04/19/23

Mobile Ad hoc Networks COE 549

Introduction to Sensor Networks

Tarek SheltamiKFUPMCCSECOE

www.ccse.kfupm.edu.sa/~tarek

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Outline

Introduction Application Areas Systems Involved Communications Challenges in SNETs Unique constraints Power Issues

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Involved Technologies

ComputationalPower

SensorTechnology

NetworkTechnology

SensorNetwork

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Application Areas Military Infrastructure security Environment & Habitat Monitoring Industrial Sensing Traffic Control Seismic Studies Life Sciences

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The Systems involved Sensor Node Internals Operating System Physical Size

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Sensor Node Internals

SENSOR

POWERSUPPLY

CPU

COMMUNICATION

NODE

INFRARED

ACOUSTIC

SEISM

IC

IMAGE

MAGNETIC

…

ELECTRO-MAGNETICINTERFACE

Some Current Node Platforms:

1. Sensoria WINS

2. Smart Dust – Dust Inc. Berkeley

3. UC Berkeley mote – Crossbow (www.xbow.com)

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Operating System - TinyOS

Custom built at UC, Berkeley for wireless sensor nodes

Component-based architecture: ensures minimum code size

Component library includes: Network protocols Sensor drivers Data acquisition tools Distributed services

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Physical Size

AWACS

LWIM III AWAIRS I

WINS

NG 2.0

Berkley

Motes

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Communication

Network Protocol Network Discovery Network Control & Routing

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Network Protocol

For wireless sensor networks: IEEE 802.11 standards

Personal Area Networks (PAN): IEEE 802.15 standard Radius of 5 to 10m Ideal application in short-range

sensors

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Network Discovery Knowledge of identity and location of

its neighbor Ad hoc protocols can be used GPS system can be used as well

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Network Control & Routing Network adapts dynamically to

conserve resources like energy and available nodes Make optimum use of bandwidth and processing

power Connectivity must emerge as needed from

algorithms Directed Diffusion routing

Data identity is separate from node identity Promotes adaptive, in-network processing

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Sensors

Passive elements: seismic, acoustic, infrared, strain, 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

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Sensor Network Challenges Low computational power Current mote processors run at < 10 MIPS

(Microprocessor without Interlocked Pipeline Stages) Not enough horsepower to do real signal processing Memory not enough to store significant data Poor communication bandwidth, current radios achieve

about 10 Kbps per mote Note that raw channel capacity is much greater

Overhead due to CSMA backoff, noise floor detection, start symbol, etc.

802.15.4 (Zigbee) radios now available at 250 Kbps But with small packets one node can only transmit

around 25 kbps

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Sensor Network Challenges.. Limited energy budget 2 AA motes provide about 2850 mAh Coin-cell Li-Ion batteries provide around 800

mAh Solar cells can generate around 5 mA/cm2 in

direct sunlight Must use low duty cycle operation to extend

lifetime beyond a few days

Portable, energy-efficient devices End-to-end quality of service Seamless operation under context

changes Context-aware operation Secure operation Sophisticated services for simple

clients

Sensor Network Challenges..

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Unique Aspects

Number of sensor nodes can be many orders of magnitude larger than number of nodes in an ad hoc network

Tens of thousands. But individual ID might not be needed.

Sensors might be very small, cheap, and prone to failure. Therefore, we need redundancy.

Extremely limited in power, and must stay operative for long time

Energy harvesting might be considered. Sensors might be densely deployed.

Opportunity for using redundancy to improve the robustness of the system

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Unique Aspects .. Very limited mobility

Helps with the design of the protocols Measurements might be correlated.

Example: measurements of temperature, pressure, humidity, etc.

Volume of transmitted data might be greatly reduced.

For many applications, nodes are randomly deployed. Thrown by a plane, carried by wind, etc.

Location-dependent Information

Changing context small movements may cause large changes caching may become ineffective dynamic transfer to nearest server for a

service

Portability Power is key

long mean-time-to-recharge, small weight, volume Risk to data due to easier privacy breach

network integrated terminals with no local storage Small user interfaces

small displays, analog inputs (speech, handwriting) instead of buttons and keyboards

Small storage capacity data compression, network storage, compressed

virtual memory, compact scripts vs. compiled code

Low Power & Energy-awareness

Battery technology is a hurdle… Typical laptop: 30% display, 30% CPU, 30% rest

wireless communication and multimedia processing incur significant power overhead

Low power circuits, architectures, protocols

Power management Right power at the right place at the right time

Battery model

Low Power & Energy-awareness..

