3. Aim
• In this paper, the authors propose an innovative hierarchical
clustering approach for wireless sensor networks to minimize
energy consumption of the network using Artificial Bee
Colony Algorithm which is a new swarm based heuristic
algorithm.
• A protocol is presented using Artificial Bee Colony Algorithm,
which tries to provide optimum cluster organization in order
to minimize energy consumption.
• In cluster based networks, the selection of cluster heads and
its members is an essential process which affects energy
consumption.
• Simulation results demonstrate that the proposed approach
provides promising solutions for the wireless sensor networks.
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5. Concept
• The sensor devices collect data from physical environment,
process it using aggregating techniques to get overall local
data, and send final data to an external base station.
• Flooding / gossiping is a basic method to transform data from
a sensor node to the base station. In flooding, data is
scattered by all the nodes as well as the base station. To
broadcast data to all over the network consumes much
energy and bandwidth.
• Clustering is one of the routing design methodologies used to
effectively manage network energy and to apply aggregation
techniques in the network.
• Data aggregation and fusion is simply used in the hierarchical
WSNs in order to decrease the number of transmitted
messages to the base station to minimize energy
consumption and bandwidth utilization.
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6. Concept
• LEACH (Low-Energy Adaptive Clustering Hierarchy), HEED
(Hybrid Energy Efficient Distributed Clustering ) and CLUDDA(
Clustered Diffusion with Dynamic Data Aggregation) are the
popular hierarchical routing methods which use data
aggregation process. Although they offers good
methodologies for implementation, yet create some
bottlenecks.
• So the authors propose a novel clustering method using
Artificial Bee Colony (ABC) algorithm to preserve network
energy and to get more performance in cluster based WSNs.
• The algorithm shows superior performance against other well
known optimization techniques.
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8. Artificial Bee colony algorithm
• It is a new swarm intelligence method inspired
by intelligent foraging behavior of honey bees.
• In ABC algorithm the colony of artificial bees is formed of three bee
groups: employed bees, onlooker(observer) bees and scout(spy)
bees.
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9. Artificial Bee colony algorithm
• It is assumed that there is only one artificial employed bee for each food
source. In other words, the number of employed bees in the colony is equal to
the number of food sources around the hive.
• Employed bees go to their food source and come back to hive and dance on
this area. The employed bee whose food source has been abandoned
becomes a scout and starts to search for finding a new food source.
• Onlookers watch the dance of employed bees and choose food sources
depending on dances.
• The main steps of the algorithm are given below:
Initial food sources are produced for all employed bees
REPEAT
– Each employed bee goes to a food source in her memory and determines
a neighbor source, then evaluates its nectar(juice) amount and dances in
the hive
– Each onlooker watches the dance of employed bees and chooses one of
their sources depending on the dances, and then goes to that source.
After choosing a neighbor around that, she evaluates its nectar amount.
– Abandoned(uncontrolled) food sources are determined and are replaced
with the new food sources discovered by scouts.
– The best food source found so far is registered.
UNTIL (requirements are met)
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10. Artificial Bee colony algorithm
• In ABC, a population based algorithm, the position of a food source represents a
possible solution to the optimization problem and the nectar amount of a food
source corresponds to the quality (fitness) of the associated solution. The number
of the employed bees is equal to the number of solutions in the population.
• There are three control parameters in the ABC:
• the first one is the number of food sources which is equal to the number of
employed or onlooker bees (SN).
• second one is the value of limit parameter.
• third one is the maximum cycle number (MCN).
• At the first step, a randomly distributed initial population (food source positions) is
generated. After initialization, the population is subjected to repeat the cycles of
the search processes of the employed, onlooker, and scout bees, respectively.
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11. Artificial Bee colony algorithm
Generate initial population xi; i=1...SN
Evaluate the population
Set cycle to 1
Repeat
FOR each employed bee
Produce new solutions
Calculate the fitness
Apply the greedy selection process
FOR each onlooker bee
Choose a solution xi depending on pi
Produce new solutions vi
Calculate the fitness
Apply the greedy selection process
If there is an Abandoned solution for the scout, then
replace it with a new solution produced
Memorize the best solution so far
Assign cycle = cycle + 1
Until cycle = MCN
Abandoned --- uncontrolled 11
13. Hierarchical WSNs clustering and proposed approach
• LEACH protocol is already proposed to maximize the network lifetime by
assigning different roles to the nodes. In this protocol, network is spited
into clusters and cluster heads are chosen randomly in definite time
intervals.
