# Internet of Everything (IoE) Taxonomies: A Survey and a Novel Knowledge-Based Taxonomy
## 6 Results
### Example of Classification of One Application with the Proposed Taxonomy  
Table 4 describes the classification of a specific application selected from [PERSON] et al. (2014) in accordance with the previously defined taxonomy. We selected the on-shelf availability application ([PERSON] et al., 2014) application due to the diversity of its sensor types and knowledge sources. \begin{table}
\begin{tabular}{c c c} \hline \hline \multicolumn{2}{c}{**Category/Dimension**} & \multicolumn{2}{c}{**Characteristics of an industry domain application (on-shelf availability application [152])**} \\ \hline \multirow{4}{*}{**Knowledge**} & \multirow{2}{*}{**Explicitness**} & **Tacit: shoppers’ experience, staff experience \(|\) Explicit: enterprise point of sale (POS) systems and inventory systems \(|\)** Implicit: algorithm and models from learning systems \\  & & **Structured: enterprise data \(|\) Semi-structured: weather data, local events, and promotion details \(|\)** Unstructured: real-time sensor data** \\  & **Trustful: data from enterprise systems \(|\) Untrustful: real-time data from shoppers’ sensors** \\  & **Outcome** & **Complements: Recommended action plans \(|\) Substitutes: predictive analytics to provide insights \\  & **Action** & **Automation: stock business processes \(|\) Transformation: insights into buyers’ behavior** \\ \hline \multirow{4}{*}{**Type**} & \multirow{4}{*}{**Presentation**} & **Cyber: predictive analytics algorithm \(|\) Physic: cameras, shoppers, staff of the store, light, infra-red, and RFID sensors \(|\) Cyber-Physical: point of sale (POS) systems** \\  & & **Electronic-based: video cameras, light, infra-red, and RFID sensors \(|\) Software-based: point of sale (POS) systems** \\  & **Nature** & **systems \(|\) Human-based: shoppers, the staff of the store \(|\) Non-human-based: shoppers’ pets \\  & **Use** & **Wearables: shoppers’ mobile devices \(|\) Surroundables: video cameras, infra-red sensors \(|\) Embedded: light, RFID sensors** \\  & & **Sensor:** video cameras, light, infra-red, and RFID sensors, shoppers, the staff of the store \(|\) Actuator: staff of the store who restock products or actuators to rectify problems \(|\)**sensor, and actuator**: staff of the store who senses and executes recommended actions \\  & **Engagement** & **Opportunistic: shoppers’ Participatory: shoppers/staff of the store** \\ \hline \multirow{4}{*}{**Observation**} & **Location** & **Coarse-grained: supply chain context \(|\) Fine-grained: store environment** \\  & **Reach** & **Full: supply chain context Partial: physical store environment** \\  & **Mobility** & **Fixed: inside the store supply chain context \(|\) Mobile: shoppers’ mobile devices** \\  & **Time** & **Full: meta-data produced and sent to the cloud \(|\) Push: forecast demands provided by systems** \\  & **Mode** & **Sense: store sensor devices \(|\) Derive: information derived from sensors \(|\)Manually provided: data** \\ \hline \multirow{4}{*}{**Capabilities**} & **Communication** & **Conceptual communication: supports the execution of recommended actions and provides a novel shopping experience** \\  & **Processing** & **Cloud: metadata produced \(|\) Fog/Edge: Edge: video streams processed locally \(|\) Mobile cloud: mobile devices from shoppers** \\  & **Storage** & **Device-level: processing video streams locally \(|\) Network level \(|\) Cluster level: metadata produced is sent to the cloud** \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Classification of IoE enablers of a specific industry domain application ([PERSON] et al., 2014), according to IoE proposed taxonomy characteristics. The on-shelf availability application [152] is an industry domain application [114] and relates to a system that benefits customers' experience by enhancing the on-shelf availability of products. The system also seeks to forecast demand and provide insights into buyers' behavior, with predictive analytics employed in business processes.