Autonomicwsn - Download 2

8/11/2019 Autonomicwsn - Download 2

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2 Autonomic Computing

2.1 Background

)he dramatic increase in com#uting devices, increased com#uting ca#acity and com#le$ity

combined !ith #o#ularity of internet resulted in #henomenal gro!th in heterogeneous net!or"sand net!or" a##lications. ith this increasing system com#le$ity, net!or" management issues

and communication #rotocols are reaching a level beyond human ability to manage and secure so

the stability of current infrastructure, systems, and data is at an increasingly greater ris" to sufferoutages and general disre#air. 0uture net!or" algorithms need to be ada#tive, robust, and

scalable !ith fully distributed and self-organi&ing architectures. Automation, self-#rotection and

self management of !ide s#read net!or"s may solve the #roblem till some e$tent.

As the conce#t of self management rooted u#, the most direct ins#iration one can thin" of !asthe autonomic function of the human central nervous system !here autonomic controls use

motor neurons to send indirect messages to organs at a sub-conscious level. )hese messages

regulate tem#erature, breathing, and heart rate !ithout conscious thought. 1bservation and

analysis of these com#le$ ada#tive systems found in nature became a ma/or source of ins#irationto design algorithms for self-managed, self-organi&ed, self-configuring and self-#rotecting

systems. )a"ing ins#iration from autonomic nervous system of the human body %23 created a

foundation for autonomic systems by ta"ing initiatives to!ards Autonomic Com#uting for

relieving humans from the burden of managing com#uter systems !hich is gro!ing enormouslytill the e$tent of unmanageability. 4567

2.2 Autonomic System

Autonomic System is a system !hich !or"s inde#endently on #redefined #olicies and rules

!ithout any human interaction and manage and configure itself on its o!n based on #redefined

rules and gained "no!ledgs over the time. %23 has defined the follo!ing four functional areasfor self management of Autonomic System: '+ef 4587(

Self-onfiguration+Automatic configuration of com#onents.

Self-,ealing+Automatic discovery, and correction of faults.Self-ptimi'ation+ Automatic monitoring and control of resources to ensure the o#timal

functioning !ith res#ect to the defined reuirements.

Self-rotection+roactive identification and #rotection from arbitrary attac"s.

2. IB! Autonomic Computing arc"itecture

%23 Autonomic Com#uting Architecture 4597 defines an abstract information frame!or" forself-managing %) systems. %n the information frame!or", an autonomic system is a collection of

autonomic elements. Each autonomic element consists of an autonomic manager 'A3( and the

managed resource '3+(. )he communication bet!een the A3 and the 3+ is done through the3+s management interfaces, !hich e$#oses t!o ty#es of hoo"s, sensors and effectors. )he

sensors are used by the A3 to obtain the internal state of the 3+, and the effectors are used by

the A3 to change the behavior of the 3+. )he A3 enables self-management of the resource

using a ;;monitoring, analysis, #lanning, and e$ecution control loo#, !ith su##orting


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"no!ledge of the com#uting environment, management #olicies, and some other related

considerations.

0igure 6. 2asic Autonomic Com#uting +eference Architecture

')his is figure is ta"en from %23 Autonomic Com#uting architectural 2lue rint 4597(

)he autonomic com#uting information model only #rovides the conce#tual guidance on

designing self-managed systems< in #ractice, the information model needs to be ma##ed to anim#lementable management and control architecture for Autonomic *et!or"s. S#ecifically,

measurement techniues, rule engines, #lanning methodologies, dynamic resource allocation

techniues, security and management schemes need to be develo#ed for autonomic elements, and

a scalable management #latform is reuired to coordinate the autonomic elements into a self-managing system.

Wireless Sensor Network #

A !ireless sensor net!or" 'S*( is a net!or" that is made of hundreds or thousands of sensor

nodes !hich are densely de#loyed in an unattended environment !ith the ca#abilities of sensing,!ireless communications and com#utations 'i.e. collecting and disseminating environmental

data(. )hese s#atially distributed autonomous devices coo#eratively monitor #hysical and

environmental conditions, such as tem#erature, sound, vibration, #ressure, motion or #ollutants,

at different locations. )he basic archetecture of ireless sensor *et!or" is sho!n in 0igure9.


