Clustering

1
Clustering in Ad hoc and Sensor Networks
2
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

3
Link-Clustered ArchitectureBaker 1981a,
1981b, Ephremides 1987

• Reduces interference in multiple-access broadcast
  environment
• Distinct clusters are formed to schedule
  transmissions in a contention-free 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
• Discover neighbors
• Select clusterhead to form clusters
• Decide on gateways between clusters

4
Link-Clustered ArchitectureBaker 1981a,
1981b, Ephremides 1987
5
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

6
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

7
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

8
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 neighbors
  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

9

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

10
Weighted Clustering Algorithm (WCA) Steps

• 1. Compute the degree dv each node v
• Coordinate distance, predefined transmission
  range.
• Compute the degree-difference for every node
• For efficient MAC (medium access control)
  functioning.
• Upper bound on of nodes a cluster head can
  handle.

11
Weighted Clustering Algorithm (WCA) Steps

• 3. Compute the sum of the distances Dv with all
  neighbors
• Energy consumption more energy for greater
  dist.
• communication.
• Power required to support a link increases
  faster than
• linearly with distance. (For cellular
  networks)

12
Weighted Clustering Algorithm (WCA) Steps

• 4. Compute the average speed of every node
  gives a measure of
• mobility Mv
• where and are the
• coordinates of the node at time and
• Component with less mobility is a better
  choice for clusterhead.

13
Weighted Clustering Algorithm (WCA) Steps

• 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 w1?v 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
• Repeat steps 2 to 7 for the remaining nodes

14
Load Balancing Factor (LBF)

• It is desirable to balance the loads among the
  clusters
• Load balancing factor (LBF) has defined as
  (should be high)

where,
is the number of clusterheads
is the cardinality of cluster i and
is the average number of neighbors of a
clusterhead (N being the total number of nodes
in the system)
15
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)
• Probability that a node is reachable from any
  other node
• ( 0 1 1 being most desirable)

16
Demonstration
Scattered nodes in the network
17

Demonstration
Clusterheads are identified
18

Demonstration
Clusters are formed
19

Demonstration
Clusters are connected
20
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

21
Performance Metric

• Number of clusterheads
• Number of reaffiliations
• a process where a node detaches from one
  clusterhead and attaches
• to another
• 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

22
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

23
Experimental Results
24
Experimental Results
25
Load Balancing
26
Connectivity
27
Performance of WCA
28
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

29
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)

30
Mapping WCA to GA
Data Encoded into chromosomes
WCA intermediate results

31
Mapping WCA to GA
ClusterHead Set for a single chromosome

32
GA Steps

•    1. Choose Initial Population
• Randomly generate the initial population.
• Pool size 50 (means 50 chromosomes)
• While (new_pool_size lt 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

33
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

34
Cfit Value Algorithm

• FitnessValue 0
• 1. For each gene in chromosome repeat step 2
  to 3
• 2. node geneI
• 3. if node is not clusterH and
• is not a member of the other clusterH and
• Nodedegree lt MAX_DEGREE ( const )
• Then it is a clusterH,
• Compute WV for this node
• insert it into clusterHSet
• fitnessValue WV

35
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 lt MAX_DEGREE)
• then
• Compute WV for this node
• insert it into clusterHSet
• fitnessValue WV

36
Performance Metric

• Number of clusterheads
• 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
• Load Distribution

37
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

38
Experimental Results
WCA
Optimized WCA
39
Experimental Results
Optimized WCA
WCA
40
Experimental Results
Optimized WCA
WCA
41
Load Balancing with WCA
42

Load Balancing with GA
The load balancing factor has improvement ten
times with GA
43
An Energy Efficient Hierarchical Clustering
Algorithm for Wireless Sensor Networks Bandyopadh
yay, 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

44
An Energy Efficient Hierarchical Clustering
Algorithm for Wireless Sensor Networks Bandyopadh
yay, 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

45
An Energy Efficient Hierarchical Clustering
Algorithm for Wireless Sensor Networks Bandyopadh
yay, 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

46
An Energy Efficient Hierarchical Clustering
Algorithm for Wireless Sensor Networks Bandyopadh
yay, 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

47
An Energy Efficient Hierarchical Clustering
Algorithm for Wireless Sensor Networks Bandyopadh
yay, 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 ? 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 hops
• 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

48
An Energy Efficient Hierarchical Clustering
Algorithm for Wireless Sensor Networks Bandyopadh
yay, 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

49
An Energy Efficient Hierarchical Clustering
Algorithm for Wireless Sensor Networks Bandyopadh
yay, 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

50
An Energy Efficient Hierarchical Clustering
Algorithm for Wireless Sensor Networks Bandyopadh
yay, 2003

• A New, Energy-Efficient, Hierarchical Clustering
  Algorithm
• Algorithm Details
• Level-1 clusterheads are chosen as follows
• 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
• This advertisement is forwarded to all the
  sensors within k1 hops of the advertising CH
• 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

51
An Energy Efficient Hierarchical Clustering
Algorithm for Wireless Sensor Networks Bandyopadh
yay, 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.

52
An Energy Efficient Hierarchical Clustering
Algorithm for Wireless Sensor Networks Bandyopadh
yay, 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

53
An Energy Efficient Hierarchical Clustering
Algorithm for Wireless Sensor Networks Bandyopadh
yay, 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

54
An Energy Efficient Hierarchical Clustering
Algorithm for Wireless Sensor Networks Bandyopadh
yay, 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?

55
An Energy Efficient Hierarchical Clustering
Algorithm for Wireless Sensor Networks Bandyopadh
yay, 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

56
An Energy Efficient Hierarchical Clustering
Algorithm for Wireless Sensor Networks Bandyopadh
yay, 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

57
An Energy Efficient Hierarchical Clustering
Algorithm for Wireless Sensor Networks Bandyopadh
yay, 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

58
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

59
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

60
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

61
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

62
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 steady-state phase
  (data transmission to BS occurs)
• 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 desired percentage of CHs r current
round G set of nodes that have not been
CHs in the last 1/P rounds
63
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

64
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

65
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

66
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

67
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

68
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

69
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

70
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.

71
TAS Topology Adaptive Clustering for Wireless
Sensor Networks Virrankoski, 2005

• Distributed Leader Election Algorithm Example
• 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

72
References

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