Lecture 13 Wireless sensor networks 13 Wireless sensor networks ... where λk is the failure rate of...

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Lecture 13 Wireless sensor networks Dr. Sasitharan Balasubramaniam Nano Communication Centre Dept. of Electronic and Communication Engineering Tampere University of Technology ([email protected])

Transcript of Lecture 13 Wireless sensor networks 13 Wireless sensor networks ... where λk is the failure rate of...

Lecture 13 Wireless sensor networks

Dr. Sasitharan Balasubramaniam Nano Communication Centre

Dept. of Electronic and Communication Engineering Tampere University of Technology

([email protected])

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Outline Introduction to Sensor Networks Sensor Network Protocols Applications

Introduction to Sensor Networks

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Introduction to Wireless Sensor Networks Sensor network

Is a subset of ad hoc networks The basic ideas are similar; a lot of differences

Sensor network definition Distributed networks of small, lightweight wireless nodes, deployed in large numbers to monitor the environment, body, or system by measurement of physical parameters such as temperature, pressure, etc.

Building of sensors Advances in micro-electro mechanical systems (MEMS)

Sensor operates …… as terminal: generates own traffic as transit node: relays traffic from other nodes

Sensor device

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http://rtcmagazine.com

Ad hoc vs. WSNs

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Vs.

Ad Hoc Networks Sensor Networks

Wireless Sensor Networks

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http://plaza.ufl.edu/cdliao/image/sensorNework.jpg

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WSN applications Emergency scenarios Telemetry Security systems Medical systems Underwater sensing Military applications RFID: identification, control, security … many others

Typical features of sensors (1)

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IEEE Communications Magazine • August 2002 103

SENSOR NETWORKSCOMMUNICATION ARCHITECTURE

The sensor nodes are usually scattered in a sensorfield as shown in Fig. 1. Each of these scatteredsensor nodes has the capabilities to collect dataand route data back to the sink. Data are routedback to the sink by a multihop infrastructurelessarchitecture through the sink as shown in Fig. 1.The sink may communicate with the task managernode via Internet or satellite. The design of thesensor network as described by Fig. 1 is influ-enced by many factors, including fault tolerance,scalability, production costs, operating environment,sensor network topology, hardware constraints,transmission media, and power consumption.

DESIGN FACTORSThe design factors are addressed by manyresearchers as surveyed in this article. However,none of these studies has a fully integrated viewof all the factors driving the design of sensornetworks and sensor nodes. These factors areimportant because they serve as a guideline todesign a protocol or an algorithm for sensor net-works. In addition, these influencing factors canbe used to compare different schemes.

Fault Tolerance — Some sensor nodes may failor be blocked due to lack of power, or havephysical damage or environmental interference.The failure of sensor nodes should not affect theoverall task of the sensor network. This is thereliability or fault tolerance issue. Fault toler-ance is the ability to sustain sensor networkfunctionalities without any interruption due tosensor node failures [1, 2]. The reliability Rk(t)or fault tolerance of a sensor node is modeled in[2] using the Poisson distribution to capture theprobability of not having a failure within thetime interval (0,t):

Rk(t) = e–λk t, (1)

where λk is the failure rate of sensor node k andt is the time period.

Sca l a b i l i t y — The number of sensor nodesdeployed in studying a phenomenon may be onthe order of hundreds or thousands. Dependingon the application, the number may reach anextreme value of millions. New schemes must beable to work with this number of nodes. Theymust also utilize the high density of the sensornetworks. The density can range from few sensornodes to few hundred sensor nodes in a region,which can be less than 10 m in diameter. Thedensity µ can be calculated according to [3] as

µ(R) = (N ⋅ π R 2) /A, (2)where N is the number of scattered sensor nodesin region A, and R is the radio transmissionrange. Basically, µ(R) gives the number of nodeswithin the transmission radius of each node inregion A.

Product i on Costs — Since sensor networksconsist of a large number of sensor nodes, thecost of a single node is very important to justifythe overall cost of the network. If the cost of the

network is more expensive than deploying tradi-tional sensors, the sensor network is not cost-jus-tified. As a result, the cost of each sensor nodehas to be kept low. The state-of-the-art technol-ogy allows a Bluetooth radio system to be lessthan US$10 [4]. Also, the price of a piconode istargeted to be less than US$1. The cost of a sen-sor node should be much less than US$1 inorder for the sensor network to be feasible. Thecost of a Bluetooth radio, which is known to bea low-cost device, is even 10 times more expen-sive than the targeted price for a sensor node.

