Transcript TP - Nadeem Javaid

Multiple Criteria Decision Making based
Clustering Technique for WSNs
By:
Mansoor Mustafa
Reg. No. FA11-REE-044
Supervisor
Dr. Safdar H. Bouk
Co-supervisor
Dr. Nadeem Javaid
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Outline
 Introduction to WSNs
 Compression with traditional Networks
 WSN Challenges
 Introduction to Clustering
 Previous Work
 Problem Statement
 Proposed Scheme
 Research Methodology
 Simulation and Results
 Conclusion
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Wireless Sensor Networks
 Composed
of a large
number of sensor nodes.
 Are densely deployed either
inside the phenomenon or
very close to it.
 Random deployment
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Comparison of WSNs with traditional
Networks
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Traditional Networks
Wireless Sensor Networks
General-purpose design; serving many applications
Single-purpose design; serving one specific application
Typical primary design concerns are network
performance and latencies; energy is not a primary
concern
Energy is the main constraint in the design of all node
and network components
Networks are designed and engineered according to
plans
Deployment, network structure, and resource use are
often ad-hoc (without planning)
Devices and networks operate in controlled and mild
environments
Sensor networks often operate in environments with
harsh conditions
Maintenance and repair are common and networks are
typically easy to access
Physical access to sensor nodes is often difficult or even
impossible
Component failure is addressed through maintenance
and repair
Component failure is expected and addressed in the
design of the network
Obtaining global network knowledge is typically feasible
and centralized management is possible
Most decisions are made localized without the support of
a central manager
WSN Challenges
 Energy
 Security
 Self-Management
 Wireless Networks
 Design Constrains
 Energy is the major challenge in WSNs
 Different MAC and Routing protocols are designed to minimize
energy consumption
 Clustering is most energy efficient routing technique in WSNS
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Introduction to Clustering
 Important method for prolonging network lifetime in
WSNs
 Divides WSN into groups, called Clusters, and in each
Cluster a Head/Leader/Manager node, called Cluster
Head (CH), is assigned by Sink/selected by consensus
from Group.
 CHs collect data from respective cluster members and
forward aggregated data to the Sink/Base Station (BS).
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Flat v/s Hierarchical/Clustered
Architecture
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Flat Architecture
Hierarchical/Clustered Architecture
Previous Work
 LEACH: which was the very first clustering protocol for
WSN.
 In LEACH, homogeneous sensor nodes (i.e. having same
initial energy) are considered
 In each round, CH responsibility is rotated among high
energy nodes in order to balance the communication load
among all nodes
 LEACH operates in two phases:
 Advertising phase
 Data transmission phase
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Previous Work …..
 Stable Election Protocol (SEP) protocol assumes that in real
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environment nodes have different energy.
SEP divides heterogeneous nodes energy environment in two
types of nodes, i.e., advance nodes and normal nodes
Advance nodes have some amount of more energy than
normal nodes
SEP assign a weighted probability to each node based on its
energy
SEP improves the cluster formation of LEACH by decreasing
the CH epoch interval of advance nodes, i.e., advance nodes
get more chances of becoming CH.
Problem Statement
 Problems with single criteria:
 Mostly based on residual energy.
 Don’t consider other information, like location of nodes, number
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of neighbor nodes etc
So normal nodes consumes more energy to send their data to
CHs.
Problems with Centralized:
Increased processing over head. Results in shorter lifetime of
nodes
Problems with single hop communication:
Data from nodes away form CHs/base station have to travel
longer distance as compared to nearer nodes, hence they die
earlier.
Proposed Scheme
 A major challenge in WSNs is selection of appropriate cluster head
 Selection of CH largely affects WSNs lifetime.
 Ideal cluster head is one which is selected on multiple criteria.
 We propose a distributed CH selection technique based
on Multiple-Criteria Decision Making (MCDM)
 We use Fuzzy-TOPSIS method of MCDM
 We consider four criteria: residual energy, number of neighbors,
distance form BS and average distance form neighbors.
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MCDM and Fuzzy-TOPSIS
 MCDM methods are used to solve the decision making
problems in field of engineering and sciences, with multiple
attributes.
 They compare and rank multiple alternatives based on degree
of desirability of their respective attributes.
 Technique for Preference by similarity to Ideal Solution
(Fuzzy-TOPSIS) consists of decision matrix with’m’ number
of a alternatives and ’n’ number of attributes for each
alternative.
