Clustering in Wireless Sensor NetworksWANG, MINGZHU

Background

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A typical clustered sensor network

Motivations

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The energy conservation is

the most important and

common objective of all

these objectives.

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LEACH (Low-energy adaptive clustering hierarchy)CH position rotated among the nodes

energy load distributed .

Number of active nodes in the network and the optimal number of clusters assumed a priori

Nodes join a target number of CHs

Node-CH communication-TDMA

5Timeline of operations in LEACH

LEACH (Low-energy adaptive clustering hierarchy)

Pros• Incorporates data fusion into routing protocols

Amount of information to base station reduced

• 4-8 times effective over direct communication in prolonging network lifetime

• Grid like area

Cons• Only single hop clusters formed

Might lead to large number of clusters

• No discussion on optimal CH selection

• All CHs should directly transmit to the data sink

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Threshold sensitive Energy Efficient sensor Network protocol (TEEN)

TEEN is designed for applications where the data should be sent to the BS when a specific event occurs.

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Pros:• Reducing the number of transmissions to the BS so that the

approach is more energy- efficient • Data-centric nature of TEEN makes it suitable for time-concerned

applications in which a quick response from the network is urgent for user

Cons• Some nodes may die while the user is not aware of their death

because it does not receive feedback.• Defining the exact value of the thresholds according to the

application is not very easy• Not suitable for the applications in which a periodical feedback

from the region is needed, like the monitoring of a forest.

Multi-hop Overlapping Clustering Algorithm (MOCA)

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• Uses a random method for CH selection, each node produces a probability p, based on which announces itself as a CH within its cluster range. This announcement is forwarded to all the nodes within the range of k hops from the CH. Then each node sends a request to all the CHs from which it has received the announcement.

• KOCA, which tries to solve the overlapping clustering problem

Clustering Communication Based on number of Neighbors (CCN)Calculating the number of neighbors

CH election

Cluster formation

Determining TDMA schedules

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Different cluster sizes

Hybrid Energy-Efficient Distributed Clustering (HEED)Cluster head selection◦ hybrid of residual energy (primary) and communication cost (secondary) such as node proximity

Number of rounds of iterations

Tentative CHs formed

Final CH until CHprob=1

Same or different power levels used for intra cluster communication

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HEEDPros

• Balanced clusters

• Low message overhead

• Uniform & non-uniform node distribution

• Inter cluster communication explained

• Out performs generic clustering protocols on various factors

Cons

• Repeated iterations complex algorithm

• Decrease of residual energy smaller probability

number of iterations increased

• Nodes with high residual energy one region of a network

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Fuzzy-based algorithms

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Fundamental block diagram of fuzzy-logic

Computing the CH election probability (chance) using fuzzy-logic (redrawn from Lee andCheng,2012b).

Compound algorithms – Hierarchical Control Clustering (HCC)

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• Tree discovery and cluster formation

• HCC conserves stability of network topology, even in dynamic environments with mobile nodes.

• A suitable approach for applications of large-scale WSNs, because of its hierarchical architecture.

Energy-efficient and dynamic clustering (EEDC)

Algorithm: Active node estimation and optimum probability of becoming cluster head

Received Signal power

Cluster formation

CH with a certain probability by wining a competition with neighbors

Data collection

Node-CH using MAC protocol-p-persistent CSMA

Data delivery

CH-BS-multi hop routing protocol

Pros• Number of clusters and CH-Dynamic

Energy dissipation-even distribution

Prolong network lifetime

• most efficient for large-scale sensor network

• Intra and inter cluster communication explained

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Basic Spider Monkey OptimizationThe algorithm mimics the foraging behaviour of spider monkeys. ◦ First, the group evaluates the distance from the food and then starts food foraging.

◦ In the second step, the positions of group members and the evaluated distance from the food sources is updated.

◦ In the next step, the local leader updates its best position within the group. All the group members start searching the food in the case of the lack of best position updation by the local leader.

◦ In fourth step, the global leader updates its ever best position. The group is splitted into smaller subgroups in the case of stagnation (no updation in global leader position for a specified time).

Inspired from the basic SMO algorithm, boolean SMO is used for binary optimization problems.

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Boolean SMO algorithm

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Setup phase

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Setup phase: sensor nodes interested for CH selection send request message to BS.

Setup phase

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Setup phase: BS selects CH using SMO and inform CH nodes with acknowledge message

CH Advertisement

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CH Advertisement phase: CH sends advertisement message to sensor nodes.

Cluster setup phase

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Cluster setup phase: Sensor nodes interested to join CH send reply message to CHs

Intra-cluster data transmission

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Intra-cluster data transmission phase: data packets are transferred from sensor nodes to the CH.

Inter-cluster data transmission

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Data packets are transferred from CH to the BS (either direct transmission or dual hop depending upon the distance between them)

Clustering Objectives

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Energy consumption

Cluster quality

CH residual energy

Scheduling time

Cluster formation voronoi diagrams

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Cluster formation voronoi diagrams

of LEACH, HCR, ERP and

SMOTECP for homogeneous setup.

a Cluster formation of LEACH. b

Cluster formation of HCR. c Cluster

formation of ERP. d Cluster formation

of SMOTECP

homogeneous setup

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heterogeneous setup

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Final comparison

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Future Challenges Most of existing clustering approaches are static so they do not have the ability to adapt to the network changes

To investigate the effect of mobility in the network

Designing clustering methods for reactive networks

Heuristic-based clustering approaches

Meeting the QoS requirements of a WSN

Energy-harvesting sensor networks

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