Navegando por Autor "Teixeira, Fernando Augusto"

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    Gathering data in wireless sensor networks by drone.
    (2020) Rezende, Josiane da Costa Vieira; Souza, Marcone Jamilson Freitas; Silva, Rone Ilídio da; Souza, Marcone Jamilson Freitas; Teixeira, Fernando Augusto; Coelho, Igor Machado; Ochi, Luiz Satoru; Penna, Puca Huachi Vaz; Coelho, Vitor Nazário; Silva, Rone Ilidio da
    The benefits of using mobile sinks or data mules for data collection in Wireless Sensor Network (WSN) have been studied in several studies. However, most of them consider only the WSN limitations and sensor nodes having no more than one data packet to transmit. This paper considers each sensor node having a relatively larger volume of data stored in its memory. That is, they have several data packets to send to sink. We also consider a drone with hovering capability, such as a quad-copter, as a mobile sink to gather this data. Hence, the mobile collector eventually has to hover to guarantee that all data will be received. Drones, however, have a limited power supply that restricts their flying time. Hence, the drone’s energy cost must also be considered to increase the amount of collected data from the WSN. This work investigates the problem of determining the best drone tour for data gathering in a WSN. We focus on minimizing the overall drone flight time needed to collect all data from the WSN. We propose an algorithm to create a subset of sensor nodes to send data to the drone during its movement and, consequently, reduce its hovering time. The proposed algorithm guarantees that the drone will stay a minimum time inside every sensor node’s radio range. The computational experiments showed that our proposal significantly outperforms the state-of-the-art methods in finding drone tours in this type of scenario.
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    RouteSpray : a multiple-copy routing algorithm based on transit routes.
    (2013) Silva, Maurício José da; Teixeira, Fernando Augusto; Oliveira, Ricardo Augusto Rabelo
    Vehicular networks represent a special type of wireless network that has gained the attention of researchers over the past few years. Routing protocols for this type of network must face several challenges, such as high mobility, high speeds and frequent network disconnections. This paper proposes a vehicular routing algorithm called RouteSpray that in addition to using vehicular routes to help make routing decisions, uses controlled spraying to forward multiple copies of messages, thus ensuring better delivery rates without overloading the network. The results of experiments performed in this study indicate that the RouteSpray algorithm delivered 13.46% more messages than other algorithms reported in the literature. In addition, the RouteSpray algorithm kept the buffer occupation 73.38% lower.
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    Wearable edge AI towards cyber-physical applications.
    (2023) Silva, Mateus Coelho; Oliveira, Ricardo Augusto Rabelo; Ribeiro, Sérvio Pontes; Bianchi, Andrea Gomes Campos; Oliveira, Ricardo Augusto Rabelo; Teixeira, Fernando Augusto; Silva, Jorge Miguel Sá; Correia, Luiz Henrique Andrade; Silva, Saul Emanuel Delabrida; Amorim, Vicente José Peixoto de
    The creation of novel technologies to support field work and research has a major impact from technologies such as the Internet of Things (IoT), Edge Computing and wearable computing. In this context, Artificial-Intelligence-based systems became more common and a trend in recent work. Environments with low connectivity and high latency in data transmission enforce the usage of Edge Computing technologies in the treatment of acquired data. Nonetheless, there is no clarity in how to transport Artificial Intelligence (AI) to Edge Computing in extreme environments, given the complexity of the requirements. This gap is more clear in the context of wearable computing, where the systems restrictions for developing systems are even harder. Thus, this work presents a protocol for developing Edge AI appliances and some case-study applications in the context of wearable devices. This study helps to evaluate the creation of Wearable Edge AI context as a novel research field.