Analyzing the Effect of Increased Distribution on a Wearable Appliance

Silva, Mateus; Oliveira, Ricardo Augusto Rabelo

doi:10.1109/compsac.2019.10176

Cited by 4 publications

(2 citation statements)

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“…We need to consider features that evaluate the proposed scenario’s individual and general performances for the architecture validation tests. Thus, we developed an experiment designed as a real-time QoS test based on similar studies concerning IoT and wireless sensor networks [ 49 , 50 ] to evaluate the real-time constraint. This test evaluates the capability of performing a set of tasks, considering both individual- and network-based conditions.…”

Section: Methodsmentioning

confidence: 99%

Wearable Edge AI Applications for Ecological Environments

Silva

Delabrida

et al. 2021

Sensors

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Ecological environments research helps to assess the impacts on forests and managing forests. The usage of novel software and hardware technologies enforces the solution of tasks related to this problem. In addition, the lack of connectivity for large data throughput raises the demand for edge-computing-based solutions towards this goal. Therefore, in this work, we evaluate the opportunity of using a Wearable edge AI concept in a forest environment. For this matter, we propose a new approach to the hardware/software co-design process. We also address the possibility of creating wearable edge AI, where the wireless personal and body area networks are platforms for building applications using edge AI. Finally, we evaluate a case study to test the possibility of performing an edge AI task in a wearable-based environment. Thus, in this work, we evaluate the system to achieve the desired task, the hardware resource and performance, and the network latency associated with each part of the process. Through this work, we validated both the design pattern review and case study. In the case study, the developed algorithms could classify diseased leaves with a circa 90% accuracy with the proposed technique in the field. This results can be reviewed in the laboratory with more modern models that reached up to 96% global accuracy. The system could also perform the desired tasks with a quality factor of 0.95, considering the usage of three devices. Finally, it detected a disease epicenter with an offset of circa 0.5 m in a 6 m × 6 m × 12 m space. These results enforce the usage of the proposed methods in the targeted environment and the proposed changes in the co-design pattern.

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Section: Methodsmentioning

confidence: 99%

Wearable Edge AI Applications for Ecological Environments

Silva

Delabrida

et al. 2021

Sensors

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“…In addition, we need to understand the limitations of the data availability in this network before any algorithmic proposal. Thus, for this evaluation, we define a mathematical model based on Quality-of-Service (QoS), like the ones presented by Boukerche and Samarah [83] and Silva and Oliveira [84]. This QoS test evaluates the information availability, providing the temporal constraints that must be evaluated when creating consuming applications.…”

Section: Case Studymentioning

confidence: 99%

Field Research Cooperative Wearable Systems: Challenges in Requirements, Design and Validation

Silva

Amorim

Ribeiro

et al. 2019

Sensors

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The widespread availability of wearable devices is evolving them into cooperative systems. Communication and distribution aspects such as the Internet of Things, Wireless Body Area Networks, and Local Wireless Networks provide the means to develop multi-device platforms. Nevertheless, the field research environment presents a specific feature set, which increases the difficulty in the adoption of this technology. In this text, we review the main aspects of Field Research Gears and Wearable Devices. This review is made aiming to understand how to create cooperative systems based on wearable devices directed to the Field Research Context. For a better understanding, we developed a case study in which we propose a cooperative system architecture and provide validation aspects. For this matter, we provide an overview of a previous device architecture and study an integration proposal.

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