Clustering in Ad hoc and Sensor Networks. –The data collected by each sensor is communicated...

Clustering in

Ad hoc and Sensor Networks

– The data collected by each sensor is communicated through the The data collected by each sensor is communicated through the

network to a single processing center that uses the datanetwork to a single processing center that uses the data

– ClusteringClustering groups nodes into groups such that each node communicate groups nodes into groups such that each node communicate

information only to clusterheads and then the clusterheads information only to clusterheads and then the clusterheads

communicate the aggregated information to the processing center, communicate the aggregated information to the processing center,

saving energy and bandwidthsaving energy and bandwidth

– The cost of transmitting a bit is higher than a computation; therefore, it The cost of transmitting a bit is higher than a computation; therefore, it

may be beneficial to organize the sensors into clustersmay be beneficial to organize the sensors into clusters

– Cluster-based control structures provides more efficient use of Cluster-based control structures provides more efficient use of

resources for large dynamic networksresources for large dynamic networks

Clustering can be used forClustering can be used for

– Transmission management Transmission management

– Backbone formationBackbone formation

– Routing Efficiency Routing Efficiency

Why Clustering?

Link-Clustered Architecture

[Baker+ 1981a, 1981b, Ephremides+ 1987]

– Reduces interference in multiple-access broadcast environmentReduces interference in multiple-access broadcast environment

– Distinct clusters are formed to schedule transmissions in a contention-Distinct clusters are formed to schedule transmissions in a contention-free wayfree way

– Each cluster has a clusterhead, one or more gateways and zero or Each cluster has a clusterhead, one or more gateways and zero or more ordinary nodesmore ordinary nodes

– Clusterhead schedules transmission and allocates resources within its Clusterhead schedules transmission and allocates resources within its clustercluster

– Gateways connect adjacent clustersGateways connect adjacent clusters

To establish link-clustered control structureTo establish link-clustered control structure

1.1. Discover neighborsDiscover neighbors

2.2. Select clusterhead to form clustersSelect clusterhead to form clusters

3.3. Decide on gateways between clustersDecide on gateways between clusters

Clusterhead

Gateway

Ordinary node

Cluster

Link-Clustered Architecture

[Baker+ 1981a, 1981b, Ephremides+ 1987]

Clusterheads

– Resemble base stations in cellular networks, but dynamicResemble base stations in cellular networks, but dynamic

– Responsible for resource allocationResponsible for resource allocation

– Maintains network topologyMaintains network topology

– Acts as routers – forwards packets from one node to anotherActs as routers – forwards packets from one node to another

– Aware of its cluster membersAware of its cluster members

– Aware of its one-hop neighboring clusterheadsAware of its one-hop neighboring clusterheads

Since clusterheads decide network topology, Since clusterheads decide network topology, electionelection

of clusterheads optimally is criticalof clusterheads optimally is critical

Previous Work

Highest-Degree Heuristic [Gerla+ 1995, Parekh 1994]

Computes the degree of a node based on the distance Computes the degree of a node based on the distance (transmission range) between the node and the other nodes(transmission range) between the node and the other nodes

The node with the maximum number of neighbors (maximum The node with the maximum number of neighbors (maximum degree) is chosen to be a clusterhead and any tie is broken degree) is chosen to be a clusterhead and any tie is broken by the node idsby the node ids

Drawbacks: A clusterhead cannot handle a large number of nodes due to A clusterhead cannot handle a large number of nodes due to

resource limitationsresource limitations Load handling capacity of the clusterhead puts an upper Load handling capacity of the clusterhead puts an upper

bound on the node-degreebound on the node-degree The throughput of the system drops as the number of nodes The throughput of the system drops as the number of nodes

in cluster increasesin cluster increases

Lowest-ID Heuristic [Baker+ 1981a-b, Ephremides+ 1987]

The node with the minimum node-id is chosen to be a clusterheadThe node with the minimum node-id is chosen to be a clusterhead A node is called a A node is called a gateway gateway if it lies within the transmission range of if it lies within the transmission range of

two or more clusterstwo or more clusters Distributed gateway Distributed gateway is a pair of nodes that reside within different is a pair of nodes that reside within different

clusters, but they are within the transmission range of each otherclusters, but they are within the transmission range of each other

Drawbacks: Since it is biased towards nodes with smaller node-ids, leading to Since it is biased towards nodes with smaller node-ids, leading to

battery drainage battery drainage It does not attempt balance the load for across all the nodesIt does not attempt balance the load for across all the nodes

Previous Work

Previous Work

Node-Weight Heuristic [Basagni 1999a, 1999b]

Node-weightsNode-weights are assigned to nodes based on the suitability are assigned to nodes based on the suitability of a node being a clusterheadof a node being a clusterhead

The node is chosen to be a clusterhead if its node-weight is The node is chosen to be a clusterhead if its node-weight is higher than any of its neighbor’s node-weights and any tie is higher than any of its neighbor’s node-weights and any tie is broken by the minimum node idsbroken by the minimum node ids

Drawbacks: No concrete criteria of assigning the node-weightsNo concrete criteria of assigning the node-weights Works well for “quasi-static” networks where the nodes do Works well for “quasi-static” networks where the nodes do

not move much or move very slowlynot move much or move very slowly

Weighted Clustering Algorithm (WCA)Weighted Clustering Algorithm (WCA)

AA clusterhead can clusterhead can ideallyideally support nodes support nodes– Ensures efficient MAC functioningEnsures efficient MAC functioning– Minimizes delay and maximizes throughputMinimizes delay and maximizes throughput

A clusterhead uses more battery power A clusterhead uses more battery power – Does extra work due to packet forwardingDoes extra work due to packet forwarding– Communicates with more number of nodesCommunicates with more number of nodes

A clusterhead should be less mobileA clusterhead should be less mobile– Helps to maintain same configuration Helps to maintain same configuration – Avoids frequent WCA invocationAvoids frequent WCA invocation

A better power usage with physically closer nodesA better power usage with physically closer nodes– More power for distant nodes due to signal attenuationMore power for distant nodes due to signal attenuation

Optimizing Clustering Algorithm in Mobile Ad hocNetworks Using Genetic Algorithmic Approach [Turgut+ 2002][Turgut+ 2002]

Weighted Clustering Algorithm (WCA) Steps1.1. Compute the Compute the degreedegree ddvv each node each node vv

Coordinate distance, predefined transmission range. Coordinate distance, predefined transmission range.

