Sensor Networks 金仲達教授清華大學資訊系統與應用研究所...

Sensor Networks

金仲達教授清華大學資訊系統與應用研究所

九十三學年度第一學期

Pervasive Computing Sensor Networks-2

Sources “Comm ’n Sense: Research Challenges in E

mbedded Networked Sensing,” D. Estrin, http://lecs.cs.ucla.edu

“A Survey on Sensor Network,”I.F. Akyildiz, W. Su, Y. Sankarasubramaniam, E. Cayirci, Georgia Institute of TechnologyIEEE Communications Magazine, Aug. 2002


Introduction Mark Weiser envisioned a world in which comp

uting is pervasive What we need is to instrument the physical wo

rld with pervasive networks of sensor-rich, embedded computation

Such systems fulfill two of Weiser’s objectives: Ubiquity: by inject computation into the physical w

orld with high spatial density Invisibility: by having the nodes and collective of no

des operate autonomously


Introduction What is required is the ability to easily deploy f

lexible sensing, computation, and actuation capabilities into our physical environments such that the devices themselves are general-purpose and can organize and adapt to support several application types


•Embed numerous distributed devices to monitor/interact with physical world

•Exploit spatially and temporally dense, in situ, sensing and actuation

•Network these devices so that they can coordinate to perform higher-level tasks.

•Requires robust distributed systems of hundreds or thousands of devices.

Vision


Sensor Nodes and Networks Sensor nodes = sensing, data processing, and

communicating capacity Sensor network: a large number of sensor nod

es that are densely deployed either inside the phenomenon or very close to it Sensor node position not engineered or predecide

dprotocols or algorithms must be self-organizing

Cooperative effort of sensor nodes with in network processing


ApplicationsScientific: eco-physiology,biocomplexity mapping

Infrastructure: Contaminant flow monitoring

Engineering: adaptivestructures

www.jamesreserve.edu


Other Applications (I) Environmental

Forest fire detection, biocomplexity mapping of the environment, flood detection, precision agriculture

Healthy Telemonitoring of human physiological data, tracki

ng and monitoring doctors and patients inside a hospital, drug administration in hospitals

Military: Monitoring friendly forces, equipment and ammuni

tion; battlefield surveillance; reconnaissance of opposing forces and terrain; targeting; battle damage assessment; nuclear, biological and chemical attack detection and reconnaissance


Other Applications (II) Home

Home automation Smart environment

Commercial Environmental control in office buildings Interactive museums Detecting and monitoring car thefts Managing inventory control Vehicle tracking and detection Monitoring product quality Monitoring disaster areas

….


Challenges Tight coupling to the physical world and

embedded in unattended “control systems” Different from traditional Internet, PDA, mobility

applications that interface primarily and directly with human users

Untethered, small form-factor, nodes present stringent energy constraints Living with small, finite, energy source is different from

fixed but reusable resources such as BW, CPU, storage Communications is primary consumer of energy

Sending a bit over 10 or 100 meters consumes as much energy as thousands/millions of operations


New Design Themes Long-lived systems that can be untethered

and unattended Low-duty cycle operation with bounded latency Exploit redundancy Tiered architectures (mix of form/energy

factors) Self-configuring systems that can be

deployed ad hoc Measure and adapt to unpredictable

environment Exploit spatial diversity and density of

sensor/actuator nodes


Approach Leverage data processing inside the

network Exploit computation near data to reduce

communication Achieve desired global behavior with

adaptive localized algorithms (i.e., do not rely on global interaction or information) Dynamic, messy (hard to model), environments

preclude pre-configured behavior Can’t afford to extract dynamic state

information needed for centralized control or even Internet-style distributed control


Why can’t we simply adapt Internet protocols and “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 Works well for the Internet, and for support of

Person-to-Person communication Embedded, energy-constrained (un-

tethered, small-form-factor), unattended systems can’t tolerate communication overhead of indirection


vs. Ad Hoc Networks Large number of sensor nodes (several

orders of magnitude higher) Densely deployed Prone to failures Network topology changes very frequently Mainly use a broadcast paradigm vs. point-

to-point in ad hoc networks Limited in power, computational

capacities, and memory May not have global identification (ID)