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0igure 9. 2asic Architecture 1f ireless Sensor *et!or". '+ef 45=7(

Each autonomic node in a sensor net!or" is ty#ically eui##ed !ith a radio transceiver or other

!ireless communications device, a #rocessing unit !hich can be a small micro-controller,

sensing unit, and an energy source, usually an al"aline battery. Sometimes, a mobili&er is neededto move sensor node from current #osition and carry out the assigned tas"s. Since the sensor may

be mobile, the base station may reuire accurate location of the node !hich is done by location

finding system. )he si&e of a single sensor node can vary from shoebo$-si&ed nodes do!n todevices the si&e of grain of dust. 45=7

0igure 8. Com#onents of a Sensor *ode '+ef 45=7(

.1 $e%uirements and &esign 'actors in Wireless Sensor Network

0ollo!ing are some of the basic reuirements and design factors of !ireless sensor net!or"!hich serve as guidelines for develo#ment of #rotocols and algorithms for S* communication

architecture.

/. #ault olerance !daptability and 0eliability+ Sensor net!or"s are reuired to o#eratethrough ada#ting to the environmental changes that sensors monitor. )he net!or"s should be

self-learning.+eliability is the ability to maintain the sensor net!or" functionalities !ithout any


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interru#tion due to sensor node failure. Sensor node may fail due to lac" of energy, #hysical

damage, communications #roblem, inactivity, or environmental interference. )he net!or" should

be able to detect failure of a node and organi'eitself, reconfigureand recoverfrom node failures!ithout loosing any information. 45>7

1. ower onsumption and ower management: 1ne of the com#onents of sensor nodes is the

#o!er source !hich can be a battery. )he !ireless sensor node being a microelectronic device,can only be eui##ed !ith a limited #o!er source 45=7. 1ver the remote inaccessible #lace !ith

less human control and e$istence, #o!er sources #lay critical role in survival of sensor nodes.

o!er source should be intelligently divided over sensing, com#utation, and communications#hases as #er reuirement. Sensors can be hibernated !hen inactive. Lots of current researches

are focusing on designing #o!er-a!are #rotocols and algorithms for !ireless sensor net!or"s.

+ecently, solar energy is also considered as an o#tion for em#o!ering remote sensor nodes

!hich are e$#osed environment.2. Network 3fficiency and 4ata !ggregation: 0looding ra! sensed data over the net!or" can

easily congest the net!or". Some critical a##lications li"e intruder detectors reuire urgent

transmission and faster #rocessing of data !hich may degrade #erformance and loose reliability

due to congestion or latency in the net!or". %ntelligent aggregation of sensed data andelimination of un!anted and redundant information and data com#ression can be a solution for

efficient resource and energy utili&ation and congestion avoidance. 3any algorithms li"edirected diffusion 45?7 are #ro#osed to facilitate data aggregation and dissemination !ithin the

conte$t of S*s.

5. $ntelligent 0outing: %n many a##lications, sensor nodes are moving nodes and can change#lace dynamically. +outing #rotocols must be ada#tive to these changes and should be self-healingandself-configuring. )he information should be #ersistent in s#ite of changes in net!or"

nodes. Lo! #rocessing ca#acity of a node creates many challenges for routing #ac"ets

throughout the neighbouring nodes intelligently. As discussed above, some a##lications mayreuire a faster communication and instant res#onse. +outing algorithms should be intelligent to

choose minimum ho# and minimum distance #aths for data transfer. 45@7

6. &anagement challenge 3anaging the communication over heterogeneous net!or"s is basicchallenge in self-managed system because #olicies and communication #rotocols #lan an

im#ortant role in net!or" communication. Also, it is necessary to balance the level of detail the

net!or" is #roviding to the client against the rate at !hich energy is being consumed !hilegathering the data. Clearly, it is #referable to have the net!or" automatically do this tuning,

rather than reuiring manual intervention.

)hese basic reuirements and design goals serve as challenge for current technology. )hough

current % routing #rotocol e$ist and have significant a##lications in current net!or"s and%nternet, they do not satisfy com#lete design reuirements in ireless sensor net!or"s because

S* nodes ty#ically has limited com#uting ca#acities and less #o!er. So S*s reuire a

different infrastructure and #rotocol stac" !hich can be im#lemented using autonomiccom#uting conce#t as !e !ill discuss in ne$t section.


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(. Wireless Sensor Networks and Autonomic Computing #

)o clarify the contribution that autonomic com#uting can bring to ireless Sensor *et!or"s'S*(, lets e$amine ho! S* design reuirements and o#erations can be tac"led using

autonomic #rinci#les.

