## 4 Evaluation and Benchmarking
### Edge Data Processing Evaluation
One of the main goals of the fog computing paradigm is that sensor values are processed using the computational resources available at the edges of the network, thus reducing the need to transfer the data to the cloud servers. We were therefore interested in examining the average volume of data exchanged by the different services executed on the edge-based processing layer. This was measured in terms of Input/Output (I/O) network operations. The results are depicted in Table 4. Recall from Section 3.2 that each sensor received at the edge-based processing layer was organized by the stream processing pipeline. The actual processing of each sensor reading took place within the generic data processing module. The processing of the message concluded within the continuous data analysis module that guaranteed that data were stored uniformly both at the cloud and the edge layers. For example, the network traffic arriving at the Tergo Raspberry Pi was at the level of 101 MBs for a total of 164,343 messages, while only one-fourth exited the service. By combining all three services, only about 5% of the network traffic was transmitted to the cloud. Clearly, the results indicated that the ability to process each sensor value by taking advantage of the processing power available significantly reduced the overall network traffic. A critical question, however, is to evaluate the resulting response time. That is, we were interested in evaluating the average time required by each different service to process a single message. In Table 5, the processing latency is measured (in ms) for each different service and for each edge device. It was evident that the more powerful Tergo Zotac and Tethys nodes achieved very low latency, which in fact could even accommodate applications that require real-time response times. However, even the low-power Tergo Raspberry Pi node, which did not support fast integer and floating-point calculations, managed to complete the processing pipeline with a response time of as low as one-tenth of a second. At this point, we were also interested in evaluating the operations required for the encryption/decryption of the messages at each different layer. Once again, it was evident that all platforms considered managed to carry out the operations without any significant delays. In this experiment, we also decided to include in the comparison the Mox nodes as well. Recall that these were the nodes that bridge the wM-Bus with the LPWAN and utilize an Arm Cortex M processor. We note that the ultra-low-power processor was capable of carrying out the cryptographic operations without imposing any significant delays. The last aspect of the evaluation related to the pilot deployment in a real-world environment was the utilization of the computational resources available at the edges of the network. Now, we were interested in understanding whether the processing power provided by each platform considered \begin{table}
\begin{tabular}{l c c c} \hline \hline \multirow{2}{*}{**Service**} & **Tergo Raspberry** & **Tergo Zotac** & **Tethys** \\  & **I/O** & **I/O** & **I/O** \\ \hline Stream Processing Pipeline & 101/26.9 & 88.6/17.3 & 89.1/15.9 \\ Generic Data Processing & 79.8/82 & 78.2/79.9 & 79.4/80.4 \\ Continuous Data Analysis & 6.62/5.37 & 2.03/1.97 & 0.45/0.46 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Network I/O operations in MB. \begin{table}
\begin{tabular}{l c c c c} \hline \hline \multirow{2}{*}{**Service**} & **Mox** & **Tergo** & **Tergo** & **Tethys** \\  & **Node** & **Raspberry** & **Zotac** & **Tethys** \\ \hline Stream Processing Pipeline & - & 1.620 & 0.016 & 0.004 \\ Generic Data Processing & - & 52.183 & 0.400 & 0.244 \\ Continuous Data Analysis & - & 63.817 & 0.186 & 0.145 \\ CMAC calculation & 1.199 & 0.036 & 0.0017 & 0.001 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Services’ processing latency (ms). sufficed to handle the load created by the sensors. Therefore, in this experiment, we examined the usage of the processors of each platform considered.