Bringing AI to the edge: a formal M&amp;S specification to deploy effective IoT architectures

Cárdenas, Román; Arroba, Patricia; Risco‐Martín, José L.

doi:10.1080/17477778.2020.1863755

Cited by 8 publications

(8 citation statements)

References 22 publications

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“…The cloud layer performs complex data analytics and machine learning tasks that require significant computing power and storage capacity. 27 Figure 1 has already illustrated the general picture of the framework architecture. Our M&S framework is fed with data that may come from the actual water body or from a database, which can, in turn, store authentic or synthetic data.…”

Section: System Architecture and Designmentioning

confidence: 99%

Simulation-driven engineering for the management of harmful algal and cyanobacterial blooms

et al. 2023

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Harmful algal and cyanobacterial blooms (HABs), occurring in inland and maritime waters, pose threats to natural environments by producing toxins that affect human and animal health. In the past, HABs have been assessed mainly by the manual collection and subsequent analysis of water samples and occasionally by automatic instruments that acquire information from fixed locations. These procedures do not provide data with the desirable spatial and temporal resolution to anticipate the formation of HABs. Hence, new tools and technologies are needed to efficiently detect, characterize and respond to HABs that threaten water quality. It is essential nowadays when the world’s water supply is under tremendous pressure because of climate change, overexploitation, and pollution. This paper introduces Discrete Event System Specification-BLOOM, a novel framework for real-time monitoring and management of HABs. Its purpose is to support high-performance hazard detection with model-based system engineering and cyber-physical systems infrastructure for dynamic environments.

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Section: System Architecture and Designmentioning

confidence: 99%

Simulation-driven engineering for the management of harmful algal and cyanobacterial blooms

et al. 2023

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“…In addition, previous studies have shown that in organizations with high‐level BDAC‐AI, through breaking through the traditional organization management mode, digital supervision (Feng et al, 2003), integration and innovation, enterprises can make rational use of resources, reduce costs, improve utilization efficiency (Chen & Xu, 2001) and make enterprises obtain more competitive dynamic capabilities (Chen et al, 2020; Gallego‐Gomez & De‐Pablos‐Heredero, 2020). Cárdenas et al (2021) also pointed out that when BDAC‐AI is high, enterprises can perceive opportunities more sensitively (Feng et al, 2003) and manage resources jointly so as to improve the dynamic capabilities of enterprises. Therefore, under the high level of BDAC‐AI, the dynamic capability of new ventures can be enhanced.…”

Section: Theoretical Background and Research Hypothesismentioning

confidence: 99%

“…At the same time, the integration of opportunity resources of new ventures affects the dynamic capabilities of enterprises (Karimi‐Alaghehband & Rivard, 2020; Teece, 2016) and directly affects the level of entrepreneurial performance (Gao et al, 2019). In the process of entrepreneurship, enterprises with strong BDAC‐AI obtain resources through a wider entrepreneurial network (Cárdenas et al, 2021; Chen et al, 2018) and enhance the relationship between opportunity resource integration and dynamic capabilities by expanding the scope of enterprise opportunity identification and resource acquisition channels and efficiency (Alkurd et al, 2020). Based on the resource‐based view and dynamic capability theory, this paper constructs a model with dynamic capability as the intermediary and BDAC‐AI as the independent variable and regulatory variable and verifies the theoretical hypothesis and research model through 225 new venture data.…”

Section: Introductionmentioning

confidence: 99%

The impact of the integration of opportunity and resources of new ventures on entrepreneurial performance: The moderating role of BDAC‐AI

Zhao

2022

Syst Res Behav Sci

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Opportunity and resources are the two key elements of entrepreneurship, and they are inseparable. Scholars have paid more attention to integrating opportunities and resources for new ventures. At the same time, the academic community has widely recognized the important role of ‘dynamic capability’ in the survival and development of enterprises. However, few studies have explored the relationship between the integration of opportunity resources and dynamic capabilities of new ventures in essence and have not revealed the mechanism of big data analysis capability supported by artificial intelligence (BDAC‐AI) in the process of improving the entrepreneurial performance of new ventures. Based on the resource‐based view and dynamic capability theory, this paper integrates opportunity resource integration and dynamic capability into a research framework for the first time, constructs a new theoretical model and discusses the impact mechanism of BDAC‐AI and opportunity resource integration on entrepreneurial performance. Based on the data of 225 new ventures, this paper finds that (1) both BDAC‐AI and opportunity resource integration of new ventures have a positive impact on dynamic capability and dynamic capability has a positive role in promoting entrepreneurial performance; (2) dynamic capability plays an intermediary role in the relationship between BDAC‐AI, opportunity resource integration and new venture performance; and (3) BDAC‐AI positively regulates opportunity resource integration. However, it has no moderating effect on the mediating effect of dynamic capabilities. The research conclusion has important theoretical significance for the improvement of new ventures' entrepreneurial performance and has important practical significance for the survival and development of new ventures under the background of artificial intelligence.

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“…The proposed model is an extension of our previous work [15]. Specifically, it includes cooling systems for EDCs and DTs of EDCs' components to implement more sophisticated resource management policies.…”

Section: Edge Computing Modelmentioning

confidence: 99%

“…-We extend our edge computing model presented in previous publications [14,15] to integrate cooling infrastructures and support dynamic and more advanced resource management policies. -We also expand the model to support a decentralized load balancing protocol to map new loads to one of the EDCs comprising the edge computing federation.…”

Section: Introductionmentioning

confidence: 99%

Modeling and simulation of smart grid-aware edge computing federations

et al. 2022

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Compute-intensive Internet of Things (IoTs) applications have led to the edge computing paradigm. Edge computing decentralizes the IT infrastructure in multiple edge data centers (EDCs) across the access networks to reduce latency and network congestion. Edge computing can benefit significantly from different aspects of smart grids to achieve lower energy consumption and greater resilience to electricity price fluctuations. This paper presents a modeling, simulation, and optimization (M&S&O) framework for analyzing and dimensioning smart grid-aware edge computing federations. This tool integrates aspects of a consumer-centric smart grid model to the resource management policies of the EDCs. To illustrate the benefits of this tool, we show a realistic case study for optimizing the energy consumption and operational expenses of an edge computing federation that provides service to a driver assistance IoT application. Results show that this approach can reduce the daily energy consumption by 20.3% and the electricity budget by 30.3%.

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Bringing AI to the edge: a formal M&S specification to deploy effective IoT architectures

Cited by 8 publications

References 22 publications

Simulation-driven engineering for the management of harmful algal and cyanobacterial blooms

Simulation-driven engineering for the management of harmful algal and cyanobacterial blooms

The impact of the integration of opportunity and resources of new ventures on entrepreneurial performance: The moderating role of BDAC‐AI

Modeling and simulation of smart grid-aware edge computing federations

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