Multidimensional Sensor Data Analysis in Cyber-Physical System: An Atypical Cube Approach

Abstract

Cyber-Physical System (CPS) is an integration of distributed sensor networks with computational devices. CPS claims many promising applications, such as traffic observation, battlefield surveillance, and sensor-network-based monitoring. One important topic in CPS research is about the atypical event analysis, that is, retrieving the events from massive sensor data and analyzing them with spatial, temporal, and other multidimensional information. Many traditional methods are not feasible for such analysis since they cannot describe the complex atypical events. In this paper, we propose a novel model of atypical cluster to effectively represent such events and efficiently retrieve them from massive data. The basic cluster is designed to summarize an individual event, and the macrocluster is used to integrate the information from multiple events. To facilitate scalable, flexible, and online analysis, the atypical cube is constructed, and a guided clustering algorithm is proposed to retrieve significant clusters in an efficient manner. We conduct experiments on real sensor datasets with the size of more than 50 GB; the results show that the proposed method can provide more accurate information with only 15% to 20% time cost of the baselines.

Nonnegative Matrix Factorization-Based Spatial-Temporal Clustering for Multiple Sensor Data Streams

“…To demonstrate the efficiency of STClu, we compare it with the Input: Matrices ( ) , ( −1) , ( −1, ) , ( −1) , and ( −1) , cluster number ( ) , and maximum iteration Output: Matrices ( ) , ( ) , ( ) , and ( −1, ) (1) use the family of Colibri methods to get ( ) (2) determine cluster number ( ) (3) initialize ( ) , ( ) , and ( −1, ) using ( −1) , ( −1) , and ( −1, ) , respectively (4) if we need to form a new partition (5) go to (8) (6) else 7let ( ) = ( −1) and ( ) = ( −1) , and return (8) while not converging and ≤ do (9) update ( ) by (9) (10) update ( ) by (10) (11) update ( ) by (11) 12update ( −1, ) by (12) (13) end while (14) return ( ) , ( ) , ( ) , and ( −1, )…”

“…It consists of a large number of sensors to monitor physical or environmental conditions [2,3]. The incoming information in CPS needs to be considered as a stream and not so much as an ever-growing data set [4,5].…”

“…As shown in Figure 1, we find that those records in an atypical event are spatially close and timely relevant. To retrieve the useful information from massive sensor data streams, Tang et al [5] proposed a model of atypical cluster to describe the atypical events and retrieve them with spatial and temporal attribute. To explore the spatial correlation between data sampled by different sensors for sensor networks, -local spatial clustering algorithm was proposed [17].…”

A comprehensive overview of cyber-physical systems: from perspective of feedback system

“…In [50], the authors propose a novel methodology to address the detection of such attacks, and further incorporate appropriate remedial actions in the estimator. In [51], the authors propose a novel model of atypical cluster to effectively represent atypical events and efficiently retrieve them from massive data. In CPS, anomalies or attacks might come from the cyber side or the physical side and thus, anomaly detection based on information from both sides needs more work to do.…”

A scalable framework for spatiotemporal analysis of location-based social media data

“…The concepts of data warehousing and data cube have been adapted to spatial data [17]. Since then, an amount of efforts have been reported to exploit the power of spatial data cube in traditional GIS and spatial analysis [e.g., 35,3], visual analytics of spatiotemporal processes [24], analysis of massive moving objects [e.g., 30,22], and closely related, analysis of mobile cyber-physical systems [e.g., 32,37].…”