IEEE Access special section editorial: Artificial intelligence enabled networking

With today’s computer networks becoming increasingly dynamic, heterogeneous, and complex, there is great interest in deploying artificial intelligence (AI) based techniques for optimization and management of computer networks. AI techniques—that subsume multidisciplinary techniques from machine learning, optimization theory, game theory, control theory, and meta-heuristics—have long been applied to optimize computer networks in many diverse settings. Such an approach is gaining increased traction with the emergence of novel networking paradigms that promise to simplify network management (e.g., cloud computing, network functions virtualization, and software-defined networking) and provide intelligent services (e.g., future 5G mobile networks). Looking ahead, greater integration of AI into networking architectures can help develop a future vision of cognitive networks that will show network-wide intelligent behavior to solve problems of network heterogeneity, performance, and quality of service (QoS)

Scalable system for smart urban transport management

Efficient management of smart transport systems requires the integration of various sensing technologies, as well as fast processing of a high volume of heterogeneous data, in order to perform smart analytics of urban networks in real time. However, dynamic response that relies on intelligent demand-side transport management is particularly challenging due to the increasing flow of transmitted sensor data. In this work, a novel smart service-driven, adaptable middleware architecture is proposed to acquire, store, manipulate, and integrate information from heterogeneous data sources in order to deliver smart analytics aimed at supporting strategic decision-making. The architecture offers adaptive and scalable data integration services for acquiring and processing dynamic data, delivering fast response time, and offering data mining and machine learning models for real-time prediction, combined with advanced visualisation techniques. The proposed solution has been implemented and validated, demonstrating its ability to provide real-time performance on the existing, operational, and large-scale bus network of a European capital city

5MART: A 5G SMART scheduling framework for optimizing QoS through reinforcement learning

The massive growth in mobile data traffic and the heterogeneity and stringency of Quality of Service (QoS) requirements of various applications have put significant pressure on the underlying network infrastructure and represent an important challenge even for the very anticipated 5G networks. In this context, the solution is to employ smart Radio Resource Management (RRM) in general and innovative packet scheduling in particular in order to offer high flexibility and cope with both current and upcoming QoS challenges. Given the increasing demand for bandwidth-hungry applications, conventional scheduling strategies face significant problems in meeting the heterogeneous QoS requirements of various application classes under dynamic network conditions. This paper proposes 5MART, a 5G smart scheduling framework that manages the QoS provisioning for heterogeneous traffic. Reinforcement learning and neural networks are jointly used to find the most suitable scheduling decisions based on current networking conditions. Simulation results show that the proposed 5MART framework can achieve up to 50% improvement in terms of time fraction (in sub-frames) when the heterogeneous QoS constraints are met with respect to other state-of-the-art scheduling solutions

Mobility Management, Quality of Service, and Security in the Design of Next Generation Wireless Network

The next generation wireless network needs to provide seamless roaming among various access technologies in a heterogeneous environment. In allowing users to access any system at anytime and anywhere, the performance of mobility-enabled protocols is important. While Mobile IPv6 is generally used to support macro-mobility, integrating Mobile IPv6 with Session Initiation Protocol (SIP) to support IP traffic will lead to improved mobility performance. Advanced resource management techniques will ensure Quality of Service (QoS) during real-time mobility within the Next Generation Network (NGN) platform. The techniques may use a QoS Manager to allow end-to-end coordination and adaptation of Quality of Service. The function of the QoS Manager also includes dynamic allocation of resources during handover. Heterogeneous networks raise many challenges in security. A security entity can be configured within the QoS Manager to allow authentication and to maintain trust relationships in order to minimize threats during system handover. The next generation network needs to meet the above requirements of mobility, QoS, and security

Ambient Networks Mobility Management for Beyond-3G Telecoms

Abstract—In heterogeneous networks such as new multi-domain and multi-technology mobile networks, handover requests could be driven by other needs such as cost reduction criteria, network resource optimization, service related requirements, etc. Thus, handover could become a critical issue if not done in an optimized way. The main objective is therefore to define concepts and functions with the aim to realize handovers characterized by new multi-dimensions (multi-domain mobile networks, multi-technologies, multi-services, multi-devices, multi-hops, multi-accesses, any-cast, etc). Furthermore it is necessary to be able to support novel requirements that have emerged due to the proliferation of mobile communication services. In the first part of this work are shown conception, definitions, concepts and functions for mobility management, in particular for handover management within "beyond-3G" Ambient Networks scenarios and architecture. Ambient Networks is an integrated project co-sponsored by the European Commission under the Information Society Technology (IST) priority within the 6th Framework Programme (FP6). [http://www.ambient-networks.org]. The two last chapters of this work illustrate implementation of new ideas proposed: in particular are shown dynamic network composition and the handover toolbox, which is very useful in order to construct a handover in an optimized way, based on needs mentioned at the beginning of this abstract