Hard w are Const ra i nts — A sensor node ismade up of four basic components, as shown inFig. 2: a sensing unit, a processing unit, a transceiv-er unit, and a power unit. They may also haveadditional application-dependent componentssuch as a location finding system, power generator,and mobilizer. Sensing units are usually composedof two subunits: sensors and analog-to-digital con-verters (ADCs). The analog signals produced bythe sensors based on the observed phenomenonare converted to digital signals by the ADC, andthen fed into the processing unit. The processingunit, which is generally associated with a smallstorage unit, manages the procedures that makethe sensor node collaborate with the other nodesto carry out the assigned sensing tasks. Atransceiver unit connects the node to the network.One of the most important components of a sen-sor node is the power unit. Power units may besupported by power scavenging units such assolar cells. There are also other subunits that are

■ Figure 1. Sensor nodes scattered in a sensor field.

Sensor field Sensor nodes

ED C B

A

Internet andsatellite

Task managernode

User

Sink

■ Figure 2. The components of a sensor node.

Sensing unitProcessing

unit

Location finding system Mobilizer

ADCSensor TransceiverProcessor

Storage

Power unit Powergenerator

Sensor has extremely limited resources Core units: Sensing, Processing, Transceiver, Power units

I. F. Akyildiz, W. Su, Y. Sankarasubramaniam, E. Cayirci, "A Survey on Sensor Networks, IEEE Communications Magazine," vol. 40, no. 8, pp. 102-116, August 2002.

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Typical features of sensors (2)

Using huge number of sensors makes the WSN High density (20 sensors/m3)

Should be…… Robust Reliable Accurate

Sensor Network topology – random and dynamic Deployment phase – thrown from a plane or rocket OR placed one by one (robots or humans) Failures in nodes (battery ran out, jamming, noise, obstacles) Redeployment of additional nodes

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WSN weak points Wireless channel

Very high error probability Omnidirectional antennas (broadcast communication) Prone to Attacks

Physical destruction risk Prone to failures (Physical damage, lack of power, environmental interference)

Lifetime Limited by battery Batteries are usually not replaceable Energy harvesting techniques (Solar ?) Wireless charging

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Power consumption 1/2 Limited power (< 0.5 Ah, 1.2v)

HUGE DESIGN FACTOR

Data sensing, processing Communications

Hearing the network: joining, routing Sending data: own + relay data

Active sensor states Data transmission Data receiving Idle

Passive sensor state Sleeping mode On/off is not effective, takes a lot of energy

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Power consumption 2/2 In active state

Very high Difference between idle and transmission is not huge

In passive state Much lower compared to active state

Power consumption, in average Transmit : receive : idle : sleeping 13/9/7/1

Important questions When to go to sleep? When to wake up again? Scheduled active/sleeping modes? The schedule can be distributed among neighbors

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Characteristics of WSNs (1) The number of nodes (Huge! Can be thousands, maybe millions. Why?)

Fault tolerance, low transmission range, huge covering areas Scalability problems for MAC/Routing!!!

Mobility Nodes can be stationary or mobile

Addressing Unique global addressing is not needed

http://ascc.okstate.edu/content/resources.

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Technical issues in WSNs Architecture Operating System Software Transmission media

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Architecture The minimal requirements for a theoretical sensor CPU

< 20 MHz

Memory < 4 КB RAM

Transmission rate < 20 kbps

Varying sizes…… Centimeters or millimeters (environmental sensors) Centimeters (underwater sensors)

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OS Main features

Optimized in accordance to architecture Embedded Small code, tens of kbytes

Developed platforms RSC WINS & Hidra Sensoria WINS UCLA’s iBadge UCLA’s Medusa MK-II MIT’s µAMPs Berkeley’s Motes

TinyOS

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Berkeley’s Motes Functions

Communication Data processing Sensing

Example Microcontroller Communications module Sensor module Measurements

Temperature Light Humidity Etc.

TinyOS

10 kbps @ 20 meters

Transmission Medium Radio (RF circuit design)

µAMPS uses Bluetooth compatible 2.4Ghz Single channel – 916 MHz

Optical Require line of sight

Infrared License free Robust to interference from electrical devices Cheaper and easier to build Require line of sight

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Sensor Network Protocols

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Protocol Stack for WSN

Protocol stack for WSN

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IEEE Communications Magazine • August 2002 105

low in power and cannot participate in routingmessages. The remaining power is reserved forsensing. The mobility management plane detectsand registers the movement of sensor nodes, soa route back to the user is always maintained,and the sensor nodes can keep track of whotheir neighbor sensor nodes are. By knowingwho the neighbor sensor nodes are, the sensornodes can balance their power and task usage.The task management plane balances and sched-ules the sensing tasks given to a specific region.Not all sensor nodes in that region are requiredto perform the sensing task at the same time. Asa result, some sensor nodes perform the taskmore than others depending on their powerlevel. These management planes are needed sothat sensor nodes can work together in a power-efficient way, route data in a mobile sensor net-work, and share resources between sensor nodes.

