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research & development

Wireless Sensor NetworksDeployment Guidelines

Dr Mischa DohlerSenior ExpertFT R&D

23 January 2007

MastersPHY – Mischa Dohler – 2/45 research & development France Telecom Group

� Review and Overview

� Wireless Sensor Networks

� Fundamental Scaling Laws

� Designing a WSN

1

2

3

Lecture's Outlook

4


1Review and Overview


Semester Review/Overview

� Introduction to Wireless Communication Systems� overview of the topic [JMG]� validity of assumptions [JMG]

� Point-to-Point Communication Modelling� wireless channel characteristics [MD]� error rates and PHY-layer modelling [JMG]

� System Modelling and Behaviour� resource sharing and MAC-layer [MD]� interference modelling [JMG]

� Practical System Studies� wireless local area network [JMG]� wireless sensor network [MD]

� Chosen Topics� scientific paper presentations [students]


Review of MAC [1/3]

� Resources allocation:� FDMA: frequency slot allocation� TDMA: time slot allocation

� CDMA: code allocation

� OFDMA: multiple carrier allocation� MC-CDMA: code & multiple carrier allocation

� Up/downlink duplex:� FDD: up & downlink in different bands but at same time

� TDD: up & downlink in same band but at different time

� Conflict management:� centralised: central entity decides when, where, how

� distributed: every user has to reserve resources himself


Review of MAC [2/3]

� TDMA is conflict-free and has the below characteristics:

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 110

0

101

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Normalised System Throughput [linear]

Nor

mal

ised

Del

ay [l

ogar

ithm

ic]

10 TDMA Users100 TDMA Users1000 TDMA Users


Review of MAC [3/3]

� ALOHA is contention-based and has the below characteristics:

10-3

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10-1

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Normalised Offered Load [logarithmic]

Nor

mal

ised

Sys

tem

Thr

ough

put [

linea

r]

Pure ALOHASlotted ALOHA


Aim of Today's Lecture

� Apply previously acquired knowledge on� wireless channel,� PHY layer performance (BER, interference, etc),

� MAC layer performance (throughput, interference, etc),

� To design a properly functioning Wireless Sensor Network (WSN):


2Wireless Sensor Networks


WSN Applications [1/7]

� University of California (UC) Berkley Center for Built Environment:� hundreds of sensors on 3 floors;� wiring cost by far outweighs sensor cost.



� Great Duck Island WSN [http://www.greatduckisland.net]:� environmental factors influence good nests and their variations;� measurement of occupancy patterns during incubation;

� use of UCB motes, IEEE 802.11b and satellite.



� UC Botanical garden sensor network project:� to help understand growth dynamics, water intake, nutrient transport;� sensors measure light, humidity, pressure and temperature;

� data sampled every 5 minutes.



� Center for Built Environment at UC Berkeley:� improve building energy efficiency, improve indoor environmental quality;� wiring is most of a sensor network's cost;

� small enough to embed in furniture or ceiling tiles.



� River bed monitoring in South of France:� floodings of coastal rivers along the Med;� experiment on-going which monitor rain fall, water level and raise alert;

� proprietary meshed network, multi-hop, to GPRS gateways.



� Automated meter reading (Les Sables d'Olonnes) [www.coronis.com]:� advantages: no appointment needed, meter need not be accessible from street,

billing for actual usage - no extrapolation, billing at appropriate interval, customer can review usage online, customer can be alerted in case of event;

� Coronis technology: hybrid sensor meshed network (low RF power nodes, higher power aggregation nodes, GPRS gateways).



� Recycling containers (Voiron, France):� reduces cost (no more random collection), reduced dissatisfaction (no more

spillovers), protects investment (real time theft alert)

� France Telecom technology: ultrasound level sensing, shock detection, local ad-hoc network and cellular backhaul.

