scispace - formally typeset
Search or ask a question
Author

Marten Fischer

Bio: Marten Fischer is an academic researcher from University of Osnabrück. The author has contributed to research in topics: Smart city & Test case. The author has an hindex of 6, co-authored 18 publications receiving 250 citations.

Papers
More filters
Journal ArticleDOI
TL;DR: The CityPulse framework supports smart city service creation by means of a distributed system for semantic discovery, data analytics, and interpretation of large-scale (near-)real-time Internet of Things data and social media data streams to break away from silo applications and enable cross-domain data integration.
Abstract: Our world and our lives are changing in many ways. Communication, networking, and computing technologies are among the most influential enablers that shape our lives today. Digital data and connected worlds of physical objects, people, and devices are rapidly changing the way we work, travel, socialize, and interact with our surroundings, and they have a profound impact on different domains, such as healthcare, environmental monitoring, urban systems, and control and management applications, among several other areas. Cities currently face an increasing demand for providing services that can have an impact on people’s everyday lives. The CityPulse framework supports smart city service creation by means of a distributed system for semantic discovery, data analytics, and interpretation of large-scale (near-)real-time Internet of Things data and social media data streams. To goal is to break away from silo applications and enable cross-domain data integration. The CityPulse framework integrates multimodal, mixed quality, uncertain and incomplete data to create reliable, dependable information and continuously adapts data processing techniques to meet the quality of information requirements from end users. Different than existing solutions that mainly offer unified views of the data, the CityPulse framework is also equipped with powerful data analytics modules that perform intelligent data aggregation, event detection, quality assessment, contextual filtering, and decision support. This paper presents the framework, describes its components, and demonstrates how they interact to support easy development of custom-made applications for citizens. The benefits and the effectiveness of the framework are demonstrated in a use-case scenario implementation presented in this paper.

199 citations

Journal ArticleDOI
TL;DR: This work proposes a novel framework with an efficient semantic data processing pipeline, allowing for real-time observation of the pulse of a city and investigates the optimization of the semantic data discovery and integration based on the proposed stream quality analysis and data aggregation techniques.
Abstract: An increasing number of cities are confronted with challenges resulting from the rapid urbanization and new demands that a rapidly growing digital economy imposes on current applications and information systems. Smart city applications enable city authorities to monitor, manage, and provide plans for public resources and infrastructures in city environments, while offering citizens and businesses to develop and use intelligent services in cities. However, providing such smart city applications gives rise to several issues, such as semantic heterogeneity and trustworthiness of data sources, and extracting up-to-date information in real time from large-scale dynamic data streams. In order to address these issues, we propose a novel framework with an efficient semantic data processing pipeline, allowing for real-time observation of the pulse of a city. The proposed framework enables efficient semantic integration of data streams, and complex event processing on top of real-time data aggregation and quality analysis in a semantic Web environment. To evaluate our system, we use real-time sensor observations that have been published via an open platform called Open Data Aarhus by the City of Aarhus. We examine the framework utilizing symbolic aggregate approximation to reduce the size of data streams, and perform quality analysis taking into account both single and multiple data streams. We also investigate the optimization of the semantic data discovery and integration based on the proposed stream quality analysis and data aggregation techniques.

55 citations

Proceedings ArticleDOI
07 Jun 2018
TL;DR: IoTCrawler targets IoT development and demonstrations with a focus on Industry 4.0, Social IoT, Smart City and Smart Energy use cases and its focus is on the integration and interoperability across different platforms, through dynamic and reconfigurable solutions for discovery and integration of data and services from legacy and new systems.
Abstract: The Internet of Things (IoT) offers an incredible innovation potential for developing smarter applications and services. However, today we see solutions in the development of vertical applications and services reflecting what used to be the early days of the Web, leading to fragmentation and intra-nets of Things. To achieve an open IoT ecosystem of systems and platforms, several key enablers are needed for effective, adaptive and scalable mechanisms for exploring and discovering IoT resources and their data/capabilities. This paper discusses our work in the EU H2020 IoTCrawler project. Its focus is on the integration and interoperability across different platforms, through dynamic and reconfigurable solutions for discovery and integration of data and services from legacy and new systems. This is complemented with adaptive, privacy-aware and secure solutions for crawling, indexing and searching in distributed IoT systems. IoTCrawler targets IoT development and demonstrations with a focus on Industry 4.0, Social IoT, Smart City and Smart Energy use cases.

18 citations

Journal ArticleDOI
24 Feb 2021-Sensors
TL;DR: The IoTCrawler framework is not only another IoT framework, it is a system of systems which connects existing solutions to offer interoperability and to overcome data fragmentation, offering solutions for crawling, indexing and searching IoT data sources, while ensuring privacy and security, adaptivity and reliability.
Abstract: Due to the rapid development of the Internet of Things (IoT) and consequently, the availability of more and more IoT data sources, mechanisms for searching and integrating IoT data sources become essential to leverage all relevant data for improving processes and services. This paper presents the IoT search framework IoTCrawler. The IoTCrawler framework is not only another IoT framework, it is a system of systems which connects existing solutions to offer interoperability and to overcome data fragmentation. In addition to its domain-independent design, IoTCrawler features a layered approach, offering solutions for crawling, indexing and searching IoT data sources, while ensuring privacy and security, adaptivity and reliability. The concept is proven by addressing a list of requirements defined for searching the IoT and an extensive evaluation. In addition, real world use cases showcase the applicability of the framework and provide examples of how it can be instantiated for new scenarios.