Communication Architecture Factors of design consideration

Transmission media Production costs Power consumption Fault tolerance NW topology HW constraints Environment Scalability


Fault Tolerance The ability to sustain sensor network function

alities without any interruption due to sensor node failures

The reliability Rk(t) or fault tolerance of a sensor node can be modeled with the Poisson distribution to capture the probability of not having a failure within the time interval (0,t) Rk(t) = exp(-λkt) , for node k


Scalability The number of sensor nodes

10 -> 100 -> 1000 -> 10000 -> …. Depending on the application

New schemes must be able to utilize the high density

The density μ(R) = (N . π R2)/A A: region area R: radio transmission range N: the number of scattered sensor nodes


Production Costs The cost of a single node is very important

to justify the overall cost of the network The cost of a sensor node should be much less

than US$1 The state-of-art technology allows a Bluetooth

radio system to be less than US$10 10 times more expensive the the targeted price


Hardware 4 basic units: sensing unit, processing unit, tra

nsceiver unit, power unit Sensing: sensors, Analog-to-digital converters (ADC

s) Additional application-dependent units

Location finding system, power generator, mobilizer….


Hardware Constraints Constraints

Size Power Operate in very high densities Low cost Dispensable Autonomous Adaptive to environment


Sensor Network Topology Topology maintenance and change in 3 phase

s Predeployment and deployment phase

Be thrown in as a mass or placed one by one Post-deployment phase

Change in sensor nodes’ position, reachability, available energy, malfunctioning, and task details

Redeployment of additional nodes phase Additional sensor nodes can be redeployed


Environment Nodes are densely deployed either very

close or directly inside the phenomenon to be observed

Usually work unattended in remote geographic areas in the interior of large machinery at the bottom of an ocean in a biologically or chemically contaminated

field in a battlefield beyond the enemy lines in a home or large building ….


Transmission Media Often by wireless medium Radio:

Used by most sensors μAMPS sensor uses a Bluetooth-compatible 2.4 GHz t

ransceiver with an integrated frequency synthesizer Infrared:

License-free, robust to interference from electrical devices

cheaper and easier to build Optical: Smart Dust mote Both infrared and optical require line of sight


Power Consumption In some application scenarios, replenishment

of power resources might be impossible Battery lifetime

In a multihop ad hoc sensor network, each node plays dual role of data originator and data router cause significant topological changes require rerouting of packets and reorganization of t

he network Power consumption

sensing, communication, and data processing


Design Issues According to Protocol Stack Physical layer:

Simple, robust modulation, transmission, receiving

MAC protocol power-aware; minimize

collision with neighbors’ broadcasts

Network layer routing data supplied by

transport layer Transport layer

maintain flow of data


Three Management Planes The power management plane, e.g.

Turn off its receiver after receiving a message Broadcasts low in power and cannot participate in

routing messages The mobility management plane

Detects and registers movement of sensor nodes maintain route back to the user, keep track of their

neighbor The task management plane

balances and schedules sensing tasks for a specific region

They are needed for sensor nodes to work power-efficiently, route data in a mobile network, share resources between sensor nodes


Physical Layer Responsibility

Frequency selection, carrier frequency generation, signal detection, modulation, and data encryption.

915 MHz industrial, scientific, and medical (ISM) band has been widely used

Long distance wireless communication can be expensive in terms of power

A good modulation is critical for reliable comm. Binary and M-ary modulation schemes

Ultra wideband (UWB) or impulse radio (IR) are promising


Physical Layer Open Issues Modulation schemes

Simple and low-power modulation schemes Strategies to overcome signal propagation

effects Hardware design

Tiny, low-power, low-cost transceiver, sensing, and processing units

Power-efficient hardware management strategies


Data Link Layer Responsibility

Multiplexing of data streams, data frame detection, medium access and error control

Reliable point-to-point and point-to-multipoint Medium Access Control protocol

creation of the network infrastructure fairly and efficiently share communication resources

Existing MAC protocols cannot be used Cellular system: infrastructure-based Bluetooth and mobile ad hoc network (MANET)

much larger number, power and radio range, frequent topology change, power conservation needed


