# Secured Big Data Analytics for Decision-Oriented Medical System Using Internet of Things
## 1 Introduction  
In recent years, the advent of the Internet of Things (IoT) has led to a paradigm shift in all areas of human-machine interaction, particularly healthcare and security [1, 2, 3]. IoMT is a subarea of the IoT, particularly related to healthcare systems and to solve medical-related issues distantly such as patient identity, diagnosis of diseases, and data storage in the cloud [4, 5]. Its elements are comprised of wearable sensors, network devices, and smartphones that collect the vital signs of the patient. Afterwards, the medical data are forwarded to centralized database servers over the 5G network. The medical experts obtain the stored information and perform some analysis on it for appropriate decision making with regards to treatment [6, 7]. However, the main issue in healthcare systems is the collaboration of the IoT environment in terms of energy constraint, storage range, accessible bandwidth, and rapid disturbance over the wireless medium [8, 9]. Wireless Sensor Networks (WSNs) are the integral components of IoT technology and Body SensorNetworks (BSNs) are the main underlying software for medical applications. A BSN is a network built in and around the human body by deploying sensors with restricted constraints. Such an enormous adoption is still ongoing, however, for early identification of diseases and diagnosis purposes [10; 11]. Combining data analytics and the IoT makes it possible to analyze medical data with versatility and a high amount of contextual awareness. Now, mobile edge cloud computing with remote sensors is also an emerging area that has a significant contribution in smart medical systems for healthcare, security, and remote management. Moreover, in edge cloud computing [12; 13; 14], local data processing imposes the least computing power, which is a critical matter as the IoT-based nodes are conventionally low power in terms of energy and computational resources [15]. Moreover, as the processing unit becomes part of the network, the time for transmission between the edge devices and the processing units declines dramatically. At the edge or preprocessed at the edge, the data is then analyzed and offloaded to the remote processing unit. In all cases, the network's performance increases enormously in terms of latency. Furthermore, by isolating edge nodes from the central network, edge computing aims to provide multi-device interoperability. Therefore, these days researchers are focused on producing an IoT-based medical system to monitor the patients' health with an efficient storage paradigm. This work presents secure big data analytics with the collaboration of mobile edge-cloud architecture, which aims to decrease the data latency performance with the support of trusted communication among medical nodes.  
The contributions of the proposed model are as follows:  
1. It adopts a greedy search in proximity neighborhood graphs for optimizing with uniform rate transmissions and increases the energy efficiency of medical systems.
2. The mobile edges have a direct or indirect association with the sink and cloud level to cope with big data analytics and offer interactive medical systems with nominal data latency.
3. The mobile edges that are the bottleneck for forwarding the data to the cloud are immune to malicious actions and kept secure against network vulnerabilities. The proposed secured algorithm maintains the inaccessibility of medical transmission from network threats and offers certifiable data to end-users.
4. The proposed model is verified with extensive experiments using simulations and it is proven to be a remarkable contribution compared to existing schemes.  
The rest of the paper is organized as follows. Section 2 presents a discussion on related work. Section 3 develops the proposed model. Section 4 illuminates the simulation-based experiments with detailed discussion. Section 5 presents conclusions.