# Moving towards Smart Cities: A Selection of Middleware for Fog-to-Cloud Services +
## 2 Related Work
### MCDA in Edge Computing
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Year** & **Paper** & **Main Contribution** & **MCDA Method Used** \\ \hline
2018 & [31] & \
\begin{tabular}{c} Propose a new multi-dimensional hierarchical performance evaluation \\ model for edge platforms. \\ \end{tabular} &
\begin{tabular}{c} AHP (Analytic Hierarchy Process) \\ \end{tabular} \\ \hline
2018 & [32] & Propose an adaptive nature inspired smartFog. & NSGA-II multi-objective \\ \hline
2018 & [33] & Make decision whether to execute tasks locally or offload them to fog servers. & Fuzzy-logic \\ \hline
2018 & [34] & Propose container migration algorithms to support mobility tasks. & Markov Decision Process \\ \hline
2015 & [35] & Propose a dynamic Service Migration in Mobile Edge-Clouds. & Markov Decision Process \\ \hline
2018 & [36] &
\begin{tabular}{c} Propose a Lightweight Auto-Scaling Mechanism for Fog Computing by \\ integrating hypervisor technique with container virtualization. \\ \end{tabular} & Fuzzy-logic \\ \hline
2018 & [37] & Propose a hybrid model for clinical decision support system in fog-cloud & Neural network \\ \hline
2018 & [38] & Propose two semi-Markov decision process-based coordinated virtual & Markov decision process \\ \hline
2018 & [39] & Optimize the scheduling of Fog computing job based on bees swarm. & Fuzzy theory \\ \hline
2018 & [40] & Propose a framework for data processing in fog computing. & fuzzy \\ \hline
2017 & [41] & Evaluate the trustworthiness of the fog servers. & Fuzzy \\ \hline
2017 & [42] & Propose a linguistic framework based on fogging for knowledge inference in & Fuzzy \\ \hline
2017 & [43] & Propose a secure trust model based in vehicular ad hoc networks. & Fuzzy logic \\ \hline
2015 & [44] &
\begin{tabular}{c} Deploy and evaluate an extended CAMA (Context-Aware Mobile Approach) \\ hub-service in fogging. \\ \end{tabular} & Fuzzy logic and neural networks \\ \hline
2017 & [45] &
\begin{tabular}{c} Describe a distributive and central traceable stream mechanism for \\ traceability process in fogging. \\ \end{tabular} & Fuzzy \\ \hline
2016 & [46] & Propose a dynamic load balancing mechanism in fog computing. & Graph repartitioning \\ \hline
2016 & [47] & Propose a framework based on fogging for managing resources in Vehicular & Fuzzy logic \\ \hline
2016 & [48] & Propose an adaptive edge router for monitoring and controlling the network & Fuzzy weighted queueing \\ \hline
2015 & [49] & \
\begin{tabular}{c} Propose a framework for selecting an adaptive mobile cloud service from \\ clouds or cloudlets. \\ \end{tabular} &
\begin{tabular}{c} AHP and TOPSIS (Technique for Order of \\ Preference by Similarity to Ideal Solution) \\ \end{tabular} \\ \hline
2015 & [50] &
\begin{tabular}{c} Select a candidate among cloud, cloudlet or mobile devices to save energy \\ consumption and increase the service availability for user (the distances of \\ the fuzzy positive-ideal solution and fuzzy negative-ideal solution are used \\ to rank the alternatives). \\ \end{tabular} & TOPSIS and Fuzzy \\ \hline
2018 & [51] & Propose an online offloading framework for mobile edge computation & Binary decision \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Applied multiple criteria decision analysis methods in edge technologies.