2 1 The innovative transport systems and the CityMobil project 10 1.1 The research questions 10 2 The state of art in the field of automatic transport systems 12 2.1 Case studies and demand studies for innovative transport systems 12 3 The design and implementation of surveys 14 3.1 Definition of experimental design 14 3.2 Questionnaire design and delivery 16 3.3 First analyses on the collected sample 18 4 Calibration of Logit Multionomial demand models 21 4.1 Methodology 21 4.2 Calibration of the “full” model. 22 4.3 Calibration of the “final” model 24 4.4 The demand analysis through the final Multinomial Logit model 25 5 The analysis of interaction between the demand and socioeconomic attributes 31 5.1 Methodology 31 5.2 Application of Mixed Logit models to the demand 31 5.3 Analysis of the interactions between demand and socioeconomic attributes through Mixed Logit models 32 5.4 Mixed Logit model and interaction between age and the demand for the CTS 38 5.5 Demand analysis with Mixed Logit model 39 6 Final analyses and conclusions 45 6.1 Comparison between the results of the analyses 45 6.2 Conclusions 48 6.3 Answers to the research questions and future developments 52

The European commission

From the Publisher:
An agent is an entity with domain knowledge, goals and actions. Multi-agent systems are a set of agents which
interact in a common environment. Multi-agent systems deal with the construction of complex systems involving multiple agents and their coordination. A multi-agent system (MAS) is a distributed computing system with
autonomous interacting intelligent agents that coordinate their actions so as to achieve its goal(s) jointly or competitively.

Introduction to Multiagent Systems

Home PURPOSE OF THE CENTER: To develop the center to address state-of-the-art research, create innovating educational programs, and support technology transfers using commercially viable results to assist the Army Research Laboratory to develop the next generation Future Combat System in the telecommunications sector that assures prevention of perceived threats, and Non Line of Sight/Beyond Line of Sight lethal support.

How to… home page

Multi-access edge computing (MEC) is an emerging ecosystem, which aims at converging telecommunication and IT services, providing a cloud computing platform at the edge of the radio access network MEC offers storage and computational resources at the edge, reducing latency for mobile end users and utilizing more efficiently the mobile backhaul and core networks This paper introduces a survey on MEC and focuses on the fundamental key enabling technologies It elaborates MEC orchestration considering both individual services and a network of MEC platforms supporting mobility, bringing light into the different orchestration deployment options In addition, this paper analyzes the MEC reference architecture and main deployment scenarios, which offer multi-tenancy support for application developers, content providers, and third parties Finally, this paper overviews the current standardization activities and elaborates further on open research challenges

/pdf/on-multi-access-edge-computing-a-survey-of-the-emerging-5g-q09cwte3qi.pdf

On Multi-Access Edge Computing: A Survey of the Emerging 5G Network Edge Cloud Architecture and Orchestration

The technologies, architectures, and methodologies traditionally used to develop distributed applications exhibit a variety of limitations and drawbacks when applied to large scale distributed settings (e.g., the Internet). In particular, they fail in providing the desired degree of configurability, scalability, and customizability. To address these issues, researchers are investigating a variety of innovative approaches. The most promising and intriguing ones are those based on the ability of moving code across the nodes of a network, exploiting the notion of mobile code. As an emerging research field, code mobility is generating a growing body of scientific literature and industrial developments. Nevertheless, the field is still characterized by the lack of a sound and comprehensive body of concepts and terms. As a consequence, it is rather difficult to understand, assess, and compare the existing approaches. In turn, this limits our ability to fully exploit them in practice, and to further promote the research work on mobile code. Indeed, a significant symptom of this situation is the lack of a commonly accepted and sound definition of the term mobile code itself. This paper presents a conceptual framework for understanding code mobility. The framework is centered around a classification that introduces three dimensions: technologies, design paradigms, and applications. The contribution of the paper is two-fold. First, it provides a set of terms and concepts to understand and compare the approaches based on the notion of mobile code. Second, it introduces criteria and guidelines that support the developer in the identification of the classes of applications that can leverage off of mobile code, in the design of these applications, and, finally, in the selection of the most appropriate implementation technologies. The presentation of the classification is intertwined with a review of state-of-the-art in the field. Finally, the use of the classification is exemplified in a case study.

