Data Mining - Concepts and Techniques.

In recent years, with the rapid development of current Internet and mobile communication technologies, the infrastructure, devices and resources in networking systems are becoming more complex and heterogeneous. In order to efficiently organize, manage, maintain and optimize networking systems, more intelligence needs to be deployed. However, due to the inherently distributed feature of traditional networks, machine learning techniques are hard to be applied and deployed to control and operate networks. Software defined networking (SDN) brings us new chances to provide intelligence inside the networks. The capabilities of SDN (e.g., logically centralized control, global view of the network, software-based traffic analysis, and dynamic updating of forwarding rules) make it easier to apply machine learning techniques. In this paper, we provide a comprehensive survey on the literature involving machine learning algorithms applied to SDN. First, the related works and background knowledge are introduced. Then, we present an overview of machine learning algorithms. In addition, we review how machine learning algorithms are applied in the realm of SDN, from the perspective of traffic classification, routing optimization, quality of service/quality of experience prediction, resource management and security. Finally, challenges and broader perspectives are discussed.

A Survey of Machine Learning Techniques Applied to Software Defined Networking (SDN): Research Issues and Challenges

Machine learning algorithms for wireless sensor networks: A survey

Nowadays, many disruptive Internet-of-Things (IoT) applications emerge, such as augmented/virtual reality online games, autonomous driving, and smart everything, which are massive in number, data intensive, computation intensive, and delay sensitive. Due to the mismatch between the fifth generation (5G) and the requirements of such massive IoT-enabled applications, there is a need for technological advancements and evolutions for wireless communications and networking toward the sixth-generation (6G) networks. 6G is expected to deliver extended 5G capabilities at a very high level, such as Tbps data rate, sub-ms latency, cm-level localization, and so on, which will play a significant role in supporting massive IoT devices to operate seamlessly with highly diverse service requirements. Motivated by the aforementioned facts, in this article, we present a comprehensive survey on 6G-enabled massive IoT. First, we present the drivers and requirements by summarizing the emerging IoT-enabled applications and the corresponding requirements, along with the limitations of 5G. Second, visions of 6G are provided in terms of core technical requirements, use cases, and trends. Third, a new network architecture provided by 6G to enable massive IoT is introduced, i.e., space–air–ground–underwater/sea networks enhanced by edge computing. Fourth, some breakthrough technologies, such as machine learning and blockchain, in 6G are introduced, where the motivations, applications, and open issues of these technologies for massive IoT are summarized. Finally, a use case of fully autonomous driving is presented to show 6G supports massive IoT.

Enabling Massive IoT Toward 6G: A Comprehensive Survey

Wireless sensor networks (WSNs) are prone to many failures such as hardware failures, software failures, and communication failures. The fault detection in WSNs is a challenging problem due to sensor resources limitation and the variety of deployment field. Furthermore, the detection has to be precise to avoid negative alerts, and rapid to limit loss. The use of machine learning seems to be one of the most convenient solutions for detecting failure in WSNs. In this paper, support vector machines (SVMs) classification method is used for this purpose. Based on statistical learning theory, SVM is used in our context to define a decision function. As a light process in term of required resources, this decision function can be easily executed at cluster heads to detect anomalous sensor. The effectiveness of SVM for fault detection in WSNs is shown through an experimental study, comparing it to latest for the same application.

Fault Detection in Wireless Sensor Networks Through SVM Classifier

Improved node localization using K-means clustering for Wireless Sensor Networks

Vehicular $ad~ hoc$ network (VANET) is a set of vehicles trying to exchange security and comfort data even if they are not directly within radio range of each other. As VANET presented several challenges, the various contributions defined are not efficient to provide reliable services. Consequently, several researches and projects have been launched to develop an intelligent transportation system that guarantees both safety and comfort for users. Although the use of metaheuristics seems to be the most convenient to overcome these issues, actually, to the best of our knowledge, there are no studies addressing the uses of those approaches in VANET. This paper surveys and discusses different metaheuristics applied to solve the routing problem within VANET. Furthermore, technical challenges and future trends are treated and presented.

A Survey on Heuristic-Based Routing Methods in Vehicular Ad-Hoc Network: Technical Challenges and Future Trends

Vehicular ad hoc networks are a special type of MANET providing vehicle to vehicle and vehicle to roadside wireless communications. Vehicular ad-hoc network (VANET) have been designed in order to assist drivers on the road with a variety of applications especially in preventing danger and saving lives. For such ends, broadcasting is a suitable scheme to convey emergency messages dissemination to the entire network. Broadcasting in VANETs is a challenging task due to the specific VANET features such as nodes mobility and frequent topology changes. VANET applications, especially those related with human life saving, are delay sensitive and have specific requirements in terms of performances and QoS. A QoS aware broadcasting scheme relies on different factors and has to deal with hard constraints. In this paper, we introduce a survey of broadcasting in vehicular networks and discussion of different performance and QoS related to broadcasting issues. Furthermore, a comparative study of QoS aware broadcasting protocols classifying them according to different taxonomies is elaborated. This survey specifies QoS requirements and performance metrics of VANET services. Furthermore, this survey focuses on QoS aware broadcasting as a challenging problem regrading VANET characteristics.

Tarek Moulahi

Papers

Fault Detection in Wireless Sensor Networks Through SVM Classifier

Improved node localization using K-means clustering for Wireless Sensor Networks

A Survey on Heuristic-Based Routing Methods in Vehicular Ad-Hoc Network: Technical Challenges and Future Trends

A survey and comparative study of QoS aware broadcasting techniques in VANET

Survey on Artificial Intelligence (AI) techniques for Vehicular Ad-hoc Networks (VANETs)