A Data Collection Method Based on the Region Division in Opportunistic Networks
关键词:
Data collection, region division, sampling frequency, time slice cycle摘要
The popularity of wearable devices and smart phones provide a great convenience for large-scale data collection. Owing to the non-uniform distribution of mobile sensors, the data quantity collected from different regions has a wide variation. So we design the region division algorithm that divides area into different density grades and sets appropriate sampling frequency on different regions. Furthermore, we propose Circle of Time Slice (CoTS) and Cardinal Number Timing Method (CNTM) to solve the sampling error when nodes move from one area to another. On this basis, we propose the Data Collection Algorithm Based on the Sampling Frequency (DC-BSF) to reduce the data redundancy. Simulations demonstrate that the method proposed in this paper can reduce data redundancy under the condition of achieving high coverage.
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参考
R. K. Ganti, F. Ye, and H. Lei, “Mobile crowd sensing: Current state and future challenges,” IEEE Communications Magazine, vol. 49, no. 2, pp. 32- 39, 2011.
B. Guo, Z. Yu, X. Zhou, et al., “From participatory sensing to mobile crowd sensing,” IEEE International Conference, pp. 593-598, 2014.
P. Luciana, P. Andrea, and C. Marco, “Opportunistic networking: Data forwarding in disconnected mobile ad hoc networks,” IEEE Communications Magazine, vol. 44, no. 11, pp. 134-141, 2006.
D. Zhang, L. Wang, H. Xiong, et al., “4W1H in mobile crowd sensing,” IEEE Communications Magazine, vol. 52, no. 8, pp. 42-48, 2014.
W. Wu, B. Guo, and Z. Yu, “Crowd sensing based urban noise map and temporal-spatial future analysis,” Journal of Computer-Aided Design & Computer Graphics, vol. 26, no. 4, pp. 638-643, 2014.
D. Zhao, H. Ma, S. Tang, et al., “COUPON: A cooperative framework for building sensing maps in mobile opportunistic networks,” IEEE Transactions on, vol. 26, no. 2, pp. 392-402, 2014.
B. Hull, V. Bychkovsky, Y. Zhang, et al., “CarTel: A distributed mobile sensor computing system,” ACM, pp. 125-138, 2006.
H. Huang, Q. Ding, and L. Li, “Research on mobile terminal crowdsourcing,” Computer and Development, vol. 24, no. 6, pp. 6-9, 2014.
X. Chen, N. E. Santos, and M. Ripeanu, “Crowdsourcing for on street smart parking,” New York, NY, USA: ACM, pp. 1-8, 2012.
V. Kotovirta, T. Toivanen, R. Tergujeff, et al., “Participatory sensing in environmental monitoring experiences,” Proc of 2012 Sixth International Conferences on IMIS, Palermo: [s. n.], pp. 155- 162, 2012.
K. Ali, D. Al Yaseen, A. Ejaz, et al., “CrowdITS: Crowdsourcing in intelligent transportation systems,” IEEE Conferences on WCNC, pp. 3307- 3311, 2012.
G. Chatzimilioudis, A. Konstantinidis, C. Laoudias, et al., “Crowd sourcing with smart phones,” IEEE Internet Computing, vol. 16, no. 5, pp. 36-44, 2012.
L. Lv, “Research on data acquisition and reconstruction algorithm of Internet of things sensor based on compressed sensing theory,” Nankai University, pp. 1-95, 2011.
L. Yao, Z. Zhao, N. An, and W. Wen, “Data acquisition and processing of wireless sensor networks in gateway,” Chinese Journal of Scientific Instrument, pp. 1577-1578, 2008.
Q. Ma, Y. Gu, T. Zhang, and G. Yu, “A heterogeneous multi-source multi-mode sensory data acquisition method based on data quality,” Chinese Journal of Computers, vol. 36, no. 10, pp. 2120-2131, 2013.
D. Zhao, “Research on data collection and incentive mechanisms in mobile crowd sensing network,” Beijing University of Posts and Telecommunications, 2014.
H. Xia, J. Chen, M. Marathe, et al., “Synthesis and refinement of detailed subnetworks in a social contact network for epidemic simulations,” 4th International Conference on Social Computing, Behavioral-cultural Modeling and Prediction, pp. 366-373, 2011.
J. Liu, Introduction to Social Network Analysis [M]. Social Science Academic Press, 2004.
J. Sun, Opportunistic Network Routing Algorithm. Post & Telecom Press, 2013.