Jasmin Pielorz

Future Internet technologies for environmental applications

This paper investigates the usability of Future Internet technologies (aka “Generic Enablers of the Future Internet”) in the context of environmental applications. The paper incorporates the best aspects of the state-of-the-art in environmental informatics with geospatial solutions and scalable processing capabilities of Internet-based tools. It specifically targets the promotion of the “Environmental Observation Web” as an observation-centric paradigm for building the next generation of environmental applications. In the Environmental Observation Web, the great majority of data are considered as observations. These can be generated from sensors (hardware), numerical simulations (models), as well as by humans (human sensors). Independently from the observation provenance and application scope, data can be represented and processed in a standardised way in order to understand environmental processes and their interdependencies. The development of cross-domain applications is then leveraged by technologies such as Cloud Computing, Internet of Things, Big Data Processing and Analytics. For example, “the cloud” can satisfy the peak-performance needs of applications which may occasionally use large amounts of processing power at a fraction of the price of a dedicated server farm. The paper also addresses the need for Specific Enablers that connect mainstream Future Internet capabilities with sensor and geospatial technologies. Main categories of such Specific Enablers are described with an overall architectural approach for developing environmental applications and exemplar use cases.

Crowdsourcing in Crisis and Disaster Management – Challenges and Considerations

With the rise of social media platforms, crowdsourcing became a powerful tool for mobilizing the public. Events such as the earthquake in Haiti or the downfall of governments in Libya and Egypt indicate its potential in crisis situations. In the scope of this paper, we discuss the relevance of crowdsourcing in the area of crisis and disaster management (CDM). Starting with a general overview of the topic, we distinguish between different types of crowds and crowdsourcing and define what is meant by crowdtasking in the area of CDM. After considering technological, societal and ethical challenges for using crowdsourcing in crisis management, applications of crowdsourcing tools in ongoing projects are described and future developments outlined.

Crowdsourcing and crowdtasking in crisis management: Lessons learned from a field experiment simulating a flooding in the city of the Hague

The EU FP7 project DRIVER conducts a number of experiments that explore new approaches for addressing known deficiencies in crisis management. The “Interaction with Citizens” experiment campaign focuses on testing the usability and acceptance of various methods and tools that facilitate crisis communication via several channels. These include: informing, alerting, micro-tasking, incident information crowdsourcing from volunteers, and usage of this information to improve situational awareness. The results highlight that volunteer motivation in a serious game like scenario is important to simulate participation in crisis events. We also argue that the scenario complexity level needs to be simple enough to avoid difficulties in communication with non-professional participants in addition to external influences in a field experiment. In this paper, we present lessons learned from the final experiment of this campaign that investigated two-way communication solutions between crisis managers and citizens or unaffiliated volunteers in a simulated flooding scenario in the city of The Hague.

Semantic Interoperability as Key to IoT Platform Federation

Semantic interoperability is the key technology to enable evolution of the Internet of Things (IoT) from its current state of independent vertical IoT silos to interconnected IoT platform federations. This paper analyzes the possible solution space on how to achieve semantic interoperability and presents five possible approaches in detail together with a discussion on implementation issues. It presents the H2020 symbIoTe project as an example on how semantic interoperability can be achieved using semantic mapping and SPARQL query re-writing. We conclude that the found approaches together with the proposed technologies have the potential to act as corner stone technologies for achieving semantic interoperability.

Optimal geospatial allocation of volunteers for crisis management

Volunteer supporters play an important role in modern crisis and disaster management. In the times of mobile Internet devices, help from thousands of volunteers can be requested within a short time span, thus relieving professional helpers from minor chores or geographically spread-out tasks. However, the simultaneous availability of many volunteers also poses new problems. In particular, the volunteer efforts must be well coordinated, or otherwise situations might emerge in which too many idle volunteers at one location become more of a burden than a relief to the professionals. In this work, we study the task of optimally assigning volunteers to selected locations, e.g. in order to perform regular measurements, to report on damage, or to distribute information or resources to the population in a crisis situation. We formulate the assignment tasks as an optimization problem and propose an effective and efficient solution procedure. Experiments on real data of the Team Österreich, consisting of over 36,000 Austrian volunteers, show the effectiveness and efficiency of our approach.

Data Fusion in the Environmental Domain

Air quality and air pollution have a very large impact on human health. The sensitivity to different pollutants varies per person, therefore it is important that citizens can get personalised air quality information. The Personal Environmental Information System (PEIS) aims at delivering just that. The PEIS takes sensor data from several data providers and employs a service-oriented architecture to deliver these observations to the user through a smartphone application. The PEIS also uses scientific models to fuse the sensor data and create new, derived observations. To make scientific models that fuse sensor data fit better in a service-oriented architecture, a software framework called Fusion4Decision was developed. This framework is based on Open Geospatial Consortium standards and allows scientific models written in languages like MATLAB or R to be available as a web service.