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Energy-efficient cache node placement using genetic algorithm in wireless sensor networks

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Abstract

Wireless sensor network (WSN) applications are required to report events and service queries with minimum delay and minimal energy consumption. The network lifetime of a WSN can be extended if the amount of communication in the network is reduced. We can achieve this by caching useful data closer to the requesting node. Caching successfully reduces data access latency and also the number of packet transmissions in the network, thereby increasing network lifetime. However, the important aspect of caching schemes is to identify nodes that can implement caching decisions and also place such cache nodes in a way that they can provide services to as many sensor nodes as possible in their vicinity. This has led to the study of optimal deployment of these cache nodes in a WSN. We carried out experiments to demonstrate the use of a multi-objective genetic algorithm (GA) for cache node placement in a WSN. In this paper, GA optimization aims to increase two parameters: sensors per cache in charge and field coverage. We also show that the GA successfully helps in selecting sensor nodes to implement caching and request forwarding decisions. Finally, we run the Scaled Power Community Index Cooperative Caching scheme (scaPCICC) on the optimized network and compare the delay and total number of overhead messages in the network. We conclude that by reducing the number of messages in the network and reducing the data access latency, the energy consumption of the network is reduced and network lifetime is increased. The experiments were run on MATLAB and ns2.

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References

  • Bouallagui S, Thi HAL, Dinh TP (2008) Design of highly nonlinear balanced Boolean functions using an hybridation of DCA and simulated annealing algorithm. In: Proceedings of second international conference MCO, Metz, France, Luxembourg, pp 579–588. doi:10.1007/978-3-540-87477-5_61

  • Barrett S (2007) Optimizing sensor placement for intruder detection with genetic algorithms. In: Proceedings of IEEE international conference on intelligence and security informatics. pp 185–188

  • Bhondekar AP, Vig R, Singla ML, Ghanshyam C, Kapur P (2009) Genetic algorithm based node placement methodology for wireless sensor networks. In: Proceedings of the international multiconference of engineers and computer scientists

  • Chagas SH, Martins JB, De Oliveira LL (2012) Genetic algorithms and simulated annealing optimization methods in wireless sensor networks localization using artificial neural networks. In: Proceedings of IEEE 55th international midwest symposium on circuits and systems. pp 928–931

  • Chand N, Joshi RC, Misra M (2006) A zone co-operation approach for efficient caching in mobile ad hoc networks. Int J Commun Syst 19:1009–1028. doi:10.1002/dac.795

    Article  Google Scholar 

  • Chand N, Joshi RC, Misra M (2007) Cooperative caching strategy in mobile ad hoc networks based on clusters. Int J Wirel Pers Commun Spec Issue Coop Wirel Netw 43:41–63

    Article  Google Scholar 

  • Chow CY, Leong HV, Chan ATS (2007) GroCoca: group-based peer-to-peer cooperative caching in mobile environment. IEEE J Sel Areas Commun 25:179–191

    Article  Google Scholar 

  • Dimokas N, Katsaros D, Manolopoulos Y (2008) Cooperative caching in wireless multimedia sensor networks. ACM Mob Netw Appl 13:337–356

    Google Scholar 

  • Dimokas N, Katsaros D, Tassiulas L, Manolopoulos Y (2011) High performance, low complexity cooperative caching for wireless sensor networks. Wirel Netw 17:717–737. doi:10.1007/s11276-010-0311-x

    Article  Google Scholar 

  • Du Y, Gupta SKS (2005) COOP: a cooperative caching service in MANETs. In: Proceedings of ICAS-ICNS 2005. Joint international conference on Tahiti, French Polynesia, pp 58–63

  • Elbeltagi E, Hegazy T, Grierson D (2005) Comparison among five evolutionary-based optimization algorithms. Adv Eng Informat 19(1):43–53. doi:10.1016/j.aei.2005.01.004

    Article  Google Scholar 

  • Ghaffari A, Nematy F, Rahmani N (2010) Redeployment of cluster heads in wireless sensor networks with genetic algorithm. In: Proceedings of fifth international conference on bio-inspired computing: theories and applications

  • Goldberg DE (2012) Genetic algorithms in search. In: Proceedings of optimization and machine learning, Pearson and Dorling Kindersley, India

