Nikolas Schmidt’s research while affiliated with Duquesne University and other places

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Publications (2)


A Survey on Edge Intelligence and Lightweight Machine Learning Support for Future Applications and Services
  • Article

January 2023

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65 Reads

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10 Citations

Journal of Data and Information Quality

Kyle Hoffpauir

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Jacob Simmons

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Nikolas Schmidt

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[...]

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As the number of devices connected to the internet has grown larger, so too has the intensity of the tasks that these devices need to perform. Modern networks are more frequently working to perform computationally intensive tasks on low-power devices and low-end hardware. Current architectures and platforms tend towards centralized and resource rich cloud computing approaches to address these deficits. However, edge computing presents a much more viable and flexible alternative. Edge computing refers to a distributed and decentralized network architecture in which demanding tasks such as image recognition, smart city services, and high-intensity data processing tasks can be distributed over a number of integrated network devices. In this paper, we provide a comprehensive survey for emerging edge intelligence applications, lightweight machine learning algorithms, and their support for future applications and services. We started by analyzing the rise of cloud computing discuss its weak points, and identify situations in which edge computing provides advantages over traditional cloud computing architectures. We then divulge into the survey - the first section identifying opportunities and domains for edge computing growth, the second identifying algorithms and approaches that can be used to enhance edge intelligence implementations, and the third specifically analyzing situations in which edge intelligence can be enhanced using any of the aforementioned algorithms or approaches. In this third section, lightweight machine learning approaches are detailed. A more in-depth analysis and discussion of future developments follows. The primary discourse of this piece is in an effort to ensure that appropriate approaches are applied adequately to artificial intelligence implementations in edge systems and mainly the lightweight machine learning approaches.


A Survey on Blockchain for Information Systems Management and Security

January 2021

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1,078 Reads

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542 Citations

Information Processing & Management

Blockchain technologies have grown in prominence in recent years, with many experts citing the potential applications of the technology in regard to different aspects of any industry, market, agency, or governmental organizations. In the brief history of blockchain, an incredible number of achievements have been made regarding how blockchain can be utilized and the impacts it might have on several industries. The sheer number and complexity of these aspects can make it difficult to address blockchain potentials and complexities, especially when trying to address its purpose and fitness for a specific task. In this survey, we provide a comprehensive review of applying blockchain as a service for applications within today’s information systems. The survey gives the reader a deeper perspective on how blockchain helps to secure and manage today information systems. The survey contains a comprehensive reporting on different instances of blockchain studies and applications proposed by the research community and their respective impacts on blockchain and its use across other applications or scenarios. Some of the most important findings this survey highlights include the fact that blockchain’s structure and modern cloud- and edge-computing paradigms are crucial in enabling a widespread adaption and development of blockchain technologies for new players in today unprecedented vibrant global market. Ensuring that blockchain is widely available through public and open-source code libraries and tools will help to ensure that the full potential of the technology is reached and that further developments can be made concerning the long-term goals of blockchain enthusiasts.

Citations (2)


... The work is categorized based on the type of Machine learning (ML) approach, technology, data generation source, research objective, and whether they consider container or VM-based virtualization. Since our proposed work focuses more on placement, we refer the reader to [28] to read more about edge intelligence. ...

Reference:

Latency aware graph-based microservice placement in the edge-cloud continuum
A Survey on Edge Intelligence and Lightweight Machine Learning Support for Future Applications and Services
  • Citing Article
  • January 2023

Journal of Data and Information Quality

... Understanding the dynamics of these projects, particularly during significant events, can provide insights into their resilience, adaptability, and overall health [1]. In recent years, blockchain technologies have emerged as a new frontier for OSS, presenting unique challenges and opportunities for collaborative development that extend and redefine traditional OSS paradigms [2]. Blockchain's convergence with OSS principles has created a distinct ecosystem with several notable characteristics. ...

A Survey on Blockchain for Information Systems Management and Security
  • Citing Article
  • January 2021

Information Processing & Management