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Node Clustering in Wireless Sensor Networks by Considering Structural Characteristics of the Network Graph

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Wireless Sensor Network (WSN) Communication in WSN WSN - Applications What is Clustering Clustering in WSN Involves grouping nodes into clusters and electing a CH ... – PowerPoint PPT presentation

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Title: Node Clustering in Wireless Sensor Networks by Considering Structural Characteristics of the Network Graph


1
Node Clustering in Wireless Sensor Networks by
Considering Structural Characteristics of the
Network Graph
  • Nikos Dimokas1
  • Dimitrios Katsaros1,2
  • Yannis Manolopoulos1

1Informatics Dept., Aristotle University,
Thessaloniki, Greece 2Computer Comm.
Engineering Dept., University of Thessaly, Volos,
Greece
4th ITNG Conference, Las Vegas, NV, 2-4/April/2007
2
Wireless Sensor Network (WSN)
  • Wireless Sensor Networks features
  • Homogeneous devices
  • Stationary nodes
  • Dispersed Network
  • Large Network size
  • Self-organized
  • All nodes acts as routers
  • No wired infrastructure
  • Potential multihop routes

3
Communication in WSN
  • Communication between two unconnected nodes is
    achieved through intermediate nodes.
  • Every node that falls inside the communication
    range r of a node u, is considered reachable.

4
WSN - Applications
  • Applications
  • Habitat monitoring
  • Disaster relief
  • Target tracking
  • Many of these applications require simple and/or
    aggregate function to be reported.
  • Clustering allows aggregation and limits data
    transmissions.

5
What is Clustering
Cluster member
Clusterhead
Gateway node
Intra-Cluster link
Cross-cluster link
  • Nodes divided in virtual group according to some
    rules
  • Nodes belonging in a group can execute different
    functions from other nodes.

6
Clustering in WSN
  • Involves grouping nodes into clusters and
    electing a CH
  • Members of a cluster can communicate with their
    CH directly
  • CH can forward the aggregated data to the central
    base station through other CHs
  • Clustering Objectives
  • Allows aggregation
  • Limits data transmission
  • Facilitate the reusability of the resources
  • CHs and gateway nodes can form a virtual backbone
    for intercluster routing
  • Cluster structure gives the impression of a
    smaller and more stable network
  • Improve network lifetime
  • Reduce network traffic and the contention for the
    channel
  • Data aggregation and updates take place in CHs

7
Relevant work Clustering
  • Based on the construction of Dominating Set
  • Nodes belonging to the DS are carrying out all
    communication
  • Running out of energy very soon
  • Based on the residual energy of each node
  • Proposed ways to rotate the role of CH among
    nodes of clusters
  • Can be easily combined with the algorithms of the
    first family
  • Our proposal the GESC protocol supports
  • dynamically estimation of CHs depending on the
    requester node, and thus improvement of network
    lifetime
  • a novel metric for characterizing node importance
  • localization
  • minimum number of messages exchanged among the
    nodes

8
Relevant work Topology Control
Minimum Spanning Tree (MST) and Localized Minimum
Spanning Tree (LMST) Calculated with Dijkstras
algorithm and Li, Hou Sha, respectively.
MST
LMST
sample graph
Relative Neighborhood Graph (RNG) An edge uv is
included in RNG iff it is not the longest edge in
any triangle uvw.
Grabriel Graph (GG) An edge uv is included in GG
iff the disk with diameter uv contains no other
node inside it.
Delaunay Triangulation (DT), Partial Delaunay
Triangulation (PDT), Yao graph (YG), etc A lot
of other (variants of) geometric structures
  • Topology Control Choosing a set of links from
    the possible ones. Not exactly our problem. So
    graph-theoretic concepts, than geometric ones.

