Clustering
in
Ad hoc and Sensor Networks
Why Clustering?
–
The data collected by each sensor is communicated through the
network to a single processing center that uses the data
–
Clustering groups nodes into groups such that each node communicate
information only to clusterheads and then the clusterheads
communicate the aggregated information to the processing center,
saving energy and bandwidth
–
The cost of transmitting a bit is higher than a computation; therefore, it
may be beneficial to organize the sensors into clusters
–
Cluster-based control structures provides more efficient use of
resources for large dynamic networks
Clustering can be used for
–
Transmission management
–
Backbone formation
–
Routing Efficiency
Link-Clustered Architecture
[Baker+ 1981a, 1981b, Ephremides+ 1987]
–
Reduces interference in multiple-access broadcast environment
–
Distinct clusters are formed to schedule transmissions in a contentionfree way
–
Each cluster has a clusterhead, one or more gateways and zero or
more ordinary nodes
–
Clusterhead schedules transmission and allocates resources within its
cluster
–
Gateways connect adjacent clusters
To establish link-clustered control structure
1.
Discover neighbors
2.
Select clusterhead to form clusters
3.
Decide on gateways between clusters
Link-Clustered Architecture
[Baker+ 1981a, 1981b, Ephremides+ 1987]
Cluster
Clusterhead
Gateway
Ordinary node
Clusterheads
– Resemble base stations in cellular networks, but dynamic
– Responsible for resource allocation
– Maintains network topology
– Acts as routers – forwards packets from one node to another
– Aware of its cluster members
– Aware of its one-hop neighboring clusterheads
Since clusterheads decide network topology,
election
of clusterheads optimally is critical
Previous Work
Highest-Degree Heuristic [Gerla+ 1995, Parekh 1994]
 Computes the degree of a node based on the distance
(transmission range) between the node and the other nodes
 The node with the maximum number of neighbors (maximum
degree) is chosen to be a clusterhead and any tie is broken
by the node ids
Drawbacks:
 A clusterhead cannot handle a large number of nodes due to
resource limitations
 Load handling capacity of the clusterhead puts an upper
bound on the node-degree
 The throughput of the system drops as the number of nodes
in cluster increases
Previous Work
Lowest-ID Heuristic [Baker+ 1981a-b, Ephremides+ 1987]
 The node with the minimum node-id is chosen to be a clusterhead
 A node is called a gateway if it lies within the transmission range of
two or more clusters
 Distributed gateway is a pair of nodes that reside within different
clusters, but they are within the transmission range of each other
Drawbacks:
 Since it is biased towards nodes with smaller node-ids, leading to
battery drainage
 It does not attempt balance the load for across all the nodes
Previous Work
Node-Weight Heuristic [Basagni 1999a, 1999b]
 Node-weights are assigned to nodes based on the suitability
of a node being a clusterhead
 The node is chosen to be a clusterhead if its node-weight is
higher than any of its neighbor’s node-weights and any tie is
broken by the minimum node ids
Drawbacks:
 No concrete criteria of assigning the node-weights
 Works well for “quasi-static” networks where the nodes do
not move much or move very slowly
Optimizing Clustering Algorithm in Mobile Ad hoc
Networks Using Genetic Algorithmic Approach
[Turgut+ 2002]
Weighted Clustering Algorithm (WCA)
 A clusterhead can ideally support  nodes
– Ensures efficient MAC functioning
– Minimizes delay and maximizes throughput
 A clusterhead uses more battery power
– Does extra work due to packet forwarding
– Communicates with more number of nodes
 A clusterhead should be less mobile
– Helps to maintain same configuration
– Avoids frequent WCA invocation
 A better power usage with physically closer nodes
– More power for distant nodes due to signal attenuation
Weighted Clustering Algorithm (WCA) Steps
1. Compute the degree dv each node v
d v  | N (v ) | 
 dist v, v   tx 
'
range
'
'
v V , v v
Coordinate distance, predefined transmission range.
2. Compute the degree-difference for every node
v  | d v   |
For efficient MAC (medium access control) functioning.
Upper bound on # of nodes a cluster head can handle.
Weighted Clustering Algorithm (WCA) Steps
3. Compute the sum of the distances Dv with all neighbors
Dv 
 dist v, v 
3
2
12
'
v N (v )
'
1
7
17
13
14
16
6
Energy consumption; more energy for greater dist.
communication.
