Towards connecting people, locations and real-world events in a cellular network

The success of personal mobile communication technologies has led an emerging expansion of the telecommunication infrastructure but also to an explosion to mobile broadband data traffic as more and more people completely rely on their mobile devices, either for work or entertainment. The continuously interaction of their mobile devices with the mobile network infrastructure creates digital traces that can be easily logged by the network operators. These digital traces can be further used, apart from billing and resource management, for large-scale population monitoring using mobile traffic analysis. They could be integrated into intelligent systems that could help at detecting exceptional events such as riots, protests or even at disaster preventions with minimal costs and improve people safety and security, or even save lives. In this paper we study the use of fully anonymized and highly aggregate cellular network data, like Call Detail Records (CDRs) to analyze the telecommunication traffic and connect people, locations and events. The results show that by analyzing the CDR data exceptional spatio-temporal patterns of mobile data can be correlated to real-world events. For example, high user network activity was mapped to religious festivals, such as Ramadan, Le Grand Magal de Touba and the Tivaouane Maouloud festival. During the Ramadan period it was noticed that the communication pattern doubled during the night with a slow start during the morning and along the day. Furthermore, a peak increase in the number of voice calls and voice calls duration in the area of Kafoutine was mapped to the Casamance Conflict in the area which resulted in four deaths. Thus, these observations could be further used to develop an intelligent system that detects exceptional events in real-time from CDRs data monitoring. Such system could be used in intelligent transportation management, urban planning, emergency situations, network resource allocation and performance optimization, etc

Evaluation of a sudden brake warning system: Effect on the response time of the following driver

This study used a video-based braking simulation dual task to carry out a preliminary evaluation of the effect of a sudden brake warning system (SBWS) in a leading passenger vehicle on the response time of the following driver. The primary task required the participants (N = 25, 16 females, full NZ license holders) to respond to sudden braking manoeuvres of a lead vehicle during day and night driving, wet and dry conditions and in rural and urban traffic, while concurrently performing a secondary tracking task using a computer mouse. The SBWS in the lead vehicle consisted of g-force controlled activation of the rear hazard lights (the rear indicators flashed), in addition to the standard brake lights. Overall, the results revealed that responses to the braking manoeuvres of the leading vehicles when the hazard lights were activated by the warning system were 0.34 s (19%) faster compared to the standard brake lights. The SBWS was particularly effective when the simulated braking scenario of the leading vehicle did not require an immediate and abrupt braking response. Given this, the SBWS may also be beneficial for allowing smoother deceleration, thus reducing fuel consumption. These preliminary findings justify a larger, more ecologically valid laboratory evaluation which may lead to a naturalistic study in order to test this new technology in ‘real world’ braking situations

PS-Sim: A Framework for Scalable Simulation of Participatory Sensing Data

Emergence of smartphone and the participatory sensing (PS) paradigm have paved the way for a new variant of pervasive computing. In PS, human user performs sensing tasks and generates notifications, typically in lieu of incentives. These notifications are real-time, large-volume, and multi-modal, which are eventually fused by the PS platform to generate a summary. One major limitation with PS is the sparsity of notifications owing to lack of active participation, thus inhibiting large scale real-life experiments for the research community. On the flip side, research community always needs ground truth to validate the efficacy of the proposed models and algorithms. Most of the PS applications involve human mobility and report generation following sensing of any event of interest in the adjacent environment. This work is an attempt to study and empirically model human participation behavior and event occurrence distributions through development of a location-sensitive data simulation framework, called PS-Sim. From extensive experiments it has been observed that the synthetic data generated by PS-Sim replicates real participation and event occurrence behaviors in PS applications, which may be considered for validation purpose in absence of the groundtruth. As a proof-of-concept, we have used real-life dataset from a vehicular traffic management application to train the models in PS-Sim and cross-validated the simulated data with other parts of the same dataset.Comment: Published and Appeared in Proceedings of IEEE International Conference on Smart Computing (SMARTCOMP-2018

Statistical characterisation of bio-aerosol background in an urban environment

In this paper we statistically characterise the bio-aerosol background in an urban environment. To do this we measure concentration levels of naturally occurring microbiological material in the atmosphere over a two month period. Naturally occurring bioaerosols can be considered as noise, as they mask the presence of signals coming from biological material of interest (such as an intentionally released biological agent). Analysis of this 'biobackground' was undertaken in the 1-10 um size range and a 3-9% contribution was found to be biological in origin - values which are in good agreement with other studies reported in the literature. A model based on the physics of turbulent mixing and dispersion was developed and validated against this analysis. The Gamma distribution (the basis of our model) is shown to comply with the scaling laws of the concentration moments of our data, which enables us to universally characterise both biological and non-biological material in the atmosphere. An application of this model is proposed to build a framework for the development of novel algorithms for bio-aerosol detection and rapid characterisation.Comment: 14 Pages, 8 Figure