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Node Clustering in Wireless Sensor Networks by Considering Structural Characteristics of the Network Graph. Nikos Dimokas 1 Dimitrios Katsaros 1,2 Yannis Manolopoulos 1. 1 Informatics Dept., Aristotle University, Thessaloniki, Greece

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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

wireless sensor network wsn
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
communication in wsn
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.
wsn applications
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.
what is clustering
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.
clustering in wsn
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
relevant work clustering
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
relevant work topology control

u

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uv not included

uv included

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uv not included

uv included

Relevant work – Topology Control

Minimum Spanning Tree (MST) and Localized Minimum Spanning Tree (LMST): Calculated with Dijkstra’s 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.
minimal dominating set
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

motivation for new clustering protocol
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) bestdecision might not be reached (even if it does exist)
well known cds algorithm
Well-known CDS algorithm

Wu and Li’s 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
well known cds algorithm1

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Well-known CDS algorithm

Wu and Li’s algorithm (Pruning Rules 1 & 2)

A node v can be taken out from the CDS if there exists a node u such that N[v] is a subset of N[u] and the ID of v is smaller than the ID of u

Open neighbor set N(v) = {u | u is a neighbor of v}

Closed neighbor set N[v] = N(v)U{v}

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

heed protocol 1 2
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.
heed protocol 2 2
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.
leach protocol 1 2
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 r+1 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.
leach protocol 2 2
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
weakness of current approaches
Weakness of current approaches
  • Some approaches can not detect all possible eliminationsbecause ordering based on node ID prevents this. As a consequence they incursignificantlyexcessive retransmissions
  • Others rely on a lot of “local” information, forinstance knowledge of k-hop neighborhood (k > 2), e.g., [WD04,WL04]
  • Other methodsare computationally expensive, incurring a cost of O(f2) or O(f3), where f is themaximum degree of a node of the ad hoc network, e.g., the methodsreported in [WL01, WD03, DW04] and [SSZ02]
  • some methods(e.g., [QVLl00,SSZ02]) do not fully exploit the compiled information; forinstance, the use of the degree of a node as its priority when deciding itspossibleinclusion in the dominating set might not result in the best local decision
terminology and assumptions
Terminology and assumptions
  • WSN is abstracted as a graph G(V,E)
  • An edge e=(u,v) exists if and onlyif u is in the transmission range ofv and vice versa. All links in the graph arebidirectional.
  • 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
a new measure of node importance
A new measure of node importance
  • Let σuw=σwu denote the number of shortest paths from uV towV (by definition, σuu=0).
  • Let σuw(v) denote the number ofshortest paths from u to w that some vertex vV lies on.
  • We define thenode importance indexNI(v) of a vertex v as:
  • Large values for the NI index of a node v indicate that this node can reach otherson relatively short paths, or that v lies on considerable fractions of shortestpaths connecting others. In the former case, it captures the fact of a possibly large degreeof node v, and in the latter case, it captures the fact that v might have one (some) “isolated” neighbors
the ni index in sample graphs
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
the ni index in a localized algorithm
The NI index in a localized algorithm
  • For any nodev, the NI indexes of the nodes in N12(v) calculatedonly for the subgraph of the 2-hop (in general, k-hop) neighborhood reveal the relative importance of the nodes in coveringN12
  • For a node u (of the 2-hop neighbourhood of anode v), the NI index of u will bedenoted as NIv(u)
ni computation
NI computation
  • At a first glance, NI computation seems expensive, i.e., O(m*n2)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 momentthat there exists only one) can be done using bfs in O(m) time, andthere exist O(n2) vertex pairs
  • Fortunately, we can do better than this by making somesmart observations. The improved algorithm (CalculateNodeImportanceIndex) is quite complicated and beyond the scope of this presentation
  • THEOREM. The complexity of the algorithm CalculateNodeImportanceIndex is O(n*m) for agraph with n vertices and m edges
evaluation setting 1 2
Evaluation setting (1/2)
  • We compare GESC to:
    • WL1+2, improved scheme incorporating therules 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.
evaluation setting 2 2
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
conclusions and future work
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 nodesand 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