Power required to support a link increases faster than
linearly with distance. (For cellular networks)
4
15
5
Weighted Clustering Algorithm (WCA) Steps
4. Compute the average speed of every node; gives a measure of
mobility Mv
1 T
Mv  T 
t 1
where
X t  X t 1  Y t Y t 1
X t , Y t 
2
X t 1,Y t 1 are the
and
coordinates of the node
2
v
at time
t
and
Yt
Yt-1
time
Xt-1 Xt
t 1
Component with less mobility is a better choice for clusterhead.
Weighted Clustering Algorithm (WCA) Steps
5. Compute the total (cumulative) time Pv a node acts as
clusterhead
Battery drainage = Power consumed
6. Calculate the combined weight Wv for each node
Wv = w1Δv + w2Dv + w3Mv + w4Pv for each node
7. Find min Wv; choose node v as the cluster head, remove all
neighbors of v for further WCA
8. Repeat steps 2 to 7 for the remaining nodes
Load Balancing Factor (LBF)
 It is desirable to balance the loads among the clusters
 Load balancing factor (LBF) has defined as (should be high)
LBF 
nc
i  x i   
2
where,
nc
xi


is the number of clusterheads
is the cardinality of cluster i
and
N  nc is the average number of neighbors of a clusterhead
nc
(N being the total number of nodes in the system)
Connectivity
 For clusters to communicate with each other, it is assumed that
clusterheads are capable of operating in dual power mode
 A clusterhead uses low power mode to communicate with its immediate
neighbors within its transmission range and high power mode is used for
communication with neighboring clusters
 Connectivity is defined as (for multiple component graph)
connectivity 
size of largest component
N
 Probability that a node is reachable from any other node
( 0 – 1; 1 being most desirable)
Demonstration
Scattered nodes in the network
Demonstration
Clusterheads are identified
Demonstration
Clusters are formed
Demonstration
Clusters are connected
Features of WCA
 Invocation of WCA is on-demand
– Reduces information exchange by less system updates
– Reduces computation/communication costs
– Manages mobility by reaffiliations
– Delays (avoids) invocation of clustering as far as possible
 WCA is distributive
– No clusterhead is over loaded
– Balances load by limiting the cluster size
Performance Metric
1. Number of clusterheads
2. Number of reaffiliations
– a process where a node detaches from one clusterhead and
attaches
to another
3. Number of dominant set updates
– when a node can no longer attach to any of the existing
clusterheads
These parameters are studied for the varying
number of nodes
transmission range
maximum displacement
Simulation Environment
 System with N nodes on a 100x100 grid
 N was varied between 20 and 60
 Nodes moved in all directions randomly
 Velocity of nodes were varied uniformly between 0 and 10
 Transmission range of nodes was varied between 0 and 70
 Ideal degree was fixed at  = 10
 Weighing factors: w1 = 0.7, w2 = 0.2, w3 = 0.05 and w4 = 0.05
Experimental Results
Max displacement = 5 (const)
Transmission range = 0 - 70
Number of nodes = 20 - 60
Ideal degree
= 10
Experimental Results
Max displacement = 1 - 10
Transmission range = 30 (const)
Number of nodes = 20 - 60
Ideal degree
= 10
Load Balancing
Connectivity
Performance of WCA
Genetic Algorithms
 Map the possible solutions of the problem to symbolic space
 Possible solutions form a pool of solutions – population
 Solution strings – chromosomes and components of chromosomes –
genes
 Genetic Algorithm operations:
– Selection
– Crossover
– Mutation
– Replacement
– Elitism
Encoding of the Chromosome
 N = # of nodes in the network
each with unique node id [1..N] used to encode
the chromosome by integer permutation
all the ids should be included without any duplication,
and without order.
For instance: N = 100 , node ids [1..100]
Pool size = 50 (50 strings of integers/chromosomes)
1
5 3 15 - - - - 1 99
2
3 7 5 - - - - 77 88
.
.
50 6 8 3 - - - - 55 44
Mapping WCA to GA
WV Values
Node Ids of all nodes
5
3
7
1
2
3
.
.
.
.