9
Minimal Dominating Set
  • A vertex set is DS (Dominating Set)
  • Any other vertex connected to one DS vertex
  • It is CDS, if it is connected
  • It is MCDS if its size is minimum among CDS
  • Discovery of the MCDS of a graph is in NP-complete

DS
CDS
10
Motivation for new clustering protocol
  • The protocol should
  • be localized, and thus distributed
  • fully exploit the locally available information
    in making the best decisions
  • be computationally efficient
  • minimize the number of message exchange among the
    nodes
  • be energy efficient and thus extend network
    lifetime. This could be achieved with the use of
    different nodes for relaying messages
  • not make use of variants, e.g., node IDs,
    because a (locally) best decision might not be
    reached (even if it does exist)

11
Well-known CDS algorithm
Wu and Lis algorithm
  • Each node exchanges its neighborhood information
    with all of its one-hop neighbors
  • Any node with two unconnected neighbors becomes a
    dominator (red)
  • The set of all the red nodes form a CDS

12
Well-known CDS algorithm
Wu and Lis algorithm (Pruning Rules 1 2)
Open neighbor set N(v) u u is a
neighbor of v Closed neighbor set Nv
N(v)Uv
A node u can be taken out from the CDS if u
has two neighbors v and w such that N(u) is
covered by N(v)UN(w) and its ID is the smallest
of the other two nodes IDs
  • A node v can be taken out from the CDS if there
    exists a node u such that Nv is a subset of
    Nu and the ID of v is smaller than the ID of u

13
Heed protocol (1/2)
  • Every sensor node has multiple power levels.
  • Periodically selects CHs according to a hybrid of
    the node residual energy and node degree.
  • TCP is the clustering process duration and TNO is
    the network operation interval.
  • Clustering is activated every TCP TNO seconds.
  • Initial number of CHs is Cprob.
  • The probability of a node to become a CH is
    CHprob.
  • The probability of a node to become a CH is
    CHprob.

14
Heed protocol (2/2)
  • Intracluster Intercluster communication
  • Intracluster communication is proportional to
  • Node degree (load distribution)
  • 1 / node degree (dense clusters)
  • If variable power levels ara allowed for
    intracluster communication then select CHs using
    average minimum reachability power.

15
Leach protocol (1/2)
  • All nodes can transmit with enough power to reach
    the BS and the nodes use power control.
  • Cluster formation during set-up phase and data
    transfer during steady-state phase.
  • Each node elects itself as CH at the beginning of
    round r1 with probability Pi(t). k is the number
    of clusters.
  • All nodes are CHs the same number of times.
  • All nodes have the same energy after N/k rounds.

16
Leach protocol (2/2)
  • Every node elects as CH the node that requires
    the least energy consumption for communication.
  • Every CH set-up a TDMA schedule and transmitted
    to the nodes. Every node could transmit data in
    the corresponding time-slot.
  • Weakness
  • Limited scalability
  • Could be complementary to clustering techniques
    based on the construction of a DS

17
Weakness of current approaches
  • Some approaches can not detect all possible
    eliminations because ordering based on node ID
    prevents this. As a consequence they incur
    significantly excessive retransmissions
  • Others rely on a lot of local information, for
    instance knowledge of k-hop neighborhood (k gt 2),
    e.g., WD04,WL04
  • Other methods are computationally expensive,
    incurring a cost of O(f2) or O(f3), where f is
    the maximum degree of a node of the ad hoc
    network, e.g., the methods reported in WL01,
    WD03, DW04 and SSZ02
  • some methods (e.g., QVLl00,SSZ02) do not fully
    exploit the compiled information for instance,
    the use of the degree of a node as its priority
    when deciding its possible inclusion in the
    dominating set might not result in the best local
    decision

18
Terminology and assumptions
  • WSN is abstracted as a graph G(V,E)
  • An edge e(u,v) exists if and only if u is in the
    transmission range of v and vice versa. All links
    in the graph are bidirectional.
  • The network is assumed to be connected
  • N1(v) the set of one hop neighbours of v
  • N2(v) the set of two hop neighbours of v
  • N12(v) combined set of N1(v) and N2(v)
  • LNv is the induced subgraph of G associated
    with vertices in N12(v)
  • dG(v,u) distance between v and u

