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

Abstract

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.

The Rising Role of Big Data Analytics and IoT in Disaster Management: Recent Advances, Taxonomy and Prospects

“…Despite the limited publications regarding BDA for disaster management, some of the recent research as compared in Table 3 shows that a variety of data sources are being utilized with various open-source BDA tools within the scope of disaster management. For instance, for flood risk management an interoperable mechanism was designed by authors in [24] to integrate heterogeneous sensors that enable access and filtering of the data in near-real-time using Spark. The approach used in their study offers a method to enhance near-real-time applications using heterogeneous data streams i.e., crowdsourced and sensor data.…”

Towards Smart-City Implementation for Crisis Management in Fast-Growing and Unplanned Cities: the Colombian Scenario

“…About infrastructure, architecture is part of the IoT topics where the implementation works for data streaming, processing, and storage. Two architectures and implementation of smart city service [19], [20], shows the heterogeneous data, and the necessity to be processing streaming and longer processing, in this way computing paradigm is also part of the research, Cloud computing for a large amount of data and permanent storage, and distributed processing with temporal storage, where is near to the users reducing latency of data [28]. The cloud layer could have Cloud data centers which are large pools of highly accessible virtualized resources [29], but cloud computing is centralized and in a crisis situation a distributed deployment could have a quick and stable response, then we can establish the minimum requirements for a smart-city with related characteristics (see Figure 4).…”

“…2.3.1 Images Some research has proven that it is useful to analyze images that aim to get information with social media unlabeled or with natural language associated [1], [5], [20], [35]. certain developments are their own as a proposal to get information to the images.…”

“…Two recent research shows an architecture implemented with similar case-studies for crisis management. Gaire et al [19], implement an architecture for crisis management in Australia, other similar implementation in Brazil for De Assis et al [20], show a summary of approaches and main functionalities, where proposes a software solution where compares and analyzes the information of each case-study suggesting new infrastructures for smart-city which it will work for crisis management in cities where requirements are minima, as the case study of the city Bucaramanga/Colombia and to extrapolate solution to cities with similar characteristics as highly dense and unplanned cities. This paper aims to review different IoT implementation for crisis management projects, where similar cities characteristics can give solution to specific needs from the citizens.…”

TerraBrasilis: A Spatial Data Analytics Infrastructure for Large-Scale Thematic Mapping

“…Space agencies have been tracking individual satellites, placed in orbit, to obtain a synoptic view with a multitude of spectral signatures that are used to provide a rapid understanding of the dynamics of land use/cover (LUC) changes, ranging from a regional to a continental scale [2]. The quality and flexibility of the large amount of spatial information can assist the scientific community in a wide range of environmental applications: deforestation modelling [3], monitoring disasters [4], estimation of total water body [5] and making a biodiversity assessment of agriculture [6,7]. As a result, some studies have led to the design of thematic maps to assist their scientific research by highlighting spatio-temporal patterns in urban and rural areas.…”