A survey of machine learning techniques applied to self organizing cellular networks

In this paper, a survey of the literature of the past fifteen years involving Machine Learning (ML) algorithms applied to self organizing cellular networks is performed. In order for future networks to overcome the current limitations and address the issues of current cellular systems, it is clear that more intelligence needs to be deployed, so that a fully autonomous and flexible network can be enabled. This paper focuses on the learning perspective of Self Organizing Networks (SON) solutions and provides, not only an overview of the most common ML techniques encountered in cellular networks, but also manages to classify each paper in terms of its learning solution, while also giving some examples. The authors also classify each paper in terms of its self-organizing use-case and discuss how each proposed solution performed. In addition, a comparison between the most commonly found ML algorithms in terms of certain SON metrics is performed and general guidelines on when to choose each ML algorithm for each SON function are proposed. Lastly, this work also provides future research directions and new paradigms that the use of more robust and intelligent algorithms, together with data gathered by operators, can bring to the cellular networks domain and fully enable the concept of SON in the near future

IEEE Access Special Section Editorial: Big Data Technology and Applications in Intelligent Transportation

During the last few years, information technology and transportation industries, along with automotive manufacturers and academia, are focusing on leveraging intelligent transportation systems (ITS) to improve services related to driver experience, connected cars, Internet data plans for vehicles, traffic infrastructure, urban transportation systems, traffic collaborative management, road traffic accidents analysis, road traffic flow prediction, public transportation service plan, personal travel route plans, and the development of an effective ecosystem for vehicles, drivers, traffic controllers, city planners, and transportation applications. Moreover, the emerging technologies of the Internet of Things (IoT) and cloud computing have provided unprecedented opportunities for the development and realization of innovative intelligent transportation systems where sensors and mobile devices can gather information and cloud computing, allowing knowledge discovery, information sharing, and supported decision making. However, the development of such data-driven ITS requires the integration, processing, and analysis of plentiful information obtained from millions of vehicles, traffic infrastructures, smartphones, and other collaborative systems like weather stations and road safety and early warning systems. The huge amount of data generated by ITS devices is only of value if utilized in data analytics for decision-making such as accident prevention and detection, controlling road risks, reducing traffic carbon emissions, and other applications which bring big data analytics into the picture

Identifying Anomalies in past en-route Trajectories with Clustering and Anomaly Detection Methods

International audienceThis paper presents a framework to identify and characterise anomalies in past en-route Mode S trajectories. The technique builds upon two previous contributions introduced in 2018: it combines a trajectory-clustering method to obtain the main flows in an airspace with autoencoding artificial neural networks to perform anomaly detection in flown trajectories. The combination of these two well-known Machine Learning techniques (ML) provides a useful reading grid associating cluster analysis with quantified level of abnormality. The methodology is applied to a sector of the French Bordeaux Area Control Center (ACC) during its 385 hours of operation over seven months of ADS-B traffic. The results provide a good taxonomy of deconfliction measures and weather-related ATC actions. The application of this work is manyfold, ranging from safety studies estimating risks of midair collision, to complexity and workload assessments of traffic when a sector is operated, or to the constitution of a database of ATC actions ensuring aircraft separation. This database could be used to train further ML techniques aimed at improving the state of the art of deconfliction algorithms

Shallow and deep networks intrusion detection system : a taxonomy and survey

Intrusion detection has attracted a considerable interest from researchers and industries. The community, after many years of research, still faces the problem of building reliable and efficient IDS that are capable of handling large quantities of data, with changing patterns in real time situations. The work presented in this manuscript classifies intrusion detection systems (IDS). Moreover, a taxonomy and survey of shallow and deep networks intrusion detection systems is presented based on previous and current works. This taxonomy and survey reviews machine learning techniques and their performance in detecting anomalies. Feature selection which influences the effectiveness of machine learning (ML) IDS is discussed to explain the role of feature selection in the classification and training phase of ML IDS. Finally, a discussion of the false and true positive alarm rates is presented to help researchers model reliable and efficient machine learning based intrusion detection systems

Evolution of 5G Network: A Precursor towards the Realtime Implementation of VANET for Safety Applications in Nigeria

  A crucial requirement for the successful real-time design and deployment of Vehicular Adhoc Networks (VANET) is to ensure high speed data rates, low latency, information security, and a wide coverage area without sacrificing the required Quality of Service (QoS) in VANET. These requirements must be met for flawless communication on the VANET. This study examines the generational patterns in mobile wireless communication and looks into the possibilities of adopting fifth generation (5G) network technology for real-time communication of road abnormalities in VANET. The current paper addresses the second phase of a project that is now underway to develop real-time road anomaly detection, characterization, and communication systems for VANET. The major goal is to reduce the amount of traffic accidents on Nigerian roadways. It will also serve as a platform for the real-time deployment and testing of various road anomaly detection algorithms, as well as schemes for communicating such detected anomalies in the VANET.   &nbsp

Spatial-Temporal Data Mining for Ocean Science: Data, Methodologies, and Opportunities

With the increasing amount of spatial-temporal~(ST) ocean data, numerous spatial-temporal data mining (STDM) studies have been conducted to address various oceanic issues, e.g., climate forecasting and disaster warning. Compared with typical ST data (e.g., traffic data), ST ocean data is more complicated with some unique characteristics, e.g., diverse regionality and high sparsity. These characteristics make it difficult to design and train STDM models. Unfortunately, an overview of these studies is still missing, hindering computer scientists to identify the research issues in ocean while discouraging researchers in ocean science from applying advanced STDM techniques. To remedy this situation, we provide a comprehensive survey to summarize existing STDM studies in ocean. Concretely, we first summarize the widely-used ST ocean datasets and identify their unique characteristics. Then, typical ST ocean data quality enhancement techniques are discussed. Next, we classify existing STDM studies for ocean into four types of tasks, i.e., prediction, event detection, pattern mining, and anomaly detection, and elaborate the techniques for these tasks. Finally, promising research opportunities are highlighted. This survey will help scientists from the fields of both computer science and ocean science have a better understanding of the fundamental concepts, key techniques, and open challenges of STDM in ocean

Web-based Geographical Visualization of Container Itineraries

Around 90% of the world cargo is transported in maritime containers, but only around 2% are physically inspected. This opens the possibility for illicit activities. A viable solution is to control containerized cargo through information-based risk analysis. Container route-based analysis has been considered a key factor in identifying potentially suspicious consignments. Essential part of itinerary analysis is the geographical visualization of the itinerary. In the present paper, we present initial work of a web-based system’s realization for interactive geographical visualization of container itinerary.JRC.G.4-Maritime affair