# Secured Big Data Analytics for Decision-Oriented Medical System Using Internet of Things
## 3 The Proposed IoMT-Enabled Model  
This section presents the detail of SBD-EC model and consists of two main phases. Figure 1 depicts the components of the proposed model. Initially, we consider that the medical devices are connected in the form of graph \(G(V,N)\). Each node \(n_{i}\)\(\varepsilon\)\(N\) and vertex \(v_{i}\)\(\varepsilon\)\(V\). The individual vertex \(v_{i}\) is associated with directly connected neighbors by a particular cost. All the medical nodes are controlled by a local coordinator that performs the functions of data aggregation. Also, the local coordinator has an indirect association with sink nodes using intermediate devices that are connected wirelessly with each other. These intermediate nodes aim to forward the medical data towards the sink node and are further connected with cloud services using mobile edges. The SBD-EC model is comprised of three main layers. In the first layer, medical sensors are coordinated with the local coordinator. In the second layer, the local coordinator is linked indirectly with the sink node using various intermediate nodes. In the third layer, the mobile edges collaborate with cloud services, decreasing the latency in data storage and processing. Furthermore, as the edges are mobile, it takes the less time collecting the medical data from an upper layer. Moreover, it is seen that most of the existing work does not consider effective authentication methods on mobile edges, and ultimately malicious users could either relay the collected data on unauthorized routes or suspension the network services. Therefore, the proposed model also offers a security algorithm and provides secured communication from mobile edges to cloud services while minimizing the communication overhead and data latency. The SBD-EC model utilizes greedy traversing with the proximity neighboring method to explore reliable routes for forwarding medical data. The greedy heuristic follows the problem-solving strategy and offers the choice for the sub-optimal selection with minimum run-time and space complexity in each iteration. It simplifies the route formation process of the SBD-EC model with a nominal computational load on the constraint IoT nodes. It makes a locally optimal decisions and each iteration leads to a globally optimal solution. In the SBD-EC model, the cost function is computed using energy consumption (\(e\)), bi-directional distance (\(dis\)), and link latency (\(ln_{t}\)) parameters, which should be minimum and optimized. The cost function follows the principle of greedy routing such that medical data is transmitted towards the destination with bordering nodes and measurements of network status. Equation (1) formulates the cost function \(Cf\) for the greedy heuristic and operates on both intra-body and inter-body communication. \[Cf\ =\ min(e\ +\ dis\ +ln_{t}) \tag{1}\]  
Figure 1: Components of the proposed medical model using IoT. The energy consumption of the medical sensors can be computed by the integration of transmitting (\(T_{e}\)) and receiving (\(R_{e}\)) factors as given in Equation (2). \[e=\ T_{e}+R_{e}/G_{e} \tag{2}\]  
where \(G_{e}\) denotes the global energy, which is comprised of maximum levels of nodes. It is declared at the time of network initialization. The minimum value of \(e\) indicates sufficient transmission power of a node for involvement in forwarding medical data. However, if the transmission power of a source node is not appropriate for direct data forwarding to the destination, then it adopts a multi-hop communication paradigm. Also, the distance factor is computed to adjust the transmission power in data forwarding and is determined using the Pythagorean theorem [31] as given in Equation (3). \[dis=\ \sqrt{di^{2}\ +\ dj^{2}} \tag{3}\]  
where \(di\) denotes the distance of the source node to the neighbor and \(dj\) denotes the average distance of the source node to the coordinator and sink node. Moreover, the link latency is also added in the decision of cost function for improving delivery performance on time, as given in Equation (4).