CACONET: Ant Colony Optimization (ACO) Based Clustering Algorithm for VANET

A vehicular ad hoc network (VANET) is a wirelessly connected network of vehicular nodes. A number of techniques, such as message ferrying, data aggregation, and vehicular node clustering aim to improve communication efficiency in VANETs. Cluster heads (CHs), selected in the process of clustering, manage inter-cluster and intra-cluster communication. The lifetime of clusters and number of CHs determines the efficiency of network. In this paper a Clustering algorithm based on Ant Colony Optimization (ACO) for VANETs (CACONET) is proposed. CACONET forms optimized clusters for robust communication. CACONET is compared empirically with state-of-the-art baseline techniques like Multi-Objective Particle Swarm Optimization (MOPSO) and Comprehensive Learning Particle Swarm Optimization (CLPSO). Experiments varying the grid size of the network, the transmission range of nodes, and number of nodes in the network were performed to evaluate the comparative effectiveness of these algorithms. For optimized clustering, the parameters considered are the transmission range, direction and speed of the nodes. The results indicate that CACONET significantly outperforms MOPSO and CLPSO.

[1]  Makoto Takizawa,et al.  A Survey on Clustering Algorithms for Wireless Sensor Networks , 2010, NBiS.

[2]  Salman Mohagheghi,et al.  Particle Swarm Optimization: Basic Concepts, Variants and Applications in Power Systems , 2008, IEEE Transactions on Evolutionary Computation.

[3]  Farrukh Aslam Khan,et al.  Clustering in Mobile Ad Hoc Networks Using Comprehensive Learning Particle Swarm Optimization (CLPSO) , 2009, FGIT-FGCN.

[4]  Jürgen Teich,et al.  Covering Pareto-optimal fronts by subswarms in multi-objective particle swarm optimization , 2004, Proceedings of the 2004 Congress on Evolutionary Computation (IEEE Cat. No.04TH8753).

[5]  Yu-Chee Tseng,et al.  The Broadcast Storm Problem in a Mobile Ad Hoc Network , 2002, Wirel. Networks.

[6]  Fredrik Rusek,et al.  Iterative receivers with channel estimation for multi-user MIMO-OFDM: complexity and performance , 2012, EURASIP Journal on Wireless Communications and Networking.

[7]  A. R. Jafarian-Moghaddam,et al.  Two New Clustering Algorithms for Vehicular Ad-Hoc Network Based on Ant Colony System , 2015, Wirel. Pers. Commun..

[8]  Winston Khoon Guan Seah,et al.  Mobility-based d-hop clustering algorithm for mobile ad hoc networks , 2004, 2004 IEEE Wireless Communications and Networking Conference (IEEE Cat. No.04TH8733).

[9]  Jing J. Liang,et al.  Comprehensive learning particle swarm optimizer for global optimization of multimodal functions , 2006, IEEE Transactions on Evolutionary Computation.

[10]  Makoto Takizawa,et al.  A Survey on Clustering Algorithms for Wireless Sensor Networks , 2010, 2010 13th International Conference on Network-Based Information Systems.

[11]  Pierre Hansen,et al.  NP-hardness of Euclidean sum-of-squares clustering , 2008, Machine Learning.

[12]  Dervis Karaboga,et al.  Dynamic clustering with improved binary artificial bee colony algorithm , 2015, Appl. Soft Comput..

[13]  Gary G. Yen,et al.  Dynamic Population Size in PSO-based Multiobjective Optimization , 2006, 2006 IEEE International Conference on Evolutionary Computation.

[14]  James Kennedy,et al.  The Behavior of Particles , 1998, Evolutionary Programming.

[15]  Sajal K. Das,et al.  WCA: A Weighted Clustering Algorithm for Mobile Ad Hoc Networks , 2002, Cluster Computing.

[16]  Marc Gravel,et al.  Scheduling continuous casting of aluminum using a multiple objective ant colony optimization metaheuristic , 2002, Eur. J. Oper. Res..

[17]  Abdelfettah Belghith,et al.  Cluster Connectivity Assurance Metrics in Vehicular ad hoc Networks , 2015, ANT/SEIT.

[18]  James Kennedy,et al.  The particle swarm: social adaptation of knowledge , 1997, Proceedings of 1997 IEEE International Conference on Evolutionary Computation (ICEC '97).

[19]  Anis Laouiti,et al.  A multi-objective genetic algorithm-based adaptive weighted clustering protocol in VANET , 2015, 2015 IEEE Congress on Evolutionary Computation (CEC).

[20]  A. Ebenezer Jeyakumar,et al.  Optimization and Quality-of-Service Protocols in VANETs: A Review , 2015 .

[21]  Kalyanmoy Deb,et al.  A fast and elitist multiobjective genetic algorithm: NSGA-II , 2002, IEEE Trans. Evol. Comput..

[22]  Ramez Elmasri,et al.  Optimizing clustering algorithm in mobile ad hoc networks using genetic algorithmic approach , 2002, Global Telecommunications Conference, 2002. GLOBECOM '02. IEEE.

[23]  Jonathan E. Fieldsend,et al.  A MOPSO Algorithm Based Exclusively on Pareto Dominance Concepts , 2005, EMO.

[24]  Zaydoun Y. Rawashdeh,et al.  A novel algorithm to form stable clusters in vehicular ad hoc networks on highways , 2012, EURASIP J. Wirel. Commun. Netw..

[25]  Prithwish Basu,et al.  A mobility based metric for clustering in mobile ad hoc networks , 2001, Proceedings 21st International Conference on Distributed Computing Systems Workshops.

[26]  Mario Gerla,et al.  Multicluster, mobile, multimedia radio network , 1995, Wirel. Networks.

[27]  Adam Lipowski,et al.  Roulette-wheel selection via stochastic acceptance , 2011, ArXiv.

[28]  Yu-Chee Tseng,et al.  The Broadcast Storm Problem in a Mobile Ad Hoc Network , 1999, Wirel. Networks.

[29]  Jong Hyuk Park,et al.  ALCA: agent learning–based clustering algorithm in vehicular ad hoc networks , 2012, Personal and Ubiquitous Computing.

[30]  Farrukh Aslam Khan,et al.  Energy-efficient clustering in mobile ad-hoc networks using multi-objective particle swarm optimization , 2012, Appl. Soft Comput..

[31]  Anthony Ephremides,et al.  The Architectural Organization of a Mobile Radio Network via a Distributed Algorithm , 1981, IEEE Trans. Commun..

[32]  Indrajit Ray,et al.  Optimal security hardening using multi-objective optimization on attack tree models of networks , 2007, CCS '07.

[33]  Manuel López-Ibáñez,et al.  Ant colony optimization , 2010, GECCO '10.

[34]  Luca Maria Gambardella,et al.  MACS-VRPTW: a multiple ant colony system for vehicle routing problems with time windows , 1999 .

[35]  Salabat Khan,et al.  A novel ant colony optimization based single path hierarchical classification algorithm for predicting gene ontology , 2014, Appl. Soft Comput..

[36]  T. Nandagopal,et al.  The Broadcast Storm Problem in a Mobile Ad Hoc Network , 1999, MobiCom 1999.