Citation: Proceedings of the 2018 Federated Conference on Computer Science and Information Systems, M. Ganzha, L. Maciaszek, M. Paprzycki (eds). ACSIS, Vol. 15, pages 737–746 (2018)
Abstract. Location Based Services play an important role in decision-making processes, company activities or in any control and policy system in modern computer organizations. Usually LBS applications provide location-specific information only when user requests them. However, Supply Chain Management applications require to push geolocalized information directly to the user. The most discussed and requested application is Geofencing, which allows to determine the topological relation between a moving object and a set of delimited geographical areas. This paper describes the design of an innovative solution for implementing proactive location-based services suitable for application scenarios with strong time constraints, such as realtime systems, called Proactive Fast and Low Resource Geofencing Algorithm.
- S. R. Garzon and B. Deva, “Infrastructure-assisted geofencing: Proactive location-based services with thin mobile clients and smart servers,” in 2015 3rd IEEE International Conference on Mobile Cloud Computing, Services, and Engineering, March 2015, pp. 61–70.
- S. Ray, A. D. Brown, N. Koudas, R. Blanco, and A. K. Goel, “Parallel in-memory trajectory-based spatiotemporal topological join,” in 2015 IEEE International Conference on Big Data (Big Data), Oct 2015, pp. 361–370.
- K. Lin, Y. Chen, M. Qiu, M. Zeng, and W. Huang, “Slgc: A fast point-in-area algorithm based on scan-line algorithm and grid compression,” in 2016 11th International Conference on Computer Science Education (ICCSE), Aug 2016, pp. 352–356.
- S. Tang, Y. Yu, R. Zimmermann, and S. Obana, “Efficient geo-fencing via hybrid hashing: A combination of bucket selection and in-bucket binary search,” ACM Trans. Spatial Algorithms Syst., vol. 1, no. 2, pp. 5:1–5:22, Jul. 2015. [Online]. Available: http://doi.acm.org/10.1145/2774219
- S. Steiniger, M. Neun, and A. Edwardes, “Foundations of location based services,” 01 2006.
- I. Junglas and R. Watson, “Location-based services,” vol. 51, pp. 65–69, 03 2008.
- V. Carchiolo, L. Compagno, M. Malgeri, N. Trapani, M. L. Previti, M. P. Loria, and M. Toja, “An efficient real-time monitoring to manage home-based oxygen therapy,” in Trends and Advances in Information Systems and Technologies, Á. Rocha, H. Adeli, L. P. Reis, and S. Costanzo, Eds. Cham: Springer International Publishing, 2018, pp. 741–749.
- M. Loria, M. Toja, V. Carchiolo, and M. Malgeri, “An efficient real-time architecture for collecting iot data,” 2017, pp. 1157–1166, cited By 1. [Online]. Available: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85039924461&doi=10.15439%2f2017F381&partnerID=40&md5=8de00d20f17d82a86236f43f33a716f8
- ESRI, “What is gis?” http://www.esri.com/what-is-gis, accessed: 2018.
- D. M. S. Sachin W. Rahate, “Geo-fencing infrastructure: Location based service,” International Research Journal of Engineering and Technology, vol. 3, 11 2016.
- ITU, “Y.2060 : Overview of the internet of things,” https://www.itu.int/rec/T-REC-Y.2060-201206-I, 06 2012, accessed: 2018.
- M. Rouse, “geo-fencing (geofencing),” http://whatis.techtarget.com/definition/geofencing, accessed; 2016.
- G. Allen, “Internet of things, industrial internet of things, industry 4.0its all connected! (no pun intended),” https://redshift.autodesk.com/industrial-internet-of-things-iot-terms/, accessed: 2015.
- M. Erwig and M. Schneider, “Developments in spatio-temporal query languages,” in Proceedings. Tenth International Workshop on Database and Expert Systems Applications. DEXA 99, 1999, pp. 441–449.
- T. SearchNetworking, “Thin client (lean client) definition,” accessed: 2016.
- M. Bauer, D. Dobre, N. Santos, and M. Schmidt, “Scalable processing of geo-tagged data in the cloud,” Nec Technical Journal, vol. 7, no. 2, 2012.
- M. A. Quddus, W. Y. Ochieng, and R. B. Noland, “Current map-matching algorithms for transport applications: State-of-the art and future research directions,” 2007.
- A. L. B. Nagendra R. Velaga, Mohammed A. Quddus, “Developing an enhanced weight-based topological map-matchingalgorithm for intelligent transport systems,” Transportation Research Part C: Emerging Technologies, vol. 17, no. 6, pp. 672–683, 12 2009.
- D. Pfoser and C. S. Jensen, “Capturing the uncertainty of moving-object representations,” in Proceedings of the 6th International Symposium on Advances in Spatial Databases, ser. SSD ’99. London, UK, UK: Springer-Verlag, 1999, pp. 111–132. [Online]. Available: http://dl.acm.org/citation.cfm?id=647226.719082
- I. E. Sutherland, R. F. Sproull, and R. A. Schumacker, “A characterization of ten hidden-surface algorithms,” ACM Comput. Surv., vol. 6, no. 1, pp. 1–55, Mar. 1974. [Online]. Available: http://doi.acm.org/10.1145/356625.356626
- G. Allen, “Harnessing the power of location based services,” http://blogs.dcvelocity.com/supply chain innovation/2016/03/harnessing-the-power-of-location-based-services.html, accessed: 2016.
- B. Rao and L. Minakakis, “Evolution of mobile location-based services,” Commun. ACM, vol. 46, no. 12, pp. 61–65, Dec. 2003. [Online]. Available: http://doi.acm.org/10.1145/953460.953490
- I. A. T. A. (IATA), “Guidance on the expanded use of passenger portable electronic devices (peds),” 2014.
- R. Ahas and lar Mark, “Location based servicesnew challenges for planning and public administration?” Futures, vol. 37, no. 6, pp. 547–561, 2005. [Online]. Available: http://www.sciencedirect.com/science/article/pii/S0016328704001521
- DOC 9674/AN 946 - WGS84 Manual, ICAO, 2002.
- T. A. S. Foundation, “ab - apache http server benchmarking tool,” https://httpd.apache.org/docs/2.4/programs/ab.html, accessed: 2018.