Time-series clustering for sensor fault detection in large-scale Cyber-Physical Systems

Large-scale Cyber-Physical Systems (CPSs) are information systems that involve a vast network of sensor nodes and other devices that stream observations in real-time and typically are deployed in uncontrolled, broad geographical terrains. Sensor node failures are inevitable and unpredictable events in large-scale CPSs, which compromise the integrity of the sensors measurements and potentially reduce the quality of CPSs services and raise serious concerns related to CPSs safety, reliability, performance, and security. While many studies were conducted to tackle the challenge of sensor nodes failure detection using domain-specific solutions, this paper proposes a novel sensor nodes failure detection approach and empirically evaluates its validity using a real-world case study. This paper investigates time-series clustering techniques as a feasible solution to identify sensor nodes malfunctions by detecting long-segmental outliers in their observations' time series. Three different time-series clustering techniques have been investigated using real-world observations collected from two various sensor node networks, one of which consists of 275 temperature sensors distributed around London. This study demonstrates that time-series clustering effectively detects sensor node's continuous (halting/repeating) and incipient faults. It also showed that the feature-based time series clustering technique is a more efficient long-segmental outliers detection mechanism compared to shape-based time-series clustering techniques such as DTW and K-Shape, mainly when applied to shorter time-series windows

Pseudomonas aeruginosa Population Structure Revisited

At present there are strong indications that Pseudomonas aeruginosa exhibits an epidemic population structure; clinical isolates are indistinguishable from environmental isolates, and they do not exhibit a specific (disease) habitat selection. However, some important issues, such as the worldwide emergence of highly transmissible P. aeruginosa clones among cystic fibrosis (CF) patients and the spread and persistence of multidrug resistant (MDR) strains in hospital wards with high antibiotic pressure, remain contentious. To further investigate the population structure of P. aeruginosa, eight parameters were analyzed and combined for 328 unrelated isolates, collected over the last 125 years from 69 localities in 30 countries on five continents, from diverse clinical (human and animal) and environmental habitats. The analysed parameters were: i) O serotype, ii) Fluorescent Amplified-Fragment Length Polymorphism (FALFP) pattern, nucleotide sequences of outer membrane protein genes, iii) oprI, iv) oprL, v) oprD, vi) pyoverdine receptor gene profile (fpvA type and fpvB prevalence), and prevalence of vii) exoenzyme genes exoS and exoU and viii) group I pilin glycosyltransferase gene tfpO. These traits were combined and analysed using biological data analysis software and visualized in the form of a minimum spanning tree (MST). We revealed a network of relationships between all analyzed parameters and non-congruence between experiments. At the same time we observed several conserved clones, characterized by an almost identical data set. These observations confirm the nonclonal epidemic population structure of P. aeruginosa, a superficially clonal structure with frequent recombinations, in which occasionally highly successful epidemic clones arise. One of these clones is the renown and widespread MDR serotype O12 clone. On the other hand, we found no evidence for a widespread CF transmissible clone. All but one of the 43 analysed CF strains belonged to a ubiquitous P. aeruginosa “core lineage” and typically exhibited the exoS+/exoU− genotype and group B oprL and oprD alleles. This is to our knowledge the first report of an MST analysis conducted on a polyphasic data set