Currently, the Internet of Things (IoT) usage observes a drastic growth in several areas and participates in the rapid universe digitalization. Henceforward, the IoT systems next generation will be more difficult to develop and monitor. Gathering real-time data created by IoT triggers some novel opportunities for businesses to take at the right time more accurate and precise decisions. However, several challenges (such as IoT systems complexity and heterogeneous data management, and IoT system scalability) restrain the elaboration of IoT systems that are smart and impel business decision-making. This paper proposes to automatize IoT systems management using an autonomic computing approach. Nevertheless, autonomic computing is insufficient for smart IoT systems development. Actually, a smart IoT system implements cognitive abilities that allow its learning and decision-making in real-time. Therefore, this study proposes a model for designing smart IoT systems. It defines a set of cognitive design patterns that delineate the dynamiccooperation between management processes (MPs) (to handle the requirements evolvement and the system's environment unpredictability) and add cognitive capabilities to IoT systems (to generate new insights, perceive big data, and communicate with users). The study's primary goal is to support the developer in designing smart IoT systems that are flexible by choosing an appropriate pattern (or a set of patterns) to meet complex system requirements.

Cognitive and Autonomic IoT System Design

Knowledge about human systems usually comes from deliberate, organized efforts. These efforts are increasingly collaborative with partnering among diverse teams of experts. This has spawned research in the means for data integration and data lineage, or provenance, to better enable sharing of data and workflows. Such research has focused on specific problems in domain representation, such as ontology domain modeling, provenance standards and methodologies, and automated workflow management. While all these contributions are highly relevant for knowledge seeking efforts, what is missing is a meta model that accounts for all elements of investigation. This work introduces the concept of investigation as a means to formalize knowledge seeking efforts involving collaborative human action. We model the investigation concept as a set of seven elements common to all knowledge-seeking efforts. Incorporated into the proposed investigation design are the concepts from emerging sensor-observation standards and W3C provenance standards. This design differs from other approaches for knowledge acquisition modeling in its focus on reifying the management of effort – here referred to as an initiative, and the abstraction and reification of analytic results – here referred to as judgments. Further, we provide and initial attempt to identify specific workflow and data model design patterns within each of the seven investigation elements. An example illustrates the various aspects of our approach.

Investigation design: The structural elements of knowledge-seeking efforts

Handle everyday research tasks with reliable, citation-backed results

Your personal Research Agent to handle research tasks with citation-backed results

Popular Tasks used by Researchers

How can I help with your research?

Meet SciSpace

Get more enhanced response by uploading the PDFs you want me to reference.

No relevant PDFs in your library

SciSpace is the AI research assistant for academics. Run systematic literature reviews on 280M+ papers, and write papers with cited sources. Trusted by 1M+ students, PhDs & researchers.

SciSpace | AI for Research

Analyze PDFs

Code & Manuscripts

Funding & Grants

Literature & Patents

Medical & Clinical Data

Systematic Review

Visualize & Present

Web & Data

Build a Google Scholar-like website for your research.

Build a website

Create charts and images for your research

Create a Chart

Write a paper for submission to a journal

Draft a manuscript

Patent Search

Design eye-catching scientific posters in minutes.

Scientific Poster Generation

Systematic Literature Review

One task is running at the moment. Your messages will be shown right after.

Drag and drop or click here to browse

Loved by <highlight>1 million+</highlight> researchers

Extract a list of specific topics and their sources from unstructured text

Topics

Compare and analyze relevant papers that matches with your search

Papers

Get insights from PDFs and bookmarked papers from your library

My library

Recent searches

Try searching for:

Catch AI-generated content in scholarly and non-scholarly content

{ai} Detector

Ai Writer

Get PDF Summaries, highlighted text explanations 

Chat with PDF

Effortlessly create in-text citations and bibliographies in APA and 2,500 other formats

Citation generator

Get explanations, summaries, and answers on academic papers

Ease up your research workflow with {scispace}'s cohort of exciting AI tools

Elevate your academic writing skills and convey your ideas the way you want

Paraphraser

Explore our range of reading and writing tools

Your file is being prepared and should be ready in a few minutes. If it's a large file, it might take a bit longer. You can close this window, and we'll email you the file when it's done.

You have reached a maximum limit of <strong>{limit}</strong> columns in the table. Remove at least <strong>1</strong> column to add or create another one.

Bryan Weaver

Author Tools

Chat about Author

Papers

Investigation design: The structural elements of knowledge-seeking efforts