# Secured Big Data Analytics for Decision-Oriented Medical System Using Internet of Things
## 4 Simulations
Figure 5 illustrates the performance analysis of the proposed model in terms of packet drop ratio as compared to existing models in varying iterations. It can be seen from the experimental results that the SBD-EC model decreases the packet drop ratio by 26%, and 59% compared to other solutions. This is due to the incorporation of energy and link latency factors, which chooses data forwarders more reliably with the updated values of the network communication system. Moreover, it selects the wireless channels for forwarding medical data with the least congestion and avoids channels with interference, which results in minimizing the packet drop ratio. Using weighted and minimized cost, only more optimal nodes can participate in the route discovery process, accordingly, the SBD-EC model decreases the probability of routing faults and high congestion levels. Figure 4: Data retrieval rate and iterations. Figure 3: Energy consumption and iterations. Such constraints improved the packet drop ratio over unreliable communication media. Furthermore, it utilizes mobile edges for the collection of medical data intelligently from the sink node and securely transmits it to the cloud layer. Figure 6 depicts the experimental results of the SBD-EC model for network throughput against existing work. It is defined as aggregate data to the receiving side and reflects the efficiency of any routing solution. It is revealed that the SBD-EC model increases the network throughput by 17% and 21% compared to other models. This is because of the proposed communication strategy of the SBD-EC model, which is split into multiple layers and each layer operates independently with the least communication overhead. Also, the multi-hop paradigm is utilized by the proposed model when needed and makes use of the cost function in the selection criteria of the data forwarder. Moreover, due to the incorporation of the link latency in the cost function, the SBD-EC model judges the transmission flow on selected time \(t\), and offers an appropriate contribution for the selection of next-hop in medical data. Such a method significantly increases the acceptance rate of medical data over unreliable communication routes with efficient energy management  
Figure 7 illustrates the performance of the SBD-EC model in the comparison of normalized routing overhead with varying iterations. Based on the results, it can be seen that it reduces the routing overhead by 41 % and 47% over the existing solutions. It is due to the fact that most of the network operations such as aggregation, fusion, etc., are performed on high-powered edge nodes. Also, most of the management of security functions is executed on the sink node that has unlimited resources and ensures network privacy against malicious threats. Furthermore, it operates a multi-criteria cost function, which minimizes the number of attempts for data retransmission and, significantly, produces a low routing overhead. Moreover, it can be seen that unlike most of the schemes that are vulnerable to  
Figure 5: Data drop ratio and iterations. Figure 6: Network throughput and iterations. high network overheads due to the exchange of several control messages regularly, the SBD-EC model decreases such practice and only transmits the node and network-related information based on certain criteria. Figure 8 illustrates the performance analysis of the SBD-EC model for second scenario in terms of packet disturbance, in comparison with other schemes. Based on the results, it is proven that the SBD-EC model improves the packet disturbance ratio by 37% and 45%. This is due to the use of heuristic cost function based on three parameters i.e., energy consumption, bi-directional distance, and link latency parameters for data routing. Such a strategy maintains the packet reception ratio among nodes and increases the efficacy for network performance. Moreover, it maintains the routes' consistency by considering the limited constraints of nodes with nominal communication load.