Automatic identification of relevant places from cellular network data

We present a methodology to automatically identify users\u2019 relevant places from cellular network data.1 In this work we used anonymized Call Detail Record (CDR) comprising information on where and when users access the cellular network. The key idea is to effectively cluster CDRs together and to weigh clusters to determine those associated to frequented places. The approach can identify users\u2019 home and work locations as well as other places (e.g., associated to leisure and night life). We evaluated our approach threefold: (i) on the basis of groundtruth information coming from a fraction of users whose relevant places were known, (ii) by comparing the resulting number of inhabitants of a given city with the number of inhabitants as extracted by the national census. (iii) Via stability analysis to verify the consistency of the extracted results across multiple time periods. Results show the effectiveness of our approach with an average 90% precision and recall

Wireless communication, identification and sensing technologies enabling integrated logistics: a study in the harbor environment

In the last decade, integrated logistics has become an important challenge in the development of wireless communication, identification and sensing technology, due to the growing complexity of logistics processes and the increasing demand for adapting systems to new requirements. The advancement of wireless technology provides a wide range of options for the maritime container terminals. Electronic devices employed in container terminals reduce the manual effort, facilitating timely information flow and enhancing control and quality of service and decision made. In this paper, we examine the technology that can be used to support integration in harbor's logistics. In the literature, most systems have been developed to address specific needs of particular harbors, but a systematic study is missing. The purpose is to provide an overview to the reader about which technology of integrated logistics can be implemented and what remains to be addressed in the future

Automatic best wireless network selection based on key performance indicators

Introducing cognitive mechanisms at the application layer may lead to the possibility of an automatic selection of the wireless network that can guarantee best perceived experience by the final user. This chapter investigates this approach based on the concept of Quality of Experience (QoE), by introducing the use of application layer parameters, namely Key Performance Indicators (KPIs). KPIs are defined for different traffic types based on experimental data. A model for an ap- plication layer cognitive engine is presented, whose goal is to identify and select, based on KPIs, the best wireless network among available ones. An experimenta- tion for the VoIP case, that foresees the use of the One-way end-to-end delay (OED) and the Mean Opinion Score (MOS) as KPIs is presented. This first implementation of the cognitive engine selects the network that, in that specific instant, offers the best QoE based on real captured data. To our knowledge, this is the first example of a cognitive engine that achieves best QoE in a context of heterogeneous wireless networks

Information & communication technologies - panacea for traffic congestion?

As road pricing, telematics and logistics evolve, information and communication technologies (ICT) aim directly at making traffic flow more efficiently in a given infrastructure. Furthermore, the virtual world gives rise to new business fields and decentralised structures which affect the development of transport indirectly. While technological progress continues to drive qualitative improvements in traffic conditions, e-business and telework in particular have, for structural reasons, a much less pronounced effect on traffic than widely presumed. ICT helps in organising traffic flows more efficiently and plays a supplementary role as transport-relevant instrument, but it is not a panacea for traffic congestion.traffic, autobahn, ICT, LBS, mobile telephony

Diffusion of mobile phones in Portugal: unexpected success?

This paper begins with a somewhat paradoxical situation: Portugal is one of the less innovative countries within the European Union by most innovation indicators but, simultaneously, it is a leading country in the diffusion of mobile phones. The remarkable evolution of the mobile telecommunications sector over the last 15 years turns the issue a little more intriguing. This paper addresses the diffusion of mobile phones in Portugal and focuses particularly on the take-off stage, which signalled a sudden change in the pattern of diffusion. The introduction of an innovation – prepaid cards – explains most of the change in the diffusion curve occurred around 1996 and the subsequent increase in the penetration rate. Prior known research has not considered pre-paid cards an important determinant of mobile phone diffusion, but pre-paid services had an enormous impact on the rate of adoption of mobile phones in many countries and it is the major take-off determinant of mobile phone diffusion in Portugal. The time lag between the launching of this innovation in Portugal and its adoption by other EU countries explains why Portugal not just caught up with the EU average mobile phone penetration rate around 1996, but moved ahead of it from then on

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