Offering seamless connectivity for Next-Generation Internet (NGI) services has become a widespread concern to achieve global sustainability goals. In this position paper, we focus on the design of a smart connectivity platform that can extend its capabilities beyond the boundaries of what today’s 5G architecture can support. The proposed platform encompasses and integrates a wide range of technologies, spanning from Non-Terrestrial Networks, NTNs (GEO/LEO satellites, and airborne) to terrestrial cellular 5G and long-range low-power Internet of Things (IoT) networks. We enhance 5G technology with novel network architectures and network intelligence to meet coverage and capacity requirements; and by strengthening infrastructure management and control components with autonomous capabilities to decouple operational costs from growth in network traffic, cloud computing, and IoT. Finally, we discuss the technical, commercial and market challenges that need to be overcome before the commercial deployment of the designed platform becomes reality.

We demonstrate Scrutinizer, a system that supports human fact checkers in translating text claims into SQL queries on an associated database. Scrutinizer coordinates teams of human fact checkers and reduces their verification time by proposing queries or query fragments over relevant data. Those proposals are based on claim text classifiers, that gradually improve during the verification of multiple claims. In addition, Scrutinizer uses tentative execution of query candidates to narrow down the set of alternatives. The verification process is controlled by a cost-based optimizer that plans effective question sequences to verify specific claims, and prioritizes claims for verification. In this demonstration, we first show how our system can assist users in verifying statistical claims. We then let users come up with new, unseen claims and show how the system effectively learns new queries with little user feedback.

We introduce the problem of anti-knowledge mining. Our goal is to create an "anti-knowledge base" that contains factual mistakes. The resulting data can be used for analysis, training, and benchmarking in the research domain of automated fact checking. Prior data sets feature manually generated fact checks of famous misclaims. Instead, we focus on the long tail of factual mistakes made by Web authors, ranging from erroneous sports results to incorrect capitals. We mine mistakes automatically, by an unsupervised approach, from Wikipedia updates that correct factual mistakes. Identifying such updates (only a small fraction of the total number of updates) is one of the primary challenges. We mine anti-knowledge by a multi-step pipeline. First, we filter out candidate updates via several simple heuristics. Next, we correlate Wikipedia updates with other statements made on the Web. Using claim occurrence frequencies as input to a probabilistic model, we infer the likelihood of corrections via an iterative expectation-maximization approach. Finally, we extract mistakes in the form of subject-predicate-object triples and rank them according to several criteria. Our end result is a data set containing over 110,000 ranked mistakes with a precision of 85% in the top 1% and a precision of over 60% in the top 25%. We demonstrate that baselines achieve significantly lower precision. Also, we exploit our data to verify several hypothesis on why users make mistakes. We finally show that the AKB can be used to find mistakes on the entire Web.

One of the most important concepts used in mobile networks, like LTE (Long Term Evolution) is service continuity. A mobile user moving from one network to another network should not lose an on-going service. In cloud-based (virtualized) LTE systems, services are hosted on Virtual Machines (VMs) that can be moved and migrated across multiple networks to such locations where these services can be well delivered to mobile users. The migration of the (1) VMs and (2) the services running on such VMs, should happen in such a way that the disruption of an on-going service is minimized. In this paper we argue that a technology that can efficiently be used for supporting service and VM migration is the ICN/CCN (Information Centric Networking / Content Centric Networking) technology.

Distributed Mobility Management (DMM) is a mobility management solution, where the mobility anchors are distributed instead of being centralized. The use of DMM can be applied in cloud-based (virtualized) Long Term Evolution (LTE) mobile network environments to (1) provide session continuity to users across personal, local, and wide area networks without interruption and (2) support traffic redirection when a virtualized LTE entity like a virtualized Packet Data Network Gateway (P-GW) running on an virtualization platform is migrated to another virtualization platform and the on-going sessions supported by this P-GW need to be maintained. In this paper we argue that the enabling technology that can efficiently be used for supporting DMM in virtualized LTE systems is the Software Defined Networking (SDN)/OpenFlow technology.

The recently standardised IEEE 802.11p Wireless Access in Vehicular Environment (WAVE) protocol is envisioned to be used for Vehicle-to-Vehicle and Vehicle-to-Infrastructure communications. We provide an analysis of 802.11p's performance when used for the exchange of small status messages known as beacons. These beacons enable vehicles to establish a Cooperative Awareness from which many applications can draw their inputs. Applications have been proposed to increase safety, efficiency and comfort of future road traffic. This paper investigates the impact of adapting settings for the Contention Window (CW) based on the traffic density in order to increase the efficiency of the 802.11p broadcasts w.r.t. reception probability and delay with a focus on real-time vehicle control. In contrast to the literature available on adapting CW settings in unicast, we find that the initial CW value of 15 proposed in the IEEE 802.11p standard performs significantly well under almost all studied circumstances.

This paper describes the Over-the-Horizon Awareness (OTHA) protocol which provides an extended view of the traffic ahead to Driver Support Systems (DSS) by means of multi-hop vehicle-to-vehicle communication. The protocol extends and adapts the TrafficFilter [1] to make it suitable for multiple-lane highway scenarios. As basis, we rely on the information required by the Congestion Assistant [2], a driver support system that aids drivers in traffic jams. Simulation results show that OTHA achieves high node reachability and information accuracy.

Reservation-based quality of service (QoS) in a mixed wireless and wireline environment requires an end-to-end signaling protocol that is capable of adapting to the idiosyncrasies of the different networks. The QoS NSIS signaling protocol (QoS-NSLP) has been created by the Next Steps In Signaling working group at the IETF to fulfill this need for an adaptive reservation protocol. It allows reservation requests to be interpreted by equipment implementing different QoS models along the path between a data sender and a data receiver. This paper describes the QoS-NSLP, and an example of a particular QoS model that is based on resource management in DiffServ (RMD). RMD provides a scalable dynamic resource management method for DiffServ networks. RMD has two basic functions to control the traffic load in a DiffServ domain: it provides admission control for flows entering the network and it has an algorithm that terminates the required amount of flows in case of congestion caused by failures (e.g. link or router) within a DiffServ domain. The admission control within the domain can be either measurement - or reservation-based. The basic signaling mechanism is described for different signaling scenarios and the expected performance of the protocol is discussed.

The wide deployment of IP technologies has driven the development of IP-based solutions for wireless networks, like the IP-based radio access networks (RAN). Such wireless networks have different characteristics than traditional IP networks, imposing very strict requirements on quality of service (QoS) solutions. Current QoS solutions are not able to satisfy these requirements. This paper describes the prototype implementation of a QoS-framework, called resource management in differentiated services (RMD), which aims to correct this situation. RMD is a framework offering scalable fine-grained QoS guarantees in differentiated services (DiffServ) networks. It extends the DiffServ architecture with new admission control and resource reservation concepts in a scalable way.