Daniel Kumper

Bio: Daniel Kumper is an academic researcher from University of Osnabrück. The author has contributed to research in topics: Bluetooth & Decision support system. The author has an hindex of 2, co-authored 4 publications receiving 160 citations.

CityPulse: Large Scale Data Analytics Framework for Smart Cities

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.

Remote configuration and deployment of sensor drivers for a medical bluetooth sensor gateway

Abstract: Outpatient nursing services expend a vast amount of time for manual nursing documentation and management of patient basic claims data. Context-driven acquisition of sensor data, documentation of observations and their automated processing can reduce this time, while availability of documented information increases. Usage of wireless sensors is a major element of long-term measurement of medical patient context. Currently a technical experienced nurse, patient or an additional technical service provider is needed on site to configure new sensors and pair them with the patient's or nurse's monitoring device. To ease configuration and maintenance this paper proposes a technique for remote configuration of a sensor gateway and secure automated pairing with Bluetooth sensors. For security reasons this paper presents a generic sensor driver interpreting XML-based protocol specifications to execute the protocol thus avoiding the installation of additional sensor drivers in the operating system for each new sensor. The concept is proven by a theoretical analysis and by an evaluation of the implementation.

Performance evaluation of a generic driver for medical Bluetooth sensors

Abstract: Outpatient nursing services expend a vast amount of time for manual documentation and management of patient basic claims data. Context-driven acquisition of sensor data, documentation of observations and their automated processing can reduce this time, while availability of documented information increases. Usage of wireless sensors is a major element of long-term measurement of medical data. Currently a technical experienced nurse, patient or an additional technical service provider is needed on site to configure new sensors and connect them with the patient's or nurse's monitoring device. To ease configuration and maintenance this paper proposes a technique for remote configuration of a sensor gateway and secure automated pairing with Bluetooth sensors. This paper presents a generic sensor driver that is interpreting XML-based protocol specifications to execute the protocol, thus avoiding the installation of additional sensor drivers in the operating system for each new sensor. It evaluates its performance compared to a binary driver. The concept is proven by an evaluation of the implementation.

Towards improving the Privacy in the MQTT Protocol

Abstract: The Internet of Things (IoT) is growing rapidly as more and more household appliances, sensors and actuators are connected to the internet and communicate with each other. Because these IoT devices usually have only limited processing and communication capabilities, an efficient communication protocol is required to reduce the protocol overhead. The Message Queue Telemetry Transport (MQTT) protocol provides such properties. MQTT is a publish/subscribe protocol, where each client can subscribe to a message topic in order to receive all messages published under that topic. The payload of a message (i.e., the content) can be encrypted to hide private information. However, to forward messages, the topic of a message needs to be read by the broker and thus cannot be encrypted and might reveal private information. This paper presents methods to avoid this problem. It features and evaluates a one-time password approach to provide a fully obfuscated communication method for MQTT topics. Thereby, the user tracking and the generation of profiles is prevented.

Machine learning for Internet of Things data analysis: A survey

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.

Integration and Exploitation of Sensor Data in Smart Cities through 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.

Security and Privacy of Smart Cities: A Survey, Research Issues and Challenges

Abstract: With recent advances of information and communication technology, smart city has been emerged as a new paradigm to dynamically optimize the resources in cities and provide better facilities and quality of life for the citizens. Smart cities involve a variety of components, including ubiquitous sensing devices, heterogeneous networks, large-scale databases, and powerful data centers to collect, transfer, store, and intelligently process real-time information. Smart cities can offer new applications and services for augmenting the daily life of citizens on making decisions, energy consumption, transportation, health-care, and education. Despite the potential vision of smart cities, security and privacy issues remain to be carefully addressed. This paper delineates a comprehensive survey of security and privacy issues of smart cities, and presents a basis for categorizing the present and future developments within this area. It also presents a thematic taxonomy of security and privacy issues of smart cities to highlight the security requirements for designing a secure smart city, identify the existing security and privacy solutions, and present open research issues and challenges of security and privacy in smart cities.