2.2. Compute the Compute the degree-differencedegree-difference for every nodefor every node

For efficient MAC (medium access control) functioning.For efficient MAC (medium access control) functioning.

Upper bound on # of nodes a cluster head can handle.Upper bound on # of nodes a cluster head can handle.

vvv

txvvdistvNvV

ranged''

,

',|)(|

|| dvv

3.3. Compute the Compute the sum of the distancessum of the distances DDvv with all neighbors with all neighbors

Energy consumption; more energy for greater dist.Energy consumption; more energy for greater dist.

communication.communication.

Power required to support a link increases faster thanPower required to support a link increases faster than

linearly with distance.linearly with distance. (For cellular networks)(For cellular networks)

)('

',vNv

vvdistvD 1

2 3

4

5

6

7

12 1314

1516

17

Weighted Clustering Algorithm (WCA) Steps

4.4. Compute the average speed of every node; gives a measure of Compute the average speed of every node; gives a measure of

mobilitymobility MMvv

where where and and are the are the

coordinates of the node at time coordinates of the node at time and and

Component with less mobility is a better choice for clusterhead.Component with less mobility is a better choice for clusterhead.

T

tYYXXM ttttTv

111

1 22

YX tt, YX tt 11,

v t 1t

Yt

Yt-1

XtXt-1

time



5.5. Compute the total (cumulative) Compute the total (cumulative) timetime PPvv a node acts as a node acts as

clusterheadclusterhead

Battery drainage = Power consumedBattery drainage = Power consumed

6.6. Calculate the Calculate the combined weightcombined weight WWvv for each node for each node

WWvv = w = w11ΔΔv v + w + w22DDv v + w + w33MMv v + w + w44PPvv for each nodefor each node

7.7. Find min Find min WWvv;; choose node choose node vv as the cluster head, remove all as the cluster head, remove all

neighbors of neighbors of vv for further WCA for further WCA

8.8. Repeat steps 2 to 7 for the remaining nodesRepeat steps 2 to 7 for the remaining nodes

Load Balancing Factor (LBF)

It is desirable to balance the loads among the clustersIt is desirable to balance the loads among the clusters

Load balancing factor (LBF) has defined as (should be high)Load balancing factor (LBF) has defined as (should be high)

i i

cLBF

xn

2

where, where,

nc is the number of clusterheads is the number of clusterheads

xi is the cardinality of cluster is the cardinality of cluster ii and and

nc

ncN is the average number of neighbors of a clusterheadis the average number of neighbors of a clusterhead

((N N being the total number of nodes in the system)being the total number of nodes in the system)

Connectivity

For clusters to communicate with each other, it is assumed that For clusters to communicate with each other, it is assumed that

clusterheads are capable of operating in clusterheads are capable of operating in dual dual power mode power mode

A clusterhead uses A clusterhead uses lowlow power mode to communicate with its immediate power mode to communicate with its immediate

neighbors within its transmission range and neighbors within its transmission range and highhigh power mode is used for power mode is used for

communication with neighboring clusterscommunication with neighboring clusters

ConnectivityConnectivity is defined as (for multiple component graph) is defined as (for multiple component graph)

Probability that a node is reachable from any other nodeProbability that a node is reachable from any other node

( 0 – 1; 1 being most desirable)( 0 – 1; 1 being most desirable)

N

componentlargestofsizetyconnectivi

Scattered nodes in the network

Demonstration

Clusterheads are identified

Demonstration

Clusters are formed

Demonstration

Clusters are connected

Demonstration

Features of WCA

Invocation of WCA is Invocation of WCA is on-demandon-demand

– Reduces information exchange by less system updates Reduces information exchange by less system updates

– Reduces computation/communication costsReduces computation/communication costs

– Manages mobility by Manages mobility by reaffiliationsreaffiliations

– Delays (avoids) invocation of clustering as far as possibleDelays (avoids) invocation of clustering as far as possible

WCA is WCA is distributivedistributive

– No clusterhead is over loadedNo clusterhead is over loaded

– Balances load by limiting the cluster sizeBalances load by limiting the cluster size

Performance Metric

1.1. Number of clusterheadsNumber of clusterheads

2.2. Number of reaffiliationsNumber of reaffiliations

– a process where a node detaches from one clusterhead and a process where a node detaches from one clusterhead and

attachesattaches

to anotherto another

3.3. Number of dominant set updatesNumber of dominant set updates

– when a node can no longer attach to any of the existing when a node can no longer attach to any of the existing

clusterheadsclusterheads

These parameters are studied for the varying These parameters are studied for the varying

number of nodesnumber of nodes

transmission rangetransmission range

maximum displacementmaximum displacement

Simulation Environment

System with N nodes on a 100x100 gridSystem with N nodes on a 100x100 grid

N was varied between 20 and 60N was varied between 20 and 60

Nodes moved in all directions randomly Nodes moved in all directions randomly

Velocity of nodes were varied uniformly between 0 and 10Velocity of nodes were varied uniformly between 0 and 10

Transmission range of nodes was varied between 0 and 70Transmission range of nodes was varied between 0 and 70

Ideal degree was fixed at = 10Ideal degree was fixed at = 10

Weighing factors: wWeighing factors: w1 1 = 0.7, w= 0.7, w22 = 0.2, w = 0.2, w33 = 0.05 and w = 0.05 and w44 = 0.05 = 0.05

Experimental Results

Max displacement = 5 (const)Transmission range = 0 - 70Number of nodes = 20 - 60Ideal degree = 10

Max displacement = 1 - 10Transmission range = 30 (const)Number of nodes = 20 - 60Ideal degree = 10


Load Balancing

Connectivity

Performance of WCA

Genetic Algorithms

Map the possible solutions of the problem to symbolic spaceMap the possible solutions of the problem to symbolic space

Possible solutions form a pool of solutions – Possible solutions form a pool of solutions – populationpopulation

Solution stringsSolution strings – chromosomes – chromosomes and components of chromosomesand components of chromosomes – –

genesgenes

Genetic Algorithm operations:Genetic Algorithm operations:

– Selection Selection

– CrossoverCrossover

– MutationMutation

– ReplacementReplacement

– ElitismElitism

Encoding of the Chromosome

N = # of nodes in the networkN = # of nodes in the network

each with unique node id [1..N] used to encodeeach with unique node id [1..N] used to encode

the chromosome by the chromosome by integer permutationinteger permutation

all the ids should be included without any duplication,all the ids should be included without any duplication,

and without order.and without order.

