Data Mining Practical Machine Learning Tools and Techniques

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The Internet of Things (IoT) is the next era of communication. Using the IoT, physical objects can be empowered to create, receive, and exchange data in a seamless manner. Various IoT applications focus on automating different tasks and are trying to empower the inanimate physical objects to act without any human intervention. The existing and upcoming IoT applications are highly promising to increase the level of comfort, efficiency, and automation for the users. To be able to implement such a world in an ever-growing fashion requires high security, privacy, authentication, and recovery from attacks. In this regard, it is imperative to make the required changes in the architecture of the IoT applications for achieving end-to-end secure IoT environments. In this paper, a detailed review of the security-related challenges and sources of threat in the IoT applications is presented. After discussing the security issues, various emerging and existing technologies focused on achieving a high degree of trust in the IoT applications are discussed. Four different technologies, blockchain, fog computing, edge computing, and machine learning, to increase the level of security in IoT are discussed.

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A Survey on IoT Security: Application Areas, Security Threats, and Solution Architectures

This paper describes a new approach to online forecasting of power production from PV systems. The method is suited to online forecasting in many applications and in this paper it is used to predict hourly values of solar power for horizons of up to 36 h. The data used is 15-min observations of solar power from 21 PV systems located on rooftops in a small village in Denmark. The suggested method is a two-stage method where first a statistical normalization of the solar power is obtained using a clear sky model. The clear sky model is found using statistical smoothing techniques. Then forecasts of the normalized solar power are calculated using adaptive linear time series models. Both autoregressive (AR) and AR with exogenous input (ARX) models are evaluated, where the latter takes numerical weather predictions (NWPs) as input. The results indicate that for forecasts up to 2 h ahead the most important input is the available observations of solar power, while for longer horizons NWPs are the most important input. A root mean square error improvement of around 35% is achieved by the ARX model compared to a proposed reference model.

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Online Short-term Solar Power Forecasting

Internet of Things (IoT) allows billions of physical objects to be connected to collect and exchange data for offering various applications, such as environmental monitoring, infrastructure management, and home automation. On the other hand, IoT has unsupported features (e.g., low latency, location awareness, and geographic distribution) that are critical for some IoT applications, including smart traffic lights, home energy management and augmented reality. To support these features, fog computing is integrated into IoT to extend computing, storage and networking resources to the network edge. Unfortunately, it is confronted with various security and privacy risks, which raise serious concerns towards users. In this survey, we review the architecture and features of fog computing and study critical roles of fog nodes, including real-time services, transient storage, data dissemination and decentralized computation. We also examine fog-assisted IoT applications based on different roles of fog nodes. Then, we present security and privacy threats towards IoT applications and discuss the security and privacy requirements in fog computing. Further, we demonstrate potential challenges to secure fog computing and review the state-of-the-art solutions used to address security and privacy issues in fog computing for IoT applications. Finally, by defining several open research issues, it is expected to draw more attention and efforts into this new architecture.

Securing Fog Computing for Internet of Things Applications: Challenges and Solutions

Fog computing is an architectural style in which network components between devices and the cloud execute application-specific logic We present the first review on fog computing within healthcare informatics, and explore, classify, and discuss different application use cases presented in the literature For that, we categorize applications into use case classes and list an inventory of application-specific tasks that can be handled by fog computing We discuss on which level of the network such fog computing tasks can be executed, and provide tradeoffs with respect to requirements relevant to healthcare Our review indicates that: 1) there is a significant number of computing tasks in healthcare that require or can benefit from fog computing principles; 2) processing on higher network tiers is required due to constraints in wireless devices and the need to aggregate data; and 3) privacy concerns and dependability prevent computation tasks to be completely moved to the cloud These findings substantiate the need for a coherent approach toward fog computing in healthcare, for which we present a list of recommended research and development actions

Fog Computing in Healthcare–A Review and Discussion

Tool support for the rapid composition, analysis and implementation of reactive services

With the evolving capabilities of devices, mobile applications are emerging towards complex reactive systems. To handle this complexity and shorten development time by increased reuse, we propose an engineering approach based on UML activities, which are used like building blocks to construct applications. Libraries of such building blocks make Android-specific features available. Tool support provides automatic formal analysis for soundness and automatic implementation. Furthermore, the approach is easily extensible, since new features can be provided by new building blocks, without changing the tools or notation. We demonstrate the method by a voice messaging application.

Engineering android applications based on UML activities

With their revision in the UML 2.x standard, activities have been extended with streaming parameters. This facilitates a reuse-oriented specification style, in which dedicated functions can be contributed by self-contained activities as building blocks: Using streaming parameters, activities can be composed together in a quite powerful manner, since streaming parameters may also pass information while activities are executing. However, to compose them correctly, we must know in which sequence an activity may emit or accept these streaming parameters. Therefore, we propose special UML state machines that specify the externally visible behavior of activities. Further, we develop an algorithm to construct these state machines automatically for an activity based on model checking. Using these behavioral contracts, activities can then be composed without looking at their internal details. Moreover, the contracts can be used during system verification to reduce the complexity of the analysis.

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Automated Encapsulation of UML Activities for Incremental Development and Verification

We outline a specification style for reactive services that focuses on UML 20 collaborations and activities as reusable specification building blocks In contrast to traditional component-based approaches, a collaboration directly describes the interactions between the components as well as the internal behavior necessary for a component to take part in it To compose services from such reusable collaborations, we use events identified as input and output pins on the activities that are connected together While our approach is formally settled in temporal logic, in this paper we focus on an example specification from the viewpoint of a service engineer

Frank Alexander Kraemer

Papers

Fog Computing in Healthcare–A Review and Discussion

Tool support for the rapid composition, analysis and implementation of reactive services

Engineering android applications based on UML activities

Automated Encapsulation of UML Activities for Incremental Development and Verification

Service Specification by Composition of Collaborations--An Example