Efficient orchestration of Node-RED IoT workflows using a vector symbolic architecture

Numerous workflow systems span multiple scientific domains and environments, and for the Internet of Things (IoT), Node-RED offers an attractive Web based user interface to execute IoT service-based workflows. However, like most workflow systems, it coordinates the workflow centrally, and cannot run within more transient environments where nodes are mobile. To address this gap, we show how Node-RED workflows can be migrated into a decentralized execution environment for operation on mobile ad-hoc networks, and we demonstrate this by converting a Node-RED based traffic congestion detection workflow to operate in a decentralized environment. The approach uses a Vector Symbolic Architecture (VSA) to dynamically convert Node-Red applications into a compact semantic vector representation that encodes the service interfaces and the workflow in which they are embedded. By extending existing services interfaces, with a simple cognitive layer that can interpret and exchange the vectors, we show how the required services can be dynamically discovered and interconnected into the required workflow in a completely decentralized manner. The resulting system provides a convenient environment where the Node-RED front-end graphical composition tool can be used to orchestrate decentralized workflows. In this paper, we further extend this work by introducing a new dynamic VSA vector compression scheme that compresses vectors for on-the-wire communication, thereby reducing communication bandwidth while maintaining the semantic information content. This algorithm utilizes the holographic properties of the symbolic vectors to perform compression taking into consideration the number of combined vectors along with similarity bounds that determine conflict with other encoded vectors used in the same context. The resulting savings make this approach extremely efficient for discovery in service-based decentralized workflows

Trustable service discovery for highly dynamic decentralized workflows

The quantity and capabilities of smart devices and sensors deployed as part of the Internet of Things (IoT) and accessible via remote microservices is set to rise dramatically as the provision of interactive data streaming increases. This introduces opportunities to rapidly construct new applications by interconnecting these microservices in different workflow configurations. The challenge is to discover the required microservices, including those from trusted partners and the wider community, whilst being able to operate robustly under diverse networking conditions. This paper outlines a workflow approach that provides decentralized discovery and orchestration of verifiably trustable services in support of multi-party operations. The approach is based on adoption of patterns from self-sovereign identity research, notably Verifiable Credentials, to share information amongst peers based on attestations of service descriptions and prior service usage in a privacy preserving and secure manner. This provides a dynamic, trust-based framework for ratifying and evaluating the qualities of different services. Collating these new service descriptions and integrating with existing decentralized workflow research based on vector symbolic architecture (VSA) provides an enhanced semantic search space for efficient and trusted service discovery that is necessary to support a diverse range of emerging edge-computing environments. An architecture for a dynamic decentralized service discovery system, is designed, and described through application to a scenario which uses trusted peers’ reported experiences of an anomaly detection service to determine service selection

Derivative estimation for longitudinal data analysis:Examining features of blood pressure measured repeatedly during pregnancy

Estimating velocity and acceleration trajectories allows novel inferences in the field of longitudinal data analysis, such as estimating change regions rather than change points, and testing group effects on nonlinear change in an outcome (ie, a nonlinear interaction). In this article, we develop derivative estimation for 2 standard approachespolynomial mixed models and spline mixed models. We compare their performance with an established methodprincipal component analysis through conditional expectation through a simulation study. We then apply the methods to repeated blood pressure (BP) measurements in a UK cohort of pregnant women, where the goals of analysis are to (i) identify and estimate regions of BP change for each individual and (ii) investigate the association between parity and BP change at the population level. The penalized spline mixed model had the lowest bias in our simulation study, and we identified evidence for BP change regions in over 75% of pregnant women. Using mean velocity difference revealed differences in BP change between women in their first pregnancy compared with those who had at least 1 previous pregnancy. We recommend the use of penalized spline mixed models for derivative estimation in longitudinal data analysis

Prostate-specific antigen patterns in US and European populations:Comparison of six diverse cohorts

Objective: To determine whether there are differences in prostate-specific antigen (PSA) levels at diagnosis or changes in PSA levels between US and European populations of men with and without prostate cancer (PCa). Subjects and Methods: We analysed repeated measures of PSA from six clinically and geographically diverse cohorts of men: two cohorts with PSA-detected PCa, two cohorts with clinically detected PCa and two cohorts without PCa. Using multilevel models, average PSA at diagnosis and PSA change over time were compared among study populations. Results: The annual percentage PSA change of 4-5% was similar between men without cancer and men with PSA-detected cancer. PSA at diagnosis was 1.7 ng/mL lower in a US cohort of men with PSA-detected PCa (95% confidence interval 1.3-2.0 ng/mL), compared with a UK cohort of men with PSA-detected PCa, but there was no evidence of a different rate of PSA change between these populations. Conclusion: We found that PSA changes over time are similar in UK and US men diagnosed through PSA testing and even in men without PCa. Further development of PSA models to monitor men on active surveillance should be undertaken in order to take advantage of these similarities. We found no evidence that guidelines for using PSA to monitor men cannot be passed between US and European studies

