Article Preview
TopIntroduction
Recent advancements made in the field of information technology, the IoT has received enormous attention and plays a vital role in our day-to-day life activities. Various interconnected sensors/devices produce a huge amount of data and exchange amongst themselves using new communication technologies. Due to the increase in connected devices and generation of a large volume of data, which impose new challenges to manage current networks effectively. The advent of cloud computing has made analysis, management, data processing, and controlling related tasks are performed in the best way at data centers that host platform and application layers. In a time-critical application such as healthcare that requires real-time data delivery. Figure 1 shows the architecture of an IoT-based remote health monitoring system, where patient health data are gathered from sensors attached to the patient. The control unit gets and calculates the data from different sensors and sends it to a base station or access point. From the base station, data can be sent to the concerned authority for necessary actions.
One of the most important concerns in IoT is the presence of heterogeneous devices which generate a huge amount of data. Management of these heterogeneous devices and generated data is an open research challenge. Integration of the new networking paradigm SDN and edge computing with the IoT will completely transform the healthcare industry. SDN and edge computing provide features control network devices centrally with help of programming (Sallabi, Naeem, Awad, & Shuaib, 2018). SDN is a promising technology that can resolve the issues and meets the need of healthcare applications for high performance. It is cost-effective and capable of delivering low latency data services by optimizing the communication and computing power of IoT devices (Li et al., 2020) (Yongdong, n.d.).
Figure 1. IoT based remote health monitoring architecture (Taneja & Narayanamoorthy, 2019)
Due to the rise in local storage capacity, edge computing is suitable to manage data adaptation and accumulation tasks. Although it is reliable data processing at the edge of devices adds a level of complexity that integrally increases risk in many applications. However, it is not easy to deploy additional computational resources close to the edge of the network and allowing them to be reachable ubiquitously. Apart from the complexity of technologies, there are many other challenges such as heterogeneity, scalability, mobility, limited energy, security, and privacy issues (Baktir, Ozgovde, & Ersoy, 2017) (Wang, Zha, Guo, & Chen, 2019). The complexities that arise due to the deployment of computational services at the edge of the network can be addressed by SDN with its inherent capability to manage resources centrally. SDN streamlines network management for the most complex networks by providing plug-and-play device setup and deployment and using the available resources very efficiently. However, it is still in the development stage and the application of SDN to Edge Computing is not yet fully resolved. Very few research papers address the combined perspectives of SDN and edge computing in IoT for healthcare applications.