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

Assessing the UK policies for broadband adoption

Broadband technology has been introduced to the business community and the public as a rapid way of exploiting the Internet. The benefits of its use (fast reliable connections, and always on) have been widely realised and broadband diffusion is one of the items at the top of the agenda for technology related polices of governments worldwide. In this paper an examination of the impact of the UK government’s polices upon broadband adoption is undertaken. Based on institutional theory a consideration of the manipulation of supply push and demand pull forces in the diffusion of broadband is offered. Using primary and secondary data sources, an analysis of the specific institutional actions related to IT diffusion as pursued by the UK government in the case of broadband is provided. Bringing the time dimension into consideration it is revealed that the UK government has shifted its attention from supply push-only strategies to more interventional ones where the demand pull forces are also mobilised. It is believed that this research will assist in the extraction of the “success factors” in government intervention that support the diffusion of technology with a view to render favourable results if applied to other national settings

Tracking Human Mobility using WiFi signals

We study six months of human mobility data, including WiFi and GPS traces recorded with high temporal resolution, and find that time series of WiFi scans contain a strong latent location signal. In fact, due to inherent stability and low entropy of human mobility, it is possible to assign location to WiFi access points based on a very small number of GPS samples and then use these access points as location beacons. Using just one GPS observation per day per person allows us to estimate the location of, and subsequently use, WiFi access points to account for 80\% of mobility across a population. These results reveal a great opportunity for using ubiquitous WiFi routers for high-resolution outdoor positioning, but also significant privacy implications of such side-channel location tracking

Inferring Person-to-person Proximity Using WiFi Signals

Today's societies are enveloped in an ever-growing telecommunication infrastructure. This infrastructure offers important opportunities for sensing and recording a multitude of human behaviors. Human mobility patterns are a prominent example of such a behavior which has been studied based on cell phone towers, Bluetooth beacons, and WiFi networks as proxies for location. However, while mobility is an important aspect of human behavior, understanding complex social systems requires studying not only the movement of individuals, but also their interactions. Sensing social interactions on a large scale is a technical challenge and many commonly used approaches---including RFID badges or Bluetooth scanning---offer only limited scalability. Here we show that it is possible, in a scalable and robust way, to accurately infer person-to-person physical proximity from the lists of WiFi access points measured by smartphones carried by the two individuals. Based on a longitudinal dataset of approximately 800 participants with ground-truth interactions collected over a year, we show that our model performs better than the current state-of-the-art. Our results demonstrate the value of WiFi signals in social sensing as well as potential threats to privacy that they imply

Supporting the Mobile In-situ Authoring of Locative Media in Rural Places: Design and Expert Evaluation of the SMAT app

Providing users with carefully authored Locative media experiences (which can be consumed via their GPS equipped smartphones or tablets) has significant potential for fostering a strong engagement with their current surroundings. However, the availability of mobile tools to support the authoring of locative media experiences in-situ, and by non-technical users, remains scarce. In this article we present the design and field-trial expert evaluation of a mobile app developed under the SHARC project (Investigating Technology Support for the Shared Curation of Local History in a Rural Community). The app is named SMAT (SHARC Mobile Authoring Tool) and supports the authoring of Locative Media experiences with a focus on the creation of POIs (Points of Interest) and associated geo-fences which trigger the pushed delivery of media items such as photos, audio clips, etc. One important requirement of SMAT is the ability to support authoring in places where connectivity is intermittent or unavailable, e.g. many rural areas

Living Without a Mobile Phone: An Autoethnography

This paper presents an autoethnography of my experiences living without a mobile phone. What started as an experiment motivated by a personal need to reduce stress, has resulted in two voluntary mobile phone breaks spread over nine years (i.e., 2002-2008 and 2014-2017). Conducting this autoethnography is the means to assess if the lack of having a phone has had any real impact in my life. Based on formative and summative analyses, four meaningful units or themes were identified (i.e., social relationships, everyday work, research career, and location and security), and judged using seven criteria for successful ethnography from existing literature. Furthermore, I discuss factors that allow me to make the choice of not having a mobile phone, as well as the relevance that the lessons gained from not having a mobile phone have on the lives of people who are involuntarily disconnected from communication infrastructures.Comment: 12 page

Robust modeling of human contact networks across different scales and proximity-sensing techniques

The problem of mapping human close-range proximity networks has been tackled using a variety of technical approaches. Wearable electronic devices, in particular, have proven to be particularly successful in a variety of settings relevant for research in social science, complex networks and infectious diseases dynamics. Each device and technology used for proximity sensing (e.g., RFIDs, Bluetooth, low-power radio or infrared communication, etc.) comes with specific biases on the close-range relations it records. Hence it is important to assess which statistical features of the empirical proximity networks are robust across different measurement techniques, and which modeling frameworks generalize well across empirical data. Here we compare time-resolved proximity networks recorded in different experimental settings and show that some important statistical features are robust across all settings considered. The observed universality calls for a simplified modeling approach. We show that one such simple model is indeed able to reproduce the main statistical distributions characterizing the empirical temporal networks