The success of future Intelligent Transportation Systems (ITS) heavily relies on the availability of wireless communications technologies that are able to fulfil the strict requirements imposed by applications such as autonomous driving. However, due to the scarce spectrum allocated to ITS, current vehicle-to-everything (V2X) networks experience a severe capacity bottleneck. In this paper, we consider a typical ITS application where images are periodically captured and transmitted by the vehicles. Using an open dataset of real video frames captured by vehicles, we show that the current bandwidth allocated to ITS is insufficient, requiring the vehicles to transmit the video at only a small portion of the frame rate at which it can be captured by the sensors. Furthermore, we show that semantic communications (SemCom) using semantic segmentation are essential to solve this problem, since the receivers are not interested in receiving high definition images but a semantic interpretation that assesses the importance of the elements in it. Our results show that, although the benefits of SemCom in terms of energy consumption are limited to algorithms with a low computational complexity, SemCom are able to greatly improve the performance in terms of maximum supported load and latency, making SemCom essential to make future ITS over 6G viable.

We introduce and explore a family of vertex-coloring problems, which, surprisingly enough, have not been considered before despite stemming from the problem of Wi-Fi channel assignment. Given a spectrum of colors, endowed with a matrix of interferences between each pair of colors, the Threshold Spectrum Coloring problem fixes the number of colors available and aims to minimize the interference threshold, i.e., the maximum of the interferences at the vertices. Conversely, the Chromatic Spectrum Coloring problem fixes a threshold and aims to minimize the number of colors for which respecting that threshold is possible. As the main theoretical results, we prove tight upper bounds for the solutions to each problem. Since both problems turn out to be NP-hard, we complete the scene with experimental results. We propose a DSATUR-based heuristic and study its performance to minimize the maximum vertex interference in Wi-Fi channel assignment, both for randomly-generated graphs and for a real-world scenario. Further, for all these graphs, we experimentally check the goodness of the theoretical bounds.

Due to the low cost of CMOS IP-based cameras, wireless surveillance sensor networks have emerged as a new application of sensor networks able to monitor public or private areas or even country borders. Since these networks are bandwidth intensive and the radioelectric spectrum is limited, especially in unlicensed bands, it is mandatory to assign frequency channels in a smart manner. In this work, we propose the application of automated negotiation techniques for frequency assignment. Results show that these techniques are very suitable for the problem, being able to obtain the best solutions among the techniques with which we have compared them.