# REISCH: Incorporating Lightweight and Reliable Algorithms into Healthcare Applications of WSNs
## Results
### Performance Analysis
#### 5.2.2 Experimental Analysis  
In this section, we evaluate the performance of REISCH in the execution of security operations in conjunction with the collected and saved data. As noted in previous sections, \(SN\)s require performance-efficient signatures to perform services for as long as possible in patients' monitoring and care. We provide tests on hash algorithms (SHA and BLAKE) and the signature algorithm (ECDSA). Additionally, we applied these algorithms to HWSN to analyze performance properties such as time, storage, and energy. Table 4 shows all the simulation parameters used in HWSN, while Table 5 shows computational operations in the REISCH scheme. All hash and signature algorithms were implemented by C language while WSN was designed in Octave under Ubuntu 16.04 LTS, processor Intel Core i5 2.6 GHz, OS type 64-bit, Memory 4 GiB, and disk 32.0 GB. The computation of energy in our scheme is based on the Micaz sensor specification. This process uses parameters such as current (0.0567), voltage (2.7), and time to extract both power and energy using \(power=current\times voltage\) and \(energy=time\times power\). We relied on real data provided by the City of Melbourne that is licensed under CC 4.0 [77]. These data were generated by sensors to monitor environmental parameters such as humidity, temperature, and light, as well as to include some information such as timestamp and ID. We divided these data into different sizes (200 K, 400 K, 800 K, and 1 M) and then converted them into an XML context. We used a large data size such as 1 M to test signature processes and security parameters in consuming sensor energy and thus the applicability of WSN. Furthermore, there are no communication channels between patients and \(SN\)s. To check performance, we implemented the SHA1-160, SHA2-256, BLAKE2s-256, BLAKE2b-512, BLAKE2 sp-256, and BLAKE2 bp-512 algorithms with 1 MB data size, as shown in Figure 17. Moreover, Figure 18 shows that ECDSA-BLAKE2 bp gives the best execution time of ECDSA-SHA1. In addition, Figures 19, 20, and 21 show execution time (minimum, maximum, and average) for hash functions when using 200 K, 400 K, 800 K, and 1 M data. We also notice that BLAKE2 bp has the best performance in terms of execution time in all figures. Additionally, Figures 22-24 show the execution time (minimum, maximum, and average) for the ECDSA algorithms when using 200 K, 400 K, 800 K, and 1 M data. Thus, the amendment to the ECDSA algorithm is entirely appropriate for the use of security measures with the longest life of the \(SN\)s from the original algorithm. \begin{table}
\begin{tabular}{l l} \hline \hline
**Parameters** & **Value** \\ \hline Area of WSN & 1000 m \(\times\) 1000 m \\ Number of \(SN\)s & 200 \\ Number of \(CH\)s & 5\% \\ Number of hops & 2 \\ Node type & Homogeneous \\ Node distribution & Random \\ \(LS\) location & (500, 500) \\ \(Dif\) & Maximum value (707.1068) \\ Initial energy & 25 J \\ Size of packet & 200 K, 400 K, 800 K and 1 M \\ Control packet size & 50 B \\ Rounds & 1000 \\ Routing protocol & LEACH \\ Propagation energy & 10 nJ/bit/m\({}^{2}\) \\ Multi-hop propagation energy & 0.0013 pJ/bit/m\({}^{4}\) \\ Aggregation energy & 5 nJ/bit/signal \\ Number of runs & 100 \\ Simulation time & 300 s \\ Simulator & Octave \\ \hline \hline \end{tabular}
\end{table} 
Table 4: [PERSON]’s simulation parameters.