THE PHYSICAL LAYERThe physical layer is responsible for frequencyselection, carrier frequency generation, signaldetection, modulation, and data encryption.Thus far, the 915 MHz industrial, scientific, andmedical (ISM) band has been widely suggestedfor sensor networks. Frequency generation andsignal detection have more to do with the under-lying hardware and transceiver design and henceare beyond the scope of our article. In the fol-lowing discussion, we focus on signal propaga-tion effects, power efficiency, and modulationschemes for sensor networks.

It is well known that long distance wirelesscommunication can be expensive, in terms ofboth energy and implementation complexity.While designing the physical layer for sensor net-works, energy minimization assumes significantimportance, over and above the propagation andfading effects. In general, the minimum outputpower required to transmit a signal over a dis-tance d is proportional to dn, where 2 < =n < 4.The exponent n is closer to four for low-lying

antennae and near-ground channels [6], as is typ-ical in sensor network communication. This canbe attributed to the partial signal cancellation bya ground-reflected ray. Measurements carriedout in [10] indicate that the power starts to dropoff with higher exponents at smaller distances forlow antenna heights. While trying to resolvethese problems, it is important that the designeris aware of inbuilt diversities and exploits this tothe fullest. For instance, multihop communica-tion in a sensor network can effectively overcomeshadowing and path loss effects, if the node den-sity is high enough. Similarly, while propagationlosses and channel capacity limit data reliability,this very fact can be used for spatial frequencyreuse. Energy-efficient physical layer solutionsare currently being pursued by researchers.Although some of these topics have beenaddressed in literature, it still remains a vastlyunexplored domain of wireless sensor networks.A discussion of some existing ideas follows.

The choice of a good modulation scheme iscritical for reliable communication in a sensornetwork. Binary and M-ary modulation schemesare compared in [8]. While an M-ary scheme canreduce the transmit on-time by sending multiplebits per symbol, it results in complex circuitryand increased radio power consumption. Thesetrade-off parameters are formulated in [8], andit is concluded that under startup power domi-nant conditions, the binary modulation scheme ismore energy-efficient . A low-power direct-sequence spread-spectrum modem architecturefor sensor networks is presented in [11]. Thislow-power architecture can be mapped to anapplication-specific integrated circuit (ASIC)technology to further improve efficiency.

Ultra wideband (UWB) or impulse radio (IR)has been used for baseband pulse radar and rang-ing systems, and has recently drawn considerableinterest for communication applications, especial-ly in indoor wireless networks. UWB employsbaseband transmission and thus requires no inter-mediate or radio carrier frequencies. Generally,pulse position modulation (PPM) is used. Themain advantage of UWB is its resilience to multi-path [12]. Low transmission power and simpletransceiver circuitry make UWB an attractive can-didate for sensor networks.

OPEN RESEARCH ISSUESThe physical layer is a largely unexplored area insensor networks. Open research issues rangefrom power-efficient transceiver design to modu-lation schemes:• Modulation schemes: Simple and low-power

modulation schemes need to be developedfor sensor networks. The modulationscheme can be either baseband, as in UWB,or passband.

• Strategies to overcome signal propagationeffects

• Hardware design: Tiny, low-power, low-costtransceiver, sensing, and processing unitsneed to be designed. Power-efficient hard-ware management strategies are also essen-tial. Some strategies are managingfrequencies of operation, reducing switch-ing power, and predicting work load in pro-cessors.

■ Figure 3. The sensor networks protocol stack.

Application layer

Transport layer

Network layer

Data link layer

Physical layer

Power m

anagement plane

Mobility m

anagement plane

Task managem

ent plane

The physical layer

is responsible

for frequency

selection, carrier

frequency

generation, signal

detection,

modulation and

data encryption.

Thus far, the

915 MHz ISM

band has been

w idely suggested

for sensor

networks.

Physical Layer Requirements:

Simple low-power modulation schemes Strategies to overcome signal propagation Simple, tiny, low-cost, hardware design

Binary (more efficient) and M-ary modulation schemes have been proposed

Ultra wideband (UWB)

Uses baseband transmission and no need for carrier frequency Low transmission power Simple transceiver circuitry

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Data Link Layer (1) Why cant conventional MAC be used in sensor networks

In cellular networks, centralized controller used to allocated access for mobile devices (impractical for sensor networks!)

Power is not an issue. Although mobile devices have battery, users can change them

Topological changes in sensor networks are very different from Ad Hoc network

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Data Link Layer (2) Self-Organizing Medium Access Control for Sensor Networks (SMACS)

Distributed algorithm

Neighbourhood discovery and channel assignment is done together

Pair of time-slots between two nodes are selected for random but fixed frequencies. Neighbours need to be time synchronized

Power conservation through random wake up and turning off radio during idle phase

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Data Link Layer (3) Cooperative Asynchronous Multi-channel MAC protocol (CAM-MAC)

Distributed algorithm

Multiple channel: 1 control channel, 3 data channel.

