A random walk based clustering with local re-computations for mobile - PowerPoint PPT Presentation

Introduction Algorithm Example execution of the algorithm Properties Simulation results Conclusion and perspectives A random walk based clustering with local re-computations for mobile ad hoc networks Kudireti ABDURUSUL ⋆ Alain BUI † Devan SOHIER † ⋆ SysCom, CReSTIC Université de Reims Champagne-Ardenne, France † PRiSM (UMR CNRS 8144) Université de Versailles St-Quentin-en-Yvelines Kudireti Abdurusul (CReSTIC) RWCMA 1 / 23

Introduction Algorithm Example execution of the algorithm Properties Simulation results Conclusion and perspectives Plan Introduction 1 Algorithm 2 Example execution of the algorithm 3 Properties 4 Simulation results 5 Conclusion and perspectives 6 Kudireti Abdurusul (CReSTIC) RWCMA 2 / 23

Introduction Algorithm Example execution of the algorithm Properties Simulation results Conclusion and perspectives Context and Related Works MANETs Decentralized wireless networks. Arbitrary Movement . Motivation Large computer networks : dividing them into several disjoint connected parts. Managed separately and be coordinated. Kudireti Abdurusul (CReSTIC) RWCMA 3 / 23

Introduction Algorithm Example execution of the algorithm Properties Simulation results Conclusion and perspectives Context and Related Works Related Works 1-hop : each node in the network is the neighbor of its clusterhead . ( eg. LCA (LCA2), DMAC, GDMAC). K-hop : any node in any cluster is at most k hops away from its clusterhead ( eg. Max-min D-hop-cluster , hierarchical clustering) Model and hypotheses Model : an asynchronous message-passing model Hypotheses Connected network Unique identifier Link bidirectional Detection of Link failure Kudireti Abdurusul (CReSTIC) RWCMA 4 / 23

Introduction Algorithm Example execution of the algorithm Properties Simulation results Conclusion and perspectives Context and Related Works Random walk based distributed Random walks scheme algorithm An algorithm involving a particular message, the token , that circulates according to a random walk scheme 1/3 At each step, a node possesses the 1/3 token 1/3 Transmission: choose one neighbor at random, and send the token to it Properties of random walks: Hitting Meeting Kudireti Abdurusul (CReSTIC) RWCMA 5 / 23

Introduction Algorithm Example execution of the algorithm Properties Simulation results Conclusion and perspectives Context and Related Works Random walk based distributed Random walks scheme algorithm An algorithm involving a particular 1/4 1/4 message, the token , that circulates 1/4 1/4 according to a random walk scheme At each step, a node possesses the token Transmission: choose one neighbor at random, and send the token to it Properties of random walks: Hitting Meeting Kudireti Abdurusul (CReSTIC) RWCMA 5 / 23

Introduction Algorithm Example execution of the algorithm Properties Simulation results Conclusion and perspectives Context and Related Works Random walk based distributed Random walks scheme algorithm An algorithm involving a particular message, the token , that circulates according to a random walk scheme At each step, a node possesses the token 1/3 1/3 Transmission: choose one neighbor at random, and send the 1/3 token to it Properties of random walks: Hitting Meeting Kudireti Abdurusul (CReSTIC) RWCMA 5 / 23

Introduction Algorithm Example execution of the algorithm Properties Simulation results Conclusion and perspectives Principle Our cluster A distributed clustering algorithm based on random walks Core Construction Cluster core Ordinary nodes Core neighbors MaxCoreSize Complete cluster and Incomplete cluster Kudireti Abdurusul (CReSTIC) RWCMA 6 / 23

Introduction Algorithm Example execution of the algorithm Properties Simulation results Conclusion and perspectives Algorithm and Messages Token message On reception of Token message Join procedure Transmit the token 1 3 2 Send the Token back 4 Delete message Kudireti Abdurusul (CReSTIC) RWCMA 7 / 23

Introduction Algorithm Example execution of the algorithm Properties Simulation results Conclusion and perspectives Algorithm and Messages Token message On reception of Token message Join procedure Transmit the token IDdes = 2 1 3 2 Send the Token back 4 Delete message Kudireti Abdurusul (CReSTIC) RWCMA 7 / 23

Introduction Algorithm Example execution of the algorithm Properties Simulation results Conclusion and perspectives Algorithm and Messages Token message On reception of Token message Join procedure Transmit the token 1 3 2 Send the Token back Node 1 timer expired send out the token 4 Delete message Kudireti Abdurusul (CReSTIC) RWCMA 7 / 23

