1,720,981 research outputs found
Design Time Methodology for the Formal Modeling and Verification of Smart Environments
Full text linkSmart Environments (SmE) are intelligent and complex due to smart connectivity and interaction of heterogeneous devices achieved by complicated and sophisticated computing algorithms. Based on their domotic and industrial applications, SmE system may be critical in terms of correctness, reliability, safety, security and other such vital factors. To achieve error-free and requirement-compliant implementation of these systems, it is advisable to enforce a design process that may guarantee these factors by adopting formal models and formal verification techniques at design time.
The e-Lite research group at Politecnico di Torino is developing solutions for SmE based on integration of commercially available home automation technologies with an intelligent ecosystem based on a central OSGi-based gateway, and distributed collaboration of intelligent applications, with the help of semantic web technologies and applications.
The main goal of my research is to study new methodologies which are used for the modeling and verification of SmE. This goal includes the development of a formal methodology which ensures the reliable implementation of the requirements on SmE, by modeling and verifying each component (users, devices, control algorithms and environment/context) and the interaction among them, especially at various stages in design time, so that all the complexities and ambiguities can be reduced
Design-time formal verification for smart environments: an exploratory perspective
Full text linkSmart environments (SmE) are richly integrated with multiple heterogeneous devices; they perform the operations in intelligent manner by considering the context and actions/behaviors of the users. Their major objective is to enable the environment to provide ease and comfort to the users. The reliance on these systems demands consistent behavior. The versatility of devices, user behavior and intricacy of communication complicate the modeling and verification of SmE's reliable behavior. Of the many available modeling and verification techniques, formal methods appear to be the most promising. Due to a large variety of implementation scenarios and support for conditional behavior/processing, the concept of SmE is applicable to diverse areas which calls for focused research. As a result, a number of modeling and verification techniques have been made available for designers. This paper explores and puts into perspective the modeling and verification techniques based on an extended literature survey. These techniques mainly focus on some specific aspects, with a few overlapping scenarios (such as user interaction, devices interaction and control, context awareness, etc.), which were of the interest to the researchers based on their specialized competencies. The techniques are categorized on the basis of various factors and formalisms considered for the modeling and verification and later analyzed. The results show that no surveyed technique maintains a holistic perspective; each technique is used for the modeling and verification of specific SmE aspects. The results further help the designers select appropriate modeling and verification techniques under given requirements and stress for more R&D effort into SmE modeling and verification researc
Autonomic goal-oriented device management for Smart Environments
No full textAbstract Modern Smart Environments (SmE) are equipped with a multitude of devices and sensors aimed at intelligent services. The variety of devices has raised a major problem of managing SmE. An increasingly adopted solution to the problem is the modeling of goals and intentions, and then using artificial intelligence to control the respective SmE accordingly. Generally, the solution advocates that the goals can be achieved by controlling the evolution of the states of the devices. In order to automatically reach a particular state, a sophisticated solution is required through which the respective commands, notifications and their correct sequence can be discovered and enforced on the real devices. In this paper, a comprehensive methodology is proposed by considering a ) the composite nature of the state of an individual device; b ) the possible variation of specific commands, notifications and their sequence based on the current states of the devices. The methodology works at two levels: design-time and runtime. At design-time, it constructs the extended data and control flow behavioral graphs of the devices by using the concepts of a model checking approach. Then, at runtime, it uses these graphs for finding the reliable evolution through which the desired goal can be fulfilled. The proposed methodology is implemented over the Domotic Effects framework and a home automation system, i.e. Domotic OSGi Gateway (Dog). The implementation and experimentation details indicate the effectiveness of the proposed approach
Modeling and Formal Verification of Smart Environments
Full text linkSmart Environments (SmE) are a growing combination of various computing frameworks (ubiquitous, pervasive etc), devices, control algorithms and a complex web of interactions. It is at the