For instance: N = 100 , node ids [1..100]For instance: N = 100 , node ids [1..100]

Pool size = 50 (50 strings of integers/chromosomes)Pool size = 50 (50 strings of integers/chromosomes)

5 13 15 99- - - -

3 777 5 88- - - -

6 558 3 44- - - -

2

50

1

.

.

Data Encoded into chromosomes

WCA intermediate results

2

50

1

.

.

.

1 353 7 12- - - 2225

3 647 5 25- - - 4534

6 448 3 56- - - 3455

Mapping WCA to GA

WV Values

.

.

15

1004

2

899

…

…

…

…

3

.

.

Node Ids of all nodes

22

10

7

863

5

3 25 127

Neighbors list

ClusterHead Set for a single chromosome

15

76

3

2 …

……

7....

Node Id of a ClusterHead

22107

5515

25 12

WV Values

.

.

.

.

Mapping WCA to GA

GA Steps

1.1. Choose Initial PopulationChoose Initial Population

Randomly generate the initial population.Randomly generate the initial population.

Pool size = 50 (means 50 chromosomes)Pool size = 50 (means 50 chromosomes)

While (new_pool_size < old_pool_size) While (new_pool_size < old_pool_size)

repeat step 3 to 6 (repeat step 2 until the number of repeat step 3 to 6 (repeat step 2 until the number of

generation or the convergence is metgeneration or the convergence is met))

2. Selection2. Selection

Compute the fitness value for each chromosome by WCompute the fitness value for each chromosome by WVV . .

Roulette Wheel method is used based on the fitness values.Roulette Wheel method is used based on the fitness values.

3. Crossover3. Crossover

X_Order1 method is used.X_Order1 method is used.

Crossover rate = 0.8 Crossover rate = 0.8

4.4. MutationMutation

Swap method is used; randomly selecting two gene at Swap method is used; randomly selecting two gene at

positions i and j.positions i and j.

Mutation rate = 0.1Mutation rate = 0.1

55. Replacement. Replacement

Append method is used. The new children will be appendedAppend method is used. The new children will be appended

into the new pool.into the new pool.

66. Elitism. Elitism

- - Check if the new children are better than the best, then replace Check if the new children are better than the best, then replace

the best by the child the best by the child

- A- Avoid being stuck on local optimavoid being stuck on local optima

GA Steps

Cfit Value Algorithm

FitnessValue = 0;FitnessValue = 0;

1.1. For each gene in chromosome repeat step 2 to 3 For each gene in chromosome repeat step 2 to 3

2.2. node = gene[I]; node = gene[I];

3.3. if node is not clusterH and if node is not clusterH and

is not a member of the other clusterH andis not a member of the other clusterH and

Nodedegree <= MAX_DEGREE ( const )Nodedegree <= MAX_DEGREE ( const )

Then it is a clusterH, Then it is a clusterH,

Compute WCompute WVV for this node for this node

insert it into clusterHSetinsert it into clusterHSet

fitnessValue += WfitnessValue += WVV;;

4.4. For each remaining node I from the network For each remaining node I from the network

If (it is not a clusterH If (it is not a clusterH andand member of other clusterH, member of other clusterH,

andand NodeDegree <= MAX_DEGREE) NodeDegree <= MAX_DEGREE)

thenthen

Compute WCompute WVV for this node for this node

insert it into clusterHSetinsert it into clusterHSet

fitnessValue += WfitnessValue += WVV;;

Cfit Value Algorithm

Performance Metric

1.1. Number of clusterheadsNumber of clusterheads

2.2. Number of reaffiliationsNumber of reaffiliations

– a process where a node detaches from one clusterhead and a process where a node detaches from one clusterhead and

attaches to anotherattaches to another

These parameters are studied for the varying These parameters are studied for the varying

number of nodesnumber of nodes

transmission rangetransmission range

maximum displacementmaximum displacement

3.3. Load Distribution Load Distribution

Simulation Environment

System with N nodes on a 100x100 gridSystem with N nodes on a 100x100 grid

N was varied between 20 and 60N was varied between 20 and 60

Nodes moved in all directions randomly Nodes moved in all directions randomly

Velocity of nodes were varied uniformly between 0 and 10Velocity of nodes were varied uniformly between 0 and 10

Transmission range of nodes was varied between 0 and 70Transmission range of nodes was varied between 0 and 70

Ideal degree was fixed at = 10Ideal degree was fixed at = 10

Weighing factors: wWeighing factors: w1 1 = 0.7, w= 0.7, w22 = 0.2, w = 0.2, w33 = 0.05 and w = 0.05 and w44 = 0.05 = 0.05


Max displacement = 5 (const)Transmission range = 0 - 70Number of nodes = 20 - 60Ideal degree = 10

WCA Optimized WCA


WCA Optimized WCA



WCA Optimized WCA


Load Balancing with WCA

Load Balancing with GA

The load balancing factor has improvement ten times with GA

– This paper proposes a distributed, randomized clustering algorithm to This paper proposes a distributed, randomized clustering algorithm to

organize the sensors in a wireless sensor network into clusters to minimize organize the sensors in a wireless sensor network into clusters to minimize

the energy used to communicate information from all nodes to the the energy used to communicate information from all nodes to the

processing centerprocessing center

– By the generation of hierarchy of clusterheads, the energy savings increase By the generation of hierarchy of clusterheads, the energy savings increase

with the number of levels in the hierarchywith the number of levels in the hierarchy

– Sensor detects events and then communicate the collected information to a Sensor detects events and then communicate the collected information to a

central location where parameters characterizing these events are central location where parameters characterizing these events are

estimatedestimated

– In the clustered environment, the data gathered by the sensors is In the clustered environment, the data gathered by the sensors is

communicated to the data processing center through a hierarchy of communicated to the data processing center through a hierarchy of

clusterheads clusterheads

– The processing center determines the final estimates of the parameters The processing center determines the final estimates of the parameters

using information communicated by the clusterheadsusing information communicated by the clusterheads

