# Resilience in the Internet of Medical Things: A Review and Case Study
## 5 Analysis of Resilience in Existing Works
### Analysis of Existing Works That Used SDN or MSA Individually in Meeting Resilience Requirements
#### 5.4.1 SDN Technology: Pros and Cons  
SDN is used in several methodologies in the literature as a standalone technology. SDN is beneficial in traffic classification, route optimization, and resource management. Initially, traffic classification and traffic scheduling play an important role in making the IoMT networks resilient through a port-based approach and deep packet inspection [88].  
Figure 3: Mapping of critical requirements into key technologies for resilient IoMT networks.  
The port-based approach works on a predefined standard port number, which has now become outdated as most applications use today's dynamic port allocation scheme [89].  
The deep packet investigation approach works on matching the predefined pattern with regular expressions in the payload of packets in traffic flow [90]. However, new applications have been increasing with an exponential growth rate today and it is impractical to maintain the pattern updates of every application. It is also a computationally expensive process and not useful for encrypted traffic flow [91]. The use of machine learning with SDN mitigates this problem as SDN collects the data of traffic flows and ML trains the models to extract the pattern recognition and apply the trained model on a new testing environment of traffic flow. In this way, ML efficiently recognizes the encrypted pattern of traffic flow for traffic classification at a much lower cost [35].  
SDN also implements route optimization in the IoMT networks by which the overloading on specific routers can be avoided. Route optimization has been conducted by using greedy approaches (such as [PERSON]'s algorithm) [92]. The problem with these approaches is that they are predefined with particular topologies. For example, [PERSON]'s algorithm uses the open shortest path first (OSPF) method for determining the path from the source to the destination by using a specific metric [93]. Moreover, they are not useful in real-time generated requirements, such as the sudden failure of routers in the topology of IoMT networks [94]. The use of ML with SDN mitigates these problems, as ML models train on the different diverse scenarios of IoMT networks and a final ML model runs on a real-time basis and detects the anomaly or any unusual behavior in the IoMT networks. This combination can predict the router or switch failure in the topology in real time.  
Resource management is another issue which is provided by SDN, as it has centralized control of IoMT networks. Dynamic resource allocation is required to maintain the QoS standard of different applications (such as bandwidth, delay, etc.). However, SDN as a standalone technology faces problems of rule placement of the controller due to the dynamic nature of the IoMT network, such as fast changes in the topologies and the size of the network being scalable [95]. Again, the use of ML with SDN addresses this problem, by bringing the optimization into resource management. ML models are trained for real-time data of IoMT networks and can predict the topology changes and sudden requirements for resource allocation. Hence, combining ML with SDN makes resource management more responsive to the rapid or dynamic requirements of IoMT networks.