From outcrop to Ground Penetrating Radar images with Point-Spread Function based convolution modelling

Postponed access: the file will be accessible after 2022-01-15Masteroppgave i geovitenskapGEOV399MAMN-GEO

A comparative node evaluation model for highly heterogeneous massive‐scale Internet of Things‐Mist networks

Internet of Things (IoT) is a new technology that is driving the connection of billions of devices around the world. Because these devices are often resource‐constrained and very heterogeneous, this presents unique challenges. To address some of these challenges, new paradigms of Edge and Fog are emerging to bring computational resources of the IoT networks from remote devices like cloud closer to the end‐devices. Mist computing is a new paradigm that attempts to make use of the more resource‐rich nodes that are closer than Edge nodes to end‐users. Since these nodes might have enough resources to host services, execute tasks or even run containers, the utilization of network resources might be improved, and delay reduced by utilizing these nodes. The nodes must, therefore, be assessed to determine which nodes should offer resources to other nodes based on their situation. In this article, a new comparative assessment model for ranking Mist nodes in highly heterogeneous massive‐scale IoT networks in order to discover nodes that can offer their resources is proposed. The Mist nodes are evaluated based on parameters like resources, connections, applications, and environmental parameters to heuristically compare the neighbors with a novel learning‐to‐rank method to predict a suitability score for each node. The most suitable neighbor is then selected based on the score, with load balancing accomplished by a second chance method. When evaluating the performance, the results show that the proposed method succeeds in identifying resource‐rich nodes, while considering the selection of other nodes.publishedVersio

SysML modeling of service-oriented system-of-systems

The success of the ongoing fourth industrial revolution largely depends on our ways to cope with the novel design challenges arising from a combination of an enormous increase in process and product complexity, as well as the expected autonomy and self-organization of complex and diverse industrial hardware–software installments, often called systems-of-systems. In this paper, we employ the service-oriented architectural paradigm, as materialized in the Eclipse Arrowhead framework, to represent modern systems engineering principles and their open structural principles and, thus, relevance to flexible and adaptive systems. As for adequately capturing the structural aspect, we propose using model-based engineering techniques and, in particular, a SysML-based specialization of systems modeling. The approach is illustrated by a real-life use-case in industrial automation.publishedVersio

TONTA: Trend-based Online Network Traffic Analysis in ad-hoc IoT networks

Internet of Things (IoT) refers to a system of interconnected heterogeneous smart devices communicatingwithout human intervention. A significant portion of existing IoT networks is under the umbrella of ad-hoc andquasi ad-hoc networks. Ad-hoc based IoT networks suffer from the lack of resource-rich network infrastructuresthat are able to perform heavyweight network management tasks using, e.g. machine learning-based NetworkTraffic Monitoring and Analysis (NTMA) techniques. Designing light-weight NTMA techniques that do notneed to be (re-) trained has received much attention due to the time complexity of the training phase. In thisstudy, a novel pattern recognition method, called Trend-based Online Network Traffic Analysis (TONTA), isproposed for ad-hoc IoT networks to monitor network performance. The proposed method uses a statisticallight-weight Trend Change Detection (TCD) method in an online manner. TONTA discovers predominant trendsand recognizes abrupt or gradual time-series dataset changes to analyze the IoT network traffic. TONTA isthen compared with RuLSIF as an offline benchmark TCD technique. The results show that TONTA detectsapproximately 60% less false positive alarms than RuLSIF.publishedVersio

How to transform UML neg into a useful construct

In UML, the operator neg is used to specify negative, or unwanted, system behaviour. We agree that being able to specify negative behaviour is important. However, the UML neg is currently not wellsuited for this purpose, the main problem being that a single operator is used with several different meanings depending on the context. In this paper we investigate some alternative definitions of neg. We also propose a solution in which neg is replaced by two new operators for specifying negative behaviour

Refining UML interactions with underspecification and nondeterminism

STAIRS is an approach to the compositional development of UML interactions, such as sequence diagrams and interaction overview diagrams. An important aspect of STAIRS is the ability to distinguish between underspecification and inherent nondeterminism through the use of potential and mandatory alternatives. This paper investigates this distinction in more detail. Refinement notions explain when (and how) both kinds of nondeterminism may be reduced during the development process. In particular, in this paper we extend STAIRS with guards, which may be used to specify the choice between alternatives. Finally, we introduce the notion of an implementation and define what it means for an implementation to be correct with respect to a specification

Time exceptions in sequence diagrams

UML sequence diagrams partially describe a system. We show how the description may be augmented with exceptions triggered by the violation of timing constraints and compare our approach to those of the UML 2.1 simple time model, the UML Testing Profile and the UML profile for Schedulability, Performance and Time. We give a formal definition of time exceptions in sequence diagrams and show that the concepts are compositional. An ATM example is used to explain and motivate the concepts

Investigating styles in variability modeling: Hierarchical vs. constrained styles

Context: A common way to represent product lines is with variability modeling. Yet, there are different ways to extract and organize relevant characteristics of variability. Comprehensibility of these models and the ease of creating models are important for the efficiency of any variability management approach. Objective: The goal of this paper is to investigate the comprehensibility of two common styles to organize variability into models - hierarchical and constrained - where the dependencies between choices are specified either through the hierarchy of the model or as cross-cutting constraints, respectively. Method: We conducted a controlled experiment with a sample of 90 participants who were students with prior training in modeling. Each participant was provided with two variability models specified in Common Variability Language (CVL) and was asked to answer questions requiring interpretation of provided models. The models included 9 to 20 nodes and 8 to 19 edges and used the main variability elements. After answering the questions, the participants were asked to create a model based on a textual description. Results: The results indicate that the hierarchical modeling style was easier to comprehend from a subjective point of view, but there was also a significant interaction effect with the degree of dependency in the models, that influenced objective comprehension. With respect to model creation, we found that the use of a constrained modeling style resulted in higher correctness of variability models. Conclusions: Prior exposure to modeling style and the degree of dependency among elements in the model determine what modeling style a participant chose when creating the model from natural language descriptions. Participants tended to choose a hierarchical style for modeling situations with high dependency and a constrained style for situations with low dependency. Furthermore, the degree of dependency also influences the comprehension of the variability model

The pragmatics of STAIRS

STAIRS is a method for the compositional development of interactions in the setting of UML 2.0. In addition to defining denotational trace semantics for the main aspects of interactions, STAIRS focuses on how interactions may be developed through successive refinement steps. In this tutorial paper, we concentrate on explaining the practical relevance of STAIRS. Guidelines are given on how to create interactions using the different STAIRS operators, and how these may be refined. The pragmatics is illustrated by a running example

Interoperability for Industrial Internet of Things Based on Service-oriented Architecture

The new Industry 4.0 envisions a future for agile and effective integration of the physical operational technologies (OT) and the cyber information technologies (IT) as well as autonomous cooperation among them. However, the wide variety and heterogeneity of industrial systems and field devices -especially on the factory floor - increase integration complexity. To address these challenges, new technologies and concepts such as the Industrial Internet of Things (IIoT), Service-oriented Architecture (SoA), Semantic Technologies, Machine Learning and Artificial Intelligence are being introduced to the industrial environment. In this paper, we focus on how industrial automation systems and field devices can be integrated into the IIoT framework and coordinated to adapt to dynamic operating environment. Specifically, this paper proposed an interoperability solution that makes use of SoA and Semantic Technologies to achieve supervised coordination of IIoT application systems. To illustrate the potential of this approach, the Service-oriented Architecture-based Arrowhead Framework is used as the fundamental framework for the implementation of the approach.acceptedVersio