Nowadays every business organization operates in ecosystems and cooperation is mandatory. If, on the one side, this increases the opportunities for the involved organizations, on the other side, every actor is a potential source of failures with impacts on the entire ecosystem. For this reason, resilience is a feature that multi-party business processes today must enforce. As resilience concerns the ability to cope with unplanned situations, managing the critical issues is usually a run-time task.\ud \ud The aim of this work is to emphasize awareness on resilience in multi-party business processes also at design-time, when a proper analysis of involved data allows the process designer to identify (possible) failures, their impact, and thus improving the process model. Using a data-centric collaboration-oriented language for processes, i.e., OMG CMMN – Case Management Model and Notation, as modeling notation, our approach allows the designer to model a flexible business process that, at run-time, results easier to manage in case of failures

This paper investigates the role of quantization in the Received Signal Strength Indicator (RSSI) information\ud used for fingerprinting (FP) applications. One of the common drawbacks of FP is the large data size and consequently the large search space and computational load as a result of either vastness of the positioning area or the finer resolution in the FP grid map: this limits the application of FP to small environments or scenarios with largely spaced grid points leading to poor localization performance. We show that the computational complexity\ud can be limited adapting the RSSI quantization w.r.t. the variance of the measured RSSI error at the target. This approach turns out to be advantageous for the deployment of FP systems based on beacons equipped with inexpensive technologies, in which the measures precision loss could be compensated by larger numbers of beacons. An appropriate quantization and design of RSSI signatures will make possible the deployment of FP in larger areas maintaining the same computational load and/or the desired localization performance

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Channel estimation for hybrid Multiple Input Multiple Output (MIMO) systems at Millimeter-Waves/sub-THz is a fundamental, despite challenging, prerequisite for an efficient design of hybrid MIMO precoding/combining. Most works propose sequential search algorithms, e.g., Compressive Sensing (CS), that are most suited to static channels and consequently cannot apply to highly dynamic scenarios such as Vehicle-to-Everything (V2X). To address the latter ones, we leverage recurrent vehicle passages to design a novel Multi Vehicular (MV) hybrid MIMO channel estimation suited for Vehicle-to-Infrastructure (V2I) and Vehicle-to-Network (V2N) systems. Our approach derives the analog precoder/combiner through a MV beam alignment procedure. For the digital precoder/combiner, we adapt the Low-Rank (LR) channel estimation method to learn the position-dependent eigenmodes of the received digital signal (after beamforming), which is used to estimate the compressed channel in the communication phase. Extensive numerical simulations, obtained with ray-tracing channel data and realistic vehicle trajectories, demonstrate the benefits of our solution in terms of both achievable spectral efficiency and mean square error compared to the unconstrained maximum likelihood estimate of the compressed digital channel, making it suitable for both 5G and future 6G systems. Most notably, in some scenarios, we obtain the performance of the optimal fully digital systems.

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