Abstract
Mobile ad hoc networks (or MANETs) are wireless networks that are spontaneously created by the neighbouring devices, without the use of any kind of infrastructure. These devices usually rely on batteries, so the lifetime of this type of network highly depends on the energy consumption of the devices composing it. Therefore, the optimisation of the energy consumption is a must in a realistic MANET. We deal in this paper with the optimisation of enhanced distance based (or EDB) broadcasting protocol for MANETs. EDB is an improved version of DB (distance based protocol), a state-of-the-art broadcasting protocol for MANETs, that is focussed on saving the energy used by the devices in the dissemination process (an essential component in MANETs), without degrading the network connectivity or the performance in the coverage of the broadcasting process. A set of parameters were identified in EDB and optimised using CellDE, a hybrid multi-objective optimisation algorithm, to maximise the coverage of the broadcast while minimising at the same time both the energy consumption and the broadcast time. The ns3 simulator was used to evaluate the different configurations of EDB. As a result, 100 different solutions are provided for every studied network, covering a wide range of design options; all of them have quality better than or similar to that provided by EDB with previously recommended values for its parameters.
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References
Alba E Dorronsoro B, (2008) Cellular genetic algorithms. Operations research/computer science interfaces. Springer, Heidelberg
Ni S-Y, Tseng Y-C, Chen Y-S, Sheu J-P (1999) The broadcast storm problem in a mobile ad hoc network. In: 5th annual ACM/IEEE international conference on Mobile computing and networking, pp 151–162
Li X, Nguyen TD, Martin RP (2003) Using adaptive range control to optimize 1-hop broadcast coverage in dense wireless networks. In: SenSys, pp 314–315
Gomez J, Campbell AT (2007) Variable-range transmission power control in wireless ad hoc networks. IEEE Trans Mob Comput 6:87–99
Cagalj M, Hubaux J-P, Enz C (2002) Minimum-energy broadcast in all-wireless networks: Np-completeness and distribution issues. In: MobiCom’02: proceedings of the 8th annual international conference on Mobile computing and networking. ACM, New York, pp 172–182
Cagalj M, Hubaux J-P, Enz CC (2005) Energy-efficient broadcasting in all-wireless networks. Wirel Netw 11:177–188
Liang W, Brent R, Xu Y, Wang Q (2009) Minimum-energy all-to-all multicasting in wireless ad hoc networks. Trans Wirel Commun 8:5490–5499
Chen X, Faloutsos M, Krishnamurthy SV (2003) Power adaptive broadcasting with local information in ad hoc networks. In: Conference on network protocols. IEEE Computer Society, Los Alamitos, p 168
Ruiz P, Bouvry P (2010) Enhanced distance based broadcasting protocol with reduced energy consumption. In: Workshop on optimization issues in energy efficient distributed systems (OPTIM), part of the 2010 international conference on high performance computing and simulation (HPCS), pp 249–258
The ns3 project website: http://www.nsnam.org/
Lacage M, Henderson TR (2006) Yet another network simulator. In: WNS2’06: proceeding from the 2006 workshop on ns-2: the IP network simulator, p. 12
Durillo J, Nebro A, Luna F, Alba E (2008) Solving three-objective optimization problems using a new hybrid cellular genetic algorithm. In: Obayashi S, Deb K, Poloni C, Hiroyasu T, Murata T (eds) Parallel problem solving from nature (PPSN X). Lecture notes in computer science, vol 5199. Springer, Berlin, pp 661–670
Feoktistov V (2006) Differential evolution—in search of solutions. Springer optimization and its applications, vol 5. Springer, Berlin
Chakraborty U (2008) Advances in differential evolution. Studies in computational intelligence, vol 142. Springer, Berlin
