42 research outputs found
V2X Communications in Highway Environments: Scheduling Challenges and Solutions for 6G Networks
No full textAs the automotive industry moves toward fully autonomous driving, the goal is to enable vehicles to operate safely without human control in all environments. Implementing Vehicle-to-Everything (V2X) communications in highway environments poses considerable challenges. Several critical services have strict network performance requirements as they deal with safety features. Existing fifth-generation (5G) base station schedulers do not discriminate among critical and non-critical automated driving functions. Therefore, in cases of increased traffic load, there is a significant drop in their performance, and, consequently, increased risk for accidents. Our paper discusses these issues and provides an adaptive scheduler called SOVANET+. The new scheduler acknowledges the Radio Access Network (RAN) load, and the requirements of critical, automated driving applications, together with channel quality, and optimizes the allocation of resources to critical services. The performance of SOVANET+ is evaluated through extensive simulations in the highway environment, an area less examined than urban scenarios. Results indicate that the adoption of SOVANET+ presents clear advantages to critical services compared to existing solutions
Customer Behaviour Analysis for Recommendation of Supermarket Ware
No full textPart 10: Mining Humanistic Data Workshop (MHDW)International audienceIn this paper, we present a prediction model based on the behaviour of each customer using data mining techniques. The proposed model utilizes a supermarket database and an additional database from Amazon Company, both containing information about customers’ purchases. Subsequently, our model analyzes these data in order to classify customers as well as products; whereas being trained and validated with real data. This model is targeted towards classifying customers according to their consuming behaviour and consequently propose new products more likely to be purchased by them. The corresponding prediction model is intended to be utilized as a tool for marketers so as to provide an analytically targeted and specified consumer behavior
V2X Communication over Cellular Networks: Capabilities and Challenges
No full textVehicular communications is expected to be one of the key applications for cellular networks during the following decades. Key international organizations have already described in detail a number of related use cases, along with their requirements. This article provides a comprehensive analysis of these use cases and a harmonized view of the requirements for the latest and most advanced autonomous driving applications. It also investigates the extent of support that 4G and 5G networks can offer to these use cases in terms of delay and spectrum needs. The paper identifies open issues and discusses trends and potential solutions
An Adaptive Scheduling Mechanism Optimized for V2N Communications over Future Cellular Networks
No full textAutomated driving requires the support of critical communication services with strict performance requirements. Existing fifth-generation (5G) schedulers residing at the base stations are not optimized to differentiate between critical and non-critical automated driving applications. Thus, when the traffic load increases, there is a significant decrease in their performance. Our paper introduces SOVANET, a beyond 5G scheduler that considers the Radio Access Network (RAN) load, as well as the requirements of critical, automated driving applications and optimizes the allocation of resources to them compared to non-critical services. The proposed scheduler is evaluated through extensive simulations and compared to the typical Proportional Fair scheduler. Results show that SOVANET’s performance for critical services presents clear benefits
Customer Behaviour Analysis for Recommendation of Supermarket Ware
No full textPart 10: Mining Humanistic Data Workshop (MHDW)International audienceIn this paper, we present a prediction model based on the behaviour of each customer using data mining techniques. The proposed model utilizes a supermarket database and an additional database from Amazon Company, both containing information about customers’ purchases. Subsequently, our model analyzes these data in order to classify customers as well as products; whereas being trained and validated with real data. This model is targeted towards classifying customers according to their consuming behaviour and consequently propose new products more likely to be purchased by them. The corresponding prediction model is intended to be utilized as a tool for marketers so as to provide an analytically targeted and specified consumer behavior
