The Smart Cyber Ecosystem for Sustainable Development

A plethora of research studies has developed various solutions to different challenges based on the traditional architecture of WLANS, aiming to optimally exploit network resources and provide high QoS to users. With the spread of AI systems and the tendency to develop AI-based solutions in all areas, researchers have been trying to adapt the ideas and tools of AI and study the possibility of using these tools for optimal operation and management of WLANs. In this section, we discuss some important studies in this field.

ML and the combination of ML and SDN have been shifting the research in WLANs to a new direction what allows more practical solutions to complex networking problems. Such solutions do not only simplify the management of network but also alleviate the complexity of algorithms and facilitate reaching optimal operation of a dynamically changing network.

The authors of [47] develop a framework solution for the control and management of WLANs based on the SDN approach. The system comprises a set of modules and ML algorithms, stressing the fact that modern and future WLANs will be intelligently controlled. The authors tried to show the strength of the developed solution by addressing the following issues: mobility, security, QoS, channel bandwidth allocation, coordinated transmission power, load balancing, and virtualization of wireless networks.

Administrators of WLANs normally deploy large number of APs to cover an area and provide users best QoS. In such scenario, a user could be under the coverage of multiple APs and thus has the potential to select the AP to which it will connect. The selection of AP is important and affects the performance a user might experience in the network as well as the overall network performance. This is because wireless networks are highly dynamic, whereas the activation of a link between a user and an AP may influence other ongoing connections in same and/or neighboring cells. In current implementations of WLANs technology, a user selects an AP from which it gets the strongest signal during a scanning phase. It has been shown in large number of research studies that the legacy selection policy does not ensure best QoS for network users. Obviously, an AP to which a user has the strongest connection might be serving large number of users; hence, its cell will be highly congested [48].

The authors of [49] propose an SDN-based AP selection scheme for WLANs. The selection is based on the analysis of achievable throughput a user might get from potential cells. The system computes the throughputs that capture the channel competition among neighboring cells. Some cells may obtain few chances to get the channel. Even in the same cell, mobile users compete with each other for channel access. The authors noticed that the airtime completion and airtime share among WLAN users play a fundamental role in determining the QoS the user will get. The authors implemented their proposed method in an SDN framework comprised of three planes: data plane, CP, and service plane. The data plane consists of a number of thin APs that are responsible for data forwarding. The CP contains a SDN-WiFi controller, while the service plane contains a set of applications such as association control, load balance, and seamless mobility management. The proposed AP selection scheme is implemented in the service plane. The applications are installed on the SDN controller, which collects necessary information from networking devices and decides the best cell for each of newly joining users.

In [50], the authors developed ML-based methods for detecting causes of unnecessary active scanning in WLANs. The authors argue that ML provides the best way to detect causes of unnecessary active scan in WLANs, where various independent and dependent parameters interact together. Both unsupervised and supervised methods are compared.

Data collected from real WLANs is used to train the ML algorithms. The authors deduced that a multilayer perceptron-based classifier model outperforms other models and accurately detect the cause of unnecessary active scanning.

The work in [51] proposes an SDN-based framework for AP selection in WLANs, considering the QoS level required by users’ flows. The authors of [52] study the user association issue in an SDN–architecture. They developed heuristic algorithms that lead to high performance assuming unsaturated heterogeneous Markovian analytical model.

The authors of [53] propose an admission control mechanism for VoIP calls in WLANs. A ML algorithm is used to predict the voice quality considering different parameters at the data link layer such as fraction of channel time used for video and normal traffic as well as estimated frame error rate for video and normal traffic.

The authors of [54] leverage on the SDN paradigm to develop an algorithm that achieves effective distribution of traffic load in WLANs. The authors try to optimally distribute network resources and improve the overall performance.

Power control is a well-known approach used to mitigate interference in wireless networks. In SDN-based management and control of WLANs, the centralized CP can be used to implement the mechanisms for power control to minimize interference through coverage optimization of WLANs cells.

Wireless interference classification is a process of identifying the type of wireless emitters exist in the local RF environment [55]. This is important for enabling coexistence of wireless technologies that operate in the same frequency band. ML-based solutions are being developed to achieve this goal.

In [56], the authors propose a RL mechanism for interference mitigation in small cell networks. The algorithm represents the state of each AP as a binary variable that indicates whether the QoS requirement is violated. The action is a selection of power values from a set of power values. The reward is defined as the achieved rate. The algorithm iterates until a predefined level of QoS is met.

The work in [57] develops a solution that uses ML-SVM for interference classification in wireless sensor networks from IEEE 802.11 signals and microwave ovens. Another deep learning approach for classification of WiFi, Zigbee, and Bluetooth was proposed in [58]. The authors defined fifteen classification tasks assuming a flat fading channel with additive white Gaussian noise. The research work of [59] compares different types of ML models for classifying signals, including deep feed-forward networks, deep convolutional networks, SVM and a multi-stage training algorithm.

In [60], the authors propose a ML-based framework for mitigating the effect of jammers in WLANs, called “DeepWifi”. The system consists of an RF front end processing unit which applies a deep learning-based auto-encoder to extract spectrum-representative features. The system leverages the advances in ML algorithms to enhance the performance and security in WLANs. A deep neural network is then trained to classify signals as idle, WiFi, or jammer. In standard WiFi, the user backs off backs off regardless of the type of interference. However, DeepWiFi which is able to classify signals backs off when the interference is from another WLAN user, allowing user to operate in degraded mode and still receive non-zero throughput.

Even with centralized control, optimal channel allocation problem in WLANs is difficult to be solved in an acceptable complexity level. Recently, researchers have been trying to leverage ML methods to find solutions in feasible time.

In [61], the authors propose a ML method for assigning channels to WLANs APs. The method is based on passive monitoring of data in each cell. Using ML, it calculates the performance loss due to interfering users and finds the best channels for the cells that minimize interference. The algorithm minimizes airtime usage of interfering links in neighboring cells. Due to the dynamic nature of WLANs, the process is repeated iteratively. The authors of [62] concluded that a central control of APs is needed even if the network is influenced by neighboring unmanaged APs. Their approach results in a self-organizing system for channel allocation in WLANs based on cooperation between APs. The authors show that the proposed system leads to a stable network of high performance.