As discussed above, there can be sensor nodes !hich are moving and can change their #ositiondynamically or even leave the net!or" coverage area. )herefore, a #re-#rogrammed

configuration for the net!or" !ill not !or". Sel'#con'iguring nodes can set u# net!or"

connections, evaluate if there are any ga#s in the S* and re#lace a moved or dead node in thenet!or". Since sensors can be de#loyed in an unattended area 'e.g., forest and ocean( or

#hysically unreachable area 'e.g., inside a building !all(, they are reuired to o#erate !ith the

minimum aid from base stations or human administrators. Although ma/ority of current sensor

a##lication have already considered this in their net!or" design, there is still a need for S* tohave the ability to reconfigure and recover itself !ithout too much human intervene, es#ecially

in inaccessible environment. 45=, 5>7

Sensor reading usually contains some noises< it may be a false #ositive due to malfunction of

sensors. Sensors are reuired to collectively sel'#"eal'i.e., detect and eliminate( false #ositivesin their sensor readings instead of transmitting them to base stations. )his can also reduce #o!er

consum#tion of sensors because data #rocessing !ithin the sensor incurs much less #o!erconsum#tion than data transmission does 4657.

Sensor nodes are generally e$#osed to much harsher conditions than standard com#uting

eui#ment, and are thus sub/ect to energy de#letion and incidental damage. 2attery failure canresult in lost sensor node. )his leads to a gradual degradation of the net!or" as individual nodes

are lost. *et!or" #aths brea" and ga#s a##ear in the coverage area. A S* needs to ada#t to the

changes, recover from losses and be sel'#protected. )his can be achieved by renegotiating

net!or" routes, monitoring voltage levels !ithin sensor node, controlling each node by an agentor base station and u#on failure activating redundant nodes to re#lace damaged ones, or by

informing some higher-level entity !hich can #rovide assistance.

As discussed in reuirements, ma$imum efficiency needs to be gained from the availableenergy as the available energy at each sensor node is limited. Sensing, rocessing and data

transfer #hases reuire lot of energy so each node should be able to sense #rocess and transfer

data intelligently hence sel'#optimi)ation is an im#ortant trait for S* #rotocols. Energysavings can be achieved by #utting the nodes into a lo! #o!er slee# mode, ready to be

reactivated !hen the need arises. 0or e$am#le, sensors may decrease their duty cycles !hen

there is no significant change in their sensor readings. )his results in less #o!er consum#tion in

the sensors. Also, !hen neighboring sensors re#ort environmental changes, a sensor may dra!inference from the re#orts and increase its duty cycle to be more !atchful for a #otential local

environmental change in the future. Bo!ever, there e$ists a trade-off in that the com#utational

cost of a globally-o#timal solution such as this is often com#utationally intractable, !hether by-bit nodes or ?=-bit base-stations.

All basic S* self-management #rinci#les com#ly !ith the conce#t of autonomic com#uting.

So %23 autonomic com#uting #rinci#les can be a##lied to !ireless sensor net!or"s to get thedesired functionality in vastly gro!ing sensor net!or" a##lications.


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*. Autonomic Wireless Sensor Network !anagement Arc"itectures #

As discussed in section 9.8, the basic Autonomic Com#uting model only #rovides the conce#tualguidance on designing self-managed systems and needs to be ma##ed to an im#lementable

management and control architecture for Autonomic *et!or"s. An architecture for Autonomic

communication and net!or"ing is an area of research lately and many architectures are #ro#osedand being develo#ed. All these architectures aim to#roduce an architectural design that enables

fle$ible, dynamic and fully autonomic formation of large-scale net!or"s in !hich the

functionalities of each constituent net!or" node are also com#osed in an autonomic fashion.3oreover, these architectures also su##ort mobile nodes and multi#le administrative domains so

these can be a##lied to !ireless sensor net!or"s for achieving desired goals and meet above

mention challenges.

0ollo!ing is the brief discussion of some visions for the design of an efficient managementarchitecture for S*s based on to# of the basic autonomic com#uting architecture.