4
8
99
100
7
10
25
22
…
…
1
1 3 7 - - - 25 35 22 12
2
3 7 5 - - - 34 64 45 25
.
.
.
12
50 6 8 3 - - - 55 44 34 56
5
63
…
8
…
Neighbors list
WCA intermediate results
Data Encoded into chromosomes
Mapping WCA to GA
WV Values
Node Id of a ClusterHead
5
7
1
3
.
.
.
.
.
.
.
.
2 76
7
10
25
15
22
…
…
12
55
…
ClusterHead Set for a single chromosome
GA Steps
1. Choose Initial Population
Randomly generate the initial population.
Pool size = 50 (means 50 chromosomes)
While (new_pool_size < old_pool_size)
repeat step 3 to 6 (repeat step 2 until the number of
generation or the convergence is met)
2. Selection
Compute the fitness value for each chromosome by WV .
Roulette Wheel method is used based on the fitness values.
3. Crossover
X_Order1 method is used.
Crossover rate = 0.8
GA Steps
4. Mutation
Swap method is used; randomly selecting two gene at
positions i and j.
Mutation rate = 0.1
5. Replacement
Append method is used. The new children will be appended
into the new pool.
6. Elitism
- Check if the new children are better than the best, then replace
the best by the child
- Avoid being stuck on local optima
Cfit Value Algorithm
FitnessValue = 0;
1. For each gene in chromosome repeat step 2 to 3
2. node = gene[I];
3. if node is not clusterH and
is not a member of the other clusterH and
Nodedegree <= MAX_DEGREE ( const )
Then it is a clusterH,
Compute WV for this node
insert it into clusterHSet
fitnessValue += WV;
Cfit Value Algorithm
4. For each remaining node I from the network
If (it is not a clusterH and member of other clusterH,
and NodeDegree <= MAX_DEGREE)
then
Compute WV for this node
insert it into clusterHSet
fitnessValue += WV;
Performance Metric
1. Number of clusterheads
2. Number of reaffiliations
– a process where a node detaches from one clusterhead and
attaches to another
These parameters are studied for the varying
number of nodes
transmission range
maximum displacement
3. Load Distribution
Simulation Environment
 System with N nodes on a 100x100 grid
 N was varied between 20 and 60
 Nodes moved in all directions randomly
 Velocity of nodes were varied uniformly between 0 and 10
 Transmission range of nodes was varied between 0 and 70
 Ideal degree was fixed at  = 10
 Weighing factors: w1 = 0.7, w2 = 0.2, w3 = 0.05 and w4 = 0.05
Experimental Results
WCA
Optimized WCA
Max displacement = 5 (const)
Transmission range = 0 - 70
Number of nodes = 20 - 60
Ideal degree
= 10
Experimental Results
WCA
Optimized WCA
Max displacement = 1 - 10
Transmission range = 30 (const)
Number of nodes = 20 - 60
Ideal degree
= 10
Experimental Results
WCA
Optimized WCA
Max displacement = 1 - 10
Transmission range = 30 (const)
Number of nodes = 20 - 60
Ideal degree
= 10
Load Balancing with WCA
Load Balancing with GA
The load balancing factor has improvement ten times with GA
An Energy Efficient Hierarchical Clustering Algorithm for
Wireless Sensor Networks
[Bandyopadhyay+, 2003]
–
This paper proposes a distributed, randomized clustering algorithm to
organize the sensors in a wireless sensor network into clusters to minimize
the energy used to communicate information from all nodes to the
processing center
–
By the generation of hierarchy of clusterheads, the energy savings increase
with the number of levels in the hierarchy
–
Sensor detects events and then communicate the collected information to a
central location where parameters characterizing these events are
estimated
–
In the clustered environment, the data gathered by the sensors is
communicated to the data processing center through a hierarchy of
clusterheads
–
The processing center determines the final estimates of the parameters
using information communicated by the clusterheads
An Energy Efficient Hierarchical Clustering Algorithm for
Wireless Sensor Networks
[Bandyopadhyay+, 2003]
–
The processing center can be a specialized device or one of the sensors
itself
–
In such clustered environment, sensor data is communicated over smaller
distances, the energy consumed in the network will be much lower than the
energy consumption when every sensor communicates directly to the
information processing center
–
The results in stochastic geometry are used to derive values of parameters
for the algorithm that minimize the energy spent in the sensor network
An Energy Efficient Hierarchical Clustering Algorithm for
Wireless Sensor Networks
[Bandyopadhyay+, 2003]
A New, Energy-Efficient, Single-Level Clustering Algorithm
–
Each sensor becomes a clusterhead (CH) with probability p and advertises
itself as a clusterhead to the sensors within its radio range – these
clusterheads are called volunteer clusterheads
–
This advertisement is forwarded to all the sensors that are no more than k
hops away from the clusterhead
–
Any sensor node that is not clusterhead itself receiving such advertisement
joins the cluster of the closest clusterhead
–
Any sensor node that is neither a clusterhead nor has joined any cluster