There are many means for powering nodes, although the reality is that various electrical sources are by far the most convenient.

Technology trends indicate that within the lifetime of CENS, nodes will likely be available that could live off ambient light.

However, this cannot be accomplished without aggressive energy management at many levels; continuous communications alone would exceed the typical energy budgets.

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Sensor Node Energy Roadmap

200020002002200220042004

10,00010,000

1,0001,000

100100

1010

11

..11

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

Battery Technology

Battery technology has historically improved at a very slow pace

NiCd improved by x2 over 30 years! require breakthroughs in chemistry

Battery Rechargeable? Gravimetric Density(Wh/lb)

Volumetric Density(Wh/l)

Alkaline-MnO2(typical AA)

NO 65.8 347

Silver oxide NO 60 500Li/MnO2 NO 105 550Zinc Air NO 140 1150NiCd YES 23 125Li-Polymer YES 65-90 300-415

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Comparison of Energy Sources

Power (Energy) Density Source of Estimates

Batteries (Zinc-Air) 1050 -1560 mWh/cm3 (1.4 V) Published data from manufacturers

Batteries(Lithium ion) 300 mWh/cm3 (3 - 4 V) Published data from manufacturers

Solar (Outdoors)

15 mW/cm2 - direct sun

0.15mW/cm2 - cloudy day. Published data and testing.

Solar (Indoor)

.006 mW/cm2 - my desk

0.57 mW/cm2 - 12 in. under a 60W bulb Testing

Vibrations 0.001 - 0.1 mW/cm3 Simulations and Testing

Acoustic Noise

3E-6 mW/cm2 at 75 Db sound level

9.6E-4 mW/cm2 at 100 Db sound level Direct Calculations from Acoustic TheoryPassive Human

Powered 1.8 mW (Shoe inserts >> 1 cm2) Published Study.

Thermal Conversion 0.0018 mW - 10 deg. C gradient Published Study.

Nuclear Reaction

80 mW/cm3

1E6 mWh/cm3 Published Data.

Fuel Cells

300 - 500 mW/cm3

~4000 mWh/cm3 Published Data.

With aggressive energy management, ENS With aggressive energy management, ENS mightmightlive off the environmentlive off the environment..

Source: UC Berkeley

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Computation & Communication

Radios benefit less from technology improvements than processors

The relative impact of the communication subsystem on the system energy consumption will grow

TransmitReceive

Encode Decode Transmit

Receive

EncodeDecode

Energy breakdown for voice Energy breakdown for MPEG

Processor: StrongARM SA-1100 at 150 MIPSRadio: Lucent WaveLAN at 2 Mbps

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Power Analysis of Mote-Like Node

Key Issue: Resource Awareness

Ad-hoc architectureSelf-configuration

Wireless communications Variability

Inherent unpredictability

Solution: adaptation

Select required performance level Operate always at peak performance

Settings based on external conditions

Fixed settings set by worst case conditions

Resource awareness“right resource at the right time and the right place”

Wireless Backbone Networks High traffic load Limited available spectrum

Focus on transmission resources

Wireless Ad-Hoc Networks Unattended operation Limited available battery

Focus on energy resources

Event Driven Model

On-Demand Model

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TinyOS is an open-source operating system designed for wireless embedded sensor networks. It features a component-based architecture which enables rapid innovation and implementation while minimizing code size as required by the severe memory constraints inherent in sensor networks. TinyOS's component library includes network protocols, distributed services, sensor drivers, and data acquisition tools – all of which can be used as-is or be further refined for a custom application. TinyOS's event-driven execution model enables fine-grained power management yet allows the scheduling flexibility made necessary by the unpredictable nature of wireless communication and physical world interfaces. TinyOS has been ported to over a dozen platforms and numerous sensor boards. A wide community uses it in simulation to develop and test various algorithms and protocols. New releases see over 10,000 downloads. Over 500 research groups and companies are using TinyOS on the Berkeley/Crossbow Motes. Numerous groups are actively contributing code to the sourceforge site and working together to establish standard, interoperable network services built from a base of direct experience and honed through competitive analysis in an open environment .