Vehicle flow prediction through probabilistic modeling

Within the area of wireless and mobile communications, ad hoc vehicular networks have generated the interest of different organizations, which has generated a topic of study and analysis for the increase of applications, devices, technology integration, security, standards, and quality of service in different areas (Zhu et al. in IEEE Trans Veh Technol 64(4):1607–1619, [1]) and (Tian et al. in A self-adaptive V2V communication system with DSRC, pp 1528–1532, [2]). This study on vehicle networks shows a great deal of opportunity and motivation to deepen the aspects that involve it, which have emerged due to the advance of wireless technologies, as well as research in the automotive industry. This allows the development of networks with spontaneous topologies with vehicles in constant movement in several simulations (Mir and Filali in LTE and IEEE 802.11p for vehicular networking: a performance evaluation, pp 1–15, [3]), with reliable vehicle flows, through the share of traffic information, considering that continuous mobility is an essential characteristic of a VANET vehicle network, which can have short changes in terms of groups of vehicles in the same direction (Lokhande and Khamitkar, 9(12):30–33, [4]). The following paper uses a road scenario called VANET to obtain a predictive characterization of vehicle flow using a probabilistic model

A framework for smart production-logistics systems based on CPS and industrial IoT

Industrial Internet of Things (IIoT) has received increasing attention from both academia and industry. However, several challenges including excessively long waiting time and a serious waste of energy still exist in the IIoT-based integration between production and logistics in job shops. To address these challenges, a framework depicting the mechanism and methodology of smart production-logistics systems is proposed to implement intelligent modeling of key manufacturing resources and investigate self-organizing configuration mechanisms. A data-driven model based on analytical target cascading is developed to implement the self-organizing configuration. A case study based on a Chinese engine manufacturer is presented to validate the feasibility and evaluate the performance of the proposed framework and the developed method. The results show that the manufacturing time and the energy consumption are reduced and the computing time is reasonable. This paper potentially enables manufacturers to deploy IIoT-based applications and improve the efficiency of production-logistics systems