston

.. as predicted by continuously monitoring DCAS systems

rainfall begins, DCAS systems reconfigure to map precipitation at fine resolution

DCAS measurements feed hydrological models

local, state, organizational emergency response teams are in action and prepared well in advance of flood waters..

Imagine….


Sensor networks intro overview example

Embedded Sensor Nets: Enabling Technologies

Embednumerous distributed devices to monitor and interact with physical world

Networkdevices tocoordinate and perform higher-level tasks

Embedded

Networked

Exploitcollaborative

Sensing, action

Control system w/

Small form factor

Untethered nodes

Sensing

Tightly coupled to physical world

Exploit spatially/temporally dense, in situ/remote, sensing/actuation


Sensor nets new design themes

Sensor Nets: New Design Themes

  • self configuring systems that adapt to unpredictable environment

    • dynamic, messy (hard to model), environments preclude pre-configured behavior

  • leverage data processing inside the network

    • exploit computation near data to reduce communication

    • collaborative signal processing

    • achieve desired global behavior with localized algorithms (distributed control)

  • long-lived, unattended, untethered, low duty cycle systems

    • energy a central concern

    • communication primary consumer of scarce energy resource


From embedded sensing to embedded control

From Embedded Sensing to Embedded Control

  • embedded in unattended “control systems”

    • control network, and act in environment

  • critical app’s extend beyond sensing to control and actuation

    • transportation, precision agriculture, medical monitoring and drug delivery, battlefield app’s

    • concerns extend beyond traditional networked systems and app’s: usability, reliability, safety

  • need systems architecture to manage interactions

    • current system development: one-off, incrementally tuned, stove-piped

    • repercussions for piecemeal uncoordinated design: insufficient longevity, interoperability, safety, robustness, scaling


Why cant we simply adapt internet protocols end to end architecture

Why cant we simply adapt Internet protocols, “end to end” architecture?

  • Internet routes data using IP Addresses in Packets and Lookup tables in routers

    • humans get data by “naming data” to a search engine

    • many levels of indirection between name and IP address

    • embedded, energy-constrained (un-tethered, small-form-factor), unattended systems cant tolerate communication overhead of indirection

  • special purpose system function(s): don’t need want Internet general purpose functionality designed for elastic applications.


Is there an broader architecture

Is there an broader architecture

: stovepipes or layers?

Duck Island ME: habitat sensing

Oklahoma: atmospheric sensing

Can we define layered (Internet-like) architecture

appropriate for wide variety of networked sensing systems?


Sample layered architecture

Sample Layered Architecture

User Queries, External Database

Resource constraints call for more tightly integrated layers

Open Question:

What are defining

Architectural

Principles?

In-network: Application processing, Data aggregation, Query processing

Data dissemination, storage, caching

Adaptive topology, Geo-Routing

MAC, Time, Location

Phy: comm, sensing, actuation, SP


Today s class overview1

Today’s class: overview

  • sensor nets: motivation

  • system design themes

  • themes

    • time and space: synchronization, location, coverage

    • in-network computation

    • “data is king”

  • illustrative sensor net application, system structure


Sensors

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


Fine grained time and location

Fine Grained Time and Location

  • unlike Internet, node time/space location essential for local/collaborative detection

    • fine-grained localization and time synchronization needed to detect events in three space and compare detections across nodes

  • GPS provides solution where available (with differential GPS providing finer granularity)

    • GPS not always available, too “costly,” too bulky

    • other approaches under study

  • localization of sensor nodes has many uses

    • beamforming for localization of targets and events

    • geographical forwarding

    • geographical addressing


Coverage measures

area coverage: fraction of area covered by sensors

detectability: probability sensors detect moving objects

node coverage: fraction of sensors covered by other sensors

control:

where to add new nodes for max coverage

how to move existing nodes for max coverage

Coverage measures

D

x

S

Given: sensor field (either known sensor locations, or spatial density)


In network processing

In Network Processing

  • communication expensive when limited

    • power

    • bandwidth

  • perform (data) processing in network

    • close to (at) data

    • forward fused/synthesized results

    • e.g., find max. of data

  • distributed data, distributed computation


Distributed representation and storage

K V

K V

K V

K V

K V

K V

K V

K V

K V

K V

Time

K V

Distributed Representation and Storage

  • Data Centric Protocols, In-network Processing goal:

    • Interpretation of spatially distributed data (Per-node processing alone is not enough)

    • network does in-network processing based on distribution of data

    • Queries automatically directed towards nodes that maintain relevant/matching data

  • pattern-triggered data collection

    • Multi-resolution data storage and retrieval

    • Distributed edge/feature detection

    • Index data for easy temporal and spatial searching

    • Finding global statistics (e.g., distribution)


Directed diffusion data centric routing

Directed Diffusion: Data Centric Routing

  • Basic idea

    • name data (not nodes) with externally relevant attributes: data type, time, location of node, SNR,

    • diffuse requests and responses across network using application driven routing (e.g., geo sensitive or not)

    • support in-network aggregation and processing

  • data sources publish data, data clients subscribe to data

    • however, all nodes may play both roles

      • node that aggregates/combines/processes incoming sensor node data becomes a source of new data

      • node that only publishes when combination of conditions arise, is client for triggering event data

    • true peer to peer system?


Traditional approach warehousing

Warehouse

Front-end

Sensor Nodes

Traditional Approach: Warehousing

  • data extracted from sensors, stored on server

  • Query processing takes place on server


Sensor database system

SensorDB

SensorDB

SensorDB

SensorDB

SensorDB

Front-end

SensorDB

SensorDB

SensorDB

Sensor Database System

  • Sensor Database System supports distributed query processing over sensor network

Sensor Nodes


Sensor database system1

Characteristics of a Sensor Network:

Streams of data

Uncertain data

Large number of nodes

Multi-hop network

No global knowledge about the network

Node failure and interference is common

Energy is the scarce resource

Limited memory

No administration, …

Can existing database techniques be reused? What are the new problems and solutions?

Representing sensor data

Representing sensor queries

Processing query fragments on sensor nodes

Distributing query fragments

Adapting to changing network conditions

Dealing with site and communication failures

Deploying and Managing a sensor database system

Sensor Database System


Performance metrics

Performance Metrics

  • High accuracy

    • Distance between ideal answer and actual answer?

    • Ratio of sensors participating in answer?

  • Low latency

    • Time between data is generated on sensors and answer is returned

  • Limited resource usage

    • Energy consumption


Today s class overview2

Today’s class: overview

  • sensor nets: motivation

  • system design themes

  • themes

    • time and space: synchronization, location, coverage

    • in-network computation

    • “data is king”

  • illustrative sensor net application, system structure


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