Automated Semantic Knowledge Acquisition From Sensor Data

The gathering of real-world data is facilitated by many pervasive data sources such as sensor devices and smartphones. The abundance of the sensory data raises the need to make the data easily available and understandable for the potential users and applications. Using semantic enhancements is one approach to structure and organize the data and to make it processable and interoperable by machines. In particular, ontologies are used to represent information and their relations in machine interpretable forms. In this context, a significant amount of work has been done to create real-world data description ontologies and data description models; however, little effort has been done in creating and constructing meaningful topical ontologies from a vast amount of sensory data by automated processes. Topical ontologies represent the knowledge from a certain domain providing a basic understanding of the concepts that serve as building blocks for further processing. There is a lack of solution that construct the structure and relations of ontologies based on real-world data. To address this challenge, we introduce a knowledge acquisition method that processes real-world data to automatically create and evolve topical ontologies based on rules that are automatically extracted from external sources. We use an extended k-means clustering method and apply a statistic model to extract and link relevant concepts from the raw sensor data and represent them in the form of a topical ontology. We use a rule-based system to label the concepts and make them understandable for the human user or semantic analysis and reasoning tools and software. The evaluation of our work shows that the construction of a topological ontology from raw sensor data is achievable with only small construction errors

Context-aware management for sensor networks

The wide field of wireless sensor networks requires that hun- dreds or even thousands of sensor nodes have to be main- tained and configured. With the upcoming initatives such as Smart Home and Internet of Things, we need new mecha- nism to discover and manage this amount of sensors. In this paper, we describe a middleware architecture that uses con- text information of sensors to supply a plug-and-play gate- way and resource management framework for heterogeneous sensor networks. Our main goals are to minimise the effort for network engineers to configure and maintain the network and supply a unified interface to access the underlying het- erogeneous network. Based on the context information such as battery status, routing information, location and radio signal strength the gateway will configure and maintain the sensor network. The sensors are associated to nearby base stations using an approach that is adapted from the 802.11 WLAN association and negotiation mechanism to provide registration and connectivity services for the underlying sen- sor devices. This abstracted connection layer can be used to integrate the underlying sensor networks into high-level ser- vices and applications such as IP-based networks and Web services

Automated group formation in decentralised environments

Collaboration towards a goal involves groups of entities collectively possessing characteristics required to accomplish the goal. Facilitating collaborations in pervasive environments requires the automated formation of such groups. The group formation process is especially challenging in decentralised environments where there is no single central entity that can coordinate the formation process. It is also important that the group formation mechanisms are generic in nature so that they can be utilised in heterogeneous target environments regardless of their domain and requirements. This paper proposes a generic approach for automating group formation in decentralised environments. © 2011 IEEE

Predicting complex events for pro-active IoT applications

The widespread use of IoT devices has opened the possibilities for many innovative applications. Almost all of these applications involve analyzing complex data streams with low latency requirements. In this regard, pattern recognition methods based on CEP have the potential to provide solutions for analyzing and correlating these complex data streams in order to detect complex events. Most of these solutions are reactive in nature as CEP acts on real-time data and does not exploit historical data. In our work, we have explored a proactive approach by exploiting historical data using machine learning methods for prediction with CEP. We propose an adaptive prediction algorithm called Adaptive Moving Window Regression (AMWR) for dynamic IoT data and evaluated it using a realworld use case. Our proposed architecture is generic and can be used across different fields for predicting complex events

Static Safety for an Actor Dedicated Process Calculus by Abstract Interpretation

The actor model eases the definition of concurrent programs with non uniform behaviors. Static analysis of such a model was previously done in a data-flow oriented way, with type systems. This approach was based on constraint set resolution and was not able to deal with precise properties for communications of behaviors. We present here a new approach, control-flow oriented, based on the abstract interpretation framework, able to deal with communication of behaviors. Within our new analyses, we are able to verify most of the previous properties we observed as well as new ones, principally based on occurrence counting