Some Proposed MAC Protocols


Example MAC Protocols Self-Organizing Medium Access Control for Se

nsor Networks (SMACS) and the Eavesdrop-And-Register (EAR) Algorithm Nodes to discover their neighbors and establish co

mmunication without the need for any local or global master nodes

No necessity for networkwide synchronization using a random wake-up schedule during connecti

on phase and turning the radio off during idle time slots

EAR attempts to offer continuous service to the mobile nodes


Data Link Open Issues MAC for mobile sensor networks

more extensive mobility in the sensor nodes and targets

Determination of lower bounds on the energy required for sensor network self-organization

Error control coding schemes Power-saving modes of operation


Network Layer Design principles

Power efficiency Sensor networks are mostly data-centric Data aggregation is useful only when it does

not hinder the collaborative effort of the sensor nodes.

An ideal sensor network has attribute-based addressing and location awareness

Also providing internetworking with external networks


Energy-Efficient Route Available power:PA Energy required (α)

Maximum minimum PA node route Min PA is larger than

the min PAs Maximum PA route Minimum energy route Minimum hop route


Data Centric Route Use interest dissemination

Sinks broadcast the interest, or Sensor nodes broadcast an advertisement and

wait for a request Often require attribute-based naming

Query by using attributes of phenomenon Data aggregation

Solve the implosion and overlap problems


Proposed Schemes Flooding

Implosion (duplicated message), overlap (both sensors detect the same event), resource blindness (not considering resource constraints)

Gossiping Relay packets to

randomly selected neighbor

Negotiation (SPIN)


More Schemes Small minimum energy communication

network Sequential assignment routing Low-energy adaptive clustering hierarchy Directed diffusion


Protocol Summary


Application Layer Protocols Sensor management

nodes do not have global identifications and are infrastructureless

Providing administrative tasks Introducing the rules related to data aggregation, attribut

e-based naming, and clustering to the sensor nodes Exchanging data related to the location finding algorithms Time synchronization of the sensor nodes Moving sensor nodes Turning sensor nodes on and off Querying the sensor network configuration and the status

of nodes, and reconfiguring the sensor network Authentication, key distribution, and security in data com

munications


Application Layer Protocols Task assignment and data advertisement

interest dissemination Advertisement of available data

Sensor query and data dissemination issue queries, respond to queries and collect incoming replies Sensor query and tasking language (SQTL) supports 3 types o

f events Receive defines events generated by a sensor node when t

he sensor node receives a message every defines events occurring periodically due to timer ti

meout expire defines events occurring when a timer is expired

Different types of SQDDP can be developed for various applications. The use of SQDDPs may be unique to each application


Research Areas Constructs for “in network” distributed

processing system organized around naming data, not nodes “programming” large collections of distributed

elements Localized algorithms that achieve system-

wide properties Time and location synchronization

energy-efficient techniques for associating time and space with data to support collaborative processing

Experimental infrastructure


Constructs for in NW Processing Nodes pull, push, store named data (using tuple

space) to create effic. processing points in NW e.g. duplicate suppression, aggregation, correlation

Nested queries reduce overhead relative to “edge processing”

Complex queries support collaborative signal proc. propagate function

describing desired locations/nodes/data (e.g. ellipse for tracking)


Self-organization with Localized Alg. Self-configuration and reconfiguration

essential to lifetime of unattended systems in dynamic, constrained energy, environment Efficient, multi-hop topology formation: node

measures neighborhood to determine participation, duty cycle, and/or power level

Beacon placement: candidate beacon measures potential reduction in localization error

Requires large solution space; not seeking unique optimal

Investigating applicability, convergence, role of selective global information


Time and Location Synchronization Common time coordinate for in situ processing,

correlation of events Developing methods that balance communication

(energy) cost with other variables (e.g., precision, scope, lifetime, cost, form factor)

Post facto pulse synchronization Common spatial coordinate for 3-space related

tasks and network operation (e.g., geo-routing) Methods not rely on GPS or RF RSSI (due to envir.) Multi-modal localization using acoustic time of fligh

t measurements, RF synchronization, and imaging to identify bad data sources (NLOS)


Experimental Infrastructure

PC-104+(off-the-shelf)

UCB Mote (Pister/Culler)