/pdf/understanding-code-mobility-1wdaf22ju4.pdf

Understanding code mobility

With the centralization of the RAN control functionality and with the pushing of Core Network functionality towards the network edge, the thus far very strong separation of concerns between RAN and core cannot be assumed any longer. Instead, almost the same network functionality for subscriber mobility and session management is placed in the same local aggregation point. This article presents a new architecture framework converging this functionality now planned for both RAN and Core Network within a single set of network functions, as well as their integration with the rest of the network components. Furthermore, the proposed new architecture is compared with the current 5G architecture and assessed against the 6G requirements, showing how it outperforms in terms of increased deployment specific customization flexibility and open interfaces.

A 6G RAN-Core Control Plane Convergence Framework

Modern industrial processes often collect redundant information from multiple sensors. It is of interest to leverage this information to form a more accurate or robust estimate of an observed quantity utilizing a sensor fusion operation. To be able to comply with quality requirements on the fused value, preservation of traceability is required. Moreover, the sensor fusion needs to be implemented within suitable digital architectures for Industrial Internet of Things (IIoT) environments. In a continuation of previous work, this is achieved by the usage of digital twins that represent the entities of two IIoT testbeds and the adoption of a method for uncertainty-aware homogeneous sensor fusion, which is presented in full detail. Metrological traceability of the fused value is established by propagating the measurement uncertainty of the input sensor according to metrological standards. The method is implemented as a modular service connected to an existing IIoT architecture. The flexibility of the method is shown by application to two different scenarios with only minimal adaption efforts. The fused sensor values are (as indicated by earlier work) robust to outliers and perform well in practical scenarios within the chosen IIoT architecture.

Application of Uncertainty-Aware Sensor Fusion in Physical Sensor Networks

Distributed Denial of Service (DDoS) attacks are a major threat for the deployment of SIP-based VoIP networks. Various solutions have been suggested to mitigate the impact of these attacks, each bringing its advantages and disadvantages. Classifying the SIP-based VoIP traffic into different queues according to the suspiciousness of the traffic, can be an efficient solution if it is correctly implemented. Unfortu-nately, adding queues to the SIP servers affects severely its performance. The current paper suggests a mechanism called “virtual queuing” that implements a priority queue on a SIP server while keeping its performance at its best. Keywords: DDoS attacks, Prioritization, Queuing, Scheduling Algorithm.

https://www.ijarcs.info/index.php/Ijarcs/article/download/11/11

Improving Distributed Denial of Service (DDOS) Attack Detection Performance using Priority Queuing

Offener Technologie-Baukasten für 5G-Campusnetze

Over the last 5 years the Communications Research Group at the University of Cape Town has been actively involved in IP Multimedia Subsystem (IMS) testbed research initiatives. This paper aims to describe the research work that was done within this testbed. The work ranges from work on an IMS client, IMS based services such as IPTV and Advertising, as well as network management work relating to QoS, Policy and Charging Control. The paper also touches on current work that involves the evolution of the mobile core network or more specifically the Evolved Packet Core (EPC). Future work involves the integration of all past research work into this new testbed as well as investigating other areas such as enhanced QoS mechanisms and Mobility control for the EPC, and the Integration of Cognitive radios in EPC / Base Stations for UMTS/LTE.

Thomas Magedanz

Papers

A 6G RAN-Core Control Plane Convergence Framework

Application of Uncertainty-Aware Sensor Fusion in Physical Sensor Networks

Improving Distributed Denial of Service (DDOS) Attack Detection Performance using Priority Queuing

Offener Technologie-Baukasten für 5G-Campusnetze

Past and current IMS testbed initiatives: The UCT IMS and EPC testbed