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A Mobility Metric Based Dynamic Clustering Algorithm for Vanets

A Mobility Metric Based Dynamic Clustering Algorithm for Vanets. Authors: Wei Fan, Yan Shi, Shanzhi Chen, Lengho Zou Proceedings of ICCTA 2011 Presented by: Sanaz Khakpour Master of Computer Science student. Aims and Objectives.

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A Mobility Metric Based Dynamic Clustering Algorithm for Vanets

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  1. A Mobility Metric Based Dynamic Clustering Algorithm for Vanets Authors: Wei Fan, Yan Shi, Shanzhi Chen, LenghoZou Proceedings of ICCTA 2011 Presented by: SanazKhakpour Master of Computer Science student

  2. Aims and Objectives A Dynamic Clustering Algorithm (DCA) is proposed in order to achieve following goals: • Create stable clusters • Increase cluster life-time • Reduce clustering reaffiliation times

  3. Contributions and Assumptions: Contribution: • Introduced mobility metric in this paper is based on velocity and acceleration of vehicles. • Adding the concept of acceleration increases accuracy. • Introducing a mobility metric which is appropriate for group mobility (suitable for VANETs) Assumption: • 1-hop cluster formation. • Nodes in the same direction can join cluster. • each node knows about the average velocity and acceleration of its neighbours.

  4. Clustering Advantageous for V2V and V2I V2I: • Connection to the Internet is done by use of roadside infrastructure. • Clustering prevents information congestion in access points. V2V: • clustering provides a layered topology for the network

  5. Mobility Metric Calculation • Each vehicle has two parameters: • Velocity • Acceleration • The traveled distance of vehicle is being calculated each T time interval as follow: D =

  6. Mobility Metric Calculation • Also, the average velocity and acceleration are being calculated: • The "Relative Velocity" and "Relative Acceleration" values between node i and j and SD is calculated as follow (based on the mentioned assumption):

  7. Mobility Metric Calculation • Each node i calculates TSD (total spatial dependence) by adding all SD values for its 1-hop neighbours: • Lastly, Cluster Relation (CR) is being calculated as the average TSD for each node: • CR determines whether a node has similar movement patterns to its neighbours or not. The node with highest CR value is selected as cluster head.

  8. Message Types • HELLO Packet: • REQUEST and REPLY Packet: • DISMISS Packet: • INVITE Packet:

  9. Clustering Algorithm: • Node types: Normal node, cluster member, cluster head • Step 1) CR calculation for all neighbours of every node is being performed. Hello messages are being transmitted between nodes. • Step 2) Nodes intend to join cluster by sending REQUEST messages to their neighbours. If there is a CH among the neighbours step 5 will run.

  10. Clustering Algorithm: • Step 3) The node checks direction field in the REPLY messages received from neighbours and only considers nodes moving in the same direction. Step 4) If the CR value of the node is smaller than all its neighbours, the node will stay a normal node. Otherwise, the algorithm will continue to find the highest CR as CH. CH send INVITE messages to its neighbours. Also, the smallest CR is defined as threshold.

  11. Clustering Algorithm: • Step 5) The node will send REQUEST message if it finds a CH in its neighbours and it will join cluster if: Threshold < Node’s CR < CH’s CR • Step 6) If N < Nth  CH sends a dismiss message

  12. Conclusion • DCA algorithm is been compared with Lowest-ID and Max-Degree algorithms by simulation. • DCA algorithm increases cluster lifetime and cluster stability due to using a metric based on similarity between vehicles’ movement. • Also, number of cluster reafilliations has been decreased in comparison to other mentioned methods.

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