Browsing by Author "Villas, Leandro Aparecido"

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    A reactive role assignment for data routing in event-based wireless sensor networks.
    (2009) Nakamura, Eduardo Freire; Ramos Filho, Heitor Soares; Villas, Leandro Aparecido; Oliveira, Horácio Antônio Braga Fernandes de; Aquino, André Luiz Lins de; Loureiro, Antônio Alfredo Ferreira
    In this work, we show how we can design a routing protocol for wireless sensor networks (WSNs) to support an information-fusion application. Regarding the application, we consider that WSNs apply information fusion techniques to detect events in the sensor field. Particularly, in event-driven scenarios there might be long intervals of inactivity. However, at a given instant, multiple sensor nodes might detect one or more events, resulting in high traffic. To save energy, the network should be able to remain in a latent state until an event occurs, then the network should organize itself to properly detect and notify the event. Based on the premise that we have an information-fusion application for event detection, we propose a role assignment algorithm, called Information-Fusion-based Role Assignment(InFRA), to organize the network by assigning roles to nodes only when events are detected. The InFRA algorithm is a distributed heuristic to the minimal Steiner tree, and it is suitable for networks with severe resource constraints, such as WSNs. Theoretical anal ysis shows that, in some cases, our algorithm has aOð1 Þ-approximation ratio. Simulation results show that the InFRA algorithm can use only 70% of the communication resources spent by a reactive version of the Centered-at-Nearest-Source algorithm.
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    Road data enrichment framework based on heterogeneous data fusion for ITS.
    (2020) Rettore, Paulo Henrique Lopes; Santos, Bruno Pereira dos; Lopes, Roberto Rigolin Ferreira; Menezes, João Guilherme Maia de; Villas, Leandro Aparecido; Loureiro, Antonio Alfredo Ferreira
    In this work, we propose the Road Data Enrichment (RoDE), a framework that fuses data from heterogeneous data sources to enhance Intelligent Transportation System (ITS) services, such as vehicle routing and traffic event detection. We describe RoDE through two services: (i) Route service, and (ii) Event service. For the first service, we present the Twitter MAPS (T-MAPS), a low-cost spatiotemporal model to improve the description of traffic conditions through Location- Based Social Media (LBSM) data. As a case study, we explain how T-MAPS is able to enhance routing and trajectory descriptions by using tweets. Our experiments compare T-MAPS’ routes against Google Maps’ routes, showing up to 62% of route similarity, even though T-MAPS uses fewer and coarse-grained data. We then propose three applications, Route Sentiment (RS), Route Infor- mation (RI), and Area Tags (AT), to enrich T-MAPS’ suggested routes. For the second service, we present the Twitter Incident (T-Incident), a low-cost learning-based road incident detection and enrichment approach built using heterogeneous data fusion. Our approach uses a learning-based model to identify patterns on social media data which is then used to describe a class of events, aiming to detect different types of events. Our model to detect events achieved scores above 90%, thus allowing incident detection and description as a RoDE application. As a result, the enriched event description allows ITS to better understand the LBSM user’s viewpoint about traffic events (e.g., jams) and points of interest (e.g., restaurants, theaters, stadiums).