core of user facilitation in a number of industrial, domestic and public areas. Based on their application areas, SmE may be critical in terms of correctness, reliability, safety, security etc. To achieve error-free and requirement-compliant implementation, these systems are designed resorting to various modeling approaches including Ontology and Statecharts. This paper attempts to consider correctness, reliability, safety and security in the design process of SmE and its related components by proposing a design time modeling and formal verification methodology. The proposed methodology covers various design features related to modeling and formal verification SmE (focusing on users, devices, environment, control algorithms and their interaction) against the set of the requirements through model checking. A realistic case study of a Bank Door Security Booth System (BDSB) is tested. The results show the successful verification of the properties related to the safety, security and desired reliable behavior of BDSB
Spatial distribution and catalytic mechanisms of beta-glucosidase activity at the root-soil interface
No full textWe compared modifications of soil zymography, a new in situ technique to visualize enzyme activities, based on contact of fluorgenic substrate-saturated membranes with soil either through the gel layer (gel zymography) or without gel application (direct zymography). We coupled zymography with quantitative measurements of enzyme kinetics to characterize catalytic mechanisms of beta-glucosidase activity at the plant-soil interface including root surface (rhizoplane), rhizosphere, and bulk soil. Direct zymography refined and focused image resolution. The area of hotspots (i.e., spots with most intensive enzyme activity) as well as color intensity ratios estimated using direct zymography exceeded by a factor of 2 the corresponding values obtained with gel zymography. As determined by direct zymography, the percentage of hotspots associated to root surfaces was 58-68 % of total hotspot area. Hotspot area comprised only 6.8 +/- 0.1 % of the total area of an image and 9.0 +/- 3 % of the root surface area. The intensity of beta-glucosidase activity, however, was up to 20 times higher in the hotspots versus bulk soil. The contribution of rhizosphere to beta-glucosidase activity of the whole image (77-82 %) was four times higher than the contribution of the root surface. Enzyme kinetic parameters indicated different enzyme systems in bulk and rhizosphere soil. Higher substrate affinity and catalytic efficiency in bulk than in rhizosphere soil suggested relative domination of microorganisms with more efficient enzyme systems in the former. Coupling direct zymography and kinetic assays enabled mapping the two-dimensional (2D) distribution of enzyme activity at the root-soil interface and estimating the catalytic properties of root-associated and soil-associated enzymes
Phenological Stage, Plant Biomass, and Drought Stress Affect Microbial Biomass and Enzyme Activities in the Rhizosphere of Enteropogon macrostachyus
No full textEffect of land use and management practices on microbial biomass and enzyme activities in subtropical top-and sub-soils
No full textLand-use change, especially from forest to intensive agriculture, is negatively impacting soil quality and sustainability. Soil biological activities are sensitive indicators of such land-use impacts. We tested two hypotheses: i) land use and management practices affect microbial properties (microbial biomass and enzyme activities) in topsoil (0-20 cm), but have no effects in subsoil (20-100 cm); and ii) microbial properties in topsoil are highest in forest, followed by organic farming and then conventional farming. Total organic C and N contents as well as microbial biomass were significantly higher in the organic farming topsoil compared with conventional farming and forest. Except xylanase and acid phosphatase, enzyme activities (beta-glucosidase, cellobiohydrolas, chitinase, sulfatase, leucine aminopeptidase and tyrosine aminopeptidase) were also higher in organic farming soil. Crop residues and rhizodeposits support higher microbial biomass, leading to enhanced enzyme activities in organic farming soil. Incorporation of rice stubble and limitation of available phosphorus explain the higher xylanase and acid phosphatase activities, respectively, in conventional farming soil. Litter removal leads to a deficiency of labile C and N, resulting in lower enzyme activities in forest soil. Total C and N contents were higher in subsoil under organic farming. Although there was no effect of land use on microbial biomass in subsoil, activities of most enzymes were higher under organic farming. Overall, our results indicate that land-use change significantly alters microbial properties in topsoil, with modest effects in subsoil. Microbial properties should be considered in environmental risk assessments and models as indicators of ecosystem disturbance caused by land-use and management practices. (C) 2017 Elsevier B.V. All rights reserved.Erasmus mundus (Experst4Asia
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