Abstract
Crowdsensing is a human-centred perception model. Through the cooperation of multiple nodes, an entire sensing task is completed. To improve the efficiency of accomplishing sensing missions, a proper and cost-effective set of service nodes is needed to perform tasks. In this paper, we propose a low-cost service node selection method based on region features, which builds on relationships between task requirements and geographical locations. The method uses DBSCAN to cluster service nodes and calculate the centre point of each cluster. The region then is divided into regions according to rules of Voronoi diagram. Local feature vectors are constructed according to the historical records in each divided region. When a particular perception task arrives, Analytic Hierarchy Process (AHP) is used to match the feature vector of each region to mission requirements to get a certain number of service nodes satisfying the characteristics. To get a lower cost output, a revised Greedy Algorithm is designed to filter the exported service nodes to get the required low-cost service nodes. Experimental results suggest that the proposed method shows promise in improving service node selection accuracy and the timeliness of finishing tasks.
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References
An, J., et al.: A crowdsourcing assignment model based on mobile crowd sensing in the internet of things. IEEE Internet Things 2(5), 358–369 (2015)
Guo, B., Yu, Z.W., Zhang, D.Q., et al.: From participatory sensing to mobile crowd sensing. In: Proceeding of the 12th IEEE International Conference on PerCom 2014, Budapest, Hungary, March 2014, pp. 593–598 (2014)
Qin, Z., Zhu, Y.: NoiseSense: a crowd sensing system for urban noise mapping service. In: Proceedings of ICPADS (2017)
Fan, X., Liu, J., Wang, Z., et al.: Navigating the last mile with crowdsourced driving information. In: Proceedings of INFOCOM, pp. 346–351 (2016)
Fiore, M., Nordio, A., Chiasserini, C.F.: Driving factors toward accurate mobile opportunistic sensing in Urban environments. IEEE Trans. Mob. Comput. 15(10), 2480–2493 (2016)
Yu, R.Y., Xia, X.Y., et al.: A location prediction algorithm with daily routines in location-based participatory sensing systems. Int. J. Distrub. Sens. N. 11(10), 1–12 (2015)
Pan, H., Crowcroft, J., Yoneki, E.: BUBBLE Rap: social-based forwarding in delay tolerant networks. IEEE Trans. Mob. Comput. 10(11), 1576–1589 (2011)
Ma, H., et al.: A multi attribute decision routing for load-balancing in crowd sensing network. Wireless Netw. 10, 1–16 (2017)
Guidi, F., Anna, G., Davide, D.: Personal mobile radars with millimeterwave massive arrays for indoor mapping. IEEE Trans. Mob. Comput. 15(6), 1471–1484 (2016)
Jin, J.W., Rothrock, L., McDermott, P.L., et al.: Using the analytic hierarchy process to examine judgment consistency in a complex multiattribute task. IEEE Trans. Syst. Man Cybern. C Appl. Rev. 40(5), 1105–1115 (2010)
Kao, B., Lee, S.D., Lee, F.K.F., et al.: Clustering uncertain data using voronoi diagrams and R-tree index. IEEE Trans. Knowl. Data Eng. 22(9), 1219–1233 (2010)
Xu, X., et al.: DBSCAN revisited, revisited: why and how you should (Still) Use DBSCAN. ACM Transactions on Database Systems 42(3), 19 (2017)
Wu, C.H., Lee, K.C., Chung, Y.C.: A Delaunay triangulation based method for wireless sensor network deployment. Comput. Commun. 30(14), 2744–2752 (2007)
Acknowledgments
This work was supported partly by the NSFC Grant No 61502380, partly by Science and Technology Program of Shenzhen (JCYJ20170816100939373).
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Peng, Z., An, J., Gui, X., Liao, D., Gui, R. (2019). A Low-Cost Service Node Selection Method in Crowdsensing Based on Region-Characteristics. In: Li, S. (eds) Green, Pervasive, and Cloud Computing. GPC 2018. Lecture Notes in Computer Science(), vol 11204. Springer, Cham. https://doi.org/10.1007/978-3-030-15093-8_25
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DOI: https://doi.org/10.1007/978-3-030-15093-8_25
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