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04/19/23
Mobile Ad hoc Networks COE 549
Introduction to Sensor Networks
Tarek SheltamiKFUPMCCSECOE
www.ccse.kfupm.edu.sa/~tarek
I-2
Outline
Introduction Application Areas Systems Involved Communications Challenges in SNETs Unique constraints Power Issues
02/14/05 3
Involved Technologies
ComputationalPower
SensorTechnology
NetworkTechnology
SensorNetwork
02/14/05 4
Application Areas Military Infrastructure security Environment & Habitat Monitoring Industrial Sensing Traffic Control Seismic Studies Life Sciences
02/14/05 5
The Systems involved Sensor Node Internals Operating System Physical Size
02/14/05 6
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)
02/14/05 7
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
02/14/05 8
Physical Size
AWACS
LWIM III AWAIRS I
WINS
NG 2.0
Berkley
Motes
02/14/05 9
Communication
Network Protocol Network Discovery Network Control & Routing
02/14/05 10
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
02/14/05 11
Network Discovery Knowledge of identity and location of
its neighbor Ad hoc protocols can be used GPS system can be used as well
02/14/05 12
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
I-13
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
I-14
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
I-15
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..
I-17
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
I-18
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.
I-23
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
I-25
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
II-26
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
II-27
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
04/19/23 31
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 .