Internet


WSNs Are Different

� WSNs bear some fundamental design differences, such as:� Application: wide variety (≠ any wireless system)� Control: decentralized (≠ cellular, broadcast, satellite)

� Info Flow: highly directed (≠ ad hoc)

� Energy: highly constrained (≠ any wireless system)� Run-Time: very long (≠ any wireless system)

� Nodes: huge amounts (≠ any wireless system)

� This means that, unlike other systems, WSNs need to be:� highly application tailored (should work for many applications)

� highly energy efficient (at all layers and functionalities)

� highly scalable (should work at arbitrary number of nodes)� highly secure (robustness, integrity and confidentiality)

� Is this possible? Let's examine some of these issues.


3Fundamental Scaling Laws


When is 'large' really large? [1/2]

� Ad hoc works well among entities with no conflicts of interest:� our circle of true friends (small number)� soldiers of ant colonies (large number)

� Problems arise with frictions, competition and interests:� three children left on their own (small number)� state without government (large number)

� 'Large' is hence not about size!

� It is about managing:� existing and emerging conflicts, and hence the

� amount of overheads needed to facilitate (fair) communication.


When is 'large' really large? [2/2]

� History has shown that if you have a fair centralised entity, use it!� pure ad hoc has not worked for several decades� cellular networks are very large but work very well

� real-world WSNs use hierarchies with centralised entities

� Hierarchical real-world WSNs include Coronis (left) and Intel (right):[www.coronis.com] [www.intel.com/research]


Grand 3 Scaling Laws

� Kumar & Gupta's Throughput Scaling Law:� quantifies theoretical network capacity limit � assuming that everybody talks with everybody

� this law is well accepted in the academic community

� Odlyzko & Tilly's Value Scaling Law:� quantifies the value of a network

� everybody has circles of friends with decreasing importance� this law, in its original form, is known in the industrial community

� Practical Hierarchical Scaling Law:� quantifies network throughput for practical systems

� assuming that everybody talks only with their respective supervisor

� this law is useful in quantifying throughput


Throughput Scaling Law[1/3]

� What is the theoretically achievable throughput for this network with:� arbitrary number of antenna elements per node,� arbitrary form of cooperation among nodes,

� arbitrary channel statistics.



� Gupta & Kumar produced a milestone capacity paper, assuming:� Gaussian communication channel, and� arbitrary form of cooperation among nodes,

� and giving very useful insights?!



� The insights which we ought to understand:� network throughput scales with , where N is total number of nodes� hence, no matter what we try, we cannot design a scalable protocol

� topologies different from pure ad hoc have to be invoked

NN log1 ⋅

0 1000 2000 3000 4000 5000 6000 7000 8000 9000 100000.05

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Number of Nodes n

Tota

l Net

wor

k Th

rou

ghpu

t


Value Scaling Law[1/3]

� People like Reed, Metcalfe, Odlyzko, etc., tried to quantify:� value of below network with increasing N� assuming different behaviours among nodes

� … and giving very different answers …



� Several value scaling laws have emerged in recent decades:� Sarnoff's Law (broadcast): ～N� Reed's Law (equal communities): ～2N

� Metcalfe's Law (equal members): ～N2

� Odlyzko's Law (non-equal members): ～N log N

� The justification behind Odlyzko & Tilly's scaling law is simple:� Zipf's Law:

• order large collection of entities by size or popularity• the entity ranked k-th, will be about 1/k of the first one

� added value of a node to talk to the remaining nodes is:• ½+1/3+1/4+ … 1/(N -1) ～ log N

� since there are n nodes, the network value is proportional to:• N log N



� The insights which we ought to understand:� value of network does not grow that fast with increasing members N� using some mathematics, one can show that clustering increases value

� for below given network of 10,000 nodes, the optimum number of clusters is 95

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Number of Clusters [logarithmic]

Tota

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alue


Hierarchical Scaling Law[1/3]

� Value scaling law did not quantify throughput; we hence assume:� below 2-tier hierarchy with clusters and cluster-heads� nodes can only communicate with respective cluster-head

� all cluster-heads need to communication among each other



� We assume for our simple throughput analysis:� 2-tier hierarchy� N total nodes, C clusters and hence M=N/C nodes per cluster

� This 2-tier hierarchy requires 2 communication phases:� first phase: all nodes communicate with respective cluster-heads� second phase: all cluster-heads communicate among each other

� Assuming all data pipes to have equal rates:� first phase: M time slots to transmit N bits� second phase: M*C*(C-1) time slots to transmit N bits to everybody

� efficiency: N/(M+M*C*(C-1)) … no new info in second phase!

