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.
References
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
Dean, J., Ghemawat, S.: MapReduce: simplified data programming on large clusters. Commun. ACM 51(1), 107–113 (2008)
Dean, J., Ghemawat, S.: MapReduce: a flexible data processing tool. Commun. ACM 53(1), 72–77 (2010)
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
Eldawy, A.: SpatialHadoop: towards flexible and scalable spatial processing using MapReduce. In: Proceedings of the ACM SIGMOD PhD Symposium, pp. 46–50, June 2014
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
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
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
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
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
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
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)
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
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
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
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)
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
MongoDB Inc.: MongoDB. https://www.mongodb.com/
Object Management Group: The OMG data-distribution service for real-time systems (DDS). http://portals.omg.org/dds/
PicoCELA: PCWL-0100 catalog. http://www.picocela.com/
The Apache Software Foundation: Apache Hadoop. http://hadoop.apache.org/
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
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.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 Springer International Publishing AG
About this paper
Cite this paper
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
Download citation
DOI: https://doi.org/10.1007/978-3-319-68282-2_3
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-68281-5
Online ISBN: 978-3-319-68282-2
eBook Packages: Computer ScienceComputer Science (R0)