 It uses relative importance of attributes instead of using
precise values.
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Research Methodology….
 Proposed Scheme is based on four phases
 Phase1: Network Deployment:
 Nodes are deployed randomly and uniformly
 Nodes are fixed
 Homogenous
 Base Station is capable of receiving, aggregating, and then
forwarding the data from the cluster heads to the desired
destinations.
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Network Deployment
Research Methodology…..
 Phase 2: Neighbor Discovery:
 Initially, all nodes broadcast a Hello packet, which contains
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node’s ID, location information and four criteria values.
Residual Energy, C1, Node Density C2, Distance to the BS,
C3, Average Distance between this node and its neighbors,
C4.
Initially, C2 and C4 fields in the Hello packet will be empty
After sharing Node ID and location information with its
neighbors, each node can easily compute C2 and C4.
Exchange it in the next Hello packet
Research Methodology….
 All the other nodes in the transmission range Tr of that node,
receive Hello packet.
 After receiving hello packet from all neighbors, a node
updates its neighborhood table (T) with neighboring node’s
ID, C1, C2, C3, C4 as well as its own information
 For n neighbors of node k, then Tk will be:
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Research Methodology…..
 Phase 3: CH selection and Cluster Formation:
 CH selection based on MCDM
 Criteria must be normalized to the similar range [0 − 1] to
fairly select a CH
 C1 and C2 are Positive Ideal Solutions (PIS)
 C3 and C4 are Negative Ideal Solutions (NIS)
 PIS
 NIS
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 After Normalization the preference or weight ’wi’ are
assigned to each criterion.
 These, weights are application specific.
 For our proposed scheme:
 Fuzzy membership function is used to categorize these normalized value of each criteria and their respective weights for
every node.
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 After that, PIS and NIS are determined from Vk matrix
 Determine separation measure:
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 Finally calculate Rank Index (R.I)
 The node with highest value in this rank index of all nodes
within its transmission range (neighbors) announces itself as
CH in that region.
 Other nodes in that region, send join request to associate
with the CH and act as member nodes
 The CH acknowledges to all of its members
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Procedure for CH change
 Phase 4: Communication
 The multi-hoping communication model is considered
 In intra-cluster, nodes within five meters range of CH, send
their data directly to CH
 Other nodes perform multi-hoping with other nodes coming
in their way to communicate with the CH.
 In inter-cluster, the CHs within twenty meters range of BS,
communicates directly to BS remaining CHs perform multihoping via other CHs.
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Research Methodology
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Simulation and Results
 Simulation Parameters:
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Results: Network Stability
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Results: Network Lifetime
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Results: Network Energy Consumption
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Results: CH Stability Ratio
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Results: Network Control Overhead
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Results: Network Throughput
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Conclusion
Simulation results show that multiple normalized criteria for
CH selection improves throughput, consumes less energy,
minimum variations in CH re-elections (cluster stability),
network lifetime, and very less control overhead, compared
to the previous clustering schemes.
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Publications
 M. Mustafa, T. Shah,
Safdar H. Bouk, Syed H. Ahmed and N. Javaid,
“Distributed Multiple Criteria based Clustering Scheme for Wireless Sensor
Networks”, accepted in IEEE Vehicular Technology Society Asia Pacific Wireless
Communications Symposium 2013
 A. Rehman, M. Mustafa, I. Israr, M. M. Yaqoob, “Survey of Wearable Sensors
with Comparative Study of Noise Reduction ECG Filters”, International Journal
of Computing and Network Technology (IJCNT), Vol 1, Issue 1, ISSN. 2210-1519
(Print), January, 2013, PP. 61-82
 A. Rehman, M. Mustafa, N. Javaid, U. Qasim, Zahoor Ali Khan, “Analytical
Survey of Wearable Sensors”, Seventh International Conference on Broadband
and Wireless Computing, Communication and Applications (BWCCA), 2012,
BioSPAN-12, Proceedings of IEEE
 M. M. Yaqoob, I. Israr, M. Mustafa, A. Rehman, “Evaluation and Analysis of
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IEEE 802.15.4 ZigBee Multi-hop Transmission in Wireless Body Area”,
International Journal of Information Technology and Electrical Engineering
(ITEE), Vol 2, Issue 1, ISSN. 2311-708X, March, 2013, PP. 33-37