Infinitely-fast diffusion in Single-File Systems

We have used Dynamic Monte Carlo (DMC) methods and analytical techniques to analyze Single-File Systems for which diffusion is infinitely-fast. We have simplified the Master Equation removing the fast reactions and we have introduced a DMC algorithm for infinitely-fast diffusion. The DMC method for fast diffusion give similar results as the standard DMC with high diffusion rates. We have investigated the influence of characteristic parameters, such as pipe length, adsorption, desorption and conversion rate constants on the steady-state properties of Single-File Systems with a reaction, looking at cases when all the sites are reactive and when only some of them are reactive. We find that the effect of fast diffusion on single-file properties of the system is absent even when diffusion is infinitely-fast. Diffusion is not important in these systems. Smaller systems are less reactive and the occupancy profiles for infinitely-long systems show an exponential behavior.Comment: 8 pages, 5 figure

A systems of systems perspective on the internet of things

The Internet of Things (IoT) refers to extending the reach of the Internet into the physical world. The realization of IoT applications involves the integrated operation of many subsystems that retain their private function. This makes IoT application deployment and integration a Systems of Systems (SoS) problem. In this paper we collect SoS properties and characteristics from the literature in order to understand common integration problems in IoT better, for which we use two running examples. We show that in particular for safety critical systems there must be means to compute and predict integrated behavior based on specifications at interfaces. We give a general coordination architecture that supports this

The case of dynamic street lighting an exploration of long-term data collection

\u3cp\u3eDynamic Street Lighting Systems (DSLS) have been advertised as a means to reduce cost, reduce light pollution and increase feelings of safety. The dynamism is based on sensing and actuation, and uses communication between the light poles. While there have been many proposals for dynamic street lighting there is actually little data available about their operation. In this paper we present results from the monitoring of an installed DSLS in Eindhoven, The Netherlands for a period of more than a year. Besides questions of energy saving, we examined commissioning problems, effects of message loss, effects of the weather and general performance characteristics. The contribution includes a novel model for describing the outcomes of metrics as a function of sensor information. The results demonstrate that energy saving can be a strong driver for the introduction of DSLS but economic savings much less; the results also show a versatile usage of the collected data.\u3c/p\u3

Pattern-based feature extraction for fault detection in quality relevant process control

\u3cp\u3eStatistical quality control (SQC) applies multivariate statistics to monitor production processes over time and detect changes in their performance in terms of meeting specification limits on key product quality metrics. These limits are imposed by customers and typically assumed to be a single target value, however, for some products, it is more reasonable to target a range of values. Under this assumption we propose a multi-stage approach for mapping operating conditions to product quality classes. We use principal component analysis (PCA) and a pattern mining algorithm to reduce dimensionality and identify predictive patterns in time series of operating conditions in order to improve the performance of the classifier. We apply this approach to an industrial machining process and obtain significant improvements over models trained using features based on the last value of each process variable.\u3c/p\u3

Behavior-driven development for real-time embedded systems

\u3cp\u3eEmbedded systems are a class of computer systems that are typically characterized by a tight interaction with the physical environment. Various methodologies have been adopted for the development of such systems, ranging from traditional waterfall to modern agile techniques. One of the agile techniques that has recently attracted increasing attention is Behavior-Driven Development (BDD). BDD promotes the engagement of all stakeholders in every development iteration to minimize the misunderstanding between technical and non-technical stakeholders and, consequently, to speed up the development process and lower the costs. In this paper, we investigate the application of BDD to the development of embedded systems, especially focusing on the testing of timing requirements for real-time embedded software. In particular, we extend BDD with time-related concepts and propose an approach to generate test code for the verification of timing behavior of real-time embedded systems. Our approach offers more automation for the development of test code compared to existing BDD tools, thus minimizing the risk of timing faults and reducing development costs and time-to-market.\u3c/p\u3

Improving broadcast performance of radio duty-cycled Internet-of-Things devices

Asynchronous Radio Duty Cycling (ARDC) protocols can make embedded networked devices more energy efficient by keeping their radio off most of the time without a need for synchronization between devices. Some ARDC protocols can operate under 6LoWPAN adaptation layer in order to enable the vision of Internet-of-Things for battery operated devices. In this paper, we propose three different protocols which are modifications of the widely accepted ARDC protocol, ContikiMAC. The proposed solutions drastically improve energy efficiency and link layer delay for broadcast packets. Moreover, the proposed solutions are backward compatible with ContikiMAC and provide high reliability against frame reception errors. We present a detailed comparison with the legacy ContikiMAC and a standardized ARDC protocol, IEEE 802.15.4e Coordinated Sampled Listening (CSL), as well as the case of no duty cycling

Designing IoT systems:patterns and managerial conflicts

\u3cp\u3eThe first step in a system design process is to perform domain analysis. This entails acquiring stakeholder concerns throughout the life cycle of the system. The second step is to design solutions addressing those stakeholder concerns. This entails applying patterns for solving known, recurring problems. For these there are architecture patterns and design patterns for architecture design and detailed design respectively. For Internet of Things (IoT) systems such patterns are hardly defined yet since experience is just evolving. In this paper, we propose our definition of an IoT pattern along with its formal specification, explained by a running example. IoT systems are characterized by the variety of stakeholders involved throughout their life cycle, therefore our pattern specification includes means for identifying possible conflicts between these stakeholders.\u3c/p\u3

Proactive dependability framework for smart environment applications

Smart environment applications demand novel solutions for managing quality of services, especially availability and reliability at run-time. The underlying systems are changing dynamically due to addition and removal of system components, changing execution environments, and resources depletion. Therefore, in such dynamic systems, the functionality and the performance of smart environment applications can be hampered by faults. In this paper, we follow a proactive approach to anticipate system state at runtime. We present a proactive dependability framework to prevent faults at runtime based on predictive analysis to increase availability and reliability of smart environment applications, and reduce manual user interventions

Synthesizing and reconstructing missing sensory modalities in behavioral context recognition

\u3cp\u3eDetection of human activities along with the associated context is of key importance for various application areas, including assisted living and well-being. To predict a user’s context in the daily-life situation a system needs to learn from multimodal data that are often imbalanced, and noisy with missing values. The model is likely to encounter missing sensors in real-life conditions as well (such as a user not wearing a smartwatch) and it fails to infer the context if any of the modalities used for training are missing. In this paper, we propose a method based on an adversarial autoencoder for handling missing sensory features and synthesizing realistic samples. We empirically demonstrate the capability of our method in comparison with classical approaches for filling in missing values on a large-scale activity recognition dataset collected in-the-wild. We develop a fully-connected classification network by extending an encoder and systematically evaluate its multi-label classification performance when several modalities are missing. Furthermore, we show class-conditional artificial data generation and its visual and quantitative analysis on context classification task; representing a strong generative power of adversarial autoencoders.\u3c/p\u3

Topology-independent algorithms based on spanning trees

We consider a class of distributed algorithms. Algorithms in this class consist of processes that communicate using a broadcast. We show that local information suffices to implement such an algo rithm on an arbitrary network. We investigate the time complexity and present some experimental results