*.1. Ser+ice#,riented Arc"itecture

Service-oriented architecture 'S1A +ef 4967( is an a##roach to build distributed systems that

deliver a##lication functionality as services to end-user a##lications or to build other services. %tdecom#oses the design of large com#le$ a##lication, and middle!are architecture into various

reusable services or function units. %n S1A the service reuester has no "no!ledge of the

technical details of the #roviders im#lementation, such as the #rogramming language,de#loyment #latform, and so forth. )he service reuester ty#ically invo"es o#erations by !ay of

messages -- a reuest message and the res#onse -- rather than through the use of A%s or file

formats. )hus, the a##lication develo#ers only need to concern the o#erational descri#tion of the

service !hich allo!s soft!are on each side of the conversation to change !ithout im#acting theother.

So far, the im#lementation and design of S1A is mostly de#endent on eb Services !ith

standardi&ed !eb technologies such as SL, 1DSA. As a result, it is not directly a##licable toall of those com#le$ technologies on those resource-constrained sensor nodes. 3A**A 467 has

#resented some initial ideas of using the conce#t of service semantics from S1A.

0igure =. 2asic 3odel of 3A**A Architecture '+ef 467(


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%n 3A**A, all the management function units sit at the lo!est level of management

architecture. )hey are designed !ith s#ecific im#lementation for individual ob/ectives in

consideration of uniue features of S*. A service, at the to# layer, can use one or more ofthose management functions. ifferent services can share the same functions, but still concern

each individual given as#ect based on the #olices and net!or" state obtained from S* models.

)he basic model of 3A**A architecture is as sho!n in figure = '+ef 467(0urthermore, S1A can s#ecially deal !ith S* uniue as#ects such heterogeneity, mobility and

ada#tation, and offers seamless management integration in the !ireless environments. Although

the s#ecial features of S1A are marvellous, there is still a large amount of research challengeneeded to address before the conce#ts of S1A can be a##ro#riately a##lied into S*s.

*.2. -olicy Based Arc"itecture

olicy-based management has #resented its robust ability to su##ort designing of self-ada#tive

decentrali&ed management service in S*s. avy S. et al. 4957 #ro#osed an autonomic

communications architecture that manages com#le$ity through #olicy-based management by

incor#orating a shared information model integrated !ith "no!ledge-based reasoningmechanisms to #rovide self-governing behavior.

)he architecture is organi&ed using four distinct architectural constructs i.e. Shared $nformation7irtual Software $nfrastructure and olicy as sho!n in figure >.

0igure >. ro#osed olicy 2ased Autonomic Architecture '+ef 4957(

)he shared information over the net!or" is managed through a virtual soft!are !hich su##ortautonomic functionality for different heterogeneous net!or"s and com#onents combined !ith

net!or" infrastructure !hich include net!or" elements and other com#uting devices. All these

three modules are governed by #olicy module. 4957

)his model is based on three im#ortant conce#ts of autonomic com#uting: '6( the sharing andreusing of common information and "no!ledge, '9( the a##lication of machine learning and


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"no!ledge-based reasoning to guide the changes in behavior of the system, and '8( an e$tensible

and fle$ible governance model that forms a closed control loo# that learns from its decisions.

Similarly, in 3A**A 467, #olicies describe a set of desired behaviours of managementcom#onents 'e.g. manager and agent( for indicating the real-time o#erations. 2ased on #olices,

managers and agents can interact !ith each other in a coo#erative fashion to achieve a desired

overall management goal such as form grou#s of nodes, control net!or" density, and "ee# thecoverage of the S* area.

$outing -rotocols 'or Autonomic Wireless Sensor Networks

*o! lets analy&e fe! !ell-"no!n routing #rotocols for !ireless sensor net!or"s and their

suitability, #ros and cons for Autonomic S*s.

.1 /looding

0looding 49=7 is an old routing mechanism used in !ireless net!or"s that may also be used inAutonomic !ireless sensor net!or"s. %n flooding, a node sends out the received data or the

management #ac"ets to its neighbors by broadcasting or flooding, unless a ma$imum number ofho#s for that #ac"et are reached or the destination of the #ac"ets is arrived. )his method

guarantees the delivery of the #ac"et to the destination. Bo!ever< there are some deficiencies

and disadvantages of flooding techniue 49=7:- %m#losion of ata #ac"ets: 0looding may cause the %m#losion effect for data #ac"ets and also

for ACF #ac"ets if used any. 1ne #ac"et ta"es multi#le routes and multi#le hosts can deliver the

same #ac"et to destination. estination has to im#lement a se#arate mechanism for du#licate

su##ression also lot of band!idth and resources are !asted in transmitting same #ac"et throughmulti#le hosts so this techniue may not be suitable for large ireless sensor net!or"s.