itself becomes a clusterhead – called forced clusterheads
–
Since the advertisement forwarding has been limited to k hops, if a sensor
does not receive a CH advertisement within time duration t (where t is the
time required for data to reach the CH from any sensor k hops away), it
means that the sensor node is not within k hops of any volunteer CHs
An Energy Efficient Hierarchical Clustering Algorithm for
Wireless Sensor Networks
[Bandyopadhyay+, 2003]
A New, Energy-Efficient, Single-Level Clustering Algorithm
–
Therefore, the sensor node becomes a forced clusterhead
–
The CH can transmit the aggregated information to the processing center
after every t units of time since all the sensors within a cluster are at most k
hops away from the CH
–
The limit on the number of hops allows the CH to reschedule their
transmissions
–
This is a distributed algorithm and does not demand clock synchronization
between the sensors
–
The energy consumed for the information gathered by the sensors to reach
the processing center will depend on the parameters p and k
–
Since the objective of this work is to organize sensors in clusters to
minimize the energy consumption, values of the parameters (p and k) must
be found to ensure the goal
An Energy Efficient Hierarchical Clustering Algorithm for
Wireless Sensor Networks
[Bandyopadhyay+, 2003]
A New, Energy-Efficient, Single-Level Clustering Algorithm
Assumptions made for the optimal parameters are as follows:
–
The sensors are distributed as per a homogeneous spatial Poisson process
of intensity λ in 2-dimensional space
–
All sensors transmit at the same power level – have the same radio range r
–
Data exchanged between two communicating sensors not within each others’
radio range is forwarded by other sensors
–
A distance of d between any sensor and its CH is equivalent to
–
Each sensor uses 1 unit of energy to transmit or receive 1 unit of data
–
A routing infrastructure is in place; when a sensor communicates data to
another sensor, only the sensors on the routing path forward the data
–
The communication environment is contention- and error-free; sensors do not
have to retransmit any data
d / r hops
An Energy Efficient Hierarchical Clustering Algorithm for
Wireless Sensor Networks
[Bandyopadhyay+, 2003]
A New, Energy-Efficient, Hierarchical Clustering Algorithm
–
This algorithm is extension of the previous one by allowing more than one
level of clustering in place
–
Assume that there are h levels in the clustering hierarchy with level 1 being
the lowest level and level h being the highest
–
The sensors communicate the gathered data to level-1 clusterheads (CHs)
–
The level-1 CHs aggregate this data and communicate the aggregated data
to level-2 CHs and so on
–
Finally, level-h CHs communicate the aggregated data or estimates based on
this aggregated data to the processing center
An Energy Efficient Hierarchical Clustering Algorithm for
Wireless Sensor Networks
[Bandyopadhyay+, 2003]
A New, Energy-Efficient, Hierarchical Clustering Algorithm
–
The cost of communicating the information from the sensors to the
processing center is the energy consumed by the sensors to communicate
the information to level-1 CHs, plus the energy consumed by the level-1 CHs
to communicate the aggregated data to level-2 CHs, …., plus the energy
consumed by the level-h CHs to communicate the aggregated data to the
information processing center
Algorithm Details
–
The algorithm works in a bottom-up fashion
–
First, it elects the level-1 clusterheads, then level-2 clusterheads, and so on
An Energy Efficient Hierarchical Clustering Algorithm for
Wireless Sensor Networks
[Bandyopadhyay+, 2003]
A New, Energy-Efficient, Hierarchical Clustering Algorithm
Algorithm Details
–
Level-1 clusterheads are chosen as follows:
o
Each sensor decides to become a level-1 CH with certain probability p1
and advertises itself as a clusterhead to the sensors within its radio
range
o
This advertisement is forwarded to all the sensors within k1 hops of the
advertising CH
o
Each sensor receiving an advertisement joins the cluster of the closest
level-1 CH; the remaining sensors become forced level-1 CHs
–
Level-1 CHs then elect themselves as level-2 CHs with a certain probability
p2 and broadcast their decision of becoming a level-2 CH
–
This decision is forwarded to all the sensors within k2 hops
An Energy Efficient Hierarchical Clustering Algorithm for
Wireless Sensor Networks
[Bandyopadhyay+, 2003]
A New, Energy-Efficient, Hierarchical Clustering Algorithm
Algorithm Details
–
The level-1 CHs that receive the advertisement from level-2 CHs joins the
cluster of the closest level-2 CH; the remaining level-1 CHs become forced
level-2 CHs
–
Clusterheads at level 3, 4, 5,…,h are chosen in similar fashion with
probabilities p3, p4, p5,...,ph respectively to generate a hierarchy of CHs, in
which any level-i CH is also CH of level (i-1), (i-2),…,1.