Negotiation and listening happens on the control channel.

Illegal negotiation of the nodes can be vetoed by a node.

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C D1 D2 D3

fc f1 f2 f3

Network Layer - Routing

Routes can be found on available power (PA) or energy required (α) Maximum PA route: Route 2, but route 4 is preferred Minimum Energy: Route 1 Minimum no. of hops: Route 3

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IEEE Communications Magazine • August 2002 109

that senses the phenomena. It has the followingpossible routes to communicate with the sink:• Route 1: Sink-A-B-T, total PA = 4, total α = 3

• Route 2: Sink-A-B-C-T, total PA = 6, totalα = 6

• Route 3: Sink-D-T, total PA = 3, total α = 4

• Route 4: Sink-E-F-T, total PA = 5, total α = 6

An energy-efficient route is selected by one ofthe following approaches.

Maximum PA route: The route that has maxi-mum total PA is preferred. The total PA is cal-culated by summing the PAs of each node alongthe route. Based on this approach, route 2 isselected in Fig. 4a. However, route 2 includesthe nodes in route 1 and an extra node. There-fore, although it has a higher total PA, it is notpower-efficient. As a result, it is important notto consider routes derived by extending routesthat can connect the sensor node to the sink asan alternative route. Eliminating route 2, weselect route 4 as our power-efficient route whenwe use the maximum PA scheme.

Minimum energy (ME) route: The route thatconsumes minimum energy to transmit the datapackets between the sink and the sensor node isthe ME route. As shown in Fig. 4a, route 1 isthe ME route.

Minimum hop (MH) route: The route thatmakes the minimum hop to reach the sink is pre-ferred. Route 3 in Fig. 4a is the most efficientroute based on this scheme. Note that the MEscheme selects the same route as the MH whenthe same amount of energy (i.e., all α are thesame) is used on every link. Therefore, whennodes broadcast with same power level withoutany power control, MH is then equivalent to ME.

Maximum minimum PA node route: Theroute along which the minimum PA is largerthan the minimum PAs of the other routes ispreferred. In Fig. 4a, route 3 is the most effi-cient and route 1 is the second most efficient.This scheme precludes the risk of using up asensor node with low PA much earlier than theothers because they are on a route with nodesthat have very high PAs.

Another important issue is that routing maybe based on the data-centric approach. In data-centric routing, interest dissemination is per-formed to assign the sensing tasks to the sensornodes. There are two approaches used for inter-est dissemination: sinks broadcast the interest[5], and sensor nodes broadcast an advertise-ment for the available data [15] and wait for arequest from the interested nodes.

Data-centric routing requires attribute-basednaming [1]. For attribute based naming, theusers are more interested in querying an

■ Figure 4. a) The power efficiency of the routes; b) an example of data aggregation; c) the SPIN protocol[15]; d) an example of directed diffusion [5].

(d)

Sink

Source

Step 1: propogate interest

(c)

Step 1

ADV

Sink

(b)

G

D

F

CBA

E

Sink

A (PA = 2)

D (PA = 3)

F (PA = 4)

C (PA = 2)

B (PA = 2)

E (PA = 1)

α1=1

α4 = 2α3 = 2

α6 = 2

α5 = 2

α2 = 1

α8 = 2

α9 = 2

α10 = 2α7 = 1

(a)

T

Step 4

ADV

Step 5

REQ

Step 6

DATA

Sink

Source

Step 2: set up gradient

Sink

Source

Step 3: send data

Step 2

REQ

Step 3

DATA

An important

function of the

data link layer is

the error control

of transmission

data. Two

important modes

of error control in

communication

networks are the

Forward Error

Correction and

Automatic Repeat

reQuest.

IEEE Communications Magazine • August 2002 109

that senses the phenomena. It has the followingpossible routes to communicate with the sink:• Route 1: Sink-A-B-T, total PA = 4, total α = 3

• Route 2: Sink-A-B-C-T, total PA = 6, totalα = 6

• Route 3: Sink-D-T, total PA = 3, total α = 4

• Route 4: Sink-E-F-T, total PA = 5, total α = 6

An energy-efficient route is selected by one ofthe following approaches.

Maximum PA route: The route that has maxi-mum total PA is preferred. The total PA is cal-culated by summing the PAs of each node alongthe route. Based on this approach, route 2 isselected in Fig. 4a. However, route 2 includesthe nodes in route 1 and an extra node. There-fore, although it has a higher total PA, it is notpower-efficient. As a result, it is important notto consider routes derived by extending routesthat can connect the sensor node to the sink asan alternative route. Eliminating route 2, weselect route 4 as our power-efficient route whenwe use the maximum PA scheme.