Context-aware stream processing for distributed IoT applications

Most of the IoT applications are distributed in nature generating large data streams which have to be analyzed in near real-time. Solutions based on Complex Event Processing (CEP) have the potential to extract high-level knowledge from these data streams but the use of CEP for distributed IoT applications is still in early phase and involves many drawbacks. The manual setting of rules for CEP is one of the major drawback. These rules are based on threshold values and currently there are no automatic methods to find the optimized threshold values. In real-time dynamic IoT environments, the context of the application is always changing and the performance of current CEP solutions are not reliable for such scenarios. In this regard, we propose an automatic and context aware method based on clustering for finding optimized threshold values for CEP rules. We have developed a lightweight CEP called CEP to run on low processing hardware which can update the rules on the run. We have demonstrated our approach using a real-world use case of Intelligent Transportation System (ITS) to detect congestion in near real-time

Silicate dust in the environment of RS Ophiuchi following the 2006 eruption

We present further Spitzer Space Telescope observations of the recurrent nova RS Ophiuchi, obtained over the period 208-430 days after the 2006 eruption. The later Spitzer IRS data show that the line emission and free-free continuum emission reported earlier is declining, revealing incontrovertible evidence for the presence of silicate emission features at 9.7 and 18microns. We conclude that the silicate dust survives the hard radiation impulse and shock blast wave from the eruption. The existence of the extant dust may have significant implications for understanding the propagation of shocks through the red giant wind and likely wind geometry.Comment: 12 pages, 4 figures, accepted for publication in ApJ (Letters

The absence of crystalline silicates in the diffuse interstellar medium

We have studied the dust along the line-of-sight towards the Galactic Center using Short Wavelength Spectrometer (SWS) data obtained with the Infrared Space Observatory (ISO). We focussed on the wavelength region from 8-13 micron which is dominated by the strong silicate absorption feature. Using the absorption profiles observed towards Galactic Center Sources (GCS) 3 and 4, which are C-rich Wolf-Rayet Stars, as reference objects, we are able to disentangle the interstellar silicate absorption and the silicate emission intrinsic to the source, toward Sgr A* and derive a very accurate profile for the intrinsic 9.7 micron band. The interstellar absorption band is smooth and featureless and is well reproduced using a mixture of 15.1% amorphous pyroxene and 84.9% of amorphous olivine by mass, all in spherical sub-micron-sized grains. There is no direct evidence for substructure due to interstellar crystalline silicates. We are able to determine an upper limit to the degree of crystallinity of silicates in the diffuse interstellar medium (ISM), and conclude that the crystalline fraction of the interstellar silicates is 0.2% (+/- 0.2%) by mass. This is much lower than the degree of crystallinity observed in silicates in the circumstellar environment of evolved stars, the main contributors of dust to the ISM. There are two possible explanations for this discrepancy. First, an amorphization process occurs in the ISM on a time scale significantly shorter than the destruction time scale, possibly caused by particle bombardment by heavyweight ions. Second, we consider the possibility that the crystalline silicates in stellar ejecta are diluted by an additional source of amorphous silicates, in particular supernovae.Comment: 33 pages, 6 figures, accepted for publication by Ap

Mantle flow in regions of complex tectonics: insights from Indonesia

Indonesia is arguably one of the tectonically most complex regions on Earth today due to its location at the junction of several major tectonic plates and its long history of collision and accretion. It is thus an ideal location to study the interaction between subducting plates and mantle convection. Seismic anisotropy can serve as a diagnostic tool for identifying various subsurface deformational processes, such as mantle flow, for example. Here, we present novel shear wave splitting results across the Indonesian region. Using three different shear phases (local S, SKS, and downgoing S) to improve spatial resolution of anisotropic fabrics allows us to distinguish several deformational features. For example, the block rotation history of Borneo is reflected in coast-parallel fast directions, which we attribute to fossil anisotropy. Furthermore, we are able to unravel the mantle flow pattern in the Sulawesi and Banda region: We detect toroidal flow around the Celebes Sea slab, oblique corner flow in the Banda wedge, and sub-slab mantle flow around the arcuate Banda slab. We present evidence for deep, sub-520 km anisotropy at the Java subduction zone. In the Sumatran backarc, we measure trench-perpendicular fast orientations, which we assume to be due to mantle flow beneath the overriding Eurasian plate. These observations will allow to test ideas of, for example, slab–mantle coupling in subduction regions