wireless sensor networks (wsn)

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1 Wireless Sensor Networks (WSN)

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Wireless Sensor Networks (WSN). Comparing to MANET. Similarities No infrastructure Multi-hop communication Differences Nodes are more resource-constrained and more prone to failure More nodes (up to hundreds or thousands) in a network Random deployment Unattended Longer life time - PowerPoint PPT Presentation

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Page 1: Wireless Sensor Networks (WSN)

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Wireless Sensor Networks (WSN)

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Comparing to MANET

• Similarities– No infrastructure– Multi-hop communication

• Differences– Nodes are more resource-constrained and more prone to failure – More nodes (up to hundreds or thousands) in a network– Random deployment– Unattended– Longer life time– Trust relationships between sensor nodes (typically belong to

the same organization)– Application-specific– …

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Usage of Sensor Networks

• Environmental observation– Water/air pollution detection– Forest fire detection– Animal habitat monitoring

• Military monitoring– Battlefield surveillance– Vehicular traffic monitoring– Tracking the position of the enemy

• Building monitoring– Monitoring climate changes/vibration

• Healthcare– Being implanted in the human body to monitor medical problems

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Some Real Examples• Great Duck Island

– A prototype sensor network is deployed to monitor the nesting grounds of elusive seabirds

– Biologist get information they need with minimal human disturbance

• Vineyard– Embedded sensors are deployed to monitor temperature in a

vineyard in Oregon’s Willamette Valley.

• Golden Gate Bridge– 200 motes organized in an ad hoc sensor network are used for

tracking stress on the bridge

• Proactive Health Research Project (Intel)– Help seniors age with dignity and independence, by developing

sensor network-based in-home technology prototypes.

• Preventive maintenance on an oil tanker in the North Sea (Intel and BP)

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Sensor Nodes

• MICA2(Motes): a popular research platform at the moment

(J. Hill, et al., “The platforms enabling wireless sensor networks,” Comm. of the ACM, June 2004/Vol 47. No. 6, pages 41-46)

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Sensor Nodes: MICA2

• The core is a small, low-cost, low-power computer– Atmel Atmega 128L processor (4MHZ), 128 KB on-board flash

memory– As powerful as 8088 CPU (in original IBM PC)– Power consumption

• 8 milliamps (running), 15 micro-amps (sleep)

• One or more sensors can be mounted• Connect to the outside world with radio

– Transmission range: 10-200 feet, Rate: 76bps– Power consumption

• 25 milliamps (Trans), 10 milliamps (Recv), <1 milliamps (off)

• Power supply: 2 AA batteries– 2,000 milliamps-hours

(http://computer.howstuffworks.com/mote.htm)

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Sensor Nodes• Four classes

(J. Hill, et al., “The platforms enabling wireless sensor networks,” Comm. of the ACM, June 2004/Vol 47. No. 6, pages 41-46)

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Physical layer and MAC sublayer

• Related Standard– IEEE 802.15.4 Wireless Medium Access Control (MAC) and

Physical Layer (PHY) Specifications for Low-Rate Wireless Personal Area Networks (LR-WPANs)

• Physical layer– 2.4-2.4835 GHZ (worldwide)– 902-928 MHz (North America) or 868-868.6 MHz (Europe)

• MAC Sublayer– Contention Access

• CSMA-CA

– Contention Free

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Other MAC Protocols (not complete)

Protocol Summery

PAMAS Power Aware Multi Access protocol with Signaling for Ad Hoc Networks: uses two separate channels, one for control packets and one for data packets. It puts the nodes to sleep when the neighbor is transmitting.

S-MAC Periodic Listen and Sleep, Collision and Overhearing Avoidance, Message Passing, Implemented over Berkeley Mote.

TDMA-based MAC protocol

Assumes a gateway per cluster where the gateway assigns the time slots for each node.

Adaptive Rate Control Random Delay

Good for per node fairness. Uses adaptive rate control to adapt the originating data and route-thru traffic. Rate control mechanism uses linear increase and multiplicative decrease where each one. Their CSMA scheme uses sensor sampling phase shift to avoid capturing effect.

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Deployment

• Random deployment– Large number of nodes; target area may be remote

and/or hostile manual deployment is impossible in many cases

• Problems caused by random deployment– Localization

• Sensor nodes must discover their locations after deployment

– Coverage• For sensing quality, a certain level of sensing coverage should be

achieved.

– Security• It is hard to store various encryption keys on nodes, since the

neighborhood cannot be know a priori.

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Localization• Basic idea of existing schemes

– Initial configuration• A small number of beacon nodes

– Know their locations by using GPS or being set manually

• A large number of nodes (non-beacon nodes) that do not know their locations

– Localization process• Beacon nodes send beacon signals to a set of non-beacon nodes• A non-beacon node obtains

– Locations of the beacon nodes

– Some features related to the distance to these beacon nodes

» Received signal strength indicator, Time to arrival, etc.

• The non-beacon node estimate its own location based on the obtained information

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Sensing Coverage

• Objectives– High sensing coverage (full coverage is ideal)– Energy efficient and low cost

• Samples of existing work– Sleep scheduling (Ye et al. 03’, Gui et al. 04’)

• Over deploying sensor nodes and make spare nodes sleep• Waking up sleeping nodes following certain sleep planning

methods

– Employing mobile sensor nodes (Wang et al.03’&05’)• Deploy a large fraction of stationary nodes and a small fraction of

mobile nodes• Stationary nodes detect sensing holes and notifies mobile nodes• Mobile nodes move to heal the holes

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Network Architectures: Flat

Sink (data collector)

•A large number of sensor nodes form a peer-to-peer ad hoc network

•They forward message for each other

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Network Architecture: Hierarchical

Sink

•The network is divided into clusters, each cluster has a head (logically or physically)

•Ordinary node cluster head; cluster heads form a backbone

Cluster head

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Network Architecture: Hierarchical

(J. Hill, et al., “The platforms enabling wireless sensor networks,” Comm. of the ACM, June 2004/Vol 47. No. 6, pages 41-46)

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Communication Patterns• A sensor network is a content-based (or data-

centric) network– In WSN, networking take place directly on contents (or

data)• In Internet/MANET, networking protocols use identifiers of nodes.