Price KV, Storn RM, Lampinen JA (2005) Differential evolution—a practical approach to global optimization. NCS. Springer, Berlin
Alba E, Tomassini M (2002) Parallelism and evolutionary algorithms. IEEE Trans Evol Comput 6:443–462
Kennedy J, Mendes R (2002) Population structure and particle swarm performance. In: IEEE International Conference on Evolutionary Computation (CEC), pp 1671–1676
Folino G, Pizzuti C, Spezzano G (2003) A scalable cellular implementation of parallel genetic programming. IEEE Trans Evol Comput 7:37–53
Alba E, Madera J, Dorronsoro B, Ochoa A, Soto M (2006) Theory and practice of cellular UMDA for discrete optimization. In: Parallel problem solving from nature (PPSN-IX), Reykjavik, Iceland, September. Lecture notes in computer science, vol 4193. Springer, Berlin, pp 242–251
Alba E (2005) Parallel metaheuristics: a new class of algorithms. Wiley, New York
Talbi E-G (2006) Parallel combinatorial optimization. Wiley, New York
Dorronsoro B, Bouvry P (2010) Improving classical and decentralized differential evolution with new mutation operator and population topologies. IEEE Trans Evol Comput. doi:10.1109/TEVC.2010.2081369
Wu S-L, Tseng Y-C, Lin C-Y, Sheu J-P (2002) A multi-channel mac protocol with power control for multi-hop mobile ad hoc networks. Comput J 45:101–110
Reumerman H, Runi M (2005) Distributed power control for reliable broadcast in inter-vehicle communication systems. In: 2nd international workshop on intelligent Transportation.
Abdullah J, Parish DJ (2007) Node connectivity index as mobility metric for GA based QoS routing in MANET. In: Mobility 2007: proceedings of the 4th international conference on mobile technology, applications and systems and the 1st international symposium on computer human interaction in mobile technology. ACM, New York, pp 104–111
Di Caro GA, Ducatelle F, Gambardella LM (2005) AntHocNet: an adaptive nature-inspired algorithm for routing in mobile ad hoc networks. Eur Trans Telecommun 16:443–455
Chiang T, Liu C, Huang Y (2007) A near-optimal multicast scheme for mobile ad hoc networks using a hybrid genetic algorithm. Expert Syst Appl 33:734–742
Huang C, Chuang Y, Hu K (2009) Using particle swarm optimization for QoS in ad-hoc multicast. Eng Appl Artif Intell 22:1188–1193
Sapienza TJ (2008) Optimizing quality of service of wireless mobile ad-hoc networks using evolutionary computation. In: Proceedings of the 4th annual workshop on Cyber security and information intelligence research (CSIIRW). ACM, New York, pp 1–5
Yen Y-S, Chan Y-K, Chao H-C, Park JH (2008) A genetic algorithm for energy-efficient based multicast routing on MANETs. Comput Commun 31:2632–2641
Alba E, Bouvry P, Dorronsoro B, Luna F, Nebro A (2005) A cellular multi-objective genetic algorithm for optimal broadcasting strategy in metropolitan MANETs. In: Nature inspired distributed computing (NIDISC) sessions of the international parallel and distributed processing symposium (IPDPS) workshop, Denver, Colorado, USA, p 192b
Alba E, Dorronsoro B, Luna F, Nebro A, Bouvry P, Hogie L (2007) A cellular multi-objective genetic algorithm for optimal broadcasting strategy in metropolitan MANETs. Comput Commun 30:685–697
Hogie L, Guinand F, Bouvry P (2004) A heuristic for efficient broadcasting in the metropolitan ad hoc network. In: 8th international conference on knowledge-based intelligent information and engineering systems, pp 727–733
Durillo JJ, Nebro AJ, Luna F, Alba E (2008) A study of master-slave approaches to parallelize NSGA-II. In: Nature inspired distributed computing (NIDISC) workshop of the international parallel and distributed processing symposium (IPDPS), p 11
León C, Miranda G, Segura C (2008) Optimizing the configuration of a broadcast protocol through parallel cooperation of multi-objective evolutionary algorithms. In: Proceedings of the 2nd international conference on advanced engineering computing and applications in sciences (ADVCOMP), pp 135–140
