@article{(Open Science Index):https://publications.waset.org/pdf/10007423,
	  title     = {Evidence Theory Enabled Quickest Change Detection Using Big Time-Series Data from Internet of Things},
	  author    = {Hossein Jafari and  Xiangfang Li and  Lijun Qian and  Alexander Aved and  Timothy Kroecker},
	  country	= {},
	  institution	= {},
	  abstract     = {Traditionally in sensor networks and recently in the
Internet of Things, numerous heterogeneous sensors are deployed
in distributed manner to monitor a phenomenon that often can be
model by an underlying stochastic process. The big time-series
data collected by the sensors must be analyzed to detect change
in the stochastic process as quickly as possible with tolerable
false alarm rate. However, sensors may have different accuracy
and sensitivity range, and they decay along time. As a result,
the big time-series data collected by the sensors will contain
uncertainties and sometimes they are conflicting. In this study, we
present a framework to take advantage of Evidence Theory (a.k.a.
Dempster-Shafer and Dezert-Smarandache Theories) capabilities of
representing and managing uncertainty and conflict to fast change
detection and effectively deal with complementary hypotheses.
Specifically, Kullback-Leibler divergence is used as the similarity
metric to calculate the distances between the estimated current
distribution with the pre- and post-change distributions. Then mass
functions are calculated and related combination rules are applied to
combine the mass values among all sensors. Furthermore, we applied
the method to estimate the minimum number of sensors needed to
combine, so computational efficiency could be improved. Cumulative
sum test is then applied on the ratio of pignistic probability to detect
and declare the change for decision making purpose. Simulation
results using both synthetic data and real data from experimental
setup demonstrate the effectiveness of the presented schemes.},
	    journal   = {International Journal of Computer and Information Engineering},
	  volume    = {11},
	  number    = {6},
	  year      = {2017},
	  pages     = {710 - 716},
	  ee        = {https://publications.waset.org/pdf/10007423},
	  url   	= {https://publications.waset.org/vol/126},
	  bibsource = {https://publications.waset.org/},
	  issn  	= {eISSN: 1307-6892},
	  publisher = {World Academy of Science, Engineering and Technology},
	  index 	= {Open Science Index 126, 2017},