# IoT Blockchain Data Vercity with Data Loss Tolerance
## 3 Results
### Result of IoT Blockchain Strength Monitoring  
Part 2 evaluated IoT blockchain data veracity strength measurement. It is worth highlighting here again that IoT blockchain strength can be measured through its capacitance value. The relationship between IoT blockchain data veracity and strength is analogous to that of measurable capacitance and inductance. IoT blockchain data veracity strength measurement was evaluated based on the flow diagram depicted in Figure 11. The simulation results are tabulated for data analysis. The IoT blockchain data veracity strength measurement was simulated with LTSPICE software with LC circuit setup. Simulation is setup as Figure 19 with 12 capacitor-clusters (as IoT blockchain node), with 10 capacitors (as transaction stream item) in each capacitor-cluster. The capacitor-clusters and its capacitors are connected in parallel for the strength to be measured as inductance current, I. The inductance current I is the energy generated when the charges Q was released by the capacitors. This will be the indicator of the strength of capacitance C.  
The simulation started with the 1 st capacitor-cluster (node) activated; there are 10 capacitors (transaction) within the cluster. The 1 st capacitor acted as the transaction was activated by setting the capacitance value from 0 to 100 \(\upmu\)F. The simulation ran, the inductance current was probed and its respective value of \"time of 1 st peak\", \"induc  
Figure 19: LTSPICE simulation and measurement. Figure 18: LTSPICE simulation result of capacitor-cluster vs. inductance current. tance current of 1 st peak\", \"time of 2 nd peak\"and \"inductance current of 2 nd peak\" are recorded. The first and second peak at the inductance current are indicated by number 1 and 2 in Figure 19. The values recorded are based on the simulation result shown in Figure 19, The small popup window shows the current value in Y-axis and time in X-axis (cursor 1 for 1 st peak and cursor 2 for 2 nd peak). The process was repeated within the first capacitor-cluster (node), then with the second capacitor (transaction) activated by setting the capacitance value from 0 to 100 \(\mu\)F. The simulation ran, the inductance current was probed and its respective values were recorded. Once the transaction capacitor reached the 10 th capacitor, this concluded the first capacitor-cluster (node) simulation for results from the first transaction to the 10 th transaction. Observation indicated that the inductance current increased with the activation of more transaction capacitors. The first capacitor-cluster (node) remained active and the simulation proceeded with the second capacitor-cluster (node) activation. Similarly, there were 10 capacitors (transaction) within the capacitor-cluster. The simulation ran under a similar process as in the first capacitor-cluster. The simulation results were recorded. Again, observation indicated that the inductance current increased with the activation of more capacitors. Likewise, the inductance current increased with the activation of more capacitor-clusters. The first and second capacitor-cluster remained active; the simulation continued with the activation of the remaining third to 12 th capacitor-cluster. The results showed a consistent increase of inductance current with more capacitor-clusters and their transaction capacitors activation. In this simulation, the IoT blockchain data veracity strength is proven measurable with its capacitance through the single inductor. The relationship between IoT blockchain data veracity and strength is thus analogous to that of measuring the capacitance and inductance current. The results are plotted in clustered chart format, as illustrated in Figure 20, the results are grouped by capacitor-clusters (node) and plotted as blue color-coded square boxes. The number of transactions are plotted in line format within the capacitor-cluster from 1 to 10. The Inductance current is the curved lines color coded in red.