- 1verla#: if t!o sensor nodes cover an overla##ing measuring region, both of them !ill

senseGdetect the same data. As a result, their neighbor nodes !ill receive du#licated data ormessages. 1verla##ing is a function of both the net!or" to#ology and the ma##ing of sensed

data to sensor nodes.

- +esource utili&ation: %n flooding, nodes do not ta"e into account the amount of energy resourceavailable to them at a given time. An Autonomic S* #rotocol must be energy resource-a!are

and ada#ts its sensing, communication and com#utation to the state of its energy.

.2 0ossiping

Dossi#ing #rotocol is an alternative to flooding mechanism. %n Dossi#ing 49>7, nodes for!ard

incoming #ac"ets to a randomly selected neighbor node. 1nce a gossi#ing node receives themessages, it for!ards the data bac" to that neighbor or to another one randomly selected

neighbor node and in this !ay route from source to destination is created. )his techniue assists

in energy conservation by randomi&ation.Although, gossi#ing can solve the im#losion #roblem, it can not avoid the overla##ing #roblem.

1n the other hand< gossi#ing distribute information slo!ly, this means it consumes energy at a

slo! rate, but the cost is long-time #ro#agation is needed to send messages to all sensor nodes so

it may not be the best suitable techniue for Autonomic ireless Sensor net!or"s.


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. S-IN

Fuli" et al. #ro#osed a family of ada#tive #rotocols for S*s, called S%* 'Sensor rotocols for

%nformation via *egotiation( 49?7. )heir design goal is to avoid the dra!bac"s of flooding

#rotocols by utili&ing data negotiation and resource-ada#tive algorithms. *odes running a S%*communication #rotocol name their data using high-level data descri#tors, called meta-data.

)hey use meta-data negotiations to eliminate the transmission of redundant data throughout the

net!or". %n addition, S%* nodes can base their communication decisions both u#on a##lication-s#ecific "no!ledge of the data and u#on "no!ledge of the resources that are available to them.

)his efficient distribution of data by sensors !ith limited energy su##ly com#lies !ith the

Autonomic Sensor net!or" reuirements and can be very effective under small net!or"s.

S%* is designed based on t!o basic ideas< '6( to o#erate efficiently and to conserve energy by

sending metadata 'i.e., sending data about sensor data instead of sending the !hole data that

sensor nodes already have or need to obtain(, and '9( nodes in a net!or" must be a!are of

changes in their o!n energy resources and ada#t to these changes to e$tend the o#erating lifetimeof the system. S%* has three ty#es of messages, namely, AV, +EH, and A)A.

- AV: !hen a node has data to send, it advertises via broadcasting this message containingmeta-data 'i.e., descri#tor( to all nodes in the net!or".

- +EH: an interested node sends this message !hen it !ishes to receive some data.

- A)A: ata message contains the actual sensor data along !ith meta-data header.S%* is a data-centric routing #rotocol !here the sensor nodes send AV message via

broadcasting for the data they have and !ait for +EH messages from interested sin"s or nodes.

S%* has some advantages in solving the #roblems associated !ith classic flooding #rotocols,

and ada#tive to to#ological changes, it has its o!n dra!bac"s li"e< '6( S%* is not scalable, '9( ifthe sin" is interested in too many events, this could ma"e the sensor nodes around it de#lete their

energy, and '8( S%*Is data advertisement techniue can not guarantee the delivery of data if the

interested nodes are far a!ay from the source node and the nodes in bet!een are not interested inthat data.

.( &irected &i''usion

irected diffusion 49@7 is most effective data dissemination and aggregation #rotocol. %t is a data-

centric and a##lication a!are routing #rotocol for ireless Sensor *et!or"s. %t aims at naming

all data generated by sensor nodes by attribute-value #airs. irected diffusion consists of severalelements< first of all, naming< !here tas" descri#tors, sent out by the sin" or ata receiver, are

named by assigning attribute-value #airs. Secondly, interests and gradients< the named tas"

descri#tion constitutes an interest that contains timestam# field and several gradient fields.Each leaf node and intermediate nodes store the interest in their interest cache. As the interests

#ro#agate throughout the net!or", the gradients from the source bac" to the sin" are set u#.

