# Bloom Filter Approach for Autonomous Data Acquisition in the Edge-Based MCS Scenario +
## 4 Results and Discussion
### Evaluation of BF Algorithm
In other words, likely, hosts will not collect the expected number of results per each _small_ area because the filter will quickly reset. At the same time, it is quite possible that within, for example, 120 min, more workers will pass through the same _small_ area, and because of the relatively high values of the parameter \(m_{MEC}\), a filter they receive may still not be updated with recently received results, which may later cause a more significant amount of redundant data in that area. Similarly, when we compare the percentage of redundant results received by ME hosts for different values of the parameter \(m_{worker}\), we can see that the amount of redundant results grows with \(m_{worker}\) because workers are instructed to produce more readings per _small_ area and need to wait longer before updating their filters. Therefore, a worker may send a redundant result because its version of the filter shows that this result is valuable and should be submitted, while in the meantime, the corresponding ME host has already received enough results for that _small_ area from other workers. Overall, the results show that even in the worst-case scenario, when workers are instructed to submit five results per each _small_ area, as shown in Figure 12d, the percentage of redundant results received by ME hosts never exceeds 6%, which proves that the proposed algorithm works exceptionally well for different input scenarios. \begin{table}
\begin{tabular}{c c c c c} \hline \hline _Window_ & \(m_{MEC}\) & \(m_{worker\_1,2}\) & \(m_{worker\_2,3}\) & \(m_{worker\_3,5}\) \\ \hline \multirow{4}{*}{1 min} & 5 & 6.73\% & 4.91\% & 6.09\% \\ \cline{2-5}  & 10 & 6.86\% & 4.91\% & 7.11\% \\ \cline{2-5}  & 15 & 6.93\% & 5.02\% & 7.34\% \\ \hline \multirow{4}{*}{120 min} & 5 & 3.41\% & 1.96\% & 1.93\% \\ \cline{2-5}  & 10 & 3.98\% & 2.39\% & 2.92\% \\ \cline{2-5}  & 15 & 4.34\% & 2.68\% & 3.27\% \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Influence of the parameter \(m_{worker}\) on the total savings. \begin{table}
\begin{tabular}{c c c c} \hline \hline _Window_ & \(m_{worker}\) & \(m_{MEC\_5,10}\) & \(m_{MEC\_10,15}\) \\ \hline \multirow{4}{*}{1 min} & 1 & 0.06\% & 0.01\% \\ \cline{2-5}  & 2 & 0.19\% & 0.08\% \\ \cline{2-5}  & 3 & 0.19\% & 0.19\% \\ \cline{2-5}  & 5 & 1.21\% & 0.42\% \\ \hline \multirow{4}{*}{120 min} & 1 & 0.95\% & 0.55\% \\ \cline{2-5}  & 2 & 1.52\% & 0.91\% \\ \cline{2-5}  & 3 & 1.95\% & 1.20\% \\ \cline{2-5}  & 5 & 2.84\% & 1.65\% \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Influence of the parameter \(m_{MEC}\) on the total savings. To conclude, we have shown that our hierarchical edge computing architecture, enhanced with the algorithm for autonomous decision-making of a mobile worker, shows great potential for energy savings in distributed MCS environments while retaining the desired sensing quality due to decreased messaging between workers and ME hosts. The main advantage of the proposed hierarchical architecture is that there is no central point of failure because the data processing is distributed across multiple ME hosts, and all workers can autonomously decide when to transmit data. At the same time, the actual savings depend on an actual MCS application and its setup. Our analysis showed that the edge architecture is suitable for massive-scale MCS services. The amount of transmitted data between mobile workers and edge hosts can be significantly reduced using the proposed BF algorithm because workers independently decide when to send data instead of periodically transmitting all data readings. Consequently, it is possible to achieve energy savings of up to 62%.