An Energy Efficient Hierarchical Clustering Algorithm for
Wireless Sensor Networks
[Bandyopadhyay+, 2003]
Advantages:
–
It is considered one of the earliest clustering algorithms in sensor
networks that incorporates energy efficiency into the design of the
algorithm
–
Since it is distributed algorithm, there is no need for clock
synchronization between sensor nodes
–
It achieves not only better energy efficiency, but also better time
complexity compared to previous work
–
The sensor nodes considered are simple nodes with fixed power level of
transmissions
–
Since the algorithm is run periodically, the probability of becoming a
clusterhead for each period is chosen to ensure that every node will get
a chance to become clusterhead – providing the functionality for load
balancing
An Energy Efficient Hierarchical Clustering Algorithm for
Wireless Sensor Networks
[Bandyopadhyay+, 2003]
Advantages:
–
Another approach to ensure load balancing is to trigger the algorithm
when the energy levels fall below a certain threshold
–
Energy savings increases as the density of the sensor nodes increases
for single level clustering
–
For the hierarchical clustering algorithm, the energy savings increase for
(i) networks of sensors with lower communication radius, (ii) lower
density of sensors in the network, and (iii) increase in the number of
hierarchy levels
An Energy Efficient Hierarchical Clustering Algorithm for
Wireless Sensor Networks
[Bandyopadhyay+, 2003]
Disadvantages:
–
The energy consumption of clusterheads has not been addressed since these
nodes will involve with more computation and communication of data to
higher level clusterheads – consequence of non-uniform power consumption
on the performance of the overall sensor network in the long run
–
An ideal network is assumed (contention- and error-free) which may not
reflect the real life scenarios
–
Possible load imbalance between different clusters
–
Overhead associated with the clusterheads selection is not considered
–
How does the network cope with sensor node failures? How is detected and
remedied?
–
How does the network handle information sent by faulty sensors?
An Energy Efficient Hierarchical Clustering Algorithm for
Wireless Sensor Networks
[Bandyopadhyay+, 2003]
Disadvantages:
–
How many forced-clusterheads can the sensor network handle? What is the
upper bound? What are the guarantees that forced-clusterhead will be able
to communicate with the neighboring clusterheads?
–
Similarly, what is the upper bound on the number of sensor nodes within
one cluster?
–
Energy is wasted by those sensor nodes closer to the processing center
than their CH, but still need to go through their CH
An Energy Efficient Hierarchical Clustering Algorithm for
Wireless Sensor Networks
[Bandyopadhyay+, 2003]
Suggestions/Improvements/Future Work:
–
What happens if a sensor node receives several join advertisements
from multiple nearby clusterheads? How does the sensor node decides
which one to join?