)hirdly, data #ro#agation, !hen the source has data for the interest, it sends out the data to theinterest 'i.e., sin"( along the interestIs gradient #ath !hich can be chosen as the shortest ho# #ath

or shortest time #ath derived from the reuest #ac"et. 0ourthly, after the interest 'sin"( starts

receiving lo! rate data events, it reinforce one #articular neighbor to dra! do!n higher uality

'higher data rate( events. )his feature of directed diffusion is achieved by data-driven local rules.


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irected diffusion assists in saving sensorsI energy by selecting good #aths by caching and

#rocessing data in-net!or" since each node has the ability for #erforming data aggregation and

caching. 1n the other hand< irected diffusion has its limitations such as< im#lementing dataaggregation reuires de#loyment of synchroni&ation techniues !hich is not reali&able in S*s.

Also, the overhead in data aggregation involves recording information.

)hese t!o dra!bac"s may contribute to the cost of sensor node hence cost is the tradeoff for#erformance in irected diffusion techniue, !hich may be acce#table for some autonomic

!ireless sensor net!or"s.

.* AC3

LEACB 'Lo! Energy Ada#tive Clustering Bierarchy( 497 is a self-organi&ing, ada#tive

clustering-based #rotocol that uses randomi&ed rotation of cluster-heads to evenly distribute theenergy load among the sensor nodes in the net!or". LEACB based on t!o basic assum#tions:

'a( base station is fi$ed and located far a!ay from the sensors, and 'b( all nodes in the net!or"

are homogeneous and energy-constrained. )he idea behind LEACB is to form clusters of the

sensor nodes de#ending on the received signal strength and use local cluster heads as routers toroute data to the base station. )he "ey features of LEACB are:

- Locali&ed coordination and control for cluster set-u# and o#eration.- +andomi&ed rotation of the cluster Jbase stationsJ or Jcluster headsJ and the corres#onding

clusters.

- Local com#ression to reduce global communication.%n LEACB, the o#eration is se#arated into fi$ed-length rounds, !here each round starts !ith a

setu# #hase follo!ed by a steady-state #hase. )he duration of a round is determined #riori.

Although, LEACB has sho!n good features to sensor net!or"s, it suffers from the follo!ing

dra!bac"s:- %t can not be a##lied to time-constrained a##lication as it results in a long latency.

- )he nodes on the route a hot s#ot to the sin" could drain their #o!er fast. )his #roblem is

"no!n as Jhot s#otJ #roblem.- )he number of clusters may not be fi$ed every round.

- %t can not be a##lied to large sensor net!or"s.

)herefore, for Autonomic !ireless sensor net!or"s !ith stable and fi$ed homogeneous nodesthe LEACB #rotocol !ill give good #erformance. 0or a Autonomic Sensor *et!or" !ith

stationary, battery #o!ered nodes it !ould be effective to use clustered based #rotocol li"e

LEACB, the most obvious reason is, its advantages such as reduced control messages, band!idth

reusability, enhanced resource allocation, im#roved #o!er control and lest !astage of energy.

. -0ASIS

EDAS%S 'o!er-Efficient DAthering in Sensor %nformation Systems( is a greedy chain-based

#o!er efficient algorithm 497. EDAS%S is based on t!o ideas i.e. Chaining, and ata 0usion. %t

uses same techniue as LEACB 'the scenario and the radio model in EDAS%S are the same asin LEACB(.

%n EDAS%S, each node can ta"e turn of being a leader of the chain, !here the chain can be

constructed using greedy algorithms that are de#loyed by the sensor nodes. EDAS%S assumes

that sensor nodes have a global "no!ledge of the net!or", nodes are stationary 'no movement of


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sensor nodes(, and nodes have location information about all other nodes. EDAS%S #erforms

data fusion e$ce#t the end nodes in the chain. EDAS%S out#erforms LEACB by eliminating the

overhead of dynamic cluster formation, minimi&ing the sum of distances that non leader-nodesmust transmit, limiting the number of transmissions and receives among all nodes, and using

only one transmission to the 2ase Station #er round.

As it is similar as LEACB #rotocol, EDAS%S also suffers from same #roblems as LEACB.Additionally, EDAS%S does not scale, so can not be a##lied to sensor net!or" !here global

"no!ledge of the net!or" is not easy to get.