Derivative estimation for longitudinal data analysis

In a previous paper we derived equivalence relations for pseudo-Wronskian determinants of Hermite polynomials. In this paper we obtain the analogous result for Laguerre and Jacobi polynomials. The equivalence formulas are richer in this case since rational Darboux transformations can be defined for four families of seed functions, as opposed to only two families in the Hermite case. The pseudo-Wronskian determinants of Laguerre and Jacobi type will thus depend on two Maya diagrams, while Hermite pseudo-Wronskians depend on just one Maya diagram. We show that these equivalence relations can be interpreted as the general transcription of shape invariance and specific discrete symmetries acting on the parameters of the isotonic oscillator and Darboux-Poschl-Teller potential.UK Medical Research Council, Grant/Award Numbers: MR/M020894/1, MC_UU_12013/7 MC_UU_12013/5; Wellcome Trust, Grant/Award Numbers:WT087997MA and 102215/2/13/2; Spanish Ministry of Economy and Competitiveness, Grant/Award number: MTM2014‐52184; National Institute forHealth Research (NIHR), Grant/AwardNumber: NF‐SI‐0611‐10196; University ofBristol; British Heart Foundation, Grant/Award Number: SP/07/008/24066; USNIH, Grant/Award Number: R01 DK07765

Efficient orchestration of Node-RED IoT workflows using a vector symbolic architecture

Numerous workflow systems span multiple scientific domains and environments, and for the Internet of Things (IoT), Node-RED offers an attractive Web based user interface to execute IoT service-based workflows. However, like most workflow systems, it coordinates the workflow centrally, and cannot run within more transient environments where nodes are mobile. To address this gap, we show how Node-RED workflows can be migrated into a decentralized execution environment for operation on mobile ad-hoc networks, and we demonstrate this by converting a Node-RED based traffic congestion detection workflow to operate in a decentralized environment. The approach uses a Vector Symbolic Architecture (VSA) to dynamically convert Node-Red applications into a compact semantic vector representation that encodes the service interfaces and the workflow in which they are embedded. By extending existing services interfaces, with a simple cognitive layer that can interpret and exchange the vectors, we show how the required services can be dynamically discovered and interconnected into the required workflow in a completely decentralized manner. The resulting system provides a convenient environment where the Node-RED front-end graphical composition tool can be used to orchestrate decentralized workflows. In this paper, we further extend this work by introducing a new dynamic VSA vector compression scheme that compresses vectors for on-the-wire communication, thereby reducing communication bandwidth while maintaining the semantic information content. This algorithm utilizes the holographic properties of the symbolic vectors to perform compression taking into consideration the number of combined vectors along with similarity bounds that determine conflict with other encoded vectors used in the same context. The resulting savings make this approach extremely efficient for discovery in service-based decentralized workflows

Derivative estimation for longitudinal data analysis: examining features of blood pressure measured repeatedly during pregnancy

Estimating velocity and acceleration trajectories allows novel inferences in the field of longitudinal data analysis, such as estimating change regions rather than change points, and testing group effects on nonlinear change in an outcome (ie, a nonlinear interaction). In this article, we develop derivative estimation for 2 standard approachespolynomial mixed models and spline mixed models. We compare their performance with an established methodprincipal component analysis through conditional expectation through a simulation study. We then apply the methods to repeated blood pressure (BP) measurements in a UK cohort of pregnant women, where the goals of analysis are to (i) identify and estimate regions of BP change for each individual and (ii) investigate the association between parity and BP change at the population level. The penalized spline mixed model had the lowest bias in our simulation study, and we identified evidence for BP change regions in over 75% of pregnant women. Using mean velocity difference revealed differences in BP change between women in their first pregnancy compared with those who had at least 1 previous pregnancy. We recommend the use of penalized spline mixed models for derivative estimation in longitudinal data analysis

Longitudinal prostate-specific antigen reference ranges:Choosing the underlying model of age-related changes

Serial measurements of prostate-specific antigen (PSA) are used as a biomarker for men diagnosed with prostate cancer following an active monitoring programme. Distinguishing pathological changes from natural age-related changes is not straightforward. Here, we compare four approaches to modelling age-related change in PSA with the aim of developing reference ranges for repeated measures of PSA. A suitable model for PSA reference ranges must satisfy two criteria. First, it must offer an accurate description of the trend of PSA on average and in individuals. Second, it must be able to make accurate predictions about new PSA observations for an individual and about the entire PSA trajectory for a new individual