The Smart Cyber Ecosystem for Sustainable Development

The main research directions that use the AI paradigm are as follows:

Expert SystemsAn expert system is a software system that relies on human expertise for decision-making. It is appropriate to deal with problems that involve incomplete information or big data.

Machine LearningML relies primarily on how the computer simulates the behavior of human learning, then restructures the knowledge and acquires new skills to continuously improve performance.

Pattern RecognitionThe concept of pattern recognition is applied to process monitoring that assumes a relationship between data patterns. The research in pattern recognition includes two main issues: the first relates to object perception and the second relates to determining the category to which the object belongs.

Neural NetworksThe concept of artificial neural networks is based on non-linear mapping between the system’s inputs and outputs. It consists of interconnected neurons arranged in layers. The layers are connected, allowing signals to propagate from the layers’ inputs across the network. A neural network stores data, learns from it, and improves its capabilities to sort new data.

Deep LearningDeep learning is the application of the concept of artificial neural networks to learning tasks that contain more than one hidden layer. It is part of a larger group of ML techniques that are based on representations of learning data. Deep learning concepts come from artificial neural network research, which opened a window to a new field of ML. Concepts of deep learning have been applied in various fields including computer vision, speech recognition systems, natural language processing systems, voice recognition systems, social networking systems, automatic translation systems, and bioinformatics systems, where the adoption of deep learning techniques has led to more effective results as compared to human experience and previous systems.

The primary goal of mobile networks is to connect mobile phone users together as well as to the Internet. Therefore, wireless network operators install large number of base stations or access points in the regions that will be covered. Each base station or access point covers a specific geographical area called a cell. Mobile networks allow users to transparently move between cells via a process called handover. Network users wish that the service provided to them is uninterrupted, whether with regard to the quality of phone calls or the speed with which they surf the Internet.

Self-Organizing Networks (SONs) is an evolving technology used to automate planning, configuration, optimization, and healing of networks. SON is included as part of the mobile networks standards such as such as Long Term Evolution (LTE). The quick evolution in wireless network industry have led to parallel operation of 2G, 3G, 4G, 5G, and emerging 6G networks that need to be managed and controlled with minimal human effort. SON is a promising technology to realize solutions for the control and management of this heterogeneous network regime. The technology suggests a set of concepts to automate network management toward a goal of improving quality of service (QoS) and reduce burdens of networks management on network administrators [1].

With SON, network administrators predefine a set of key performance indicators (KPIs) regarding QoS and other operational functions. Then, the network uses modules and algorithms to self-monitor and optimize its parameters, trying to achieve the predefined KPIs. This is considered as a closed loop control process, by which a network gains understanding of the operation environment and users’ behavior and adapts its parameters accordingly to achieve the intended performance goal, but at same time avoid any misconfiguration of parameters that may lead to service disturbances [2]. In the following subsection, we elaborate more on the features of SONs illustrated in Figure 2.1.

Figure 2.1SON features.

Mobile communications networks are heterogeneous networks comprised of multiple technologies, such as LTE, EDGE, and UMTS. The number of mobile users is incredibly increasing which makes the installation and configuration of base stations a tedious process. Therefore, self-configuration is a process that reduces the time required for these tasks.

Self-configuration provides an initial setup of the network elements. It consists of three stages. The first stage relates to automatic connection to the network, security procedure, and establishing a secure connection between network elements and the network core. The second stage is the programming of network elements, while the third stage relates to the configuration of radio parameters.

Mobile networks are dynamic in nature. This pertains to traffic characteristics, the volume and variability of data exchanged between network elements, the joining of new users, the leave of others, and the movement of users among network cells. This results in variations of network performance as well as the level of service that users are experiencing. Therefore, self-optimization aims to maintain an optimal performance level for all network elements, through analysis of data measured and exchanged by network elements.

The larger the network size, the more likely that failures will occur. The objective of self-healing is to continuously monitor the network in order to automatically detect and recover from unexpected possible failures. In future networks, it is expected that self-healing enables the network to predict faults and automatically take the necessary measures to avoid service degradation and disruptions.

KPIs are simple indicators that represent network performance. Here, we present examples of some important indicators:

Channel Quality Indicator: This represents the connection quality to all users in a cell. Obstacles and multipath fading are major factors that impact channel quality.

Handover Rate Indicator: This represents the mobility pattern of network users. It indicates the signaling traffic on the backbone network units which affects the overall network performance.

Cell Load Indicator: This represents the amount of load on a cell, in terms of users, traffic load, or a cost function.

Quality of Experience (QoE): This represents the satisfaction level of all users in the network or within each cell. Such indicator would characterize the QoS level users are experiencing.

It is important to discuss the fundamental optimization tasks of SONs. In this section, we present some important tasks:

Coverage: Coverage optimization is a process through which a network tries to cover an intended area with minimal number of base stations and transmit power levels.

Capacity: Capacity optimization refers to the process of providing users with the best possible QoS using minimal radio resources. This would imply radio frequency assignment and interference mitigation techniques.

Mobility: Mobility optimization deals with the process of ensuring transparent user movement between cells and at the same time minimizing the number of unnecessary handover requests.

Load Balancing: This refers to the process of distributing the load among network base stations, trying to maximize the QoE in the network and minimize the overhead on core network elements.