Information-Theoretic Performance Analysis of Sensor Networks via Markov Modeling of Time Series Data

2018 ◽  
Vol 48 (6) ◽  
pp. 1898-1909 ◽  
Author(s):  
Yue Li ◽  
Devesh K. Jha ◽  
Asok Ray ◽  
Thomas A. Wettergren
Keyword(s):  
Time Series ◽  
Sensor Networks ◽  
Performance Analysis ◽  
Time Series Data ◽  
Series Data ◽  
Markov Modeling ◽  
Information Theoretic

scholarly journals An Information Theoretic Approach for Creating 3D Spatial Images from 4D Time Series Data

2017 ◽  
Vol 23 (S1) ◽  
pp. 100-101
Author(s):  
Willy Wriggers ◽  
Julio Kovacs ◽  
Federica Castellani ◽  
P. Thomas Vernier ◽  
Dean J. Krusienski
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Series Data ◽  
Theoretic Approach ◽  
Information Theoretic ◽  
Information Theoretic Approach

scholarly journals Symbolic analysis-based reduced order Markov modeling of time series data

2018 ◽  
Vol 149 ◽  
pp. 68-81 ◽  
Author(s):  
Devesh K. Jha ◽  
Nurali Virani ◽  
Jan Reimann ◽  
Abhishek Srivastav ◽  
Asok Ray
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Symbolic Analysis ◽  
Series Data ◽  
Markov Modeling ◽  
Reduced Order

Based on ETEO Pattern Abnormal Event Detection in Wireless Sensor Networks

2014 ◽  
Vol 926-930 ◽  
pp. 1886-1889
Author(s):  
Bo Tian ◽  
Dian Hong Wang ◽  
Fen Xiong Chen ◽  
Zheng Pu Zhang
Keyword(s):  
Time Series ◽  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Time Series Data ◽  
Feature Space ◽  
Real Data ◽  
Wireless Sensor ◽  
Series Data ◽  
Edge Operator ◽  
Abnormal Events

This paper presents a new algorithm for the detection of abnormal events in Wireless Sensor Networks (WSN). Abnormal events are sets of data points that correspond to interesting patterns in the underlying phenomenon that the network monitors. This algorithm is inspired from time-series data mining techniques and transforms a stream of sensor readings into an Extension Temporal Edge Operator (ETEO) of time series pattern representation, and then extracts the three eigenvalue of each sub-pattern, that is, patterns length, patterns slope and patterns mean to map time series to feature space, and finally uses local outlier factor to detect abnormal pattern in this feature space. Experiments on synthetic and real data show that the definition of pattern outlier is reasonable and this algorithm is efficient to detect outliers in WSN.

Sign in / Sign up

Export Citation Format

Share Document