The Smart Cyber Ecosystem for Sustainable Development

Nowadays, communication networks are getting more complex and their configuration and management to achieve performance goals have become a challenging task. This is due to the following:

The significant increase in the number of network users.

The increase of the number of required networking elements at the network core.

The huge number of mobile applications.

The diversity of traffic.

The idea of cognitive networks is to improve the performance of networks and reduce the effort required for their configuration and management. Unlike current technologies, in which networking elements are unable to make intelligent decisions, the elements of a cognitive network have the ability to learn and dynamically self-adjust as response to changing channel and network conditions. Cognitive network elements utilize the principles of logic and learning in order to improve performance. Decisions are made to improve the overall network performance, rather than the performance of individual network elements. Thus, cognitive networks achieve the goal of intelligent, self-adjustment, and improved network performance, by intelligently finding optimal values of many adjustable parameters. They are required to learn the relationships among network parameters of the entire protocol stack.

As we indicated, a cognitive network should provide better performance to users. The cognition can be used to improve: utilization of network resources, QoS, security, access, control, or any other issue related to network management.

It must be emphasized that cognition is not only related to wireless networks, but also the idea applies to the management of network infrastructure and the various network elements [3]. To stimulate transition to cognitive networks, their performance must outweigh all additional complexities that they require. The question is how to measure the cost of a cognitive network. Such cost would primarily depend on the communications required to apply cognition, the architecture complexity, maintenance cost, and the operational complexity. For example, in wired networks, user’s behavior is clear and easily predictable, and therefore, it may not be interesting for some people to employ cognition with this type of networks. On the contrary, wireless networks often include heterogeneous elements and have characteristics that cannot be easily predicted, making them the best candidates to adopt the cognition concept.

Cognitive networks should use different measures, tools, and patterns as inputs to the decision-making processes. Then, they come up with results in the form of procedures or commands that can be implemented in modifiable network elements. It is important to note that the cognitive network must adapt to changes in the environment in which it operates and anticipate problems before they occur. Their architecture must be flexible, scalable and be supportive of future improvements and extensions.

Several research studies have been discussing the architecture and functionalities of cognitive networks. There is a need to rethink about network intelligence from being dependent on resource management to understanding the needs of network users and then transferring intelligence also to the elements of the network.

The central mechanism of the cognitive network is the cognitive process. This process implements real learning and decides the appropriate responses and actions based on observations in the network. The operation of the cognitive process mainly depends on whether its implementation is central or distributive as well as on the amount of state network information.

ML is a subset of AI. The aim of ML is to develop algorithms that can learn from data and solve specific problems in some context as human do [4]. ML has been proving its ability to overcome the challenges and complexities of mathematical formulation and solution of complex problems, including wired and wireless networking problems that require effective methods to quickly respond to dynamical changes of channels as well as the increasing diversification of services. Dynamic ML algorithms are able to process data and learn from it. They are replacement of complex algorithms which are written in a fixed way to conduct specific tasks.

The basic concept of ML is through training data that is used as input to the learning algorithm. The learning algorithm then produces a new set of rules, based on inferences from data, which results in a new algorithm. The new algorithm is officially referred to as the ML model. Traditional algorithms are comprised of a set of pre-programmed instructions used by the processor in the operation and management of a system. However, instructions of ML algorithms are formed based on real-life data acquired from the system environment. Thus, a machine is fed a large amount of data, it will analyze and classify data, then use the gained experience to improve its own algorithm and process data in a better way in the future. The strength of ML algorithms lies in their ability to infer new instructions or policies from data. The more data is available for the learning algorithms during the training phase, the more ML algorithms will be able to carry out their tasks efficiently and with greater accuracy.

Depending on the type of tasks, there are two types of ML:

Regression LearningIt is also called prediction model, used when the output is a numerical value that cannot be enumerated. The algorithm is requested to predict continuous results. Error metrics are used to measure the quality of the model. Example metrics are Mean Absolute Error, Mean Squared Error, and Root Mean Squared Error.

Classification LearningThe algorithm is asked to classify samples. It is of two subtypes: binary classification models and multiple classification models. Accuracy is used to measure the quality of a model.

The main difference between the algorithms for classification and regression is the type of output variable. Methods with quantitative outcomes are called regressions or continuous variable predictions. Methods with qualitative outputs are called classifications or discrete variable predictions.

A formal definition of a ML algorithm is “A Computer program is said to learn from experience E with respect to some class of tasks T and performance measure P, if its performance at tasks T, as measured by P, improves with experience E” [5].

Tasks: A task defines a way to process an object or data. An example task is classification, which is a process of assigning a class label to an input object or data point. Regression is another task example, which involves assigning a real value to an object or data point.

Performance Measure: Defines the criteria by which a ML algorithm is evaluated. In classification algorithms, accuracy refers to the percentage of correct assignment of class labels to objects or data points. Normally, data is divided into two sets. The first is used for training, while the second is used for testing.

The Experience: It refers to the knowledge that a ML gains while learning. It divides the ML algorithms into the types explained in the next subsection.

Intelligent machines learn from the data available in their environment. The process of applying ML consists of two phases: The training phase and the decision-making phase. In the training phase, ML techniques are used to learn the system model using training dataset. In the decision-making phase, the machine shall be able to estimate the output for each input data point using the trained model.

According to the training method, ML techniques can be classified into four general types. Many advanced ML techniques are based on those general types. Figure 2.2 illustrates these types.