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SMART DUST
Outline
Overview of Sensor NetworksSmartdust NetworksRadio Smartdust NetworksSimulation ResultsConcluding Remarks
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Wireless Sensor Networks
Processor Memory
Sensor
Interconnect
Radio
Communication
Computation
Data Storage
Sensing
Base station
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Applications of sensor networks
Motivating application: monitoring Mars (ΑΡΗΣ)Environmental monitoring:
Air, soil and water controllingHabitat and complex systems monitoringSeismic detectionVulcan activity detectionToxic waste control
Military surveillanceDemining action (ΑΦΑΛΑΤΩΣΗ)
« Networked sensors will revolutionize information gathering and processing both in urbanenvironments and in inhospitable terrain »
Challenges
Ad hoc deployment
Unattended operation
Untethered (χαλαρά !)
Dynamic changes
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Focus
Energy efficiencyLocalizationRouting
Energy in Wireless Networks
Processor Memory
Sensor
Interconnect
Radio
CommunicationEnergy
ComputationEnergy
Data StorageEnergy
SensingEnergy
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Energy efficiency
Computing subsystem (nJ/instruction)Communication subsystem (nJ/bit)Sensing subsystem (nJ/sampling)Power supply subsystem (1J/mm3)
Localization
Localization schema
Sensors with local positionBase station receives information
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Localization techniques
Coarse-grainedProximity based localization: trilaterationLinear programming techniquesBeacon placement techniques
• Random, Max, Grid
Fine-grainedTimingSignal strengthDirectionality
Routing
Flat routing protocolsSAR (Sequential Assignment Routing)Directed DiffusionSPIN (Sensor Protocols for Information via Negotiation)Adaptive Local Routing Cooperative Signal Processing
• Noncoherent Processing• Coherent Processing
Hierarchical Routing ProtocolsLEACH (Low Energy Adaptive Clustering Hierarchy)TEEN (Threshold sensitive Energy Efficient Sensor Network Protocol)PEGASIS (Power-Efficient Gathering in Sensor Information Systems)Two level clustering algorithm
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Example of routing protocol
The generated data is named by attributesThe sink request data by interest Intermediate nodes propagate the interestGradients are established for the interests expressed by the sink
Sink
Source
Event
InterestGradients
Data
Direct diffusion:
Diffusion protocols
Deficiencies of diffusion protocols:
A
B C
D
(a) (a)
(a) (a)
No clustering
Reverse communication
Implosion
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Outline 2
Overview of Sensor NetworksSmartdust NetworksRadio Smartdust NetworksSimulation ResultsConcluding Remarks
Analogy Buffon 1
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Mote The Smart Dust Project
Massively distributed sensor networkSensor = mote
sensing systemcomputational abilitypower supplylaser communication
Motes are inexpensive enough to be deployedby the millions.
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Problem:
The missing ingredient is the networking
LocalizationRoutingEnergy efficiency
Random topology makes the problemextremely difficultThe large number of motes makes it worse
Maybe not …
It is known that probabilistic methods and asymptotic behavior combine well…
If we could find the right model …
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Modeling the network
Random graph G(n,p)
Problem:
it does not capture the communication capability ofthe motes
Our model
Random scaled sector graphs
A mote can orient its laser beam in any direction of a prescribed scanning area of α radians and distance r
Directed Graph Model.
αr
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More realistic Distribution of motes
Motes are distributed at random but we need a reference system !!!!
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Precision and scalability
Imaginary squared gridof s cells (size s-1/2 vs s-1/2 )
This grid gives thereference position for the motes
s is the sensing precisionof the networks s controls the scalability of the network
We must guarantee atleast one mote per cell
Parameters of a Smart Dust Net
n: number of motesr: laser range of the
motesα: laser scanning angles: number of cells
po: probability mote isoperativepb: prob. motescommunicates with the BTSpc: prob. that two motes are aligned
BTS: Base station
Terrain = grid
αr
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Observations
Communication is unidirectional
We must assure minimum s and n to guarantee:
CoverageConnectivityBroadcast/routing algorithms
Our results: coverage
SizeTo assure complete coverage, n should be at
least
n = ((1+ ε) /po ) (s ln s)
with ε a constant depending on the parameters of the moteTherefore, n = O (s ln s)
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Our results: connectivity
ConnectivityIf
n = O (s ln s)with high probability:
For all x and y there is a directed path from x to y
and another from y to x.
Localization algorithms
Motes communicating with BTS receive theirposition
Motes knowing their position performs a scanning along its angle sending coordinates
Every mote that does not know its coordinatewaits to receive four positions and computescoordinates
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Observations
The probability that after a constant number of steps all operative interior motes know their position, rapidlyapproaches 1.
For instance for po = 0.75
For 1000 motes after 5 steps: 91.9% of motes know their positionFor 2000 motes after 5 steps: 96.2% of motes know their positionFor 5000 motes after 5 steps: 98.1% of motes know their positionFor 10000 motes after 5 steps: 98.2% of motes know their positionFor 15000 motes after 5 steps: 98.3% of motes know their position
Broadcasting BTS to motes
A mote has an information that must becommunicated to all the remaining motes in the network
Broadcast:
All motes at level i perform a full scan sending the message and its level number and sleeps
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Routing motes to BTS
A set of communicating motes have acommunication system that allows them tocollect and send information to a master monitoring system.
Other motes will send their measurement and act as routers.
Energy consumption
Broadcast:The energy consumption per mote is that of a scanning plus the energy lost in awakening and sleeping.
Routing:The energy is bounded by a scanning per communicating mote plus transmission to the BTS
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Outline 3
Overview of Sensor NetworksSmartdust NetworksRadio Smartdust NetworksSimulation ResultsConcluding Remarks
Radio Communication Model
Optical vs Radio Transmission:Sector (α)Circle (R)Line (L)
L
R
α
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Collisions
Every mote in the red area has interference
How to deal with collisions?
How to deal with collisions?
Direct approach is impossible
Geometric approach becomes too complex
To take profit of the random power !
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Our broadcast with collisions
Basic principle:
If a mote receives a message without collisionResends the messageTurns itself off
else the other alive motes just wait
Energy consumption: only one operation per mote
Our probabilistic broadcast
Basic principle:If a mote receives a message without collision
then with probability p• resends the message• turns itself off
else with probability 1-p • just wait
else all the other alive motes just waitEnergy consumption: only one operation per moteLess collisions but 1/p slowdown
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Our ln n broadcast
Basic principle:If a mote receives a message without collision then chooses randomly a k smaller than ln n
• waits for clock cycle number k• sends the message• turns itself off
else all the other alive motes just wait
Energy consumption: only one operation per moteLess collisions and only ln n slowdown
Outline 4
Overview of Sensor NetworksSmartdust NetworksRadio Smartdust NetworksSimulation ResultsConcluding Remarks
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Last step K=25 Outline 5
Overview of Sensor NetworksSmartdust NetworksRadio Smartdust NetworksSimulation ResultsConcluding Remarks
81
Open research areas
Distributed processingData organized systemsMassive programming
Specialized algorithmsAggregationAdaptative reliabilityEnergy mappingAutoconfiguration
Synchronization issuesExperimental infrastructureEvolutive modeling
Very rich research area
When we imagine this