Issues of using wireless sensor network to monitor urban air quality

Frequent monitoring of urban environment has now been regulated in most EU countries. Due to the design and cost of high-quality sensors, the current approach using these sensors may not provide data with an appropriate spatial and temporal resolution. As a result, using a wireless sensor network constructed by a large number of low-cost sensors is becoming increasingly popular to support the monitoring of urban environments. However, in practice, there are many issues that prevent such networks to be widely adopted. In this paper, we use data and lessons learnt from three real deployments to illustrate those issues. The issues are classified into three main categories and discussed according to the different sensing stages. In the end, we summarise a list of open challenges which we believe are significant for the future research

Improving data quality for low-cost environmental sensors

Using low-cost sensors to monitor the urban environment has become increasingly popular, as they can provide better data resolution than current practices. However, these low-cost sensors often produce poorer data quality, and so the data may not be utilised directly without processing. This thesis presents a two-phase solution for improving the data quality of low-cost environmental sensors. The solution consists of a novel method for anomaly detection and removal, and a process of sensor calibration. In the first phase, an anomaly model is utilised to identify the anomalies, which is constructed using a Bayesian-based approach. New contextual information is used to build the anomaly model, that is to the best of our knowledge the first time it has been used for such purpose. The result shows that this solution is more practical and robust than the existing approaches. In the second phase, a systematic comparison of the state-of-the-art calibration approaches is performed. The comparison aims to understand the difference between the methods, and the result shows a regression based method could provide a more predicable result and require much less computational resources. As a result, a regression based method is used for calibrating sensors in this work. In contrast to the existing approaches, the proposed method for calibration is able to systematically and automatically select the calibration parameters. The parameter selection ensures the best set of parameters are used in the model, which makes the calibration process less sensitive to different environmental conditions. The overall evaluations are performed using real datasets. The results show the data quality in terms of general accuracy against the reference instruments can be significantly improved, especially for sensors at roadside

An Improved Sensor Calibration with Anomaly Detection and Removal

Sensor calibration is a widely adopted process for improving data quality of low-cost sensors. However, such a process may not address data issues caused by anomalies. Anomalies are considered as data errors that are inconsistent to the actual physical phenomena. This paper presents an improved sensor calibration, which applies a process for detection and removal of anomalies before the sensor calibration process. A Bayesian-based method is used for anomaly detection that takes advantage of cross-sensitive parameters in a sensor array. The method utilises dependencies between cross-sensitive parameters, which allows underlying physical phenomena to be modelled and anomalies to be detected. The calibration approach is based on stepwise regression, which automatically and systematically selects suitable supporting parameters for a calibration function. The evaluation for anomaly detection shows that the results are better than the state-of-the-art methods, in terms of accuracy, precision and completeness. The overall evaluation confirms that data quality can be further enhanced when anomalies are removed before the calibration

Fast Parametric Model Checking through Model Fragmentation

Parametric model checking (PMC) computes algebraic formulae that express key non-functional properties of a system (reliability, performance, etc.) as rational functions of the system and environment parameters. In software engineering, PMC formulae can be used during design, e.g., to analyse the sensitivity of different system architectures to parametric variability, or to find optimal system configurations. They can also be used at runtime, e.g., to check if non-functional requirements are still satisfied after environmental changes, or to select new configurations after such changes. However, current PMC techniques do not scale well to systems with complex behaviour and more than a few parameters. Our paper introduces a fast PMC (fPMC) approach that overcomes this limitation, extending the applicability of PMC to a broader class of systems than previously possible. To this end, fPMC partitions the Markov models that PMC operates with into fragments whose reachability properties are analysed independently, and obtains PMC reachability formulae by combining the results of these fragment analyses. To demonstrate the effectiveness of fPMC, we show how our fPMC tool can analyse three systems (taken from the research literature, and belonging to different application domains) with which current PMC techniques and tools struggle

PRESTO: Predicting System-level Disruptions through Parametric Model Checking

Self-adaptive systems are expected to mitigate disruptions by con- tinually adjusting their configuration and behaviour. This mitiga- tion is often reactive. Typically, environmental or internal changes trigger a system response only after a violation of the system re- quirements. Despite a broad agreement that prevention is better than cure in self-adaptation, proactive adaptation methods are underrepresented within the repertoire of solutions available to the developers of self-adaptive systems. To address this gap, we present a work-in-progress approach for the prediction of system- level disruptions (PRESTO) through parametric model checking. Intended for use in the analysis step of the MAPE-K (Monitor- Analyse-Plan-Execute over a shared Knowledge) feedback control loop of self-adaptive systems, PRESTO comprises two stages. First, time-series analysis is applied to monitoring data in order to iden- tify trends in the values of individual system and/or environment parameters. Next, future non-functional requirement violations are predicted by using parametric model checking, in order to establish the potential impact of these trends on the reliability and perfor- mance of the system. We illustrate the application of PRESTO in a case study from the autonomous farming domain

The predictive value of coronary microvascular dysfunction for left ventricular reverse remodelling in dilated cardiomyopathy

