Machine Learning for Disseminating Cooperative Awareness Messages in Cellular V2V Communications

<div>This paper develops a novel Machine Learning (ML)-based strategy to distribute aperiodic Cooperative Awareness Messages (CAMs) through cellular Vehicle-to-Vehicle (V2V) communications. According to it, an ML algorithm is employed by each vehicle to forecast its future CAM generation times; then, the vehicle autonomously selects the radio resources for message broadcasting on the basis of the forecast provided by the algorithm. This action is combined with a wise analysis of the radio resources available for transmission, that identifies subchannels where collisions might occur, to avoid selecting them.</div><div>Extensive simulations show that the accuracy in the prediction of the CAMs’ temporal pattern is excellent. Exploiting this knowledge in the strategy for radio resource assignment, and carefully identifying idle resources, allows to outperform the legacy LTE-V2X Mode 4 in all respects.</div>

Machine Learning for Disseminating Cooperative Awareness Messages in Cellular V2V Communications

<div>This paper develops a novel Machine Learning (ML)-based strategy to distribute aperiodic Cooperative Awareness Messages (CAMs) through cellular Vehicle-to-Vehicle (V2V) communications. According to it, an ML algorithm is employed by each vehicle to forecast its future CAM generation times; then, the vehicle autonomously selects the radio resources for message broadcasting on the basis of the forecast provided by the algorithm. This action is combined with a wise analysis of the radio resources available for transmission, that identifies subchannels where collisions might occur, to avoid selecting them.</div><div>Extensive simulations show that the accuracy in the prediction of the CAMs’ temporal pattern is excellent. Exploiting this knowledge in the strategy for radio resource assignment, and carefully identifying idle resources, allows to outperform the legacy LTE-V2X Mode 4 in all respects.</div>

Tourism governance during the COVID-19 pandemic crisis: A proposal for a sustainable model to restore the tourism industry

Unsustainable models of governance belonging to a widespread neoliberal mindset in developed countries have commonly been applied in the tourism industry. The management of the COVID-19 pandemic crisis has provided exemplary lessons regarding the application of sustainable models of governance. Through a participatory research, guidances are provided to tackle the COVID-19 effects in the tourist sector, namely in the Spanish southwestern region of Sierra de Gata. Seventeen indicators are proposed to enhance the safety measures, commitment of tourist authorities, communities empowered and protection of common resources among tourism industry, tourist authority and communities to spread cooperative awareness, mutual trust and shared objectives. Using a sample of 161 tourism companies, we tested a model of tourism governance with two focus groups during May and October 2020. Structural equation modelling (SEM) was utilized. Based on the data attained from a questionnaire and interviews, a sustainable tourism model to recover the threatened tourism sector is proposed. Indeed, our results can be used to draw theoretical and practical conclusions such as 1.) connecting private and public interactions to tackle the spread of the virus and strategies to recover the damaged tourist sector, 2.) to develop corporative values among the tourist industry and communities, 3.) to enhance governance models (trusts, consortia, tourist boards, clusters) to promote cooperation, 4.) to improve the local participation of companies, communities and associations in decision-making, and 5.) to prioritize qualitative development goals over quantitative ones, in the touristic territory. These conclusions are applicable to other regions suffering from the damaging consequences of the pandemic.