# Resource Management and Secure Data Exchange for Mobile Sensors Using Ethereum Blockchain
## 5 Results
### Accomplished Result
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Approaches**} & **Validation** & **Latency** & **Throughput** & **Overhead** & \multirow{2}{*}{**Cost**} & **Processing** \\  & **Time (s)** & **(s)** & **(bps)** & **(bps)** & & **Time (s)** \\ \hline [PERSON] et al. [28] & 0.5887 & 0.4221 & 1788 & 4350 & 0.6278 & 0.6118 \\ [PERSON] et al. [29] & 0.7021 & 0.2847 & 2670 & 4989 & 0.6893 & 0.6773 \\ [PERSON] et al. [37] & 0.6098 & 0.17882 & 1987 & 5610 & 0.5995 & 0.5982 \\ [PERSON] et al. [48] & 0.7276 & 0.4782 & 2900 & 3599 & 0.6833 & 0.5289 \\ [PERSON] et al. [38] & 0.5692 & 0.1982 & 2550 & 4705 & 0.7621 & 0.5974 \\ [PERSON] et al. [49] & 0.7445 & 0.387 & 1721 & 4691 & 0.5932 & 0.7964 \\ [PERSON] et al. [39] & 0.631 & 0.2109 & 2600 & 6920 & 0.5885 & 0.6983 \\ [PERSON] et al. [43] & 0.6933 & 0.4835 & 1950 & 4555 & 0.7402 & 0.7451 \\ [PERSON] et al. [44] & 0.5991 & 0.1769 & 2250 & 4985 & 0.6298 & 0.6502 \\ [PERSON] et al. [45] & 0.7109 & 0.2976 & 1871 & 5370 & 0.6002 & 0.7864 \\ [PERSON] et al. [46] & 0.7305 & 0.3501 & 1900 & 4130 & 0.7631 & 0.7582 \\ [PERSON] et al. [47] & 0.6831 & 0.3201 & 2100 & 6301 & 0.5831 & 0.6903 \\ Proposed & 0.2668 & 0.0378 & 4500 & 1200 & 0.388 & 0.2985 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Numerical outcomes of assessment. Figure 6: Comparative assessment of validation time [28; 29; 37; 38; 39; 43; 44; 45; 46; 47; 48; 49]. Figure 8: Comparative assessment of throughput [28; 29; 37; 38; 39; 43; 44; 45; 46; 47; 48; 49]. Figure 7: Comparative assessment of latency [28; 29; 37; 38; 43; 44; 45; 46; 47; 48; 49]. Figure 10: Comparative assessment of cost [28; 29; 37; 38; 39; 43; 44; 45; 46; 47; 48; 49]. Figure 9: Comparative assessment of overhead [28; 29; 37; 38; 39; 43; 44; 45; 46; 47; 48; 49]. This section illustrates the learning outcomes obtained from the numerical outcome in Table 4 and the respective graphical outcomes in Figures 6-11. The discussion of each performance metric is as follows:  
* **Validation Time**: This performance parameter represents the time required to validate the user's request. This involves enrolling users, validating their identities, and computing the trust score. The aim is to determine the capability of the proposed system in terms of its responsiveness to process multiple requests from concurrent users in peak traffic situations. The outcome presented in Figure 6 eventually showed a faster validation time. The learning outcomes of this performance metric are as follows:
* _Novelty_: The main novelty of this analysis is that the proposed system does not use any form of complex encryption (apart from hashing during validation of the sensor's identity) to offer a faster validation time. This is not witnessed in existing approaches ([PERSON] et al. [45], [PERSON] et al. [44], [PERSON] et al. [43], and [PERSON] et al. [39]), where a complex form of encryption method was applied. This is unlikely in the majority of the existing approaches to blockchain. The secondary novelty of the analysis is associated with a simplified consensus method that allows the AP to interchange roles based on three entities (follower, auxiliary, and leader). The consensus methods in existing approaches perform quite a more extensive set of operations without defining the precise role of sensors, leading to increased validation times. Hence, the processing of such a consensus is faster and can accurately map the dynamic state of the network to facilitate an appropriate security action. Therefore, the proposed system is anticipated to exhibit faster responsiveness when exposed to real-time scenarios based on an extensive analysis of various traffic loads. _Evidential Justification_: The existing approaches show that the implementation strategy is more inclined toward blockchain design than normalizing the communication model or enrollment process. For example, the validation times for [PERSON] et al. [48] and [PERSON] et al. [49] are expected to increase because of their dependency on the iterative training operation.