An Energy Efficient Hierarchical Clustering Algorithm for Wireless Sensor Networks[Bandyopadhyay+, 2003]

– The processing center can be a specialized device or one of the sensors The processing center can be a specialized device or one of the sensors

itselfitself

– In such clustered environment, sensor data is communicated over smaller In such clustered environment, sensor data is communicated over smaller

distances, the energy consumed in the network will be much lower than the distances, the energy consumed in the network will be much lower than the

energy consumption when every sensor communicates directly to the energy consumption when every sensor communicates directly to the

information processing centerinformation processing center

– The results in stochastic geometry are used to derive values of parameters The results in stochastic geometry are used to derive values of parameters

for the algorithm that minimize the energy spent in the sensor networkfor the algorithm that minimize the energy spent in the sensor network


A New, Energy-Efficient, Single-Level Clustering AlgorithmA New, Energy-Efficient, Single-Level Clustering Algorithm

– Each sensor becomes a clusterhead (CH) with probability Each sensor becomes a clusterhead (CH) with probability p p and advertises and advertises

itself as a clusterhead to the sensors within its radio range – these itself as a clusterhead to the sensors within its radio range – these

clusterheads are called clusterheads are called volunteer clusterheadsvolunteer clusterheads

– This advertisement is forwarded to all the sensors that are no more than This advertisement is forwarded to all the sensors that are no more than kk

hops away from the clusterheadhops away from the clusterhead

– Any sensor node that is not clusterhead itself receiving such advertisement Any sensor node that is not clusterhead itself receiving such advertisement

joins the cluster of the closest clusterheadjoins the cluster of the closest clusterhead

– Any sensor node that is neither a clusterhead nor has joined any cluster Any sensor node that is neither a clusterhead nor has joined any cluster

itself becomes a clusterhead – called itself becomes a clusterhead – called forced clusterheadsforced clusterheads

– Since the advertisement forwarding has been limited to Since the advertisement forwarding has been limited to kk hops, if a sensor hops, if a sensor

does not receive a CH advertisement within time duration does not receive a CH advertisement within time duration tt (where (where tt is the is the

time required for data to reach the CH from any sensor time required for data to reach the CH from any sensor kk hops away), it hops away), it

means that the sensor node is not within k hops of any volunteer CHsmeans that the sensor node is not within k hops of any volunteer CHs



– Therefore, the sensor node becomes a forced clusterheadTherefore, the sensor node becomes a forced clusterhead

– The CH can transmit the aggregated information to the processing center The CH can transmit the aggregated information to the processing center

after every t units of time since all the sensors within a cluster are at most k after every t units of time since all the sensors within a cluster are at most k

hops away from the CHhops away from the CH

– The limit on the number of hops allows the CH to reschedule their The limit on the number of hops allows the CH to reschedule their

transmissionstransmissions

– This is a distributed algorithm and does not demand clock synchronization This is a distributed algorithm and does not demand clock synchronization

between the sensorsbetween the sensors

– The energy consumed for the information gathered by the sensors to reach The energy consumed for the information gathered by the sensors to reach

the processing center will depend on the parameters the processing center will depend on the parameters pp and and kk

– Since the objective of this work is to organize sensors in clusters to Since the objective of this work is to organize sensors in clusters to

minimize the energy consumption, values of the parameters (minimize the energy consumption, values of the parameters (pp and and kk) must ) must

be found to ensure the goalbe found to ensure the goal



Assumptions made for the optimal parameters are as follows:Assumptions made for the optimal parameters are as follows:

– The sensors are distributed as per a homogeneous spatial Poisson process The sensors are distributed as per a homogeneous spatial Poisson process

of intensity of intensity λλ in 2-dimensional space in 2-dimensional space

– All sensors transmit at the same power level – have the same radio range All sensors transmit at the same power level – have the same radio range rr

– Data exchanged between two communicating sensors not within each others’ Data exchanged between two communicating sensors not within each others’

radio range is forwarded by other sensorsradio range is forwarded by other sensors

– A distance of A distance of dd between any sensor and its CH is equivalent to hops between any sensor and its CH is equivalent to hops

– Each sensor uses 1 unit of energy to transmit or receive 1 unit of dataEach sensor uses 1 unit of energy to transmit or receive 1 unit of data

– A routing infrastructure is in place; when a sensor communicates data to A routing infrastructure is in place; when a sensor communicates data to

another sensor, only the sensors on the routing path forward the dataanother sensor, only the sensors on the routing path forward the data

– The communication environment is contention- and error-free; sensors do not The communication environment is contention- and error-free; sensors do not

have to retransmit any datahave to retransmit any data


d / r

A New, Energy-Efficient, Hierarchical Clustering AlgorithmA New, Energy-Efficient, Hierarchical Clustering Algorithm

– This algorithm is extension of the previous one by allowing more than one This algorithm is extension of the previous one by allowing more than one

level of clustering in placelevel of clustering in place

– Assume that there are Assume that there are hh levels in the clustering hierarchy with level 1 being levels in the clustering hierarchy with level 1 being

the lowest level and level the lowest level and level hh being the highest being the highest

– The sensors communicate the gathered data to level-1 clusterheads (CHs)The sensors communicate the gathered data to level-1 clusterheads (CHs)

– The level-1 CHs aggregate this data and communicate the aggregated data The level-1 CHs aggregate this data and communicate the aggregated data

to level-2 CHs and so onto level-2 CHs and so on

– Finally, level-h CHs communicate the aggregated data or estimates based on Finally, level-h CHs communicate the aggregated data or estimates based on

this aggregated data to the processing centerthis aggregated data to the processing center



– The cost of communicating the information from the sensors to the The cost of communicating the information from the sensors to the

processing center is the energy consumed by the sensors to communicate processing center is the energy consumed by the sensors to communicate

the information to level-1 CHs, plus the energy consumed by the level-1 CHs the information to level-1 CHs, plus the energy consumed by the level-1 CHs

to communicate the aggregated data to level-2 CHs, …., plus the energy to communicate the aggregated data to level-2 CHs, …., plus the energy

consumed by the level-h CHs to communicate the aggregated data to the consumed by the level-h CHs to communicate the aggregated data to the

information processing centerinformation processing center

Algorithm DetailsAlgorithm Details

– The algorithm works in a bottom-up fashionThe algorithm works in a bottom-up fashion

