# Internet of Everything (IoE) Taxonomies: A Survey and a Novel Knowledge-Based Taxonomy
## 3 Research Methodology  
Taxonomies are interpretations of reality and represent sense-making structures [87] (p. 51) for organizing information and knowledge into hierarchical relationships between the terms. This involves uncovering how the theories evolve, which enables researchers to study the essences and their relationships in the research territory [88].  
As a form of classification [88], a taxonomy for IoE sensors that considers knowledge enablers elucidates how some types of sensors are pooled and used in diverse application domains and how issues with capabilities and observations can affect the quality of services and knowledge creation. In order to develop a taxonomy to guide the identification of critical knowledge in IoE applications and an in-depth classification of IoE enablers (sensors and actuators), we surveyed existing taxonomies related to IoT and IoE. The first step in developing our taxonomy was to review the existing classification schemes, semantic descriptions, and taxonomies, which could suggest design implications for IoE systems.  
The methodological guidelines suggested by [PERSON] and [PERSON] [89] for literature reviews guided this survey. Our review included contributions from the ACM Digital Library, IEEE Digital Library, ISI Web of Science, Science@Direct, and Scopus databases, which we considered to be the most relevant for finding specific studies in journal and conference papers in English. The following specific search string was sought: (\"Internet of everything\" OR \"IoE\" OR \"Internet of things\" OR \"IoT\") AND (\"taxonomy\") in the \"Title\", \"Abstract\", or \"Keywords\" fields.  
We designed the search string to retrieve from the databases as many studies as possible that were relevant to the review, even if the query results returned articles not relevant to the survey. Relevant studies not retrieved after the first query were also included in a second iteration analysis in June 2020, considering studies likely to be explicitly related to IoE. Furthermore, most contributions were survey papers for IoT, which indicates a lack of maturity in work in the field of IoE.  
We selected only studies published in English in journals (already published and in press), conference proceedings, books, and technical reports. After discarding the duplicates, a total of 394 candidate articles remained from the initial search (Table 1).  
Each candidate article was subjected to the following series of steps before its eventual selection: (1) evaluate the title and read the summary; (2) retrieve the selected papers and read the introduction and conclusions; and (3) critically assess the contribution considering the degree of adherence to IoE applications and the contribution's relevance.  
Finally, after applying the filters, 76 articles relevant to this literature review remained. The studies were diverse and promoted different approaches. From the list of papers selected, it was possible to extract works related to IoT and IoE taxonomies, thus revealing the proposed IoE taxonomy. A qualitative analysis of the results summarized the main findings and provided some guidelines and a comprehensive overview of the topic that supported the novel knowledge-based IoE taxonomy proposal.