Data assessment and prioritization in mobile networks for real-time prediction of spatial information using machine learning

A new framework of data assessment and prioritization for real-time prediction of spatial information is presented. The real-time prediction of spatial information is promising for next-generation mobile networks. Recent developments in machine learning technology have enabled prediction of spatial information, which will be quite useful for smart mobility services including navigation, driving assistance, and self-driving. Other key enablers for forming spatial information are image sensors in mobile devices like smartphones and tablets and in vehicles such as cars and drones and real-time cognitive computing like automatic number/license plate recognition systems and object recognition systems. However, since image data collected by mobile devices and vehicles need to be delivered to the server in real time to extract input data for real-time prediction, the uplink transmission speed of mobile networks is a major impediment. This paper proposes a framework of data assessment and prioritization that reduces the uplink traffic volume while maintaining the prediction accuracy of spatial information. In our framework, machine learning is used to estimate the importance of each data element and to predict spatial information under the limitation of available data. A numerical evaluation using an actual vehicle mobility dataset demonstrated the validity of the proposed framework. Two extension schemes in our framework, which use the ensemble of importance scores obtained from multiple feature selection methods, are also presented to improve its robustness against various machine learning and feature selection methods. We discuss the performance of those schemes through numerical evaluation

Design of a New High Bandwidth Network for Agricultural Machines

Ethernet is by now the most adopted bus for fast digital communications in many environments, from household entertainment to PLC robotics in industrial assembly lines. Even in automotive industry, the interest in this technology is increasingly growing, pushed forward by research and by the need of high throughput that high dynamics distributed control demands. Although 100base-TX physical layer (PHY) does not seem to meet EMC requirements for vehicular and heavy-duty environments, OPEN Alliance BroadR Reach (soon becoming IEEE standard as IEEE 802.3bw) technology is the most promising and already adopted Ethernet-compatible PHY, reaching 100Mbps over an unshielded twisted pair. An agricultural machine is usually a system including tractor and one or more implements attached to it, to the back or to the front. Nowadays, a specific CAN-based distributed control network support treatments and applications, namely ISOBUS, defined by ISO 11783. This work deals with architectural and technological aspects of advanced Ethernet networks in order to provide a high-throughput deterministic network for in-vehicle distributed control for agricultural machinery. Two main paths of investigation will be presented: one concerning the prioritization of standard Ethernet taking advantage of standard ways of prioritization in well-established technologies; the other changing the channel access method of Ethernet using an industrial fieldbus, chosen after careful investigation. The prioritization of standard Ethernet is performed at two, non-mutual exclusive layers of the ISO OSI stack: one at L3, using the diffserv (former TOS) Ip field; one at L2, using the priorities defined in IEEE 802.1p, used in IEEE 802.1q (VLAN). These choices have several implications in the specific field of application of the agricultural machines. The change of the access method, instead, focused on the adoption of a specific fieldbus, in order to grant deterministic access to the medium and reliability of communications for safety-relevant applications. After a survey, that will be reported, the Powerlink fieldbus was chosen and some modifications will be discussed in order to suit the scope of the research