## 3 Results and Discussion
### Simulation for Data Accuracy in Randomly Placed Non-Hierarchical Bidirectional Graph  
3.1 Simulations for Data Accuracy of a Deterministic Constant (Slowly Varying) Parameter Having Bounded Sensor Noise  
The link distance is unchanged and the BER of a link is varied by varying the path loss exponent to obtain the average bit error rate of a link in the network using analytical model given in [56]. First, a network MSE comparison of data fusion techniques with data cycles is obtained as shown in Figure 19.  
Bit error rates of 0.05 and 0.0003 were selected for comparison as it was done for tree structure. Only the MSE gap is changed with data gathering cycles. For all fusion methods, the network MSE for high communication errors is always higher than that for low communication errors for any data gathering cycle.  
The proposed method shows the least deviation and thus the highest stability on the network MSE against number of data gathering cycles. It shows higher fluctuations of network MSE for initial data gathering cycles (for up to about 40 cycles) for both BER s than when number of data cycles are much higher. Similar variation with data cycles is observed in AVG method but its variations are  
Figure 18: Network lifetime comparison for 3 information fusion methods in a tree-based IoT platform having 38 nodes.  
higher than proposed method and consume a greater number of data cycles for the network MSE to get stabilized.  
However, in contrast, the MSE performance with data cycles is different for data fusion scheme EBDKF under high communication errors. As proved by Figure 19, the variation of proposed method with data cycles even for high communication errors is almost same as that for low communication errors. When it comes to the diffusion technique, the network MSE is most of the time lesser than 1.0 for first 50 data gathering cycles for BER of 0.05 case. After that, the network MSE value is often greater than 1.0. On the other hand, for low communication error scenario, EBDKF tend to be less accurate for initial data gathering cycles; this is because we can observe smaller fluctuations in MSE within first 50 data cycles and highly stable MSE for data cycles closer to 600 of EBDKF for BER of 0.0003 in Figure 19. Therefore, it can be deduced that accuracy of diffusion technique is better for low data gathering cycles under high communication errors and vice versa. The proposed method has clearly outperformed both AVG and EBDKF under BER of 0.05 for any data gathering cycle except for data cycles 13 to 15 and 45 to 49.  
We plot the network MSE versus the communication errors for AVG, proposed method and EBDKF for the previously discussed unstructured sensor network as shown in Figure 20.  
The result obtained in Figure 20 is very similar to the result obtained for tree structure seen in Figure 12. Further, conclusions derived based on Figure 19 are also very similar to those derived on tree structure. Therefore, similar conclusions can be derived on the accuracy of unstructured network for three different fusion techniques. Therefore, the accuracy-wise performance of the information fusion techniques can be expected to be almost unchanged when the network gets nonhierarch  
Figure 19: Accuracy comparison with Data gathering cycles for BER s of 0.0003 and 0.05 (**a**): For first 600 Data gathering cycles (**b**): For first 50 Data gathering cycles.