Minimum energy (ME) route: The route thatconsumes minimum energy to transmit the datapackets between the sink and the sensor node isthe ME route. As shown in Fig. 4a, route 1 isthe ME route.

Minimum hop (MH) route: The route thatmakes the minimum hop to reach the sink is pre-ferred. Route 3 in Fig. 4a is the most efficientroute based on this scheme. Note that the MEscheme selects the same route as the MH whenthe same amount of energy (i.e., all α are thesame) is used on every link. Therefore, whennodes broadcast with same power level withoutany power control, MH is then equivalent to ME.

Maximum minimum PA node route: Theroute along which the minimum PA is largerthan the minimum PAs of the other routes ispreferred. In Fig. 4a, route 3 is the most effi-cient and route 1 is the second most efficient.This scheme precludes the risk of using up asensor node with low PA much earlier than theothers because they are on a route with nodesthat have very high PAs.

Another important issue is that routing maybe based on the data-centric approach. In data-centric routing, interest dissemination is per-formed to assign the sensing tasks to the sensornodes. There are two approaches used for inter-est dissemination: sinks broadcast the interest[5], and sensor nodes broadcast an advertise-ment for the available data [15] and wait for arequest from the interested nodes.

Data-centric routing requires attribute-basednaming [1]. For attribute based naming, theusers are more interested in querying an

■ Figure 4. a) The power efficiency of the routes; b) an example of data aggregation; c) the SPIN protocol[15]; d) an example of directed diffusion [5].

(d)

Sink

Source

Step 1: propogate interest

(c)

Step 1

ADV

Sink

(b)

G

D

F

CBA

E

Sink

A (PA = 2)

D (PA = 3)

F (PA = 4)

C (PA = 2)

B (PA = 2)

E (PA = 1)

α1=1

α4 = 2α3 = 2

α6 = 2

α5 = 2

α2 = 1

α8 = 2

α9 = 2

α10 = 2α7 = 1

(a)

T

Step 4

ADV

Step 5

REQ

Step 6

DATA

Sink

Source

Step 2: set up gradient

Sink

Source

Step 3: send data

Step 2

REQ

Step 3

DATA

An important

function of the

data link layer is

the error control

of transmission

data. Two

important modes

of error control in

communication

networks are the

Forward Error

Correction and

Automatic Repeat

reQuest.

Network Layer – Other Routing Algo. Data Centric Routing

Interest dissemination is performed to assign the sensing tasks to the sensor nodes 2 approaches: sinks broadcasts interests, sensor nodes broadcast an advertisement

Requires attribute based naming Support the user’s query For example: The areas where the temperature is over 70°F.

Data Aggregation and Fusion Solves huge data transmission

in the network Data are processed and aggregated along the path

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IEEE Communications Magazine • August 2002 109

that senses the phenomena. It has the followingpossible routes to communicate with the sink:• Route 1: Sink-A-B-T, total PA = 4, total α = 3

• Route 2: Sink-A-B-C-T, total PA = 6, totalα = 6

• Route 3: Sink-D-T, total PA = 3, total α = 4

• Route 4: Sink-E-F-T, total PA = 5, total α = 6

An energy-efficient route is selected by one ofthe following approaches.

Maximum PA route: The route that has maxi-mum total PA is preferred. The total PA is cal-culated by summing the PAs of each node alongthe route. Based on this approach, route 2 isselected in Fig. 4a. However, route 2 includesthe nodes in route 1 and an extra node. There-fore, although it has a higher total PA, it is notpower-efficient. As a result, it is important notto consider routes derived by extending routesthat can connect the sensor node to the sink asan alternative route. Eliminating route 2, weselect route 4 as our power-efficient route whenwe use the maximum PA scheme.

Minimum energy (ME) route: The route thatconsumes minimum energy to transmit the datapackets between the sink and the sensor node isthe ME route. As shown in Fig. 4a, route 1 isthe ME route.

Minimum hop (MH) route: The route thatmakes the minimum hop to reach the sink is pre-ferred. Route 3 in Fig. 4a is the most efficientroute based on this scheme. Note that the MEscheme selects the same route as the MH whenthe same amount of energy (i.e., all α are thesame) is used on every link. Therefore, whennodes broadcast with same power level withoutany power control, MH is then equivalent to ME.

Maximum minimum PA node route: Theroute along which the minimum PA is largerthan the minimum PAs of the other routes ispreferred. In Fig. 4a, route 3 is the most effi-cient and route 1 is the second most efficient.This scheme precludes the risk of using up asensor node with low PA much earlier than theothers because they are on a route with nodesthat have very high PAs.