Possible solution: the decision can be made to join to the cluster with the
minimum number of members such that sensor nodes are evenly
distributed among the clusters
–
Error and contention in communication is not considered
Possible solution: results may be verified with the real MAC protocol and
traffic conditions under a simulator or a test-bed
–
The capabilities of the processing center should be more than the
regular sensor nodes
An Energy Efficient Hierarchical Clustering Algorithm for
Wireless Sensor Networks
[Bandyopadhyay+, 2003]
Suggestions/Improvements/Future Work:
–
Further energy efficiency can be achieved if the clusterheads can be in
active or inactive mode (energy saving mode)
–
Depending on the distance from the clusterheads, the sensor nodes may
choose to transmit data towards clusterhead in various power levels (for
instance, low vs. high)
–
In multi-hop mode, the sensor nodes closest to the clusterhead have the
most energy drainage due to data forwarding
Possible solution: a scheme allowing the sensor nodes to alternate
between single-hop and multiple-hop mode periodically
Energy-Efficient Communication Protocol
Architecture for Wireless Microsensor
Networks (LEACH Protocol)
[Heinzelman+ 2000, 2002]
–
LEACH (Low-Energy Adaptive Clustering Hierarchy) is a clustering-based
protocol that utilizes the randomized rotation of local cluster base stations
to evenly distribute the energy load within the network of sensors
–
It is a distributed, does not require any control information from base station
(BS) and the nodes do not need to have knowledge of global network for
LEACH to function
–
The energy saving of LEACH is achieved by combining compression with
data routing
–
Key features of LEACH include:

Localized coordination and control of cluster set-up and operation

Randomized rotation of the cluster base stations or clusterheads and their
clusters

Local compression of information to reduce global communication
LEACH
[Heinzelman+ 2000, 2002]
–
Considered microsensor network has the following characteristics:

The base station is fixed and located far from the sensors

All the sensor nodes are homogeneous and energy constrained
–
Communication between sensor nodes and the base station is expensive and no
high energy nodes exist to achieve communication
–
By using clusters to transmit data to the BS, only few nodes need to transmit for
larger distances to the BS while other nodes in each cluster use small transmit
distances
–
LEACH achieves superior performance compared to classical clustering algorithms
by using adaptive clustering and rotating clusterheads; assisting the total energy of
the system to be distributed among all the nodes
–
By performing load computation in each cluster, amount of data to be transmitted to
BS is reduced. Therefore, large reduction in the energy dissipation is achieved
since communication is more expensive than computation
LEACH
[Heinzelman+ 2000, 2002]
Algorithm Overview
–
The nodes are grouped into local clusters with one node acting as the local base
station (BS) or clusterhead (CH)
–
The CHs are rotated in random fashion among the various sensors
–
Local data fusion is achieved to compress the data being sent from clusters to the
BS; resulting the reduction in the energy dissipation and increase in the network
lifetime
–
Sensor elect themselves to be local BSs at any any given time with a certain
probability and these CHs broadcast their status to other sensor nodes
–
Each node decided which CH to join based on the minimum communication energy
–
Upon clusters formation, each CH creates a schedule for the nodes in its cluster
such that radio components of each non-clusterhead node need to be turned OFF
always except during the transmit time
–
The CH aggregates all the data received from the nodes in its cluster before
transmitting the compressed data to BS
LEACH
[Heinzelman+ 2000, 2002]
Algorithm Overview
–
The transmission between CH and BS requires high energy transmission
–
In order to evenly distribute energy usage among the sensor nodes, clusterheads
are self-elected at different time intervals
–
The nodes decides to become a CH depending on the amount of energy it has left
–
The decisions to become CH are made independently of the other nodes
–
The system can determine the optimal number of CHs prior to election procedure
based on parameters such as network topology and relative costs of computation
vs. communication (Optimal number of CHs considered is 5% of the nodes)
–
It has been observed that nodes die in a random fashion
–
No communication exists between CHs
–
Each node has same probability to become a CH
LEACH
[Heinzelman+ 2000, 2002]
Algorithm Details
–
The operation of LEACH is achieved by rounds
–
Each round begins with a set-up phase (clusters are selected) followed by steadystate phase (data transmission to BS occurs)
1.
