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Transcript of Clustering in Wireless Sensor Networksdimitris/5311/WSN-5.pdf · Clustering in Wireless Sensor...
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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