Another important issue is that routing maybe based on the data-centric approach. In data-centric routing, interest dissemination is per-formed to assign the sensing tasks to the sensornodes. There are two approaches used for inter-est dissemination: sinks broadcast the interest[5], and sensor nodes broadcast an advertise-ment for the available data [15] and wait for arequest from the interested nodes.

Data-centric routing requires attribute-basednaming [1]. For attribute based naming, theusers are more interested in querying an

■ Figure 4. a) The power efficiency of the routes; b) an example of data aggregation; c) the SPIN protocol[15]; d) an example of directed diffusion [5].

(d)

Sink

Source

Step 1: propogate interest

(c)

Step 1

ADV

Sink

(b)

G

D

F

CBA

E

Sink

A (PA = 2)

D (PA = 3)

F (PA = 4)

C (PA = 2)

B (PA = 2)

E (PA = 1)

α1=1

α4 = 2α3 = 2

α6 = 2

α5 = 2

α2 = 1

α8 = 2

α9 = 2

α10 = 2α7 = 1

(a)

T

Step 4

ADV

Step 5

REQ

Step 6

DATA

Sink

Source

Step 2: set up gradient

Sink

Source

Step 3: send data

Step 2

REQ

Step 3

DATA

An important

function of the

data link layer is

the error control

of transmission

data. Two

important modes

of error control in

communication

networks are the

Forward Error

Correction and

Automatic Repeat

reQuest.

Network Layer Flooding

Each node receiving data will broadcast to the neighbours, unless maximum hop-count has been reached. Very reliable! Drawback: Implosion and Energy expensive

Gossiping Packets are sent randomly to some neighboring nodes Does not lead to Implosion Drawback: Propagation of packet takes a long time

Sensor Protocol for Information via Negotiation (SPIN) Data centric routing, where sensor nodes

broadcast an advertisement for available data and wait for a request from interested sinks.

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IEEE Communications Magazine • August 2002 109

that senses the phenomena. It has the followingpossible routes to communicate with the sink:• Route 1: Sink-A-B-T, total PA = 4, total α = 3

• Route 2: Sink-A-B-C-T, total PA = 6, totalα = 6

• Route 3: Sink-D-T, total PA = 3, total α = 4

• Route 4: Sink-E-F-T, total PA = 5, total α = 6

An energy-efficient route is selected by one ofthe following approaches.

Maximum PA route: The route that has maxi-mum total PA is preferred. The total PA is cal-culated by summing the PAs of each node alongthe route. Based on this approach, route 2 isselected in Fig. 4a. However, route 2 includesthe nodes in route 1 and an extra node. There-fore, although it has a higher total PA, it is notpower-efficient. As a result, it is important notto consider routes derived by extending routesthat can connect the sensor node to the sink asan alternative route. Eliminating route 2, weselect route 4 as our power-efficient route whenwe use the maximum PA scheme.

Minimum energy (ME) route: The route thatconsumes minimum energy to transmit the datapackets between the sink and the sensor node isthe ME route. As shown in Fig. 4a, route 1 isthe ME route.

Minimum hop (MH) route: The route thatmakes the minimum hop to reach the sink is pre-ferred. Route 3 in Fig. 4a is the most efficientroute based on this scheme. Note that the MEscheme selects the same route as the MH whenthe same amount of energy (i.e., all α are thesame) is used on every link. Therefore, whennodes broadcast with same power level withoutany power control, MH is then equivalent to ME.

Maximum minimum PA node route: Theroute along which the minimum PA is largerthan the minimum PAs of the other routes ispreferred. In Fig. 4a, route 3 is the most effi-cient and route 1 is the second most efficient.This scheme precludes the risk of using up asensor node with low PA much earlier than theothers because they are on a route with nodesthat have very high PAs.

Another important issue is that routing maybe based on the data-centric approach. In data-centric routing, interest dissemination is per-formed to assign the sensing tasks to the sensornodes. There are two approaches used for inter-est dissemination: sinks broadcast the interest[5], and sensor nodes broadcast an advertise-ment for the available data [15] and wait for arequest from the interested nodes.

Data-centric routing requires attribute-basednaming [1]. For attribute based naming, theusers are more interested in querying an

■ Figure 4. a) The power efficiency of the routes; b) an example of data aggregation; c) the SPIN protocol[15]; d) an example of directed diffusion [5].

(d)

Sink

Source

Step 1: propogate interest

(c)

Step 1

ADV

Sink

(b)

G

D

F

CBA

E

Sink

A (PA = 2)

D (PA = 3)

F (PA = 4)

C (PA = 2)

B (PA = 2)

E (PA = 1)

α1=1

α4 = 2α3 = 2

α6 = 2

α5 = 2

α2 = 1

α8 = 2

α9 = 2

α10 = 2α7 = 1

(a)

T

Step 4

ADV

Step 5

REQ

Step 6

DATA

Sink

Source

Step 2: set up gradient

Sink

Source

Step 3: send data

Step 2

REQ

Step 3

DATA

An important

function of the

data link layer is

the error control

of transmission

data. Two

important modes

of error control in

communication

networks are the

Forward Error

Correction and

Automatic Repeat

reQuest.

