# A Comprehensive Survey on Knowledge-Defined Networking
## 8 Discussion
### Design Guidelines
#### 8.3.7 Different Network Scenarios  
Knowledge-Defined Optical Networks  
An optical network is a telecommunications network that uses optical fibers to transmit information by converting electrical signals into optical signals that are transmitted over long distances with low signal attenuation and high bandwidth capacity, providing high-speed, reliable, and secure communication [565]. In optical networks, KDN can be specifically used to predict fiber cuts and other physical disruptions. KDN can predict if an optical fiber is likely to fail, enabling network administrators to perform maintenance before it fails. As optical networks are sensitive to changes in environmental conditions such as temperature, humidity, etc., KDN systems can be designed to account for these changes and adjust network settings accordingly. In order to achieve the above tasks, optical networks should be designed such that data from a large number of sensors on optical fibers are collected and analyzed.  
A vehicular network is an autonomous collection of mobile devices that communicate with each other over wireless links and cooperate with each other in a distributed manner to provide network functionality in the absence of fixed infrastructure [566]. These networks are characterized by dynamic network topology, road-constrained mobility patterns, and self-organizing, dynamic, and volatile links between the wireless nodes [567]. A vehicular network uses different network communication technologies, such as Wi-Fi, cellular, and Dedicated Short Range Communication (DSRC). KDN can be designed to enable seamless handoffs between network communication technologies by using knowledge on network availability, quality, signal strength, etc. for a given application. Furthermore, KDN can be used for efficient resource allocation in vehicular networks. For instance, it can allocate more resources to safety-critical applications such as collision avoidance systems while allocating lower resources to infotainment applications. Furthermore, a knowledge-defined vehicular network can be designed to prioritize traffic received from emergency vehicles and use knowledge on congestion to optimize traffic flows.  
Knowledge-Defined Internet of Things  
An Internet of Things (IoT) network is an interconnected system of devices that can exchange information and communicate with one another while producing a vast amount of data that must be processed and transmitted to the cloud [568]. By analyzing patterns in data traffic and routing it to the most effective pathways, KDN may be developed with cloud computing applications to optimize the network and lower latency and network congestion. Furthermore, typical security methods such as firewalls are frequently insufficient to protect IoT devices from cyberattacks. KDN may be created to gather data from devices and produce information using a knowledge generation model to identify anomalous activity or behavior out of the ordinary in the network and to identify new dangers in order to stop them.  
Knowledge-Defined Mobile Networks  
A mobile network is a wireless communication system that allows mobile devices to connect to a telecommunications network and communicate with each other and other devices [569]. KDN can be designed to optimize handover procedures by analyzing data on signal strength, network congestion, etc. so that it can be handed over to the best network, ensuring a seamless transition and avoiding dropped calls.  
Knowledge-Defined Wireless Sensor Networks  
A wireless sensor network is a group of interconnected sensor nodes that communicate wirelessly to collect and transmit data from the physical environment [67]. KDN can be designed to predict optimal sensor placement to ensure full coverage and avoid overlap. KDN can be used to improve data collection in wireless sensor networks by analyzing network data and identifying the sensors that are producing the most accurate data. This allows for the identification and replacement of faulty sensors, leading to more reliable data collection.