In order to provide an industry-grade 5G Private Network (PN) to the enterprises, the ultra-reliability communication becomes a key feature to successfully deploy 5G PNs for the future industry 4.0 applications. This paper article will use the free5GC, which is the world's first open-source 5G core (5GC) network that complies with 3rd Generation Partnership Project (3GPP) R15 standards, developed in the laboratory as the base to develop a redundant transmission technique for Ultra-Reliable Communication in 5G Core Networks. We compare the its performance and reliability with the original a single protocol data unit (PDU) session in free5GC. Moreover, it confirmed that the reliability it brings would enable users to have a better user experience.

Existing global navigation satellite systems (GNSSs) provide insufficient accuracy in terms of GNSS elevation and are limited by two-dimensional maps. Therefore, when traveling along a road that branches off to an overpass, it is impossible to immediately determine whether the vehicle is on the overpass. Inaccurate navigation not only causes anxiety for some drivers but also can lead to car accidents. This problem has existed since the invention of GNSSs. To the best of our knowledge, we are the first to conduct a large number of on-road experiments at different locations using off-the-shelf smartphones equipped with barometers. Based on the preliminary results of the wind tunnel and on-road experiments, we propose barometer-assisted road-layer detection (BARLD), which uses the changes in atmospheric pressure to determine therelativealtitude. Comprehensive experiments in many different scenarios, including on overpasses, underpasses, and in parking lots, are discussed in this paper. The results confirm that the BARLD can effectively assist consumer-grade GNSSs to determine road layers. The source code of BARLD is publicly available, which would benefit the research community.

Accurate positioning in global navigation satellite system (GNSS)-denied environments, such as tunnels and underpasses, remains a challenge. Navigation systems for such environments need to strike a balance between price and precision. In this paper, we propose magnetic vehicular positioning (MVP), a navigation system that guides drivers in GNSS-denied environments. The key idea of MVP is to extract magnetic fingerprints from geomagnetic field anomalies. By comparing the measured magnetic field against a magnetic map, positioning can be achieved without GNSS signals. Our proposed matching algorithm allows MVP to provide 5.14 m positioning accuracy. We conducted large-scale real-road experiments for 36 months in two countries and 56 tunnels to demonstrate the effectiveness of the proposed system. Because MVP can be deployed on off-the-shelf smartphones, our approach makes accurate navigation in GNSS-denied environments affordable.