20/04/16 29

WSN and public network interconnection

Transit Network

sensors

Data base

The Internet

WSN client

sensors

Gateway

Gateways

Gateways

Self-Organization Sensor nodes must be autonomous! Participating entities do not need a central authority Entities interact with each other and react to changes from the environment (Emergent Behaviour) Leads to flexible, adaptive, failure-robust, and scalable systems

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C. Prehofer, C Bettstetter, "Self-Organization in Communication Networks: Principles and Design Paradigms, IEEE Communications Magazine,” July 2005.

IEEE Communications Magazine • July 2005 79

We regard a system consisting of several enti-ties. We call this system organized if it has a cer-tain structure and functionality [4]. Structuremeans that the entities are arranged in a particu-lar manner and interact (communicate) witheach other in some way. Functionality meansthat the overall system fulfills a certain purpose.For example, a school of small fish tries toachieve a group structure that protects the fishagainst enemies (Fig. 1).

A system is self-organized if it is organizedwithout any external or central dedicated controlentity. In other words, the individual entitiesinteract directly with each other in a distributedpeer-to-peer fashion. Interaction between theentities is usually localized. For example, in aschool of fish each individual fish bases its behav-ior on its observation of the position and speedof its immediate neighbors, rather than on thebehavior of a “central fish” or that of the wholeschool.

But self-organization is more than just dis-tributed and localized control. It is about therelationship between the behavior of the individ-ual entities (the microscopic level) and theresulting structure and functionality of the over-all system (the macroscopic level). In self-orga-nized systems, the application of rather simplebehavior at the microscopic level leads to sophis-ticated organization of the overall system. Thisphenomenon is called emergent behavior. Whyand how emergent behavior occurs is still notwell understood.

Another important characteristic of self-orga-nized systems is their adaptability with respect tochanges in the system or environment. In fact,the entities continuously adapt to changes in acoordinated manner, such that the system alwaysreorganizes as a reaction to different internaland external triggers for change. By doing so, ittries to converge toward desired beneficial struc-tures while avoiding other structures. Combiningthis intrinsic adaptability with the distributednature of self-organized systems leads to one oftheir major advantages: robustness against failureand damage. There is no single point of failure,and the system can repair or correct damagewithout external help. In this way, a good self-organizing system will degrade gracefully ratherthan break down suddenly. Another importantproperty is that many self-organizing systemsshow a high level of scalability, which means thatthe system still works if the number of entities isvery large. For instance, the self-organized groupformation of fish works well in schools withthousands of fish.

In summary, as illustrated in Fig. 1, self-orga-nization can be defined as the emergence of sys-tem-wide adaptive structure and functionalityfrom simple local interactions between individu-al entities.

SELF-ORGANIZATION INCOMMUNICATION NETWORKS

Unsurprisingly, the notion of self-organizationhas also found its way into the area of communi-cation and computer networks. The main goalsare to minimize the need for configuration,

develop protocols that facilitate network opera-tion, and enable new types of communicationsnetworks, such as completely decentralized adhoc and sensor networks [4–8].

A typical example of the emergence of self-organized functions is in the field of IP addressallocation. Traditionally, an administrator con-figures each computer manually with an IPaddress from a specific address space that hasbeen allocated to the administrator by a higher-level authority. This traditional approach has noelements of self-organization; instead, it requiressignificant human intervention and creates avery stiff address structure. A first step towardmore self-organization was made with the intro-duction of the Dynamic Host Configuration Pro-tocol (DHCP). Using this protocol, computersare able to automatically obtain an IP addressfrom a server installed by the administrator inhis or her domain. This allows computers toadapt to changes in their environment (e.g., toobtain a new IP address when they move to adifferent network). Although DHCP relievesusers of configuring their IP settings, it stillrequires network administrators to install dedi-cated DHCP servers. This problem has beenaddressed with the standardization of IPv6 state-less autoconfiguration (self-configuration). Nowa computer can query its current router for asubnet prefix and then form an IP address on itsown: it simply combines the prefix with its lower-layer unique identifier (e.g., its medium accesscontrol [MAC] address). This state-of-the-artapproach already includes many aspects of self-organization: no dedicated address server isneeded, and communication is localized. Theaddress allocation becomes more flexible androbust. Looking into the future, additionalapproaches to IP address autoconfiguration areunder development. In ad hoc networks, forexample, distributed autoconfiguration tech-niques are needed that deal with merging andseparating networks caused by node mobility [9].