García S, Luque C, Cervantes A, Galván I (2009) Multiobjective algorithms hybridization to optimize broadcasting parameters in mobile ad-hoc networks. In: Proceedings of the 10th international work-conference on artificial neural networks part I: bio-inspired systems: computational and ambient intelligence. Lecture notes in computer science, vol 5517. Springer, Heidelberg, pp 728–735
León C, Miranda G, Segura C (2009) Optimizing the broadcast in MANETs using a team of evolutionary algorithms. In: 6th international conference on large-scale scientific computing (LSSC07). Lecture notes in computer science, vol 4818. Springer, Heidelberg, pp 569–576
García S, Luque C, Cervantes A, Galván I (2009) Multiobjective algorithms hybridization to optimize broadcasting parameters in mobile ad-hoc networks. In: Proceedings of the 10th international work-conference on artificial neural networks: part I: bio-inspired systems: computational and ambient intelligence, pp 728–735
García-Nieto J, Alba E (2010) Automatic parameter tuning with metaheuristics of the AODV routing protocol for vehicular ad-hoc networks. In: EvoApplications. Lecture notes in computer science (LNC), vol 6025. Springer, Heidelberg, pp 21–30
García-Nieto J, Toutouh J, Alba E (2010) Automatic tuning of communication protocols for vehicular ad hoc networks using metaheuristics. Eng Appl Artif Intell 32:795–805
Basagni S, Conti M, Giordano S, Stojmenovic I (2004) Mobile ad hoc networking. Wiley-IEEE Press, New York
Coello Coello CA, Lamont GB, Veldhuizen DA (2007) Evolutionary algorithms for solving multi-objective problems, 2nd edn. Springer, Berlin
Deb K (2001) Multi-objective optimization using evolutionary algorithms. Wiley, New York
Danoy G, Dorronsoro B, Bouvry P (2009) Overcoming partitioning in large ad hoc networks using genetic algorithms. In: Proc. of the genetic and evolutionary computation conference (GECCO). ACM, New York, pp 1347–1354
Kennedy J, Mendes R (2006) Neighborhood topologies in fully informed and best-of-neighborhood particle swarms. IEEE Trans Syst Man Cybern, Part C, Appl Rev 36:515–519
Nebro AJ, Durillo JJ, Luna F, Dorronsoro B, Alba E (2009) MOCell: A cellular genetic algorithm for multiobjective optimization. Int J Intell Syst 24:726–746 Special issue: Nature inspired cooperative strategies for optimization
Zitzler E, Laumanns M, Thiele L (2001) SPEA2: Improving the strength Pareto evolutionary algorithm. Tech rep 103, Computer Engineering and Networks Laboratory (TIK), ETH Zurich
Durillo JJ, Nebro AJ, Alba E (2010) The jMetal framework for multiobjective optimization: Design and architecture. In: IEEE world congress con computational intelligence (WCCI), pp 4138–4325
Groenevelt RB, Altman E, Nain P (2006) Relaying in mobile ad hoc networks: The Brownian motion mobility model. J Wirel Netw, pp 561–571
Veldhuzen DAV (1999) Multiobjective evolutionary algorithms: classifications, analyses, and new innovations. PhD thesis, Department of Electrical and Computer Engineering, Graduate School of Engineering, Air Force Institute of Technology, Wright-Patterson AFB, Ohio
Deb K, Pratap A, Agarwal S, Meyarivan T (2002) A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Trans Evol Comput 6:182–197
Nebro AJ, Luna F, Alba E, Dorronsoro B, Durillo JJ, Beham A (2008) AbYSS: Adapting scatter search to multiobjective optimization. IEEE Trans Evol Comput 12:439–457
Zitzler E, Thiele L (1999) Multiobjective evolutionary algorithms: a comparative case study and the strength Pareto approach. IEEE Trans Evol Comput 3:257–271
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Ruiz, P., Dorronsoro, B., Valentini, G. et al. Optimisation of the enhanced distance based broadcasting protocol for MANETs. J Supercomput 62, 1213–1240 (2012). https://doi.org/10.1007/s11227-011-0564-x
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DOI: https://doi.org/10.1007/s11227-011-0564-x