Advertisement Phase:
–
Initially, each node need to decide to become a CH for the current round based
on the suggested percentage of CHs for the network (set prior to this phase)
and the number times the node has acted as a CH
–
The node (n) decides by choosing a random number between 0 and 1
–
If this random number is less than T(n), the nodes become a CH for this round
–
The threshold is set as follows:
P
T(n) =
1 – P * (rmod 1P )
0
If n C G
Otherwise
P = desired percentage of CHs
r = current round
G = set of nodes that have not
been CHs in the last 1/P rounds
LEACH
[Heinzelman+ 2000, 2002]
Algorithm Details
1. Advertisement Phase:
–
Assumptions are (i) each node starts with the same amount of energy and (ii)
each CHs consumes relatively same amount of energy for each node
–
Each node elected as CH broadcasts an advertisement message to the rest
–
During this “clusterhead-advertisement” phase, the non-clusterhead nodes
hear the ads of all CHs and decide which CH to join
–
A node joins to a CH in which it hears with its advertisement with the highest
signal strength
2. Cluster Set-Up Phase:
–
Each node informs its clusterhead that it will be member of the cluster
3. Schedule Creation:
–
Upon receiving all the join messages from its members, CH creates a TDMA
schedule about their allowed transmission time based on the total number of
members in the cluster
LEACH
[Heinzelman+ 2000, 2002]
Algorithm Details
4. Data Transmission:
–
Each node starts data transmission to their CH based on their TDMA schedule
–
The radio of each cluster member nodes can be turned OFF until their
allocated transmission time comes; minimizing the energy dissipation
–
The CH nodes must keep its receiver ON to receive all the data
–
Once all the data is received, the CH compresses the data to send it to BS
Multiple Clusters
–
In order to minimize the radio interference between nearby clusters, each CH
chooses randomly from a list of spreading CDMA codes and it informs its
cluster members to transmit using this code
–
The neighboring CHs radio signals will be filtered out to avoid corruption in the
transmission
LEACH
[Heinzelman+ 2000, 2002]
Advantages:
–
Localized coordination to enable scalability, and robustness for dynamic
networks
–
Incorporates data fusion into the routing protocol in order to reduce the
amount of information transmitted to BS
–
Distributes energy dissipation evenly throughout the sensors, thus increasing
the system lifetime of the network
LEACH
[Heinzelman+ 2000, 2002]
Disadvantages:
–
How to decide the percentage of cluster heads for a network? The topology,
density and number of nodes of a network could be different from other networks
–
No suggestions about when the re-election needs to be invoked
–
The clusterheads farther away from the base station will use higher power and
die more quickly than the nearby ones
LEACH
[Heinzelman+ 2000, 2002]
Suggestions/Improvements/Future Work:
–
Extensions can be included to have hierarchical clustering where each CH
will communicate with “super-clusterhead” until the top layer of hierarchy in
which the data needs to be sent to BS
–
The degree and remaining energy of a node may be considered as
parameters to decide a clusterhead in a round. If a clusterhead with a limited
power used up its power in a round, the data to be transmitting may be lost
–
Since TDMA schedule is used, a large delay may be introduced between
event detection and notification at base station. Therefore, the protocol is not
suitable for a real-time application
TAS: Topology Adaptive Clustering for Wireless Sensor
Networks [Virrankoski+, 2005]
–
TASC is a distributed algorithm that partitions the network into a set of
locally isotropic, non-overlapping clusters without prior knowledge of the
number of clusters, cluster size and node coordinates
–
Spatial grouping of nodes with respect to regions of close proximity and
similar deployment density benefits

Improving the ease of network management

Efficient data aggregation and compression of sensor data

Formation of hierarchies and node localization
–
The set of weights that encode distance, connectivity, and density
information within the locality of each node are derived
–
These weights form the terrain for holding a coordinated leader election in
which each node selects the node closer to the center of mass of its
neighborhood to become its leader
TAS: Topology Adaptive Clustering for Wireless Sensor
Networks [Virrankoski+, 2005]
–
The algorithm employs a dynamic density reachability criterion which allows
the grouping of nodes according to their neighborhood density properties
–
Assumptions made:

Nodes are aware of their 2-hop neighborhood

Distances between nodes
Clustering objectives:
–
A clustering algorithm should partition the network so that the nodes inside
each cluster have high correlation in sensor measurements and are evenly
spaced in order to maximize gains and reduce errors due to ill geometric
positioning as in the case of node localization
–
TASC requires only minimum number of nodes in a cluster
–
The goal is to partition networks with density non-uniformities, into a set of
smaller locally isotropic clusters by grouping nodes with similar density
attributes
TAS: Topology Adaptive Clustering for Wireless Sensor
Networks [Virrankoski+, 2005]
Distributed Leader Election Algorithm
–
Two main components: node weights and density reachability
–
Two phases: nomination and voting followed by a merging phase

In first phase, each node considers weights of 2-hop neighbors,
nominates the node with maximum weight as an election candidate
and notifies the nodes in its neighborhood of this nomination

In second phase, each node elects the closest candidate as its leader.