• Cluster heads are responsible for collecting information inside the clusters
and sending this information to the base. While nodes inside the clusters
communicate in a small region, they consume low energy and cluster
heads consumes more energy due to the communication with remote
base station.
• Since the role of cluster head in the clusters distributed randomly in each
tour, the energy consumption for each node is equalized.
• some disadvantages are in question??
• First, all the nodes in network should have the ability of communication
with the base.
• 2nd , the distances to cluster heads from the member nodes as well as to
base station from cluster heads are not taken into account.
• Hence due to the both disadvantages in LEACH ,more n/w energy may be
consumed.(problem)
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14. Hierarchical WSNs clustering and proposed approach
• The authors of LEACH algorithm further solved this problem by
distributing the cluster heads uniformly, but the network assumed in this
study requires node positioning systems like GPS, which cause the system
to be more expensive, on sensor nodes. Also, GPS systems require
additional energy consumption and needs large size of hardware.
• So the authors of this paper proposed a low cost solutions to the existing
problem by introducing the implementations of ABC algorithm.
The proposed algorithm consists of two main phases.
(WSNCABC-wireless sensor network clustering using artificial bee colony)
• 1.setup phase
Each node sends the calculated distances to the base station.
Base station selects the cluster heads at each round using ABC algorithm.
Once the base station declares the cluster heads to the network , then
each node sends a request membership massage to the nearest cluster
head.
The cluster head after getting the request membership massage ,forms
the initial network configuration and declares the formulation of cluster
structure.
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15. Hierarchical WSNs clustering and proposed approach
• 2.Data gathering phase
Cluster members sense the physical data from environment and store in
their individual buffer.
Then cluster head gathers sensed data from member nodes one by one
using TDMA MAC protocol over a single channel and aggregates those
data.
Now the cluster head is ready to send the gathered and aggregated data
to the base station.
Centralized WSN application 15
17. Simulation results
• Here the authors evaluate the performance of "Wireless Sensor Network
Clustering using Artificial Bee Algorithm" (WSNCABC) via simulations, and
compare it to basic transmission algorithm (direct communication) and a
popular hierarchical routing method named "Low Energy Adaptive
Clustering Hierarchy" (LEACH).
• It is assumed that every node has a capability of communicating with
sensor nodes in the network region as well as the base station.
• In the simulations the same settings and assumptions are used for both
LEACH and WSNCABC algorithms to be able to make a reliable
comparisons.
• In order to verify the success of the proposed approach, direct
communication method and LEACH were used to make the comparisons.
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18. Simulation results
• In left figure, a network of 100 nodes is randomly deployed on an area of 500x500
m2, and base station is placed at the point of X=250 m, Y=575 m. Cluster heads are
selected using LEACH algorithm(shown in squares), and members of the clusters
are displayed with different markers.
• In right Fig, a network of 100 nodes is randomly deployed on the same area, and
cluster heads are chosen using ABC algorithm with the configuration described
before. The cluster heads are uniformly selected providing that clusters have
approximately equal sized of regions.
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19. Simulation results
• Demonstrates the residual energy of the network versus total received
items.
• it is seen that normalized residual energy of the network decreases as the
number of received items increase in time, and the network using
WSNCABC algorithm provides more performance by receiving 1.7 times
more message items than the network using LEACH Algorithm when
network energy is depleted by 50%.
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20. Simulation results
• It shows that WSNCABC algorithm clearly improves network lifetime over other two
algorithms by spending less energy.
• It also be observed that while the network is in more communication, bigger number of
rounds implies the nodes have more energy in the network with WSNCABC algorithm.
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22. Conclusion
• This paper proposed a new approach for hierarchical WSN
routing operations having aim to minimize the energy
consumption of the network , that can be accomplished by
using Artificial Bee Colony Algorithm so as to obtain optimum
clusters with minimum energy consumption for
communication.
• The implementation of ABC algorithm to the clustering
problem in WSNs is the first study in the literature.
• Simulation outputs show that WSNCABC algorithm
outperforms over direct transmission and LEACH Algorithm.
• Hence ABC algorithm seems to be a promising solution for
successful operations in cluster based WSNs.
Thank You! 22
