# Security Framework for IoT Based Real-Time Health Applications
## 2 Background and Related Work  
The indoor and outdoor patients are responsible for their data safety by enhancing their security awareness regarding cyber-attacks. Meanwhile, facility workers, and vendors are accountable for delivering improved hardware devices to defend indoor and outdoor patient data [19]. As IoT is in the developing phase, it lacks security morals. The data interchanging with the patient's real-time health monitoring systems would be secured in the sense of authenticity, integrity, and confidentiality. The current safety conditions require co-operative academic efforts to overcome the security challenges in the telemedicine and real-time health monitoring systems [20]. IoT is the blend of different technologies, and data is exchanged between devices by different communication levels. Each communication level requires different security activities. Many scientists have recommended biometric safety arrangements because they deal with individuals' unique actions to provide scalable security solutions. Biometric systems are becoming very popular among the masses because of their use for identity verification and authentication. However, many challenges are being faced by these systems, such as cost, real-time implementation, hardware maintenance, cyber-attacks like Denial of Services [21], eavesdropping, and data manipulation. Man in the middle attacks, spoofing, and brute force attacks occur on the perception layer in biometric systems from where the individuals enter their data into the biometric systems. These attacks create problems in the system for decision-making. A framework was developed to address key issues in the layered architecture of an IoT-based health system as it collects the complete data of the patients and sends it to the cloud which is accessed locally and globally [22]. The proposed model was tested using the Contiki real-time operating system. The model consisted of three layers in which each layer had a specific function; however, the framework lacked the security infrastructure to combine the current administrative tasks. The Mobicare plan was recommended for mobile healthcare applications. This system gives the patients access to a nursing and tracing system about the current and accurate monitoring of their health status. The system supports professional staff to analyze the current health information while the patient is not present [23]. Technological change is a continuous process; thus, Mobicare has some challenging research issues,including body sensor network security, reliable and protected sensor code updates, and potential opportunities. Some solutions offer key scenarios for e-health applications. Instead, this research has provided a robust methodology for combining undisclosed sharing schemes and connecting identities to form social groups of patients suffering from a similar disease [24]. mHealth provided a social network. The groups are divided into separate blocks, and the block department helps the block director collect detailed information from patients' mobile devices. Although the system does not provide a solution to malicious invaders, these invaders are not maliciously affecting the patients; instead, it helps the patients in adverse situations by sharing some sensitive information about their safety [25]. Wireless body area networks (WBANs) systems now have technologies overcoming restrictions such as power, processing, scalability, managing, and computing, by adopting cloud computing technology. The unification of WBANs and cloud-computing as a sensor cloud infrastructure (SC-I) supports the healthcare environment by real-time patient tracking, and early disease detection [26]. However, this lacks standard architecture, policy compliance, typical data collection, patient behavior management, medically coded passwords, data sharing, data disclosure, data management, and multiple data organization [27].