Creating a Sensor Tier for the EMULSION IoT Platform with Low-Cost Electronic Modules

Ганчев, Иван; Ji, Zhanlin

doi:10.1088/1742-6596/2226/1/012009

Cited by 2 publications

(1 citation statement)

References 13 publications

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“…The EMULSION IoT platform is successfully used as a basis for the development of various IoT prototype systems for different purposes, such as smart environment monitoring and control [16,17], rented bicycles OAM, operation and control of smart electric boilers, and pure water monitoring and control [15,18]. Future research work will be focused on further elaboration of novel techniques, algorithms, and models for the effective and efficient recommendation of IoT services, provided by the prototype systems, built on top of EMULSION, and accessible anytimeanywhere-anyhow through any kind of mobile device via the 'best' available wireless access network, in accordance with the ABC&S communication paradigm [19].…”

Section: Emulsionmentioning

confidence: 99%

In pursuit of horizontality in the development of IoT platforms

Ganchev

2023

MEM

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This paper presents an overview of the horizontal approach for the creation of Internet of Things (IoT) platforms, which allows to overcome many of the disadvantages of the vertical approach, by allowing a provider to supply only a horizontal slice in the delivery of IoT services, thus meeting better today’s expectations for efficiency and effectiveness, flexibility and scalability, openness and easy adjustment to new use cases and application scenarios, and multi-purpose use of the IoT systems built on top of such platforms. The state of the art in the area of IoT platforms development is presented along with some examples of horizontal IoT platform solutions in use today. The EMULSION IoT platform, developed by following the horizontal approach, is briefly described at the end.

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Section: Emulsionmentioning

confidence: 99%

In pursuit of horizontality in the development of IoT platforms

Ganchev

2023

MEM

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Integrating Deep Learning and Reinforcement Learning for Enhanced Financial Risk Forecasting in Supply Chain Management

Cui,

Yao

2024

J Knowl Econ

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In today’s dynamic business landscape, the integration of supply chain management and financial risk forecasting is imperative for sustained success. This research paper introduces a groundbreaking approach that seamlessly merges deep autoencoder (DAE) models with reinforcement learning (RL) techniques to enhance financial risk forecasting within the realm of supply chain management. The primary objective of this research is to optimize financial decision-making processes by extracting key feature representations from financial data and leveraging RL for decision optimization. To achieve this, the paper presents the PSO-SDAE model, a novel and sophisticated approach to financial risk forecasting. By incorporating advanced noise reduction features and optimization algorithms, the PSO-SDAE model significantly enhances the accuracy and reliability of financial risk predictions. Notably, the PSO-SDAE model goes beyond traditional forecasting methods by addressing the need for real-time decision-making in the rapidly evolving landscape of financial risk management. This is achieved through the utilization of a distributed RL algorithm, which expedites the processing of supply chain data while maintaining both efficiency and accuracy. The results of our study showcase the exceptional precision of the PSO-SDAE model in predicting financial risks, underscoring its efficacy for proactive risk management within supply chain operations. Moreover, the augmented processing speed of the model enables real-time analysis and decision-making — a critical capability in today’s fast-paced business environment.

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Creating a Sensor Tier for the EMULSION IoT Platform with Low-Cost Electronic Modules

Cited by 2 publications

References 13 publications

In pursuit of horizontality in the development of IoT platforms

In pursuit of horizontality in the development of IoT platforms

Integrating Deep Learning and Reinforcement Learning for Enhanced Financial Risk Forecasting in Supply Chain Management

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