12 citations

Proceedings ArticleDOI
16 Nov 2020
TL;DR: This work shows a performance analysis for Hyperledger Fabric v2.0 and focusses on the evaluation of throughput, latency and error rate, together with the overall scalability of the Fabric Blockchain platform.
Abstract: Hyperledger Fabric is currently one of the most popular business Blockchain platforms. With its included functionality of executing custom smart contracts, Fabric has become one of the most widely used frameworks, e.g. for industry 4.0 applications. Though, most applications require a previously known data throughput. For a potentially interested developer, it is not trivial to decide, whether a given Fabric network configuration will meet the required expectations in regards to performance. Thus this work shows a performance analysis for Hyperledger Fabric v2.0 and focusses on the evaluation of throughput, latency and error rate, together with the overall scalability of the Fabric Blockchain platform. The results show, that Fabric v2.0 outperforms previous versions in almost any regard in addition to an improved smart contract lifecycle.

10 citations


Cited by
More filters
Journal ArticleDOI
TL;DR: This article assesses the different machine learning methods that deal with the challenges in IoT data by considering smart cities as the main use case and presents a taxonomy of machine learning algorithms explaining how different techniques are applied to the data in order to extract higher level information.

690 citations

Journal ArticleDOI
TL;DR: In this article, the authors present a taxonomy of machine learning algorithms that can be applied to the data in order to extract higher level information, and a use case of applying Support Vector Machine (SVM) on Aarhus Smart City traffic data is presented for more detailed exploration.
Abstract: Rapid developments in hardware, software, and communication technologies have allowed the emergence of Internet-connected sensory devices that provide observation and data measurement from the physical world. By 2020, it is estimated that the total number of Internet-connected devices being used will be between 25 and 50 billion. As the numbers grow and technologies become more mature, the volume of data published will increase. Internet-connected devices technology, referred to as Internet of Things (IoT), continues to extend the current Internet by providing connectivity and interaction between the physical and cyber worlds. In addition to increased volume, the IoT generates Big Data characterized by velocity in terms of time and location dependency, with a variety of multiple modalities and varying data quality. Intelligent processing and analysis of this Big Data is the key to developing smart IoT applications. This article assesses the different machine learning methods that deal with the challenges in IoT data by considering smart cities as the main use case. The key contribution of this study is presentation of a taxonomy of machine learning algorithms explaining how different techniques are applied to the data in order to extract higher level information. The potential and challenges of machine learning for IoT data analytics will also be discussed. A use case of applying Support Vector Machine (SVM) on Aarhus Smart City traffic data is presented for a more detailed exploration.

375 citations

Journal ArticleDOI
01 Nov 2018-Cities
TL;DR: In this paper, a systematic review of the literature on smart cities, focusing on those aimed at conceptual development and providing empirical evidence base, is presented, where the authors identify three types of drivers of smart cities: community, technology, and policy.

296 citations

Journal ArticleDOI
19 Mar 2019-Sensors
TL;DR: This work proposed a generic approach to enabling spatiotemporal capabilities in information services for smart cities, adopted a multidisciplinary approach to achieving data integration and real-time processing, and developed a reference architecture for the development of event-driven applications.
Abstract: Smart cities are urban environments where Internet of Things (IoT) devices provide a continuous source of data about urban phenomena such as traffic and air pollution. The exploitation of the spatial properties of data enables situation and context awareness. However, the integration and analysis of data from IoT sensing devices remain a crucial challenge for the development of IoT applications in smart cities. Existing approaches provide no or limited ability to perform spatial data analysis, even when spatial information plays a significant role in decision making across many disciplines. This work proposes a generic approach to enabling spatiotemporal capabilities in information services for smart cities. We adopted a multidisciplinary approach to achieving data integration and real-time processing, and developed a reference architecture for the development of event-driven applications. This type of applications seamlessly integrates IoT sensing devices, complex event processing, and spatiotemporal analytics through a processing workflow for the detection of geographic events. Through the implementation and testing of a system prototype, built upon an existing sensor network, we demonstrated the feasibility, performance, and scalability of event-driven applications to achieve real-time processing capabilities and detect geographic events.

173 citations

Journal ArticleDOI
TL;DR: A survey of smart city initiatives and analyze their key concepts and different data management techniques by applying a complex literature matrix including terms, like smart people, smart economy, smart governance, smart mobility, smart environment, and smart living.
Abstract: Intelligent systems are wanting for cities to cope with limited spaces and resources across the world. As a result, smart cities emerged mainly as a result of highly innovative ICT industries and markets, and additionally, they have started to use novel solutions taking advantage of the Internet of Things (IoT), big data and cloud computing technologies to establish a profound connection between each component and layer of a city. Several key technologies congregate to build a working smart city considering human requirements. Even though the smart city concept is an advanced solution for today’s cities, recently, more living spaces should be discovered, and the concept of a smart city could be moved to these alternative living spaces, namely floating cities. The concept of a floating city emerged as a novel solution due to rising sea levels and land scarcity in order to provide alternative living spaces for humanity. In this article, our main research question is to raise awareness on the current state of smart city concepts across the world by understanding the key future trends, including floating cities, by motivating researchers and scientists through new IoT technologies and applications. Therefore, we present a survey of smart city initiatives and analyze their key concepts and different data management techniques. We performed a detailed literature survey and review by applying a complex literature matrix including terms, like smart people, smart economy, smart governance, smart mobility, smart environment, and smart living. We also discuss multiple perspectives of smart floating cities in detail. With the proposed approach, recent advances and practical future opportunities for smart cities can be revealed.

170 citations