Software• Directed Diffusion• TinyOS (UCB/Culler)• Measurement, Simulation

Berkeley Motes & TinyOS孫文宏


Berkeley Motes 1st generation

2nd generation


System of MICA Motes


MICA Motes Processor and radio board -

MPR300

Sensor board – MTS310

Base station/interface board - MIB300


MICA Motes


Sensor Board

2.25 in

1.25 in

Microphone

AccelerometerLightSensor

TemperatureSensor

Sounder Magnetometer


Processor/Radio Board


TinyOS TinyOS = application/binary image, executabl

e on an ATmega processor event-driven, 2-level scheduling, single-shared stac

k no kernel, no process management, no memory m

anagement,no virtual memory

simple FIFOscheduler, partof the main

CommunicationActuating Sensing Communication

Application (User Components)

Main (includes Scheduler)

Hardware Abstractions


TinyOSf:\avrgcc \cygwin \tinyos-1.x\apps {cnt_to_leds, cnt_to_rfm, sense, …}

\docs {connector.pdf, tossim.pdf, …} \tools {toscheck, inject, verify, …} \tos {shared/system components, …}

……………………..


Programming Model Application Component

2 types: modules and configurations. Module Configuration

A configuration is a component that "wires" other components together. Every NesC application has a single top-level configuration.

Interface


Programming Model

application:configuration

comp1:module

comp3

comp4comp2:configuration


Reference Crossbow

http://www.xbow.comMICA Motes http://www.xbow.com/Products/Wireless_Sensor_Networks.htm

TinyOS

http://today.cs.berkeley.edu/tos/ TinyOS supporthttp://today.cs.berkeley.edu/tos/support.html TinyOS tutorial

http://today.cs.berkeley.edu/tos/tinyos-1.x/doc/tutorial/index.html

PADSFTP/TinyOS

Directed Diffusion: A Scalable and

Robust Communication

Paradigm for Sensor Networks

Chalermek Intanagonwiwat (USC/ISI)Ramesh Govindan (USC/ISI)

Deborah Estrin (USC/ISI and UCLA)


The Goal Embed numerous

devices to monitor and interact with physical world

Network these devices so that they can coordinate to perform higher-level tasks

Requires robust robust distributed systems of distributed systems of tens of thousands of tens of thousands of devicesdevices


The Challenge: Dynamics! The physical world is dynamic

Dynamic operating conditions Dynamic availability of resources

… particularly energy! Devices must adapt automatically to the

environment Too many devices for manual configuration Environmental conditions are unpredictable

Unattended and un-tethered operation is key to many applications


Energy Is the Bottleneck Resource Communication VS Computation Cost

E R4 10 m: 5000 ops/transmitted bit 100 m: 50,000,000 ops/transmitted bit

Short distance communication => multi-hop Cannot assume global knowledge, cannot

pre-configure networks Get desired global behavior thru localized

interactions Empirically adapt to observed environment

Can leverage data processing/aggregation inside the network


Research Theme What communication primitives can be employed i

n such unattended sensor networks? Assume no structured sensor fields, but task-specific A user of the network contact one of the sensors in the fiel

d and pose queries (interrogation): e.g., “Give me periodic reports about animal location in

region A every t seconds” Interrogation propagated to sensor nodes in region A Sensor nodes in region A are tasked to collect data Data are sent back to the users every t seconds

Dissemination mechanisms for tasks and events?


Issues to Be Addressed Scalable to thousands of sensor nodes Sensor nodes may fail, lose battery power,

be temporarily unable to communication, …=> communication mechanisms must be robust

Minimize energy usage

=> a data dissemination mechanism for sensorsDirected Diffusion


Directed Diffusion In-network data processing (aggregation,

caching) Distributed algorithm with localized interaction Application-aware communication primitives

expressed in terms of named data (not in terms of the nodes generating or requesting data)=> data-centric

Data generated by sensors named by attribute-value Sensor nodes need not have globally unique address,

but need to distinguish between neighbors


Basic Ideas A node requests data by sending interests for

named data (diffusion) Gradients are set up in network to draw events Data matching the interest is drawn towards

that node along multiple reverse paths The network reinforces one or more paths Intermediate nodes can cache, transform, or

aggregate data, and may direct interests based on previously cached data

Interest/data propagation, aggregation decided by localized interactions (with local naming)


