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Design of Distributed Calculation Scheme Using Network Address Translation for Ad-hoc Wireless Positioning Network

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Book cover Information Search, Integration, and Personlization (ISIP 2016)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 760))

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Abstract

We have developed an ad-hoc wireless positioning network (AWPN) to realize on-demand indoor location-based services [10]. This paper extends our AWPN to handle huge number of localization requests. In AWPN, WiFi APs measure received signal strength (RSS) of WiFi signals and send the RSS information to a localization server via a WiFi mesh network. The maximum number of WiFi devices is therefore limited by computational resources on the localization server. We push this limit by introducing a new distributed calculation scheme: we use the MapReduce computation framework and perform map processes on APs and reduce processes on localization servers. We also utilize a network router capable of network address translation (NAT) for shuffle processes to provide scalability. We implemented and evaluated our distributed calculation scheme to demonstrate that our scheme almost evenly distributes localization calculations to multiple localization servers with approximately 26% variations.

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References

  1. Chen, R., Chen, H.: Tiled-MapReduce: efficient and flexible MapReduce processing on multicore with tiling. ACM Trans. Archit. Code Optim. (TACO) 10(1), 3:1–3:30 (2013). Article no. 3

    Google Scholar 

  2. Dean, J., Ghemawat, S.: MapReduce: simplified data programming on large clusters. Commun. ACM 51(1), 107–113 (2008)

    Article  Google Scholar 

  3. Dean, J., Ghemawat, S.: MapReduce: a flexible data processing tool. Commun. ACM 53(1), 72–77 (2010)

    Article  Google Scholar 

  4. Ekanayake, J., Li, H., Zhang, B., Gunarathne, T., Bae, S.H.: Twister: a runtime for iterative MapReduce. In: Proceedings of the ACM International Symposium on High Performance Distributed Computing (HPDC), pp. 810–818, June 2010

    Google Scholar 

  5. Eldawy, A.: SpatialHadoop: towards flexible and scalable spatial processing using MapReduce. In: Proceedings of the ACM SIGMOD PhD Symposium, pp. 46–50, June 2014

    Google Scholar 

  6. Elsayed, T., Lin, J., Oard, D.W.: Pairwise document similarity in large collections with MapReduce. In: Proceedings of the ACL, Human Language Technologies: Short Papers (HLT-Short), pp. 265–268, June 2008

    Google Scholar 

  7. Fadika, Z., Govindaraju, M.: DELMA: dynamically ELastic MApReduce framework for CPU-intensive applications. In: Proceedings of IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing (CCGrid), pp. 454–463, May 2011

    Google Scholar 

  8. Ghoting, A., Kambadur, P., Pednault, E., Kannan, R.: NIMBLE: a toolkit for the implementation of parallel data mining and machine learning algorithms on MapReduce. In: Proceedings of the ACM KDD, pp. 334–342, August 2011

    Google Scholar 

  9. Ghoting, A., Krishnamurthy, R., Pednault, E., Reinwald, B., Sindhwani, V., Tatikonda, S., Tian, Y., Vaithyanathan, S.: SystemML: declarative machine learning on MapReduce. In: Proceedings of IEEE International Conference on Data Engineering (ICDE), pp. 231–242, April 2011

    Google Scholar 

  10. Ishida, S., Tagashira, S., Arakawa, Y., Fukuda, A.: On-demand indoor location-based service using ad-hoc wireless positioning network. In: Proceedings of the IEEE International Conference on Embedded Software and Systems (ICESS), pp. 1005–1013, August 2015

    Google Scholar 

  11. Jammes, F., Mensch, A., Smit, H.: Service-oriented device communications using the devices profile for web services. In: Proceedings of the ACM International Workshop on Middleware for Pervasive and Ad-Hoc Computing (MPAC), November–December 2005

    Google Scholar 

  12. Jiang, D., Wu, S., Chen, G., Ooi, B.C., Tan, K.L., Ku, J.: epiC: an extensible and scalable system for processing big data. VLDB J. 25(1), 3–26 (2016)

    Article  Google Scholar 

  13. Jin, C., Vecchiola, C., Buyya, R.: MRPGA: an extention of MapReduce for parallelizing genetic algorithms. In: Proceedings of the IEEE International Conference on eScience, pp. 214–221, December 2008

    Google Scholar 

  14. Kreps, J., Narkhede, N., Rao, J.: Kafka: a distributed messaging system for log processing. In: Proceedings of the International Workshop on Networking Meets Databases (NetDB), pp. 1–7, June 2011

    Google Scholar 

  15. Low, Y., Bickson, D., Gonzalez, J., Guestrin, C., Kyrola, A., Hellerstein, J.M.: Distributed GraphLab: a framework for machine learning and data mining in the cloud. In: Proceedings of the International Conference on Very Large Scale Data Bases (VLDB), pp. 716–727, August 2012

    Article  Google Scholar 

  16. McKenna, A., Hanna, M., Banks, E., Sivachenko, A., Cibulskis, K., Kernytsky, A., Garimella, K., Altshuler, D., Gabriel, S., Daly, M., DePristo, M.A.: The genome analysis toolkit: a MapReduce framework for analyzing next-generation DNA sequencing data. Genome Res. 20(9), 1297–1303 (2010)

    Article  Google Scholar 

  17. Miwa, N., Tagashira, S., Matsuda, H., Tsutsui, T., Arakawa, Y., Fukuda, A.: A multilateration-based localization scheme for adhoc wireless positioning networks used in information-oriented construction. In: Proceedings of the IEEE International Conference on Advanced Information Networking and Applications (AINA), pp. 690–695, March 2013

    Google Scholar 

  18. MongoDB Inc.: MongoDB. https://www.mongodb.com/

  19. Object Management Group: The OMG data-distribution service for real-time systems (DDS). http://portals.omg.org/dds/

  20. PicoCELA: PCWL-0100 catalog. http://www.picocela.com/

  21. The Apache Software Foundation: Apache Hadoop. http://hadoop.apache.org/

  22. Zhao, W., Ma, H., He, Q.: Parallel K-means clustering based on MapReduce. In: Jaatun, M.G., Zhao, G., Rong, C. (eds.) CloudCom 2009. LNCS, vol. 5931, pp. 674–679. Springer, Heidelberg (2009). doi:10.1007/978-3-642-10665-1_71

    Chapter  Google Scholar 

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Acknowledgments

This work was supported in part by JSPS KAKENHI Grant Numbers 15H05708, 15K12021, 16K16048, and 17H01741, and the Cooperative Research Project of the Research Institute of Electrical Communication, Tohoku University.

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Correspondence to Shigemi Ishida .

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Kajimura, J., Ishida, S., Tagashira, S., Fukuda, A. (2017). Design of Distributed Calculation Scheme Using Network Address Translation for Ad-hoc Wireless Positioning Network. In: Kotzinos, D., Laurent, D., Petit, JM., Spyratos, N., Tanaka, Y. (eds) Information Search, Integration, and Personlization. ISIP 2016. Communications in Computer and Information Science, vol 760. Springer, Cham. https://doi.org/10.1007/978-3-319-68282-2_3

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  • DOI: https://doi.org/10.1007/978-3-319-68282-2_3

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