– First, it elects the level-1 clusterheads, then level-2 clusterheads, and so onFirst, it elects the level-1 clusterheads, then level-2 clusterheads, and so on




– Level-1 clusterheads are chosen as follows:Level-1 clusterheads are chosen as follows:

o Each sensor decides to become a level-1 CH with certain probability Each sensor decides to become a level-1 CH with certain probability pp11

and advertises itself as a clusterhead to the sensors within its radio and advertises itself as a clusterhead to the sensors within its radio

rangerange

o This advertisement is forwarded to all the sensors within This advertisement is forwarded to all the sensors within kk11 hops of the hops of the

advertising CHadvertising CH

o Each sensor receiving an advertisement joins the cluster of the closest Each sensor receiving an advertisement joins the cluster of the closest

level-1 CH; the remaining sensors become forced level-1 CHslevel-1 CH; the remaining sensors become forced level-1 CHs

– Level-1 CHs then elect themselves as level-2 CHs with a certain probability Level-1 CHs then elect themselves as level-2 CHs with a certain probability

pp2 2 and broadcast their decision of becoming a level-2 CHand broadcast their decision of becoming a level-2 CH

– This decision is forwarded to all the sensors within This decision is forwarded to all the sensors within kk22 hops hops




– The level-1 CHs that receive the advertisement from level-2 CHs joins the The level-1 CHs that receive the advertisement from level-2 CHs joins the

cluster of the closest level-2 CH; the remaining level-1 CHs become forced cluster of the closest level-2 CH; the remaining level-1 CHs become forced

level-2 CHslevel-2 CHs

– Clusterheads at level Clusterheads at level 3, 4, 5,…,h3, 4, 5,…,h are chosen in similar fashion with are chosen in similar fashion with

probabilities probabilities pp33, p, p44, p, p55,...,p,...,phh respectively to generate a hierarchy of CHs, in respectively to generate a hierarchy of CHs, in

which any level-i CH is also CH of level (i-1), (i-2),…,1.which any level-i CH is also CH of level (i-1), (i-2),…,1.


Advantages:Advantages:

– It is considered one of the earliest clustering algorithms in sensor It is considered one of the earliest clustering algorithms in sensor

networks that incorporates energy efficiency into the design of the networks that incorporates energy efficiency into the design of the

algorithmalgorithm

– Since it is distributed algorithm, there is no need for clock Since it is distributed algorithm, there is no need for clock

synchronization between sensor nodessynchronization between sensor nodes

– It achieves not only better energy efficiency, but also better time It achieves not only better energy efficiency, but also better time

complexity compared to previous workcomplexity compared to previous work

– The sensor nodes considered are simple nodes with fixed power level of The sensor nodes considered are simple nodes with fixed power level of

transmissionstransmissions

– Since the algorithm is run periodically, the probability of becoming a Since the algorithm is run periodically, the probability of becoming a

clusterhead for each period is chosen to ensure that every node will get clusterhead for each period is chosen to ensure that every node will get

a chance to become clusterhead – providing the functionality for load a chance to become clusterhead – providing the functionality for load

balancing balancing



– Another approach to ensure load balancing is to trigger the algorithm Another approach to ensure load balancing is to trigger the algorithm

when the energy levels fall below a certain thresholdwhen the energy levels fall below a certain threshold

– Energy savings increases as the density of the sensor nodes increases Energy savings increases as the density of the sensor nodes increases

for single level clusteringfor single level clustering

– For the hierarchical clustering algorithm, the energy savings increase for For the hierarchical clustering algorithm, the energy savings increase for

(i) networks of sensors with lower communication radius, (ii) lower (i) networks of sensors with lower communication radius, (ii) lower

density of sensors in the network, and (iii) increase in the number of density of sensors in the network, and (iii) increase in the number of

hierarchy levelshierarchy levels


Disadvantages:Disadvantages:

– The energy consumption of clusterheads has not been addressed since these The energy consumption of clusterheads has not been addressed since these

nodes will involve with more computation and communication of data to nodes will involve with more computation and communication of data to

higher level clusterheads – consequence of non-uniform power consumption higher level clusterheads – consequence of non-uniform power consumption

on the performance of the overall sensor network in the long runon the performance of the overall sensor network in the long run

– An ideal network is assumed (contention- and error-free) which may not An ideal network is assumed (contention- and error-free) which may not

reflect the real life scenariosreflect the real life scenarios

– Possible load imbalance between different clustersPossible load imbalance between different clusters

– Overhead associated with the clusterheads selection is not consideredOverhead associated with the clusterheads selection is not considered

– How does the network cope with sensor node failures? How is detected and How does the network cope with sensor node failures? How is detected and

remedied?remedied?

– How does the network handle information sent by faulty sensors?How does the network handle information sent by faulty sensors?


Disadvantages:Disadvantages:

– How many forced-clusterheads can the sensor network handle? What is the How many forced-clusterheads can the sensor network handle? What is the

upper bound? What are the guarantees that forced-clusterhead will be able upper bound? What are the guarantees that forced-clusterhead will be able

to communicate with the neighboring clusterheads?to communicate with the neighboring clusterheads?

– Similarly, what is the upper bound on the number of sensor nodes within Similarly, what is the upper bound on the number of sensor nodes within

one cluster?one cluster?

– Energy is wasted by those sensor nodes closer to the processing center Energy is wasted by those sensor nodes closer to the processing center

than their CH, but still need to go through their CHthan their CH, but still need to go through their CH


Suggestions/Improvements/Future Work:Suggestions/Improvements/Future Work:

– What happens if a sensor node receives several join advertisements What happens if a sensor node receives several join advertisements

from multiple nearby clusterheads? How does the sensor node decides from multiple nearby clusterheads? How does the sensor node decides

which one to join? which one to join?

