Open AccessA Node Selection Paradigm for Crowdsourcing Service Based on Region Feature in Crowd Sensing
Author(s) -
Zhenlong Peng,
Xiaolin Gui,
Jian An,
Liao Dong,
Ningchao Cai,
Ruowei Gui
Publication year - 2018
Publication title -
mathematical problems in engineering
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.262
H-Index - 62
eISSN - 1026-7077
pISSN - 1024-123X
DOI - 10.1155/2018/6434083
Subject(s) - computer science , crowdsourcing , data mining , dbscan , service (business) , cluster analysis , node (physics) , task (project management) , voronoi diagram , overhead (engineering) , feature (linguistics) , set (abstract data type) , feature vector , artificial intelligence , engineering , canopy clustering algorithm , fuzzy clustering , linguistics , philosophy , geometry , economy , structural engineering , systems engineering , mathematics , world wide web , economics , programming language , operating system
Crowd sensing is a human-centered sensing model. Through the cooperation of multiple nodes, an entire sensing task is completed. To improve the efficiency of sensing missions, a cost-effective set of service nodes, which is easy to fit in performing different tasks, is needed. In this paper, we propose a low-cost service node selection method based on region features, which builds on the relationship between task requirements and geographical locations. The method uses Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm to cluster service nodes and calculate the center point of each cluster. The area then is divided into regions according to rules of Voronoi diagrams. Local feature vectors are constructed according to the historical records in each divided region. When a particular sensing 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.