  • Hoyingcharoen P, Teerapabkajorndet W (2011) Fault tolerant sensor placement optimization with minimum detection probability guaranteed. In: Proceedings of eighth international workshop on the design of reliable communication networks (DRCN)

  • Karpate A, Ali HH (2011) An energy-aware genetic algorithm for managing self-organized wireless sensor networks. In: Proceedings of the IFIP wireless days conference. Niagara Falls, Canada, pp 1–6

  • Law WHO, Kumar M, Venkatesh S (2002) A cooperative cache architecture in supporting caching multimedia objects in MANETs. In: Proceedings of the 5th ACM international workshop on wireless mobile multimedia (WoWMoM), in conjunction with the 8th international conference on mobile computing and networking (MobiCom) Atlanta, Georgia, USA, pp 56–63

  • Mollanejad A, Khanli LM, Zeynali M (2010) DBSR: Dynamic base station repositioning using genetic algorithm in wireless sensor network. In: Proceedings of second international conference on computer engineering and applications. pp 521–525

  • Oh SC, Tan CH, Kong FW, Tan YS, Ng KH, Ng GW, Tai K (2007) Multiobjective optimization of sensor network deployment by a genetic algorithm. In: Proceedings of IEEE congress on evolutionary computation, pp 3917–3921. doi:10.1109/CEC.2007.4424981

  • Robatmili M, Mohammadi M, Movaghar A, Dehghan M (2008) Finding the sensors location and the number of sensors in sensor networks with a genetic algorithm. In: Proceedings of 16th IEEE international conference on networks, ICON 2008. doi:10.1109/ICON.2008.4772651

  • Romoozi M, Romoozi M, Babaei H (2009) Genetic algorithm for energy efficient and coverage-preserved positioning in wireless sensor networks. In: Proceedings of second international conference on computer science and its applications

  • Shen H, Das SK, Kumar M, Wang Z (2004) Cooperative caching with optimal radius in hybrid wireless networks. In: Proceedings of the international IFIP-TC6 networking conference (NETWORKING), Lecture notes on computer science. Athens, pp 841–853

  • Shen H, Joseph MS, Kumar M, Das SK (2005) PRe-CinCt: a scheme for cooperative caching in mobile peer-to-peer systems. In: Proceedings of the 19th IEEE international parallel and distributed processing symposium (IPDPS), pp 57

  • Sun P, Ma J, Ni K (2012) A genetic simulated annealing hybrid algorithm for relay nodes deployment optimization in industrial wireless sensor networks. In: Proceedings of IEEE international conference on computational intelligence for measurement systems and applications (CIMSA)

  • Wang Y, Li X, Tian J (2011) Blackboard mechanism based genetic algorithm for dynamic deployment of mobile sensor networks. In: Proceedings of international conference on electronic and mechanical engineering and information technology

  • Yin L, Cao G (2006) Supporting cooperative caching in ad hoc networks. IEEE Trans Mob Comput 5:77–89

    Article  Google Scholar 

  • Yong M, Huludao L, Yu Y, Yan W (2009) Optimization design of coal mine body sensor network based on genetic algorithm. In: Proceedings of international conference on network security, wireless communications and trusted computing. doi:10.1109/NSWCTC.2009.347

  • Youssef W, Younis M (2007) Intelligent gateways placement for reduced data latency in wireless sensor networks. In: Proceedings of the 32nd IEEE international conference on communications (ICC 2007), Glasgow, Scotland, UK

  • Zhao C, Yu Z, Chen P (2007) Optimal deployment of nodes based on genetic algorithm in heterogeneous sensor networks. In: Proceedings of international conference on wireless communication networking and mobile computing. pp 2743–2746

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Acknowledgments

We wish to thank Ms. H. M. Divya for her support and contribution to this work.

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The authors declare that they have no conflict of interest.

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Correspondence to Juhi R. Srivastava.

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Communicated by V. Loia.

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Srivastava, J.R., Sudarshan, T.S.B. Energy-efficient cache node placement using genetic algorithm in wireless sensor networks. Soft Comput 19, 3145–3158 (2015). https://doi.org/10.1007/s00500-014-1473-8

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  • DOI: https://doi.org/10.1007/s00500-014-1473-8

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