Naming Task descriptions are named by a list of

attribute-value pairs

This specifies an interest for data matching the attributes


Basic Directed DiffusionSetting up gradients (flooding)

Source

Sink

Interest = InterrogationGradient = Who is interested

Broadcast periodically

Data rate = 1ms


Basic Directed Diffusion

Source

Sink

Sending data and reinforcing the best path

Low rate event Reinforcement = Increased intereste.g. 1st neighbor sending the event


Multiple Sources and Sinks


Directed Diffusion and Dynamics

Recoveringfrom node failure

Source

Sink

Low rate event

High rate eventReinforcement


Directed Diffusion and Dynamics

Source

Sink

Stable path

Low rate event

High rate event


Local Behavior Choices For propagating

interests In our example, floodIn our example, flood More sophisticated

behaviors possible: e.g. based on cached information, GPS

For data transmission Multi-path delivery with Multi-path delivery with

selective quality along selective quality along different pathsdifferent paths

probabilistic forwarding single-path delivery,

etc.

For setting up gradients data-rate gradients data-rate gradients

are set up towards are set up towards neighbors who send neighbors who send an interestan interest..

Others possible: probabilistic gradients, energy gradients, etc.

For reinforcement reinforce paths, or parts reinforce paths, or parts

thereof, based on thereof, based on observed delaysobserved delays, losses, variances etc.

other variants: inhibit certain paths because resource levels are low


Simulation Study of Diffusion Key metric

Average Dissipated Energy per event delivered indicates energy efficiency and network lifetime

Compare diffusion to flooding centrally computed tree (omniscient multicast)


Diffusion Simulation Details -2Simulator: ns - NNNNN:50 250 :40 :1.9510 -3 nodes/m2 NN NNNNNNN(9.8 ) MAC: Modified Contention-based MAC NNNNNNN NNNNN:[ 2000]

660 mW in transmission, 395 mW in reception, and 35 mw in idle


Diffusion Simulation Surveillance application

5 sources are randomly selected within a 70m x 70m corner in the field

5 sinks are randomly selected across the field NNNN NN N NNNNNNNNNN2 / NNNNNNNNNN0 . 0 2 / Eventsize: 64byt es NNNNN:36 All sources send the same location estimate for

base experiments


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Average Dissipated Energy (Standard 802.11 Energy M

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FloodingFlooding

Diffusion can outperform flooding and even Diffusion can outperform flooding and even omniscient multicast. WHY ?omniscient multicast. WHY ?

Average Dissipated Energy (Sensor Radio Energy Model

)


Impact of In-network Processing

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Diffusion With Diffusion With SuppressionSuppression

Diffusion Without Diffusion Without SuppressionSuppression

Application-level suppression allows diffusion to Application-level suppression allows diffusion to reduce traffic and to surpass omniscient multicast.reduce traffic and to surpass omniscient multicast.


Impact of Negative Reinforcement

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Diffusion With Negative Diffusion With Negative ReinforcementReinforcement

Diffusion Without Diffusion Without Negative ReinforcementNegative Reinforcement

Reducing high-rate paths in steady state is criticalReducing high-rate paths in steady state is critical


Summary of Diffusion Results Under the investigated scenarios, diffusion out

performed omniscient multicast and flooding - Application level data dissemination has the p

otential to improve energy efficiency significantly Duplicate suppression is only one simple example o

ut of many possible ways. Aggregation (in progress)

All layers have to be carefully designed Not only network but also MAC and application level

Experimentation on our testbed in progress


M MMM Information SCADDS project

http://www.isi.edu/scadds

- 2ns : network simulator (with diffusion su) - -http://www.isi.edu/nsnam/dist/ns src snapshot.t

ar.gz

NNN NNNNNNN NNN NNNNNNNN http://www.isi.edu/scadds/testbeds.html

Sensor Networks 金仲達教授清華大學資訊系統與應用研究所...

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Transcript of Sensor Networks 金仲達教授清華大學資訊系統與應用研究所...

Sensor Networks 金仲達教授 清華大學資訊系統與應用研究所...

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Transcript of Sensor Networks 金仲達教授 清華大學資訊系統與應用研究所...

Sensor Networks 金仲達教授清華大學資訊系統與應用研究所...

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