# Secured Big Data Analytics for Decision-Oriented Medical System Using Internet of Things
## 4 Simulations  
This section presents the simulation scenarios and the obtained results. The experiments were performed using 15 medical sensors and one coordinator node that is placed on the patient body. The performance tests are evaluated using two different scenarios (i) the number of iterations in terms of network throughput, data retrieval latency, normalized routing overhead, energy consumption, and packet drop ratio (ii) the speed of mobile edges in terms of packet disruption, retransmitted packets, data delivery ratio, and breaches attack. In the implementation phase, we considered a platform with medical sensors, mobile edges, sink nodes, and cloud servers. Initially, the energy level of all the medical sensors was 2 j. The data flow among healthcare devices was considered at a constant bit rate (CBR). A few high-powered nodes were randomly deployed in the vicinity of sink and cloud systems that acted as mobile edges. The transmission range of medical sensors was set to 2 m. We considered 5 to 10 nodes as malicious to evaluate the security performance in the presence of network threats. To simulate the proposed model with existing schemes, a well-known network simulator NS-3 was utilized, as considered in different work [33; 34]. The performance evaluation was carried out using the simulation parameters as illustrated in Table 1.  
Figure 3 depicts the performance analysis of the SBD-EC model in terms of energy consumption against other schemes. Based on the simulation experiments, it can be seen that the SBD-EC model improves energy consumption by 18% and 32% compared to other models. This is due to utilizing greedy heuristics for the selection of the next hop for forwarding medical data. Furthermore, the stability period of medical sensors is increased due to the computation of cost function which decreases the additional overheads on it. Moreover, as the SBD-EC model employs a multi-criteria selection for the computing of cost function which is more adjustable in constraint-oriented applications as compared to other solutions. The proposed strategy not only confirms data routing via reliable nodes, but it also balances the energy consumption in negotiating through shorter routes. Unlike other solutions, the SBD-EC model decreases the calls for route maintenance and exchange of beacon messages based on network needs, and thereby results in the least energy consumption among selected forwarders. \begin{table}
\begin{tabular}{l l} \hline \hline
**Parameter** & **Value** \\ \hline Medical sensors & 15 \\ Malicious nodes & 5–10 \\ Transmission power & 2 m \\ Time interval & 2 s \\ Mobile edge nodes & 10 \\ No. of the sink node & 1 \\ Payload size & 512 bytes \\ Initial energy & 2 j \\ Observing field & 15 m \(\times\) 15 m \\ Simulation time & 400 s \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Simulation parameters. Figure 4 illustrates the performance of the SBD-EC model against existing solutions in terms of data retrieval rate. Based on the results, it can be observed that it significantly improves the data retrieval rate over the existing solutions by 37% and 46% respectively. This is due to the fact that the SBD-EC model avoids forwarding the data on interrupted routes and utilizes the constraints of medical sensors very efficiently. Also, the SBD-EC model utilizes a smaller number of hops in transmitting the medical data by evaluating the neighbors' information. With the consideration of more reliable paths using greedy heuristics, the SBD-EC model decreases the chances for route re-construction and network faults, which improves the data retrieval rate from storage servers. Moreover, by making use of security algorithms among mobile edge and cloud storage, the SBD-EC model avoids the possibility of malicious actions altering the route directions while forwarding the data. Furthermore, the proposed model is constantly monitoring the link latency and in case of network congestion, it removes such a node from routing decisions and maintains the timely delivery of sensitive patient data.