AimsTo evaluate the degree of coronary microvascular dysfunction (CMD) in dilated cardiomyopathy (DCM) patients by cardiac magnetic resonance (CMR) first-pass perfusion parameters and to examine the correlation between myocardial perfusion and left ventricle reverse remodelling (LVRR).MethodsIn this study, 94 DCM patients and 35 healthy controls matched for age and sex were included. Myocardial perfusion parameters, including upslope, time to maximum signal intensity (Timemax), maximum signal intensity (SImax), baseline signal intensity (SIbaseline), and the difference between maximum and baseline signal intensity (SImax−baseline) were measured. Additionally, left ventricular (LV) structure, function parameters, and late gadolinium enhancement (LGE) were also recorded. The parameters were compared between healthy controls and DCM patients. Univariable and multivariable logistic regression analyses were used to determine the predictors of LVRR.ResultsWith a median follow-up period of 12 months [interquartile range (IQR), 8–13], 41 DCM patients (44%) achieved LVRR. Compared with healthy controls, DCM patients presented CMD with reduced upslope, SIbaseline, and increased Timemax (all p < 0.01). Timemax, SImax, and SImax−baseline were further decreased in LVRR than non-LVRR group (Timemax: 60.35 [IQR, 51.46–74.71] vs. 72.41 [IQR, 59.68–97.70], p = 0.017; SImax: 723.52 [IQR, 209.76–909.27] vs. 810.92 [IQR, 581.30–996.89], p = 0.049; SImax−baseline: 462.99 [IQR, 152.25–580.43] vs. 551.13 [IQR, 402.57–675.36], p = 0.038). In the analysis of multivariate logistic regression, Timemax [odds ratio (OR) 0.98; 95% confidence interval (CI) 0.95–1.00; p = 0.032)], heart rate (OR 1.04; 95% CI 1.01–1.08; p = 0.029), LV remodelling index (OR 1.73; 95% CI 1.06–3.00; p = 0.038) and LGE extent (OR 0.85; 95% CI 0.73–0.96; p = 0.021) were independent predictors of LVRR.ConclusionsCMD could be found in DCM patients and was more impaired in patients with non-LVRR than LVRR patients. Timemax at baseline was an independent predictor of LVRR in DCM

Evolutionary-Guided Synthesis of Verified Pareto-Optimal MDP Policies

We present a new approach for synthesising Pareto- optimal Markov decision process (MDP) policies that satisfy complex combinations of quality-of-service (QoS) software requirements. These policies correspond to optimal designs or configurations of software systems, and are obtained by translating MDP models of these systems into parametric Markov chains, and using multi-objective genetic algorithms to synthesise Pareto-optimal parameter values that define the required MDP policies. We use case studies from the service-based systems and robotic control software domains to show that our MDP policy synthesis approach can handle a wide range of QoS requirement combinations unsupported by current probabilistic model checkers. Moreover, for requirement combinations supported by these model checkers, our approach generates better Pareto-optimal policy sets according to established quality metrics

The Uyghur population and genetic susceptibility to type 2 diabetes: potential role for variants in CAPN10, APM1 and FUT6 genes

Genome-wide association studies have successfully identified over 70 loci associated with the risk of type 2 diabetes mellitus (T2DM) in multiple populations of European ancestry. However, the risk attributable to an individual variant is modest and does not yet provide convincing evidence for clinical utility. Association between these established genetic variants and T2DM in general populations is hitherto understudied in the isolated populations, such as the Uyghurs, resident in Hetian, far southern Xinjiang Uyghur Autonomous Region, China. In this case–control study, we genotyped 13 single-nucleotide polymorphisms (SNPs) at 10 genes associated with diabetes in 130 cases with T2DM and 135 healthy controls of Uyghur, a Chinese minority ethnic group. Three of the 13 SNPs demonstrated significant association with T2DM in the Uyghur population. There were significant differences between the T2DM patients and controls in the risk allele distributions of rs3792267 (CAPN10) (P = 0.002), rs1501299 (APM1) (P = 0.017), and rs3760776 (FUT6) (P = 0.031). Allelic carriers of rs3792267-A, rs1501299-T, and rs3760776-T had a 2.24-fold [OR (95% CI): 1.35–3.71], 0.59-fold [OR (95% CI): 0.39–0.91], 0.57-fold [OR (95% CI): 0.34–0.95] increased risk for T2DM respectively. We further confirmed that the cumulative risk allelic scores calculated from the 13 susceptibility loci for T2DM differed significantly between the T2DM patients and controls (P = 0.001), and the effect of obesity/overweight on T2DM was only observed in the subjects with a combined risk allelic score under a value of 17. This study observed that the SNPs rs3792267 in CAPN10, rs1501299 in APM1, and rs3760776 in FUT6 might serve as potential susceptible biomarkers for T2DM in Uyghurs. The cumulative risk allelic scores of multiple loci with modest individual effects are also significant risk factors in Uyghurs for T2DM, particularly among non-obese individuals. This is the first investigation having observed/found genetic variations on genetic loci functionally linked with glycosylation associated with the risk of T2DM in a Uyghur population. © 2016 The Authors. Journal of Cellular and Molecular Medicine published by John Wiley & Sons Ltd and Foundation for Cellular and Molecular Medicine