� Assuming cluster-heads' pipes to be r times stronger:� first phase: M time slots to transmit N bits

� second phase: M*C*(C-1)/r time slots to transmit N bits to everybody

� efficiency: N/(M+M*C*(C-1)/r) … no new info in second phase!



� The insights which we ought to understand:� with equal data pipes, there is little sense in doing clustering� with cluster-heads being more powerful, an optimum cluster size can be achieved

� this behaviour, of course, changes with different underlying assumptions

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Number of Clusters

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sensors @ 1 kbps & cluster-heads @ 1 kbpssensors @ 1 kbps & cluster-heads @ 100 kbps (ZigBee)sensors @ 1 kbps & cluster-heads @ 1 Mbps (Bluetooth)


4Designing a WSN


Functions, Layers & Tools

� Business case dictates design process:


Business Case

� Services:� create ubiquitous service experience using cellular, WLAN, fixed nets & sensors;� allow existing customer base to build their own ambient environments;

� facilitate a service platform (e.g. automatic meter readings).

� CAPEX (capital expenditure):� research and production or purchase of WSN nodes;

� roll-out of wireless sensor nodes (enormous savings due to absence of cables);� deployment of service solutions (software mainly).

� OPEX (operational expenditure):� replacement of faulty nodes, batteries and software bugs;

� customer service for clientele.


Requirements – Scenario [1/3]

� Scenario of environmental monitoring in Grenoble:� build reliable, autonomous, solutions for event reporting;� transfer sensed data quickly to appropriate processing units;

� make sure that no problems arise with the scaling of the network;

� facilitate installation (self-organising) and automatic reparation (self-healing).

processing unit

processing unit


Requirements – Characteristics [2/3]

� Data characteristics:� data measurements: every 15 minutes; radius of 100m; T, pH, wind, CO2, etc;� data reporting: regular reporting (4h); triggered alarm (radical changes);

� data rate and size: 1ksps with packets of over-the-air 128 symbols at 400MHz;

� packet error rate: 10% maximum;� data confidentiality: no security, but reliability and integrity;

� Node characteristics:� node types: energy constrained, not rechargeable; 3.6V, 3200mAh;� node IDs: per default, the physical ID is the same as the logical ID;

� node behaviour: ramp-up/birth; operation/life; depletion/death;

� Topology characteristics:� roll-out: random, 1 node in average every 100 meters;

� node density: 1e-4 nodes / m2;

� topology: 2D - flat or hierarchical;� flow direction: multiple sinks possible.


Requirements – Channel [3/3]

� WSN node location:� at ground level;� embedded into walls, objects;

� in rich clutter environments; rarely LOS.

� Pathloss:� -40 dB power loss per decade distance.

� Shadowing:� 6dB standard deviation.

� Fading:� frequency-flat (mainly because of low data rates);� little time selective (average clutter mobility of 1m/s);

� highly spatial selective (because of rich clutter).


Techno Design – PHY [1/8]

� BPSK Modulation:� 128 over-the-air BPSK symbols = 128 bits packet;

� Interleaver:� channel coherence time ～ 1/fDoppler = c/v/fc = 3e8/1/400e6=750ms;� symbol duration = 1ms;

� interleaver size is hence 750 � too big!

� hence no interleaving in time; interleaving in frequency possible.

� Channel coder:� either no channel coder, i.e. (128-CRC) bits are useful bits;� BCH code which can detect 2 errors, i.e. (128-16-CRC) bits useful.

� Cyclic redundancy check is imperative, e.g. CRC = 12 bits.



