Implementation and Evaluation of a Cooperative Vehicle-to-Pedestrian Safety Application

While the development of Vehicle-to-Vehicle (V2V) safety applications based on Dedicated Short-Range Communications (DSRC) has been extensively undergoing standardization for more than a decade, such applications are extremely missing for Vulnerable Road Users (VRUs). Nonexistence of collaborative systems between VRUs and vehicles was the main reason for this lack of attention. Recent developments in Wi-Fi Direct and DSRC-enabled smartphones are changing this perspective. Leveraging the existing V2V platforms, we propose a new framework using a DSRC-enabled smartphone to extend safety benefits to VRUs. The interoperability of applications between vehicles and portable DSRC enabled devices is achieved through the SAE J2735 Personal Safety Message (PSM). However, considering the fact that VRU movement dynamics, response times, and crash scenarios are fundamentally different from vehicles, a specific framework should be designed for VRU safety applications to study their performance. In this article, we first propose an end-to-end Vehicle-to-Pedestrian (V2P) framework to provide situational awareness and hazard detection based on the most common and injury-prone crash scenarios. The details of our VRU safety module, including target classification and collision detection algorithms, are explained next. Furthermore, we propose and evaluate a mitigating solution for congestion and power consumption issues in such systems. Finally, the whole system is implemented and analyzed for realistic crash scenarios

Anticipatory Mobile Computing: A Survey of the State of the Art and Research Challenges

Today's mobile phones are far from mere communication devices they were ten years ago. Equipped with sophisticated sensors and advanced computing hardware, phones can be used to infer users' location, activity, social setting and more. As devices become increasingly intelligent, their capabilities evolve beyond inferring context to predicting it, and then reasoning and acting upon the predicted context. This article provides an overview of the current state of the art in mobile sensing and context prediction paving the way for full-fledged anticipatory mobile computing. We present a survey of phenomena that mobile phones can infer and predict, and offer a description of machine learning techniques used for such predictions. We then discuss proactive decision making and decision delivery via the user-device feedback loop. Finally, we discuss the challenges and opportunities of anticipatory mobile computing.Comment: 29 pages, 5 figure

On the analysis of youTube QoE in cellular networks through in-smartphone measurements

International audienceCellular-network operators are becoming increasingly interested in knowing the Quality of Experience (QoE) of their customers. QoE measurements represent today a main source of information to monitor, analyze, and subsequently manage operational networks. In this paper, we focus on the analysis of YouTube QoE in cellular networks, using QoE and distributed network measurements collected in real users' smart-phones. Relying on YoMoApp, a well-known tool for collecting YouTube smartphone measurements and QoE feedback in a crowdsourcing fashion, we have built a dataset covering about 360 different cellular users around the globe, throughout the past five years. Using this dataset, we study the characteristics of different QoE-relevant features for YouTube in smartphones. Measurements reveal a constant improvement of YouTube QoE in cellular networks over time, as well as an enhancement of the YouTube video streaming functioning in smartphones. Using the gathered measurements, we additionally investigate two case studies for YouTube QoE monitoring and analysis in cellular networks: the machine-learning-based prediction of QoE-relevant metrics from network-level measurements, and the modeling and assessment of YouTube QoE and user engagement in cellular networks and smartphone devices. Last but not least, we introduce the YoMoApp cloud dashboard to openly share smartphone YouTube QoE measurements, which allows anyone using the YoMoApp smartphone app to get immediate access to all the raw measurements collected at her devices

Build an app and they will come? Lessons learnt from trialling the GetThereBus app in rural communities

Acknowledgements The research described here was supported by the award made by the RCUK Digital Economy programme to the dot.rural Digital Economy Hub; award reference: EP/G066051/1.Peer reviewedPostprin

Relevant Affect Factors of Smartphone Mobile Data Traffic

Smartphones are used to access a wide range of different information and communication services and perform functions based on data transfer. A number of subscription contracts for smartphones is rapidly increasing, and the development of mobile communications network provides higher speed of data transfer. The continuous increase in the average amount of data traffic per one subscriber contract leads to an increase in the total Mobile Data Traffic (MDT), globally. This research represents a summary of factors that affect the amount of smartphone MDT. Previous literature shows only a few of the factors individually that affect the realization of smartphone MDT. The results of the research clarify the ways which influence the amount of MDT generated by a smartphone. This paper increases the awareness of the users of the methods of generating smartphone MDT. The research also allows users to specify parameters that affect the prediction of generated MDT of a smartphone