EDistributed Energy-Efficient Hierarchical Clustering for Wireless Sensor Networks

8/14/2019 EDistributed Energy-Efficient Hierarchical Clustering for Wireless Sensor Networks

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UBI532 - WSN Spring 2009 1

Distributed Clustering in Ad-hoc Sensor Networks :A Hybrid, Energy-Efficient Approach

HEED (Hybrid Energy Efficient Distributed Clustering)

Paper By

Ossama Younis and Sonia Fahmy

Department of Computer Sciences, Purdue University

Presentation By Deniz zsoyeller


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Contributions of HEED

HEED is a new energy-efficient approach for clustering nodes insensor networks.

Periodically selects cluster heads according to their residual energy and asecondary parameter , such as node proximity to its neighbors or nodedegree.

The clustering process terminates rapidly .

The protocol incurs low overhead in terms of processing cycles andmessages exchanged.

Achieves fairly uniform cluster head distribution across the network.

Considers cluster quality, e.g., load-balanced clusters or dense clusters .


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Sensor Networks Overview (1)

Sensor Nodes are usually:

Typically less mobile and more denselydeployed than mobile ad-hoc networks(MANETs).

Limited in processing, memory, andcommunication capabilities

Constrained in battery lifetime

Left unattended e.g., in hostileenvironments, which makes it difficultimpossible to re-charge or replace theirbatteries.

Sensor networks have recently emerged as an important computingplatform.


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Reasons Of Energy Consumption

Energy consumption in a sensor node can be due to eitherEnergy consumption in a sensor node can be due to eitherusefuluseful oror wastefulwasteful sources.sources.

Useful energy consumption can be due to

Transmitting/ receiving data, processing query requests, andforwarding queries/data to neighboring nodes.

Wasteful energy consumption can be due to

Idle listening to the media, retransmitting due to packet collisions,overhearing, and generating/handling control packets.


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How To Reduce Energy Consumption ?

Due To Wasteful Sources ?Due To Wasteful Sources ? Several MAC protocols attempt to reduce.

Due To Useful Sources ?Due To Useful Sources ? A number of protocols have also been proposed toreduce useful energy consumption.

These protocols can be classified into three classes :These p

rotocols can be classified into three classes :

First class controls the transmission power level at each node to increasenetwork capacity while keeping the network connected.

Second class makes routing decisions based on power optimization goals.Third class decides which nodes should participate in the network operation(be awake) and which should not (remain asleep) . (nodes require of locations knowledge via GPS-capable antennae / message exchange).


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Topology management

Cell-based approach Cluster-based approachobserver


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How To Reduce Energy Consumption ? (2)

Hierarchical clustering techniques can aid in reducing useful energyconsumption.

Clustering is particularly useful for :Clustering is particularly useful for :

Applications that require scalability to hundreds or thousands of nodes.

(need for load balancing and efficient resource utilization.)

Applications requiring efficient data aggregation

Routing protocols

One-to-many, many-to-one or one-to-all (broadcast) communication.

For example,For example, in many-to-one communication, clustering can supportdata fusion and reduce communication interference.


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Clustering And Clusterheads

The essential operation in sensor nodeclustering is to :

Select a set of cluster heads among thenodes in the network.Cluster the rest of the nodes with these

heads.

Cluster heads are responsible for :Coordination among the nodes withintheir clusters (intra-cluster coordination) .

Communication with each other and/orwith external observers on behalf of theirclusters (inter-cluster communication) .

observer

CH CH

CH


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Network Lifetime

What Is Network Lifetime ?What Is Network Lifetime ?Time until the first node / the last node in the network depletes its energy(dies).For example,For example, in a military field where sensors are monitoring chemicalactivity, the lifetime of a sensor is critical for maximum field coverage.

How To Prolong Network Lifetime ?How To ProlongNetwork Lifetime ?

1. Reducing the number of nodes contending for channel access,

2. Summarizing network state information and updates at the cluster headsthrough intra-cluster coordination,

3. Routing among cluster heads, which has a relatively small network diameter.


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Clustering Can Reduce The Communication Overhead

Clustering can reduce thecommunication overhead for bothsingle-hop and multi-hop networks.

1. Sensors periodically transmit

information to a remote observer(base station).

2. With clustering, nodes transmittheir information to their clusterheads.

3. A cluster head aggregates thereceived information and forwardsit over to the observer.


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Primary Goals Of HEED

1. Prolonging network lifetime by distributing energy consumption

2. Terminating the clustering process within a constant number of iterations / steps,

3. Minimizing control overhead (to be linear in the number of Nodes)

4. Producing well-distributed cluster heads and compact clusters.


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HEED Assumptions

A set of n sensor nodes are dispersed uniformly and independently in arectangular field.