Possible solution:Possible solution: the decision can be made to join to the cluster with the the decision can be made to join to the cluster with the

minimum number of members such that sensor nodes are evenly minimum number of members such that sensor nodes are evenly

distributed among the clustersdistributed among the clusters

– Error and contention in communication is not considered Error and contention in communication is not considered

Possible solution: Possible solution: results may be verified with the real MAC protocol and results may be verified with the real MAC protocol and

traffic conditions under a simulator or a test-bedtraffic conditions under a simulator or a test-bed

– The capabilities of the processing center should be more than the The capabilities of the processing center should be more than the

regular sensor nodesregular sensor nodes



– Further energy efficiency can be achieved if the clusterheads can be in Further energy efficiency can be achieved if the clusterheads can be in

active or inactive mode (energy saving mode)active or inactive mode (energy saving mode)

– Depending on the distance from the clusterheads, the sensor nodes may Depending on the distance from the clusterheads, the sensor nodes may

choose to transmit data towards clusterhead in various power levels (for choose to transmit data towards clusterhead in various power levels (for

instance, low vs. high)instance, low vs. high)

– In multi-hop mode, the sensor nodes closest to the clusterhead have the In multi-hop mode, the sensor nodes closest to the clusterhead have the

most energy drainage due to data forwardingmost energy drainage due to data forwarding

Possible solution:Possible solution: a scheme allowing the sensor nodes to alternate a scheme allowing the sensor nodes to alternate

between single-hop and multiple-hop mode periodicallybetween single-hop and multiple-hop mode periodically


– LEACHLEACH (Low-Energy Adaptive Clustering Hierarchy) is a clustering-based (Low-Energy Adaptive Clustering Hierarchy) is a clustering-based

protocol that utilizes the randomized rotation of local cluster base stations protocol that utilizes the randomized rotation of local cluster base stations

to evenly distribute the energy load within the network of sensorsto evenly distribute the energy load within the network of sensors

– It is a distributed, does not require any control information from base station It is a distributed, does not require any control information from base station

(BS) and the nodes do not need to have knowledge of global network for (BS) and the nodes do not need to have knowledge of global network for

LEACH to functionLEACH to function

– The energy saving of LEACH is achieved by combining compression with The energy saving of LEACH is achieved by combining compression with

data routingdata routing

– Key features of LEACH include:Key features of LEACH include:

Localized coordination and control of cluster set-up and operationLocalized coordination and control of cluster set-up and operation

Randomized rotation of the cluster base stations or clusterheads and their Randomized rotation of the cluster base stations or clusterheads and their

clustersclusters

Local compression of information to reduce global communicationLocal compression of information to reduce global communication

Energy-Efficient Communication Protocol Architecture for Wireless Microsensor Networks (LEACH Protocol)[Heinzelman+ 2000, 2002]

– Considered microsensor network has the following characteristics:Considered microsensor network has the following characteristics:

The base station is fixed and located far from the sensorsThe base station is fixed and located far from the sensors

All the sensor nodes are homogeneous and energy constrainedAll the sensor nodes are homogeneous and energy constrained

– Communication between sensor nodes and the base station is expensive and no Communication between sensor nodes and the base station is expensive and no

high energy nodes exist to achieve communicationhigh energy nodes exist to achieve communication

– By using clusters to transmit data to the BS, only few nodes need to transmit for By using clusters to transmit data to the BS, only few nodes need to transmit for

larger distances to the BS while other nodes in each cluster use small transmit larger distances to the BS while other nodes in each cluster use small transmit

distancesdistances

– LEACH achieves superior performance compared to classical clustering algorithms LEACH achieves superior performance compared to classical clustering algorithms

by using adaptive clustering and rotating clusterheads; assisting the total energy of by using adaptive clustering and rotating clusterheads; assisting the total energy of

the system to be distributed among all the nodesthe system to be distributed among all the nodes

– By performing load computation in each cluster, amount of data to be transmitted to By performing load computation in each cluster, amount of data to be transmitted to

BS is reduced. Therefore, large reduction in the energy dissipation is achieved BS is reduced. Therefore, large reduction in the energy dissipation is achieved

since communication is more expensive than computationsince communication is more expensive than computation

LEACH [Heinzelman+ 2000, 2002]

Algorithm OverviewAlgorithm Overview

– The nodes are grouped into local clusters with one node acting as the local base The nodes are grouped into local clusters with one node acting as the local base

station (BS) or clusterhead (CH)station (BS) or clusterhead (CH)

– The CHs are rotated in random fashion among the various sensorsThe CHs are rotated in random fashion among the various sensors

– Local data fusion is achieved to compress the data being sent from clusters to the Local data fusion is achieved to compress the data being sent from clusters to the

BS; resulting the reduction in the energy dissipation and increase in the network BS; resulting the reduction in the energy dissipation and increase in the network

lifetimelifetime

– Sensor elect themselves to be local BSs at any any given time with a certain Sensor elect themselves to be local BSs at any any given time with a certain

probability and these CHs broadcast their status to other sensor nodesprobability and these CHs broadcast their status to other sensor nodes

– Each node decided which CH to join based on the minimum communication energyEach node decided which CH to join based on the minimum communication energy

– Upon clusters formation, each CH creates a schedule for the nodes in its cluster Upon clusters formation, each CH creates a schedule for the nodes in its cluster

such that radio components of each non-clusterhead node need to be turned OFF such that radio components of each non-clusterhead node need to be turned OFF

always except during the transmit timealways except during the transmit time

– The CH aggregates all the data received from the nodes in its cluster before The CH aggregates all the data received from the nodes in its cluster before

transmitting the compressed data to BStransmitting the compressed data to BS


Algorithm OverviewAlgorithm Overview

– The transmission between CH and BS requires high energy transmissionThe transmission between CH and BS requires high energy transmission

– In order to evenly distribute energy usage among the sensor nodes, clusterheads In order to evenly distribute energy usage among the sensor nodes, clusterheads

are self-elected at different time intervalsare self-elected at different time intervals

– The nodes decides to become a CH depending on the amount of energy it has leftThe nodes decides to become a CH depending on the amount of energy it has left

– The decisions to become CH are made independently of the other nodesThe decisions to become CH are made independently of the other nodes

– The system can determine the optimal number of CHs prior to election procedure The system can determine the optimal number of CHs prior to election procedure

based on parameters such as network topology and relative costs of computation based on parameters such as network topology and relative costs of computation

vs. communication (Optimal number of CHs considered is 5% of the nodes)vs. communication (Optimal number of CHs considered is 5% of the nodes)

– It has been observed that nodes die in a random fashionIt has been observed that nodes die in a random fashion

– No communication exists between CHsNo communication exists between CHs

– Each node has same probability to become a CHEach node has same probability to become a CH