Introduction Algorithm Example execution of the algorithm Properties Simulation results Conclusion and perspectives Algorithm and Messages Token message On reception of Token message Join procedure Transmit the token 1 3 2 Send the Token back Pgreen < Pblue 4 Delete message Kudireti Abdurusul (CReSTIC) RWCMA 7 / 23

Introduction Algorithm Example execution of the algorithm Properties Simulation results Conclusion and perspectives Algorithm and Messages Token message Delete message On reception of Delete message if P r = P e then Broadcast Delete ( P r , O r ) 1 3 2 message, isCore = false , reset timer 4 Kudireti Abdurusul (CReSTIC) RWCMA 8 / 23

Introduction Algorithm Example execution of the algorithm Properties Simulation results Conclusion and perspectives Algorithm and Messages Link failure On detecting a link ( i , j ) ∈ E failure on node i if ( i , j ) ∈ Core ∧ isCore i = true then 1 3 2 Delete procedure 4 re-initialization if isCore i = false re-initialization Kudireti Abdurusul (CReSTIC) RWCMA 9 / 23

Introduction Algorithm Example execution of the algorithm Properties Simulation results Conclusion and perspectives Example execution of the algorithm Ad-hoc network with 11 nodes 3 1 4 9 5 7 8 11 6 2 10 Kudireti Abdurusul (CReSTIC) RWCMA 10 / 23

Introduction Algorithm Example execution of the algorithm Properties Simulation results Conclusion and perspectives Example execution of the algorithm The modeling of this ad hoc network of ( MaxCoreSize = 3) 3 1 4 9 5 7 8 11 6 2 10 Kudireti Abdurusul (CReSTIC) RWCMA 10 / 23

Introduction Algorithm Example execution of the algorithm Properties Simulation results Conclusion and perspectives Example execution of the algorithm Second state Timer Expiration 3 P=1, K=1 1 4 Core nodes 9 Timer Expiration P=7, K=1 5 7 Ordinary nodes 8 11 Token to Destination node 6 2 Delete message 10 Kudireti Abdurusul (CReSTIC) RWCMA 11 / 23

Introduction Algorithm Example execution of the algorithm Properties Simulation results Conclusion and perspectives Example execution of the algorithm Second state 3 P=1, K=2 1 4 Core nodes 9 P=7, K=2 5 7 Ordinary nodes 8 11 Token to Destination node 6 2 Delete message 10 Kudireti Abdurusul (CReSTIC) RWCMA 11 / 23

Introduction Algorithm Example execution of the algorithm Properties Simulation results Conclusion and perspectives Example execution of the algorithm Third state 3 1 4 Core nodes 9 P=1 < P=7 Delete Procedure in the red cluster 5 7 Ordinary nodes 8 11 Token to Destination node 6 2 Delete message 10 Kudireti Abdurusul (CReSTIC) RWCMA 12 / 23

Introduction Algorithm Example execution of the algorithm Properties Simulation results Conclusion and perspectives Example execution of the algorithm Fourth state 3 1 4 Core nodes 9 Detecting Link Failure 5 7 Ordinary nodes 8 11 Token to Destination node 6 Timer Expiration 2 Delete message 10 Kudireti Abdurusul (CReSTIC) RWCMA 13 / 23

Introduction Algorithm Example execution of the algorithm Properties Simulation results Conclusion and perspectives Example execution of the algorithm Fifth state 3 1 4 Core nodes 9 Delete Procedure in the green cluster 5 7 Ordinary nodes 8 11 Token to Destination node 6 2 Delete message 10 Kudireti Abdurusul (CReSTIC) RWCMA 14 / 23

Introduction Algorithm Example execution of the algorithm Properties Simulation results Conclusion and perspectives Example execution of the algorithm Steady state 3 1 Local Re−clustering 4 Core nodes 9 Ordinary nodes 5 7 8 11 Token to Destination node 6 2 Delete message 10 Result 2 clusters with MaxCoreSize = 3 Kudireti Abdurusul (CReSTIC) RWCMA 15 / 23

A random walk based clustering with local re-computations for mobile - PowerPoint PPT Presentation

Introduction Algorithm Example execution of the algorithm Properties Simulation results Conclusion and perspectives A random walk based clustering with local re-computations for mobile ad hoc networks Kudireti ABDURUSUL Alain BUI

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