Antonio Virdis

Ensuring compliance with the stringent latency requirements of edge services requires close cooperation between the network and computing components. Within mobile 5G networks, the nomadic behavior of users may impact the performance of edge services, prompting the need for workload migration techniques. These techniques allow services to follow users by moving between edge nodes. This paper introduces an innovative approach for edge service continuity by integrating Segment Routing over IPv6 (SRv6) into the 5G core data plane alongside the ETSI multi-access edge computing (MEC) architecture. Our approach maintains compatibility with non-SRv6 5G network components. We use SRv6 for packet steering and Software-Defined Networking (SDN) for dynamic network configuration. Leveraging the SRv6 Network Programming paradigm, we achieve lossless workload migration by implementing a packet buffer as a virtual network function. Our buffer may be dynamically allocated and configured within the network. We test our proposed solution on a small-scale testbed consisting of an Open Network Operating System (ONOS) SDN controller and a core network made of P4 BMv2 switches, emulated using Mininet. A comparison with a non-SRv6 alternative that uses IPv6 routing shows the higher scalability and flexibility of our approach in terms of the number of rules to be installed and time required for configuration.

Indoor short-range localization is crucial in many Industry 4.0 applications. Production monitoring for assembly lines, for instance, requires fine-grained positioning for parts or goods in order to keep track of the production process and the stations traversed by each product. Due to the unavailability of the Global Positioning System (GPS) for indoor positioning, a different approach is required. In this paper, we propose a specific design for short-range indoor positioning based on the analysis of the Received Signal Strength Indicator (RSSI) of Bluetooth beacons. To this aim, different machine learning techniques are considered and assessed: regressors, Convolution Neural Network (CNN) and Recurrent Neural Network (RNN). A realistic testbed is created to collect data for the training of the models and to assess the performance of each technique. Our analysis highlights the best models and the most convenient and suitable configuration for indoor localization. Finally, the localization accuracy is calculated in the considered use case, i.e., production monitoring. Our results show that the best performance is obtained using the K-Nearest Neighbors technique, which results in a good performance for general localization and in a high level of accuracy, 99%, for industrial production monitoring.