� Packet error rate versus distance (moral: don't use code!):

0 100 200 300 400 500 600 700 800 900 10000

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1

Distance [m]

Pac

ket E

rror

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e

no code, no shadowingwith code, no shadowingno code, with shadowingwith code, with shadowing



� PHY efficiency considering packet errors, code & CRC overhead:

0 100 200 300 400 500 600 700 800 900 10000

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Distance [m]

PH

Y-L

ayer

Effi

cien

cy

no code, no shadowingwith code, no shadowingno code, with shadowingwith code, with shadowing


Techno Design – MAC [4/8]

� Medium Access Control protocols need to:� facilitate communication in a distributed fashion;� resolve resource conflicts between nodes;

� minimise energy consumption.

� Sources of energy wastage:� idle listening (listening to an idle channel);

� packet collisions (requiring re-transmission);� overhearing (reception of irrelevant frames or signals);

� overhead (transmission and reception of control frames).

� Typical energy consumption ratios (unitless):� hibernate: 1

� idle: 150� Tx & Rx: 10000



� Throughput is same for traditional non-energy optimised MACs:

at low loads, all MACshave same throughput

offered load

thro

ug

hp

ut



� Energy-optimised MACs yield a better power consumption versus (low) traffic arrival [C. Enz, et al., 2004]:



� Even better MACs are micro-frame preamble (MFP) sampling protocols compared to low power listening (LPL) protocols [A. Bachir, et al., 2006]:


Techno Design – NTW [8/8]

� Example: Wavenis uses some basic but effective NTW rules:� A network features a “virtual hierarchical” network.� A new device initiates the connection to become member of existing network.

� The Quality of Service (QoS) of a device consists of a combination of RSSI, the remaining battery energy, the number of children.

� Initial conditions depend on the application & the network topology.

� This allowed below topology to be successfully organised:� before after


Implementation Design

� Energy is the main implementation design driver:� not renewable batteries (sensor needs to use <1mW; Moor's Law doesn't apply)� energy harvesting (regular re-charge; e.g. 1 mW/cm2 in direct sunlight).


5Closing Remarks


Some Thoughts

� Wireless sensor networks are highly application driven.

� Unlike other wireless systems, wireless senor networks are highly energy-constrained.

� Designing WSNs is a cross-community exercise (IT, telecom, etc). Novel approaches borrowed from physics, biology, etc, are needed to understand such systems.

� WSNs mean business; mainly, because machine-to-machine exhibit less "inhibitions" to communicate.


Acronyms

� A list of some important acronyms used in the lecture:� BCH Bose, Ray-Chaudhuri, Hocquenghem (Channel Code)� BPSK Binary Phase Shift Keying (Modulation)

� CAPEX Capital Expenditure

� CRC Cyclic Redundancy Check� CSMA Carrier Sensing Multiple Access

� GPRS General Packet Radio Service

� LPL Low Power Listening (MAC Protocol)� MAC Medium Access Control (Layer)

� MFP Micro-Frame Preamble (MAC Protocol)

� NTW Network (Layer)

� OPEX Operational Expenditure� PHY Physical (Layer)

� QoS Quality of Service

� Rx, Tx Receiver, Transmit


Advanced Topics

� If you really want to get into resource allocation mechanisms, here some important topics which I didn't have time to deal with:

� trade-offs between existing WSN MAC protocols

� routing protocols and associated trade-offs

� auto-organisation & auto-healing mechanisms� cross-layer designs to improve energy efficiency


My Recommendations

� Some good books related to this lecture are:� Ian Akyildiz, et al. “Wireless Sensor Networks”.� Ananthram Swami, et al. “Wireless Sensor Networks: Signal Processing and

Communications”.� Holger Karl, et al. “Protocols and Architectures for Wireless Sensor Networks”.

� Some good articles related to this lecture are:� Ian Akyildiz, et al. "A survey on sensor networks," IEEE Communications

Magazine, August 2002.

� Some good online articles related to this lecture can be found at:� http://teachware.distlab.dk/� http://www.comsoc.org/freetutorials/nsc/

� … there is always http://en.wikipedia.org/


Credits

� Some graphs/figures in this lecture have been reproduced from:

� Dominique Barthel's lecture on "Ad Hoc & Sensor Networks", 2006.

� Abdelmalik Bachir's PhD thesis and presentations, 2006.

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