Nodes that end up in clusters that are smaller than a pre-specified
minimum cluster size are dismantled and their node members join
bigger existing clusters. It includes all shortest paths between all pairs
of nodes that are located in path S.
TAS: Topology Adaptive Clustering for Wireless Sensor
Networks [Virrankoski+, 2005]
Distributed Leader Election Algorithm Example
A
B
C
D
E
4
7
8
7
4
F 0.49
4 B 0.86
A 1.29
5
3
D 10.15
C 0.84
4
G1
E 11.46
H 0.51
–
Define the weights to be the number of times a node is found on a shortest path
when computing a weight for node
–
Node A can be found on the paths AB, AC, AD, and AE, its weight = 4
–
Node C receives a weight of 8
References
[Baker+ 1981a] D.J. Baker and A. Ephremides, A Distributed Algorithm for Organizing Mobile Radio
Telecommunication Networks, Proceedings of the 2nd International Conference on Distributed
Computer Systems, April 1981, pp. 476-483.
[Baker+ 1981b] D.J. Baker and A. Ephremides, The Architectural Organization of a Mobile Radio
Network via a Distributed Algorithm, IEEE Transactions on Communications COM-29(11), 1981,
pp. 1694-1701.
[Bandyopadhyay+ 2003] S. Bandyopadhyay and E.J. Coyle, An Energy Efficient Hierarchical
Clustering Algorithm for Wireless Sensor Networks, IEEE INFOCOM 2003, San Francisco, CA,
March 30 – April 3, 2003.
[Basagni 1999a] S. Basagni, Distributed Clustering for Ad hoc Networks, Proceedings of International
Symposium on Parallel Architectures, Algorithms and Networks, June 1999, pp. 310-315.
[Basagni 1999b] S. Basagni, Distributive and Mobility-Adaptive Clustering for Multimedia Support in
Multi-hop Wireless Networks, Proceedings of Vehicular Technology Conference, VTC, Vol. 2,
1999-Fall, pp. 889-893.
[Ephremides+ 1987] A. Ephremides J.E. Wieselthier and D.J. Baker, A Design Concept for Reliable
Mobile Radio Networks with Frequency Hopping Signaling, Proceedings of IEEE, Vol. 75(1),
1987, pp. 56-73.
References
[Gerla+ 1995] M. Gerla and J.T. Tsai, Multicluster, mobile, multimedia radio network, Wireless
Networks, Vol. 1, No. 3, 1995, pp. 255-265.
[Heinzelman+ 2002] W. Heinzelman, A.P. Chandrakasan and H. Balakrishnan, An Application-Specific
Protocol Architecture for Wireless Microsensor Networks, IEEE Transactions on Wireless
Communications, Vol. 1, No. 4, October 2002, pp. 660-670.
[Heinzelman+ 2000] W. Heinzelman, A.P. Chandrakasan and H. Balakrishnan, Energy-Efficient
Communication Protocol for Wireless Microsensor Networks, IEEE Proceedings of the Hawaii
International Conference on System Sciences, January 4-7, 2000, Maui, Hawaii.
[Parekh 1994] A.K. Parekh, Selecting Routers in Ad-hoc Wireless Networks, Proceedings of the
SBT/IEEE International Telecommunications Symposium, August 1994.
[Turgut+ 2002] D. Turgut, S. K. Das, R. Elmasri, and B. Turgut, Optimizing Clustering Algorithm in
Mobile Ad hoc Networks Using Genetic Algorithmic Approach, Proceedings of IEEE GLOBECOM
2002, Taipei, Taiwan, November 17-21, 2002.
[Virrankoski+ 2005] R. Virrankoski, D. Lymberopoulos, and A. Savvides, TASC: Topology Adaptive
Spatial Clustering for Sensor Networks, IEEE INFOCOM 2005.