– The operation of LEACH is achieved by The operation of LEACH is achieved by roundsrounds

– Each round begins with a set-up phase (clusters are selected) followed by steady-Each round begins with a set-up phase (clusters are selected) followed by steady-

state phase (data transmission to BS occurs)state phase (data transmission to BS occurs)

1.1. Advertisement Phase: Advertisement Phase:

– Initially, each node need to decide to become a CH for the current round based Initially, each node need to decide to become a CH for the current round based

on the suggested percentage of CHs for the network (set prior to this phase) on the suggested percentage of CHs for the network (set prior to this phase)

and the number times the node has acted as a CHand the number times the node has acted as a CH

– The node (n) decides by choosing a random number between 0 and 1The node (n) decides by choosing a random number between 0 and 1

– If this random number is less than T(n), the nodes become a CH for this roundIf this random number is less than T(n), the nodes become a CH for this round

– The threshold is set as follows:The threshold is set as follows:

1P

P

1 – P * (rmod )

0 Otherwise T(n) =

If n C G P = desired percentage of CHsr = current roundG = set of nodes that have not been CHs in the last 1/P rounds



1. Advertisement Phase: 1. Advertisement Phase:

– Assumptions are (i) each node starts with the same amount of energy and (ii) Assumptions are (i) each node starts with the same amount of energy and (ii)

each CHs consumes relatively same amount of energy for each nodeeach CHs consumes relatively same amount of energy for each node

– Each node elected as CH broadcasts an advertisement message to the restEach node elected as CH broadcasts an advertisement message to the rest

– During this “clusterhead-advertisement” During this “clusterhead-advertisement” phase, the non-clusterhead nodes phase, the non-clusterhead nodes

hear the ads of all CHs and decide which CH to joinhear the ads of all CHs and decide which CH to join

– A node joins to a CH in which it hears with its advertisement with the A node joins to a CH in which it hears with its advertisement with the highest highest

signal strengthsignal strength

2. Cluster Set-Up Phase:2. Cluster Set-Up Phase:

– Each node informs its clusterhead that it will be member of the clusterEach node informs its clusterhead that it will be member of the cluster

3. Schedule Creation:3. Schedule Creation:

– Upon receiving all the join messages from its members, CH creates a TDMA Upon receiving all the join messages from its members, CH creates a TDMA

schedule about their allowed transmission time based on the total number of schedule about their allowed transmission time based on the total number of

members in the clustermembers in the cluster



4. Data Transmission: 4. Data Transmission:

– Each node starts data transmission to their CH based on their TDMA scheduleEach node starts data transmission to their CH based on their TDMA schedule

– The radio of each cluster member nodes can be turned OFF until their The radio of each cluster member nodes can be turned OFF until their

allocated transmission time comes; minimizing the energy dissipationallocated transmission time comes; minimizing the energy dissipation

– The CH nodes must keep its receiver ON to receive all the dataThe CH nodes must keep its receiver ON to receive all the data

– Once all the data is received, the CH compresses the data to send it to BSOnce all the data is received, the CH compresses the data to send it to BS

Multiple ClustersMultiple Clusters

– In order to minimize the radio interference between nearby clusters, each CH In order to minimize the radio interference between nearby clusters, each CH

chooses randomly from a list of spreading CDMA codes and it informs its chooses randomly from a list of spreading CDMA codes and it informs its

cluster members to transmit using this codecluster members to transmit using this code

– The neighboring CHs radio signals will be filtered out to avoid corruption in the The neighboring CHs radio signals will be filtered out to avoid corruption in the

transmissiontransmission



– Localized coordination to enable scalability, and robustness for dynamic Localized coordination to enable scalability, and robustness for dynamic

networksnetworks

– Incorporates data fusion into the routing protocol in order to reduce the Incorporates data fusion into the routing protocol in order to reduce the

amount of information transmitted to BSamount of information transmitted to BS

– Distributes energy dissipation evenly throughout the sensors, thus increasing Distributes energy dissipation evenly throughout the sensors, thus increasing

the system lifetime of the networkthe system lifetime of the network


DisadvantagesDisadvantages::

– How to decide the percentage of cluster heads for a network? The topology, How to decide the percentage of cluster heads for a network? The topology,

density and number of nodes of a network could be different from other networksdensity and number of nodes of a network could be different from other networks

– No suggestions about when the re-election needs to be invokedNo suggestions about when the re-election needs to be invoked

– The clusterheads farther away from the base station will use higher power and The clusterheads farther away from the base station will use higher power and

die more quickly than the nearby onesdie more quickly than the nearby ones



– Extensions can be included to have hierarchical clustering where each CH Extensions can be included to have hierarchical clustering where each CH

will communicate with “super-clusterhead” until the top layer of hierarchy in will communicate with “super-clusterhead” until the top layer of hierarchy in

which the data needs to be sent to BSwhich the data needs to be sent to BS

– The degree and remaining energy of a node may be considered as The degree and remaining energy of a node may be considered as

parameters to decide a clusterhead in a round. If a clusterhead with a limited parameters to decide a clusterhead in a round. If a clusterhead with a limited

power used up its power in a round, the data to be transmitting may be lostpower used up its power in a round, the data to be transmitting may be lost

– Since TDMA schedule is used, a large delay may be introduced between Since TDMA schedule is used, a large delay may be introduced between

event detection and notification at base station. Therefore, the protocol is not event detection and notification at base station. Therefore, the protocol is not

suitable for a real-time applicationsuitable for a real-time application


– TASCTASC is a distributed algorithm that partitions the network into a set of is a distributed algorithm that partitions the network into a set of

locally isotropic, non-overlapping clusters without prior knowledge of the locally isotropic, non-overlapping clusters without prior knowledge of the

number of clusters, cluster size and node coordinatesnumber of clusters, cluster size and node coordinates

– Spatial grouping of nodes with respect to regions of close proximity and Spatial grouping of nodes with respect to regions of close proximity and

similar deployment density benefitssimilar deployment density benefits

Improving the ease of network managementImproving the ease of network management

Efficient data aggregation and compression of sensor dataEfficient data aggregation and compression of sensor data

Formation of hierarchies and node localizationFormation of hierarchies and node localization

– The set of weights that encode distance, connectivity, and density The set of weights that encode distance, connectivity, and density

information within the locality of each node are derivedinformation within the locality of each node are derived