19
A new measure of node importance
  • Let suwswu denote the number of shortest paths
    from u ? V to w ? V (by definition, suu0).
  • Let suw(v) denote the number of shortest paths
    from u to w that some vertex v ? V lies on.
  • We define the node importance index NI(v) of a
    vertex v as
  • Large values for the NI index of a node v
    indicate that this node can reach others on
    relatively short paths, or that v lies on
    considerable fractions of shortest paths
    connecting others. In the former case, it
    captures the fact of a possibly large degree of
    node v, and in the latter case, it captures the
    fact that v might have one (some) isolated
    neighbors

20
The NI index in sample graphs
In parenthesis, the NI index of the respective
node i.e., 7(156) node with ID 7 has NI equal
to 156.
  • Nodes with large NI
  • Articulation nodes (in bridges), e.g., 3, 4, 7,
    16, 18
  • With large fanout, e.g., 14, 8, U
  • Therefore geodesic nodes

21
The NI index in a localized algorithm
  • For any node v, the NI indexes of the nodes in
    N12(v) calculated only for the subgraph of the
    2-hop (in general, k-hop) neighborhood reveal the
    relative importance of the nodes in covering N12
  • For a node u (of the 2-hop neighbourhood of a
    node v), the NI index of u will be denoted as
    NIv(u)

22
NI computation
  • At a first glance, NI computation seems
    expensive, i.e., O(mn2) operations in total for
    a 2-hop neighbourhood, which consists of n nodes
    and m links
  • calculating the shortest path between a
    particular pair of vertices (assume for the
    moment that there exists only one) can be done
    using bfs in O(m) time, and there exist O(n2)
    vertex pairs
  • Fortunately, we can do better than this by making
    some smart observations. The improved algorithm
    (CalculateNodeImportanceIndex) is quite
    complicated and beyond the scope of this
    presentation
  • THEOREM. The complexity of the algorithm
    CalculateNodeImportanceIndex is O(nm) for a
    graph with n vertices and m edges

23
Pseudocode for CalculateNodeImportanceIndex (1/2)
24
Pseudocode for CalculateNodeImportanceIndex (2/2)
25
Evaluation setting (1/2)
  • We compare GESC to
  • WL 12, improved scheme incorporating the rules
    indicated
  • MPR, the MultiPoint Relaying method described in
    QVL00
  • SSZ, reported in SSZ02, which was selected as a
    Fast Breaking Paper for October 2003
  • Implementation of protocols using J-Sim
    simulation library
  • Sensor network topologies with 100, 300, 500
    nodes.
  • Each topology consists of square grid units
  • Each sensor node is uniformly distributed between
    the point (0,0) and (100,100)
  • Two sensor nodes are neighbors if they are placed
    in the same or adjacent grid units.

26
Evaluation setting (2/2)
  • Varying levels of node degree from 4 to 10
  • Run each protocol at least 100 times for each
    different node degree. Each time a different node
    is selected to start broadcasting
  • Performance metric
  • Energy dissipation
  • Broadcast messages
  • Latency

27
Impact of the nodes (1/2)
28
Impact of the nodes (2/2)
29
Impact of the average node degree
30
Impact of energy consumption
31
Conclusions and Future Work
  • Defined and investigated a novel distributed
    clustering protocol for WSN based on a novel
    localized metric
  • The calculation of this metric is very efficient,
    linear in the number of nodes and linear in the
    number of links
  • Proved that it is very efficient in terms of
    communication cost and in terms of prolonging
    network lifetime
  • The protocol is able to reap significant
    performance gains, reducing the number of
    rebroadcasting nodes
  • Simulated an environment to evaluate the
    performance of the protocol and competitive
    protocols using J-Sim simulator
  • Comparison with protocols based on residual
    energy (LEACH,HEED)
  • GESC GEodegic Sensor Clustering has been
    proven to prevail
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