Another example of self-organization is theTransport Control Protocol (TCP), which imple-ments a decentralized mechanism to handle con-gestion in the Internet. In simplified terms, TCPuses a control loop for the sending rate: it reducesthe rate upon packet losses and increases it ifpackets arrive correctly. In this way, there is noexplicit management of network resources, butthese are shared among all participants in a rea-sonably fair manner. This example of traffic adap-tation shows that decentralized control can beeffective and scalable. Furthermore, it shows that

Figure 1. Illustration of the main principles of a self-organizing system.

Local view, peer-to-peerinteraction, and simplebehavior rules of eachindividual entity

Adaptive, robust, andscalable structurefunctionality of the overallsystem

BETTSTETTER LAYOUT 6/20/05 10:10 AM Page 79

Bio-inspired Self-Organization

Underwater sensor networks (Wokoma et al)

Quorum sensing: Coordination of cells with no global awareness (e.g. Vibrio Fischeri)

Quorum sensing based clustering for under water sensor networks

Rate of Oceanographic signal with respect to space

Evaluation: Energy dissipation

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Quorum Sensing

Energy Harvesting Renewable Energy approaches (Solar and Wind)

Thermal Energy

Mechanical Energy Vibrations

Biochemical energy harvesting

Wireless energy harvesting RF energy harvesting Inductive coupling (electromagnetic induction)

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Actors “Actors” or “actuators”

Connect sensor network to a public network

Features of actors Should not be limited with resources Should have security functions implemented

WSN segment With single actor With more than single actor

Applications

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Emergency cases 1/3 Emergency cases require

Fast response Actions coordination

Example of usage Fire brigade operation

Coordination Fireman current location Current health condition tracking

3D network and building models Fireman coordination advantages

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Telemetry Dynamic tracking of environmental parameters

Temperature Humidity Pressure Light Sound Vibration Wind parameters Water quality …many others

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Red forests climate monitoring [1/2] So-Cal: unique forests of sequoia and red trees

Climate is very specific 70% of H2O cycle on the upper ground Humidity monitoring

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Red forests climate monitoring [2/2]

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Real implementations Forest fire detection

California, USA A lot of private houses are destroyed

Seismic activity monitoring Naples, Italy -> Vesuvius volcano Underwater volcano Axial Volcano, USA

Environmental monitoring and ecology Manoa, Hawaii

Climate monitoring Precise climate models e.g. deserts Statistics collection: vineyards

Solar activity monitoring Flooding monitoring

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Real implementations Animals habits/migration monitoring

Tree fogs, Switzerland Duck Island, www.greatduckisland.net Zebras, Kenya Wild horses, USA

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Medical applications Two broad classes

Monitoring Heart rate, pressure, EKG, oxygen saturation, etc.

Body Area Network

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http://wirelesshealth.virginia.edu/projects/platforms-and-technologies

Code Blue

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http://fiji.eecs.harvard.edu/CodeBlue

Underwater Sensor Networks (1)

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http://www.ece.gatech.edu/research/labs/bwn/UWASN/

Underwater Sensor Networks (2)

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http://www.ece.gatech.edu/research/labs/bwn/UWASN/

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Areas of interest Navigation Water quality and chemical analysis Salinity level monitoring Seismic activity monitoring Tsunami warning Distributed military operations

Challenges of water environment Difficulties with radio propagation… acoustics? Corrosion Water surface equipment

Antenna flooding, 3D non-stationarity, etc.

Underwater Sensor Networks (3)

Deep-ocean Assessment and Reporting of Tsunamis (DART)

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http://www.ndbc.noaa.gov

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Security systems Surveillance Intrusion detection Environmental protection

Military apps Anti-terrorists security Boarder control

Ground sensor network

On body sensor networks for soldiers

http://www.lockheedmartin.com

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Other applications Precision agriculture

Construction monitoring

Road traffic surveillance

Many others……

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Implementations Crossbow

http://www.xbow.com

Moteiv http://www.moteiv.com/

Sensicast http://www.sesnsicast.com

Sensoria http://www.sensoria.com

Many others USA, Europe, China

Background Reading I. F. Akyildiz, W. Su, Y. Sankarasubramaniam, E. Cayirci, "A Survey on Sensor Networks, IEEE Communications Magazine," vol. 40, no. 8, pp. 102-116, August 2002. I. F. Akyildiz, W. Su, Y. Sankarasubramaniam, E. Cayirci, "Wireless Sensor Networks: A Survey", Computer Networks (Elsevier) Journal, vol. 38, no. 4, pp. 393-422, March 2002. I. F. Akyildiz, D. Pompili, T. Melodia, "Underwater Acoustic Sensor Networks: Research Challenges," Ad Hoc Networks (Elsevier) Journal, March 2005.

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