– These weights form the terrain for holding a coordinated leader election in These weights form the terrain for holding a coordinated leader election in

which each node selects the node closer to the center of mass of its which each node selects the node closer to the center of mass of its

neighborhood to become its leaderneighborhood to become its leader

TAS: Topology Adaptive Clustering for Wireless Sensor

Networks [Virrankoski+, 2005]

– The algorithm employs a dynamic density reachability criterion which allows The algorithm employs a dynamic density reachability criterion which allows

the grouping of nodes according to their neighborhood density propertiesthe grouping of nodes according to their neighborhood density properties

– Assumptions made:Assumptions made:

Nodes are aware of their 2-hop neighborhoodNodes are aware of their 2-hop neighborhood

Distances between nodesDistances between nodes

Clustering objectives:Clustering objectives:

– A clustering algorithm should partition the network so that the nodes inside A clustering algorithm should partition the network so that the nodes inside

each cluster have high correlation in sensor measurements and are evenly each cluster have high correlation in sensor measurements and are evenly

spaced in order to maximize gains and reduce errors due to ill geometric spaced in order to maximize gains and reduce errors due to ill geometric

positioning as in the case of node localizationpositioning as in the case of node localization

– TASC requires only minimum number of nodes in a clusterTASC requires only minimum number of nodes in a cluster

– The goal is to partition networks with density non-uniformities, into a set of The goal is to partition networks with density non-uniformities, into a set of

smaller locally isotropic clusters by grouping nodes with similar density smaller locally isotropic clusters by grouping nodes with similar density

attributesattributes



Distributed Leader Election AlgorithmDistributed Leader Election Algorithm

– Two main components: Two main components: node weightsnode weights and and density reachabilitydensity reachability

– Two phases: Two phases: nomination and votingnomination and voting followed by a followed by a mergingmerging phase phase

In first phase, each node considers weights of 2-hop neighbors, In first phase, each node considers weights of 2-hop neighbors,

nominates the node with maximum weight as an election candidate nominates the node with maximum weight as an election candidate

and notifies the nodes in its neighborhood of this nominationand notifies the nodes in its neighborhood of this nomination

In second phase, each node elects the closest candidate as its leader. In second phase, each node elects the closest candidate as its leader.

Nodes that end up in clusters that are smaller than a pre-specified Nodes that end up in clusters that are smaller than a pre-specified

minimum cluster size are dismantled and their node members join minimum cluster size are dismantled and their node members join

bigger existing clusters. It includes all shortest paths between all pairs bigger existing clusters. It includes all shortest paths between all pairs

of nodes that are located in path S.of nodes that are located in path S.



Distributed Leader Election Algorithm ExampleDistributed Leader Election Algorithm Example

– Define the weights to be the number of times a node is found on a shortest path Define the weights to be the number of times a node is found on a shortest path

when computing a weight for nodewhen computing a weight for node

– Node A can be found on the paths AB, AC, AD, and AE, its weight = 4 Node A can be found on the paths AB, AC, AD, and AE, its weight = 4

– Node C receives a weight of 8Node C receives a weight of 8



A C D EB

4 7 8 7 4

B 0.86

A 1.29

D 10.15

C 0.84

G 1

F 0.49

E 11.46

H 0.51

45

4

3

[Baker+ 1981a] D.J. Baker and A. Ephremides, A Distributed Algorithm for Organizing Mobile Radio Telecommunication Networks, Proceedings of the 2nd International Conference on Distributed Computer Systems, April 1981, pp. 476-483.

[Baker+ 1981b] D.J. Baker and A. Ephremides, The Architectural Organization of a Mobile Radio Network via a Distributed Algorithm, IEEE Transactions on Communications COM-29(11), 1981, pp. 1694-1701.

[Bandyopadhyay+ 2003] S. Bandyopadhyay and E.J. Coyle, An Energy Efficient Hierarchical Clustering Algorithm for Wireless Sensor Networks, IEEE INFOCOM 2003, San Francisco, CA, March 30 – April 3, 2003.

[Basagni 1999a] S. Basagni, Distributed Clustering for Ad hoc Networks, Proceedings of International Symposium on Parallel Architectures, Algorithms and Networks, June 1999, pp. 310-315.

[Basagni 1999b] S. Basagni, Distributive and Mobility-Adaptive Clustering for Multimedia Support in Multi-hop Wireless Networks, Proceedings of Vehicular Technology Conference, VTC, Vol. 2, 1999-Fall, pp. 889-893.

[Ephremides+ 1987] A. Ephremides J.E. Wieselthier and D.J. Baker, A Design Concept for Reliable Mobile Radio Networks with Frequency Hopping Signaling, Proceedings of IEEE, Vol. 75(1), 1987, pp. 56-73.

References

[Gerla+ 1995] M. Gerla and J.T. Tsai, Multicluster, mobile, multimedia radio network, Wireless Networks, Vol. 1, No. 3, 1995, pp. 255-265.

[Heinzelman+ 2002] W. Heinzelman, A.P. Chandrakasan and H. Balakrishnan, An Application-Specific Protocol Architecture for Wireless Microsensor Networks, IEEE Transactions on Wireless Communications, Vol. 1, No. 4, October 2002, pp. 660-670.

[Heinzelman+ 2000] W. Heinzelman, A.P. Chandrakasan and H. Balakrishnan, Energy-Efficient Communication Protocol for Wireless Microsensor Networks, IEEE Proceedings of the Hawaii International Conference on System Sciences, January 4-7, 2000, Maui, Hawaii.

[Parekh 1994] A.K. Parekh, Selecting Routers in Ad-hoc Wireless Networks, Proceedings of the SBT/IEEE International Telecommunications Symposium, August 1994.

[Turgut+ 2002] D. Turgut, S. K. Das, R. Elmasri, and B. Turgut, Optimizing Clustering Algorithm in Mobile Ad hoc Networks Using Genetic Algorithmic Approach, Proceedings of IEEE GLOBECOM 2002, Taipei, Taiwan, November 17-21, 2002.

[Virrankoski+ 2005] R. Virrankoski, D. Lymberopoulos, and A. Savvides, TASC: Topology Adaptive Spatial Clustering for Sensor Networks, IEEE INFOCOM 2005.

References

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