– Contents can collected, processed and stored in the network

• Desirable interaction paradigm in WSN: Publish/Subscribe– Entities can publish data under certain names– Entities can subscribe to updates of such named data

(H. Karl, “Ad hoc and sensor networks Chapter 12: Data-centric and content-based networking”)

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Naming

• Content based naming– Tasks are named by a list of attribute – value

pairs– Task description specifies an interest for data

matching the attributes • Animal tracking:

Interest ( Task ) DescriptionType = four-legged animalInterval = 20 msDuration = 1 minuteLocation = [-100, -100; 200, 400]

RequestRequest

Node dataType =four-legged animalInstance = elephantLocation = [125, 220]Confidence = 0.85Time = 02:10:35

ReplyReply

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Communication Patterns

• External storage-based pattern – fixed subscriber

• Sink-initiated pattern– subscriber initiated

• Source-initiated pattern– publisher initiated

• Rendezvous-based pattern– Intermediate entities help to match publishers and subscribers

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External Storage-based Pattern

source

sink

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Sink-initiated Pattern: Directed Diffusion

(Intanagonwiwat et al., “Directed Diffusion for wireless sensor networking,”, IEEE/ACM Transactions on Networking (TON), Feb, 2003)

• The sink periodically broadcasts interest messages to each of its neighbors

• Every node maintains an interest cache– Each item corresponds to a distinct interest– No information about the sink– Interest aggregation : identical type, completely

overlap rectangle attributes• Each entry in the cache has several fields

– Timestamp: last received matching interest– Several gradients: data rate, duration, direction

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• Set up gradient: Constrained or Directional flooding based on location.

Source

Sink

Interest

Gradient

Directed Diffusion

(Intanagonwiwat et al., “Directed Diffusion for wireless sensor networking,”, IEEE/ACM Transactions on Networking (TON), Feb, 2003)

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Source

Sink

Gradient

Data

Directed Diffusion

(Intanagonwiwat et al., “Directed Diffusion for wireless sensor networking,”, IEEE/ACM Transactions on Networking (TON), Feb, 2003)

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Source-initiated Patterns

Source

Dissemination Node

Sink

Data Announcement

Query

Data

Immediate DisseminationNode

(Ye et al., “TTDD: A Two-tier Data Dissemination Model for Large-Scale Wireless Sensor Networks,” Mobicom’02)

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PDA

Rendezvous-based pattern

(Ratnasamy et al., “Data-centric Storage in Sensor Networks with GHT,” Mobile Networks and Applications, 2004.)

• Data with the same name are stored at the same place

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Rendezvous-based pattern

(Ratnasamy et al., “Data-centric Storage in Sensor Networks with GHT,” Mobile Networks and Applications, 2004.)

• Fault tolerance consideration

• If a storing fails, it is replaced by another node closest to itself

• To protect the stored data, data can be replicated in multiple nodes

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Routing• Address-centric (AC) protocol

– Each source independently sends data along the shortest path to sink based on the route that the queries took (“end-to-end routing”)

• Data-centric (DC) protocol– The sources send data to the sink, but routing nodes en-route look at

the content of the data and perform some form of aggregation/consolidation function on the data originating at multiple sources.

(Krishnamachari et al., “Modeling data-centric routing in wireless sensor networks”, 2002.)

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Data Aggregation• Types

– Removing redundancy– In-network processing

Redundancy

Sensing range of A

Sensing range of B

After aggregation, the redundancy is removed

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• Aggregation is implemented by three functions– Merging function f, initializer i and evaluator e.

• General form <z> = f (<x>,<y>)– <x> and <y> are multi valued partial state records– <z> is partial state record resulting from application of

f to <x> and <y>

• E.g. f is the merging function of AVERAGE

f(<S1,C1>,<S2,C2>)=<S1 + S2, C1 + C2>i(x) = <x,1>

e(<S,C>) = S/C where S and C are Sum and Count. <3,1> <5,1>

e(<15,3>) = 15/3 = 5

i(3)=<3,1> i(5)=<5,1>

i(7)=<7,1>

f(<3,1>,<5,1>) = <8,2>

f(<8,2>,<7,1>) = <15,3>

Data Aggregation: in-network processing

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• Major challenges– Resource constraints in computation, storage

and communication• Public key is too expensive

– Private key operations in MICA2: a few milliseconds– RSA-based public key operations: tens of seconds (50-

60)– ECC-based public key operations: tens of seconds

(~30s)• Security mechanisms must be low-cost

– Unattended deployment & lack of temper resistance

• Must address node compromise

Security Issues

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• Existing research– Low-cost key management

• Pair-wise key & group-wise key– Message and entity Authentication– Securing protocols in sensor networks

• Localization• Data aggregation• …

– Privacy/Anonymity• Protecting data source

(details will be discussed in later classes)

Security Issues

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• Zigbee Aliance

– The ZigBee Alliance is an association of companies working together to enable reliable, cost-effective, low-power, wirelessly networked, monitoring and control products based on an open global standard.

– Focus• Defining the network, security and application software

layers    • Providing interoperability and conformance testing

specifications   • Promoting the ZigBee brand globally to build market

awareness   • Managing the evolution of the technology

– Members: more than 100 companies

Standardization of Sensor Networks

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Zigbee Specification (June 2005)