Flávio Eduardo Aoki Horita

Development of a spatial decision support system for flood risk management in Brazil that combines volunteered geographic information with wireless sensor networks

Effective flood risk management requires updated information to ensure that the correct decisions can be made. This can be provided by Wireless Sensor Networks (WSN) which are a low-cost means of collecting updated information about rivers. Another valuable resource is Volunteered Geographic Information (VGI) which is a comparatively new means of improving the coverage of monitored areas because it is able to supply supplementary information to the WSN and thus support decision-making in flood risk management. However, there still remains the problem of how to combine WSN data with VGI. In this paper, an attempt is made to investigate AGORA-DS, which is a Spatial Decision Support System (SDSS) that is able to make flood risk management more effective by combining these data sources, i.e. WSN with VGI. This approach is built over a conceptual model that complies with the interoperable standards laid down by the Open Geospatial Consortium (OGC) - e.g. Sensor Observation Service (SOS) and Web Feature Service (WFS) - and seeks to combine and present unified information in a web-based decision support tool. This work was deployed in a real scenario of flood risk management in the town of Sao Carlos in Brazil. The evidence obtained from this deployment confirmed that interoperable standards can support the integration of data from distinct data sources. In addition, they also show that VGI is able to provide information about areas of the river basin which lack data since there is no appropriate station in the area. Hence it provides a valuable support for the WSN data. It can thus be concluded that AGORA-DS is able to combine information provided by WSN and VGI, and provide useful information for supporting flood risk management. HighlightsA conceptual framework integrates information from sensors and volunteers.Interoperable standards are employed for integrating the heterogeneous data sources.Lessons were learned from the deployment in a real scenario of flood risk management.Volunteered Geographic Information improves the coverage of monitored areas.

Bridging the gap between decision-making and emerging big data sources

With the emergence of big data and new data sources, a challenge posed to todays organizations consists of identifying how to align their decision-making and organizational processes to data that could help them make better-informed decisions. This paper presents a study in the context of disaster management in Brazil that applies oDMN+, a framework that connects decision-making with data sources through an extended modeling notation and a modeling process. The study results revealed that the framework is an effective approach for improving the understanding of how to leverage big data in the organizations decision-making. An extended model-based framework connects decision-making to big data sources.A better understanding of decision-making is achieved with the framework.A modeling process is outlined for systematically using the framework in practice.Decision-making can be improved through the use of standard models and notations.Lessons were learned from a case study on a Brazilian disaster early-warning center.

oDMN: An Integrated Model to Connect Decision-Making Needs to Emerging Data Sources in Disaster Management

Disaster managers depend on timely and accurate information for task-related decision-making in highly complex and dynamic environments. New data sources, like online social media provide an increasing volume of data that promises improvements in situation awareness. But it remains difficult to focus data collection on information needs and integrate relevant information back into decision-making. In this paper, we present the observation-aware Decision Model and Notation (oDMN), which connects tasks, decisions, information and data sources based on standardized models and notations as well as on domain-specific information models. The integrated model allows for deriving information requirements and determining the impact of incoming observations on relevant tasks and decisions. To demonstrate its usefulness, we apply the model to a case centered on logistics operations during the 2015 Nepal earthquake response. The results show that the model is indeed able to formally connect tasks, decisions, information and data sources, and thus support better decision-making.

Dynamic Sensor Management: Extending Sensor Web for Near Real-Time Mobile Sensor Integration in Dynamic Scenarios

Wireless Sensor Networks (WSN) are often composed of a wide range of sensor nodes, which may vary greatly in their type of hardware platform, as well as their sensing and mobility capabilities. The ability of a sensor to move is a particularly important feature in dynamic scenarios, since mobile sensors can fill the gap caused by the failures of those that are stationary, and thus extend the lifetime and span of a WSN. However, there remains the problems of intersensory communication in the field when integrating mobile sensors into the Sensor Web in dynamic scenarios since it does not have the necessary interoperability for automatically managing the different types of sensor data and activities involved in such scenarios. This paper tackles this problem by adopting an approach consisting of an enhanced messaging protocol and a dynamic sensor management component. In validating the proposal, two different realistic scenarios were simulated to evaluate the achieved results in terms of interoperability and performance. The results provided evidence that the proposal complies with Sensor Web standards as well as being suitable for near real-time data publication, and is thus able to support applications in dynamic scenarios.

Software architecture and reference architecture of software-intensive systems and systems-of-systems: contributions to the state of the art

Complex software-intensive systems are more and more required as a solution for diverse critical application domains; at the same time, software architecture and also reference architecture have attracted attention as means to more adequately produce and evolve such systems. The main goal of this paper is to summarize our principal contributions in software architecture and reference architecture of software-intensive systems, including Systems-of-Systems. We intend this work can also inspire the opening of other related research lines towards founding the sustainability of such software-intensive systems.

A Service-Oriented Middleware for Integrated Management of Crowdsourced and Sensor Data Streams in Disaster Management

The increasing number of sensors used in diverse applications has provided a massive number of continuous, unbounded, rapid data and requires the management of distinct protocols, interfaces and intermittent connections. As traditional sensor networks are error-prone and difficult to maintain, the study highlights the emerging role of “citizens as sensors” as a complementary data source to increase public awareness. To this end, an interoperable, reusable middleware for managing spatial, temporal, and thematic data using Sensor Web Enablement initiative services and a processing engine was designed, implemented, and deployed. The study found that its approach provided effective sensor data-stream access, publication, and filtering in dynamic scenarios such as disaster management, as well as it enables batch and stream management integration. Also, an interoperability analytics testing of a flood citizen observatory highlighted even variable data such as those provided by the crowd can be integrated with sensor data stream. Our approach, thus, offers a mean to improve near-real-time applications.