Cascaded WLAN-FWA Networking and Computing Architecture for Pervasive In-Home Healthcare

Pervasive healthcare is a promising assisted-living solution for chronic patients. However, current cutting-edge communication technologies are not able to strictly meet the requirements of these applications, especially in the case of life-threatening events. To bridge this gap, this paper proposes a new architecture to support indoor healthcare monitoring, with a focus on epileptic patients. Several novel elements are introduced. The first element is the cascading of a WLAN and a cellular network, where IEEE 802.11ax is used for the wireless local area network to collect physiological and environmental data in-home and 5G-enabled Fixed Wireless Access links transfer them to a remote hospital. The second element is the extension of the network slicing concept to the WLAN, and the introduction of two new slice types to support both regular monitoring and emergency handling. Moreover, the inclusion of local computing capabilities at the WLAN router, together with a mobile edge computing resource, represents a further architectural enhancement. Local computation is required to trigger not only health-related alarms, but also the network slicing change in case of emergency: in fact, proper radio resource scheduling is necessary for the cascaded networks to handle healthcare traffic together with other promiscuous everyday communication services. Numerical results demonstrate the effectiveness of the proposed approach while highlighting the performance gain achieved with respect to baseline solutions

On the Evaluation of the NB-IoT Random Access Procedure in Monitoring Infrastructures

NarrowBand IoT (NB-IoT) is emerging as a promising communication technology offering a reliable wireless connection to a large number of devices employed in pervasive monitoring scenarios, such as Smart City, Precision Agriculture, and Industry 4.0. Since most of the NB-IoT transmissions occur in the uplink, the random access channel (that is the primary interface between devices and the base station) may usually become the main bottleneck of the entire system. For this reason, analytical models and simulation tools able to investigate its behavior in different scenarios are of the utmost importance for driving current and future research activities. Unfortunately, scientific literature partially addresses the current open issues by means of simplified and, in many cases, not standard-compliant approaches. To provide a significant step forward in this direction, the contribution of this paper is three-folded. First, it presents a flexible, open-source, and 3GPP-compliant implementation of the NB-IoT random access procedure. Second, it formulates an analytical model capturing both collision and success probabilities associated with the aforementioned procedure. Third, it presents the cross-validation of both the analytical model and the simulation tool, by taking into account reference applications scenarios of sensor networks enabling periodic reporting in monitoring infrastructures. Obtained results prove the remarkable accuracy, demonstrating a well-calibrated instrument, which will be also useful for future research activities

Architecting 5G RAN Slicing for Location Aware Vehicle to Infrastructure Communications: The Autonomous Tram Use Case

In this paper, we propose a Radio Access Network (RAN) slicing mechanism for mission-critical services in the context of location-aware Vehicle to Infrastructure (V2I) communications. In particular, the available location information is used to design an ad-hoc wireless network with both inter and intra-slice radio isolation obtained through the reservation of bandwidth (BW) resources. In this general setting, a particular emphasis is given to the use case of autonomous driving for Lightrails and Tramways systems. We assess the effectiveness of the proposed mechanism for guaranteeing isolation and for providing the required Quality of Service (QoS) through simulations. In order to evaluate realistic and reference scenario settings, we leverage a customized version of the system-level 5G-air-simulator

Strategies and performance of the CMS silicon tracker alignment during LHC Run 2

The strategies for and the performance of the CMS silicon tracking system alignment during the 2015–2018 data-taking period of the LHC are described. The alignment procedures during and after data taking are explained. Alignment scenarios are also derived for use in the simulation of the detector response. Systematic effects, related to intrinsic symmetries of the alignment task or to external constraints, are discussed and illustrated for different scenarios

Precision measurement of the structure of the CMS inner tracking system using nuclear interactions

The structure of the CMS inner tracking system has been studied using nuclear interactions of hadrons striking its material. Data from proton-proton collisions at a center-of-mass energy of 13 TeV recorded in 2015 at the LHC are used to reconstruct millions of secondary vertices from these nuclear interactions. Precise positions of the beam pipe and the inner tracking system elements, such as the pixel detector support tube, and barrel pixel detector inner shield and support rails, are determined using these vertices. These measurements are important for detector simulations, detector upgrades, and to identify any changes in the positions of inactive elements