## 3 Related Work
### Discussions  
Table 1 presents a comparison between existing ontologies and optimization techniques for ERP semantic description and services composition in terms of (1) reusability, (2) extensibility, (3) dynamicity, (4) adaptability, (5) usage constraints (time-space constraints, services dependencies, energy), and (6) information coverage of thing diversity, thing mobility, service diversity, and events sharing. These criteria were chosen for several reasons. Reusability refers to how existing concepts can be reused across multiple applications, as well as the choice of extensibility criteria that determine whether or not the model is extensible for domain-specific business models. The choice of dynamicity refers to how existing models monitor context changes and adapt business processes dynamically. The choice of adaptability refers to how existing techniques adapt business processes and improves their quality. Usage constraints refers to how existing models describe spatiotemporal constraints. Limited resources and device constraints are parameters needed in managing sensor mobility in terms of energy consumption. We have identified the common limitations of the studied approaches, particularly for integrating hybrid heterogeneous services providers (IoT, fog, and Cloud) with a heuristic-based optimization strategy to dynamically compose rich services for ERP in smart factories. Most existing works have neglected both energy and QoS optimization when monitoring one or more products to ensure their safety. We need to integrate mobility-related features to represent mobile devices and monitor changes in their locations and cover  
\begin{table}
\begin{tabular}{c c c c c c c c c c c c c c c c c c} \hline \hline  & & & & & & & & & & & & & & & & & & & & & & & \\ \cline{2-16} \multirow{6}{*}{**Related Works**} & \multirow{2}{*}{**Criteria**} & & & & & & & & & & & & & & & & & & & & & & \\ \cline{2-16}  & & [PERSON] et al. [3] & \(\surd\) & \(\surd\) & \(\surd\) & \(\times\) & \(\times\) & \(\times\) & \(\times\) & \(\times\) & \(\times\) & \(\times\) & \(\surd\) & \(\times\) & \(\times\) & \(\times\) \\  & & OntoSLAM [4] & \(\surd\) & \(\surd\) & \(\surd\) & \(\times\) & \(\surd\) & \(\surd\) & \(\times\) & \(\times\) & \(\surd\) & \(\surd\) & \(\times\) & \(\times\) & \(\times\) & \(\times\) \\  & & ISRO [5] & \(\surd\) & \(\surd\) & \(\surd\) & \(\times\) & \(\surd\) & \(\surd\) & \(\times\) & \(\times\) & \(\surd\) & \(\surd\) & \(\times\) & \(\times\) & \(\times\) & \(\times\) \\  & & POS [6] & \(\surd\) & \(\surd\) & \(\surd\) & \(\times\) & \(\surd\) & \(\surd\) & \(\times\) & \(\surd\) & \(\surd\) & \(\times\) & \(\times\) & \(\times\) & \(\times\) & \(\times\) \\  & & CORA [7] & \(\times\) & \(\surd\) & \(\times\) & \(\times\) & \(\surd\) & \(\surd\) & \(\times\) & \(\surd\) & \(\times\) & \(\times\) & \(\times\) & \(\times\) & \(\times\) & \(\times\) \\  & & KnowRob [8] & \(\surd\) & \(\surd\) & \(\times\) & \(\times\) & \(\surd\) & \(\surd\) & \(\times\) & \(\surd\) & \(\surd\) & \(\surd\) & \(\times\) & \(\times\) & \(\times\) & \(\times\) \\  & & SAWSDL [9] & \(\surd\) & \(\surd\) & \(\surd\) & \(\times\) & \(\surd\) & \(\surd\) &age area. Moreover, when examining service optimization approaches, we notice that most works in [10; 11; 12; 13; 14; 15; 16; 17; 18; 19; 20; 21; 22; 23; 36] use bio-inspired and meta-heuristics approaches, enabling the optimization of QoS criteria for composite Cloud service regardless of their semantics, such as category, role, energy, spatial temporal constraints, etc. Furthermore, other related works [3; 4; 5; 6; 7; 8; 9; 23; 25; 26; 27; 28; 29; 38] integrate IoT and Cloud services-related semantic features to handle service heterogeneity but without optimizing QoS and energy for IoT-Fog-Cloud ERP. We notice that no approach covers semantic service description for IoT, fog, and Cloud ERP with QoS and energy optimization under sensor mobility and context changes.