Sensor nodes are1. quasi-stationary.2. location-unaware, i.e. not equipped with GPS capable antenna.3. equally significant (have similar capabilities (processing /

communication)).4. left unattended after deployment.5. Each node has a fixed number of transmission power levels.6. The network serves multiple mobile/stationary observers, which

implies that energy consumption is not uniform for all nodes.


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No Assumptions Are Made About :

1. Homogeneity of node dispersion in the field,

2. Network density or diameter,

3. Distribution of energy consumption among sensor nodes,

4. Proximity of querying observers.


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HEED Requirements1. Each node is mapped to exactly one cluster.

2. The node can directly communicate with its cluster head (via a single hop).

3. Clustering is completely distributed.Each node independently makes its decisions based on local information.

1. Clustering terminates within a fixed number of iterations.

2. At the end of each T CP , each node is either a cluster head, or an ordinarynode that belongs to exactly one cluster.

3. Clustering should be efficient in terms of processing complexity andmessage exchange.

4. Cluster heads are well-distributed over the sensor field.


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Approach

HEED ishybrid :Clustering is based on two parameters

HEED isdistributed :Every node only uses information from its 1-hop neighbors (withincluster range)

HEED isenergy-efficient :Elects cluster heads that are rich in residual energy


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Communication Cost DefinitionsCost Definitions

A node joins theCH with the min.

degree

Power : Fixed forall nodes

Goal :Load Distribution

(Balancing)

AMRP(Average Min.Reachability

P ower)

Goal :For minimumintra - cluster

communicationenergy

A node joins theCH with the max.

degree

Power : Fixed forall nodes

Goal :For dense clusters

(AMRP)is the mean of the minimum power levels required by all M nodes withinthe cluster range to reach CH u. (a good estimate for the communication cost)


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Probability Of Becoming A Cluster Head

Before a node starts executing HEED, it sets its probability of becoming acluster head, CH prob , as follows :

CH prob = C prob * ( E r / E max )

C prob : Initial percentage of cluster heads among all N nodes (say 5%)

E residual : Estimated current residual energy in the Node

E max : A reference maximum energy (corresponding to a fully chargedbattery), which is typically identical for all nodes.

The CH prob value of a node is not allowed to fall below a certain threshold p min (e.g., 104)


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HEED Algorithm at nodev

I. Initialization

II. Main Processing(Repeat)

III. Finalization

Discover neighbors within cluster range (S nbr )

Compute and broadcast cost to S nbrCompute the initial cluster head probabilityCHprob = max(Cprob * (Er/Emax ) , pmin )

If v received some cluster head messages, choose onehead with min costIf v does not have a cluster head, elect to becomea cluster head with CH prob.

CHprob= min(CHprob* 2, 1)

Repeat until CHprobreaches 1

If cluster head is found, join its clusterOtherwise, elect to be cluster head


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HEED Analysis (1)HEED has the following lemmas :

Lemma 1 : HEED terminates in N iter = O(1) iterationsBrief Proof :

The worst case : low E residual . Then CH prob = p min . However, CH prob doublesin every step, and phase II of the protocol terminates one step

(iteration) after CH prob reaches 1

Lemma 2 : At the end of phase III of the HEED protocol, a node is either acluster head or a regular node that belongs to a cluster.

Lemma 3 : HEED has a worst case processing time complexity of O(N) pernode, where N is the number of nodes in the network .


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HEED Analysis (2)

Lemma 4 : HEED has a worst case message exchange complexity of O(1) pernode, i.e., O(N) in the network.

An ordinary node is silent until it sends one join message to a clusterhead.

The number of these join messages in the network is less than N , sinceat least one node will decide to be a cluster head during the clusteringprocess.

Hence, the number of messages exchanged in the network is

upper-bound by N iter

N , i.e., O(N) since N iter

is constant.


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HEED Analysis (3)

Lemma 5 : The probability that two nodes within each others cluster rangeare both cluster heads is small, i.e., cluster heads are well-distributed.

Consider the following worst case scenario :

Assume that v 1 and v 2 are two isolated neighboring nodes, each one doesnot have any other neighbor in close proximity.

We compute the probability, p nbr , that at the end of phase III, both of them are cluster heads (assume that they are fully synchronized).

Assume that neither of the two nodes decides to be a cluster head beforeits CH prob reaches 1. Otherwise, one of them will concede to the other.