.4 0A$

DEA+ 'Deogra#hical and Energy A!are +outing( 4857 is a recursive data dissemination #rotocol

for S*s. %t uses energy a!are and geogra#hically informed neighbor selection heuristics toroute a #ac"et to the targeted region. ithin that region, it uses a recursive a geogra#hic

informed mechanism to disseminate the #ac"et. DEA+, li"e other sensor net!or"s #rotocols,

develo#ed according to some assum#tions in mind:

- Sensor nodes are static 'i.e., immobile(.- )here is an e$istence of a locali&ation system that enables each node to "no! its current

#osition.- Sensor nodes are energy-constrained accom#anied !ith location information about all other

nodes 'i.e., each node "no!s its location and its energy level, and its neighborIs location and

remaining energy level.- )he lin" that connects nodes is bi-directional.

DEA+ has t!o #hases: '6( for!arding the #ac"ets to!ard the targeted region, and '9( for!arding

the #ac"ets !ithin the targeted region +.

Although DEA+ reduces the energy consum#tion for the route set u#. %t is not scalable and doesnot su##ort data diffusion.

2ased on the analysis, com#atibility survey of the e$isting #rotocols for ireless Sensor*et!or"s, !e can conclude that some of the #rotocols can more or less be a##lied for routing in

Autonomic ireless Sensor *et!or"s !ith fe! modifications de#ending u#on the net!or"

structure and functionality. 1verall, there are some "ey features, an efficient routing #rotocol forAutonomic ireless Sensor *et!or"s should have are: 4867

- &ata Aggregation5+educing the data si&e uic"ly using com#utation !ill #lay a "ey role in

su##orting efficient uery #rocessing, and reducing the overall net!or" overhead. Bence saving

the #o!er.- &ynamic clustering5 ynamic clustering architecture is very im#ortant because such

architecture !ill #reclude cluster heads from de#leting their energy uic"ly. Bence, long

net!or"Is lifetime.

-6"res"old 'or sensor nodes on data transmission and dissemination5this !ill hel# in saving

energy by reducing unnecessary transmissions 'i.e., redundancy( and giving the net!or" long

lifetime.- $andomi)ed pat" selection5multi-#ath selection to destination could im#rove fault tolerance

and handle the overhead of net!or" load.


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- !obility5most of the current #rotocols assume that sensor nodes are static 'i.e., immobile(.

Bo!ever, for some a##lications, nodes need to be mobile. Bence, ne! routing algorithms are

needed to handle the mobility and net!or" to#ology changes.- Sel'#con'iguration5since sensor nodes are #rone to failure due to some factors or ne! sensor

nodes may /oin the net!or", an u#date, self-configuration, self-healing, and ada#tation to

changes in net!or" to#ology or environmental changes should be considered.- Security5 there is a des#erate need to develo# distributed security a##roaches for !ireless

sensor net!or". Bence, achieving secure routing.

- Huality-of-Service, de#endability, and locali&ation need to be considered and given moreattention.

- )ime synchroni&ation.

4. Brie' ,+er+iew o' researc" pro7ects on Autonomic Networks

Bere is a brief overvie! of the current research #ro/ects based on Architecture for Autonomic

*et!or" communication and Self-3anagement !hich !ill serve as guidelines for AutonomicS*s and !ill bring revolution to S*s and its a##lications.

4.1. Bison

2%S1* !as a three-year #ro/ect funded by the Euro#ean Commission. 2%S1* aimedconfronting the com#le$ity e$#losion #roblem by building robust *et!or" %nformation Systems

that are self-organi&ing and self-re#airing.

2%S1* develo#ed techniues and tools for building robust, self-organi&ing and ada#tive

*et!or" %nformation System as ensembles of autonomous agents by dra!ing ins#iration frombiological #rocesses and mechanisms li"e ant colonies for routing in overlay net!or"s using

s!arm intelligence, lifecycle of ictyostelium for load balancing, e#idemics for aggregation and

immune system for search. 2%S1* e$#lored the use of ideas derived from com#le$ ada#tive systems 'CAS( to enable the

construction of robust and self-organi&ing information systems for de#loyment in highly

dynamic net!or" environments. )he #ro/ect #ro#osed solutions to im#ortant #roblems arising inoverlay net!or"s and mobile ad-hoc net!or"s by develo#ing algorithms for routing in mobile

ad-hoc net!or"s, to#ology control in sensor net!or"s along !ith data aggregation and content

search algorithms for #eer to #eer net!or"s. 4667

4.2. ANA 8Autonomic Network Arc"itecture9

A*A frame!or" is built on the ob/ective to #rovide an architectural frame!or" that allo!s the

accommodation of and communication bet!een various net!or"s, ranging from small scale

ersonal Area *et!or"s, through '3obile( Ad hoc *et!or"s and s#ecial #ur#ose net!or"s suchas Sensor *et!or"s, to global scale net!or"s, in #articular the %nternet. A*A frame!or"

s#ecifies ho! net!or"s interact.