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Inter-Cluster Communication (1)

Rt : inter-cluster transmission rangeRc : the cluster transmission range

Lemma 6 : (Blough and Santi02)Assume n nodes are dispersed uniformly and independently in an area R=[0,L] 2

Assume that the area is divided into square cells of size ( Rc / 2) (Rc / 2).If Rc2n = aL 2lnL, for some a > 0 , Rc > 1 , thenlim n,N E (number of empty cells) = 0, so each cell contains at least onenode

Rc / 2

Rc / 2 Rc

L


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Inter-Cluster Communication (2)

2.7R c

2.7R c

R t

CH1

CH2

Lemma 7 : There exists at least one cluster head in any ( 2 + 1/2) Rc (2 + 1/2) Rc area.

Lemma 8 : For any two cluster heads v1 and v2 in two neighboring areas Aand B of size (2+ 12) Rc (2+ 12) Rc , v1 and v2 can communicate if

Rt 6 Rc.


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Inter-Cluster Communication (3)Theorem 1 : HEED produces a connected multi-hop cluster head graph (structure )

Proof (by contradiction) : Assume previous 3 lemmas hold.

Assume that HEED produces two connected components (graphs) of cluster heads.

G1 = ( V 1,E 1) and G2 = ( V 2,E 2), such that any v 1 V 1 can not communicate with anyv 2 V 2.

Assume that V 2 lies on the right of V 1, and that a cluster head v 1 V 1 lies on therightmost border of V 1.

v 1 is able to communicate with a cluster head v 2 on its right side, since the

condition in Lemma 8 holds.v 2 must reside inside V 2, which contradicts with the initial assumption that aCluster head in one component cannot communicate with one in the othercomponent. Therefore, V 1 and V 2 are connected.


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Performance Evaluation (1)Simulation Environment

1000 nodes uniformly spread across 2000 x 2000Minimum probability for becoming a cluster head ( p min) - 0.0005Initial CH prob = C prob= 5% and Cluster radius 25 to 400 m.Residual Energy levels 20 and # of Experiments 100

Comparison with generic weightbased clustering protocols (GC) WCA, DCA,etc. because

Distributed clustering only based on local informationSelected cluster heads with the highest weights (residual energy)A node has only one cluster headNo assumptions about node dispersion fieldNumber of iterations is function of network diameterTime and message complexities are O(N) and O(1)Guaranteed that no two cluster heads are neighbor


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Performance Evaluation (2)

Number of iterations to terminate

Number of iterations in HEED can be deterministically computed usingLemma 1, which is independent of the cluster radius.

For GC, the number of iterations grows quickly as the cluster radiusincreases. (radius implies neigbors for each node )

GC takes only 3 iterations to terminate for a cluster radius of 25. Thenumber of iterations, however, grows to 85 for a cluster radius of 400.

HEED takes 6 iterations to terminate for all cluster ranges.(C prob= 5%, Eresidualis close to E max)


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ClusterheadCharacteristics (1)

HEED cluster heads arecomparable to those selected byGC in terms of number,distribution, and energyavailability.

HEED cannot guarantee optimalhead selection in terms of energy, since it uses thesecondary parameter to resolveconflicts.

GC, a weight-based approach,does guarantee that the highestenergy node will be the clusterhead within its cluster range.


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Clusterhead Characteristics (2)

If it is required to balance load oncluster heads, then it is important tohave clusters with small variance inthe number of nodes they cover.

The maximum degree cost type and

GC show similar results.For minimum degree cost, thestandard deviation is the lowest,because ties are broken by joining thesmaller degree node, thus balancingthe cluster sizes.

AMRP results lie between the twoextremes. Therefore, AMRP provides a

compromise between load balancingand cluster density.


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ClusterheadCharacteristics &

Node SyncronizationHEED produces ahigherpercentage of non-single node clusters than GC for all cost types.

The maximum number of nodes ina cluster in HEED is on theaverage smaller than that of GCfor all cost types

Node synchronization is not

critical for the operation of HEED.selected cluster heads in bothcases have comparable residualenergy.


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ClusteringApplications

Use of HEED forEnergy efficient routing protocolsEfficient Data Aggregation

Because prolonging networklifetime is especially important for unattended networks used

in environmental monitoring.

HEED vs. gen-LEACHHEED clustering improves networklifetime over gen-LEACH clusteringfor all cost types.HEED expends less energy inclustering than gen-LEACH.HEED prolongs network lifetime,compared to gen-LEACH and todirect communication.


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Conclusion And Future Work

Authors have proposed HEED clustering.

HEED is fast and has low overhead.HEED can provide features such as load-balancing.HEED isindependent of :

Homogeneity of node dispersion in the fieldNetwork density or diameterDistribution of energy consumption among nodesProximity of querying observers

Future WorkExtend the protocol to multi-level hierarchies.Cluster size constraints in HEEDIncorporate multiple external mobile observers into HEED.

EDistributed Energy-Efficient Hierarchical Clustering for Wireless Sensor Networks

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