A*A introduces the core conce#t of Jnet!or" com#artments.J )he com#artment abstraction

allo!s atomi&ation or decom#osition of communication systems and net!or"s into smaller and


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more easily manageable units. 0or e$am#le, com#artments !ill allo! decom#osition of todays

global % net!or" into a##ro#riate sub-net!or"s, !hich can be managed more autonomously

from the overall net!or" 'e.g., a different addressing or routing scheme can be a##lied insideeach com#artment(. 46=7

0igure ?. A*A 0rame!or" and *et!or" com#artments '+ef 46=7(

A 'net!or"( com#artment im#lements the o#erational rules and administrative #olicies for a

given communication conte$t. Com#artments ty#ically #erform functions li"e registration and

degradation, #olicy enforcement, identifier management and resolution and +outing. 46=7Addressing and naming are left to com#artments. )he main advantages of this a##roach are:

*o need to im#ose a uniue !ay to resolve names and manage a uniue global addressing

scheme. %t is o#en to future addressing and naming schemes.

4.. 3aggle

Baggle is a ne! autonomic net!or"ing architecture designed to enable communication in the#resence of intermittent net!or" connectivity, !hich e$#loits autonomic o##ortunistic

communications 'i.e., in the absence of end-to-end communication infrastructures(. Baggle node

architecture ta"es ins#iration from human communication model. 46>7)he main com#onents of Baggle are:

A re+olutionary paradigm 'or autonomic communication, based on advanced local

for!arding and sensitive to realistic human mobility

A simple and power'ul arc"itectureoriented to o##ortunistic message relaying, and

based on #rivacy, authentication, trust and advanced data handling

An o#en environment for the easy #roliferation of a##lications and services.

4.(. CASCA&AS


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CASCAAS 'Com#onent-!are for Autonomic Situation-a!are Communications, and

ynamically Ada#table Services( is an ongoing #ro/ect li"e A*A and Baggle.

)he overall goal of CASCAAS is identifying, develo#ing, and evaluating architectures andsolutions based on a general-#ur#ose com#onent model for autonomic communication services7 Baggle ro/ect. htt#:GG!!!.haggle#ro/ect.org

46?7 CASCAAS #ro/ect. htt#:GG!!!.cascadas-#ro/ect.org

46@7 Sch!iebert L., Du#ta S., einmann M., K0esearch hallenges in Wireless Networks of%iomedical Sensors in #roceedings of the @th annual international conference on 3obile

com#uting and net!or"ing.Available from : htt#:GG#ortal.acm.orgGcitation.cfmP

idQ86?9

467 Autonomic Com#uting i"i.htt#:GGen.!i"i#edia.orgG!i"iGAutonomicNcom#uting

467 Linnyer 2eatrys +ui&, M.3.S.*., Antonio A.0. Loureiro, 8&!NN!+ ! &anagement!rchitecture for Wireless Sensor Networks.9%EEE Communications 3aga&ine, 9558. =6'9(:

#. 66?-69>. Available from :htt#:GG!!!.lisha.ufsc.brGRlucasGdocsG+ui&-9558.#df

4957 avy S. et al. Kolicy-%ased !rchitecture to 3nable !utonomic ommunications9. Availablefrom :

htt#:GGtech#ubs.motorola.comGdo!nloadG%C135556=6=55G%C135556=6=55.#df

4967 Colan, 3. Service-1riented Architecture e$#ands the vision of eb services, art 6. Mune,

955= Available from : htt#:GG!!!-69.ibm.comGdevelo#er!or"sGlibraryG!s-soaintro.html

4997 Dianni A., i Caro, 0rederic" ucatelle, Luca 3. Dambardella, K2%S1*: 2iology-%ns#ired

techniues for Self-1rgani&ation in dynamic *et!or"s Available from :htt#:GG!!!.idsia.chGRfrederic"Gbison.#df

4987 ireless Sensor *et!or"s i"i. htt#:GGen.!i"i#edia.orgG!i"iGirelessNsensorNnet!or"

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for Wireless Sensor Networking,J %EEEGAC3 )ransactions on *et!or"ing, vol. 66, ##. 9-6?,

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