A large-scale study of cultural differences using urban data about eating and drinking preferences

Traditional ways to study urban social behavior, e.g. surveys, are costly and do not scale. Recently, some studies have been showing new ways of obtaining data through location-based social networks (LBSNs), such as Foursquare, which could revolutionize the study of urban social behavior. We use Foursquare check-ins to represent user preferences regarding eating and drinking habits. Considering datasets differing in terms of volume of data and observation window size, our results indicate that spatio-temporal eating and drinking habits of users voluntarily expressed in LBSNs has the potential to explain cultural habits of the users. From this, we propose a methodology to identify cultural boundaries and similarities across populations at different scales, e.g., countries, cities, or neighborhoods. This methodology is extensively evaluated in several aspects. For instance, by proposing some variations of it disregarding some of the considered dimensions, as well as analyzing the results using datasets from different periods and window of observation. The results indicate that our proposed methodology is a promising approach for automatic cultural habits separation, which could enable new urban services

Cloud-assisted Computing for Event-driven Mobile Services

Today, software developers for desktop computing build request and respond applications to do what end users tell them to do and answer what they ask. In mobile computing, software developers will need to develop sense and response applications that will interact with the end user. These applications will notify or ask users what they want based on what they have sensed or on a personal profile. Mobile cloud computing has the potential to empower mobile users with capabilities not found in mobile devices, combining different and heterogeneous data sets. In this work, we discuss the importance and challenges in designing event-driven mobile services that will detect conditions of interest to users and notify them accordingly.19216117

The impact of mobility on Mobile Ad Hoc Networks through the perspective of complex networks

Mobile Ad Hoc Networks (MANETs) are wireless networks where nodes’ exchange of messages does not rely on any previously deployed infrastructure. Portable devices that are capable of wireless communication have become extremely popular making possible the establishment of wide ubiquitous networks. Users connected to such networks can access the provided services anywhere and anytime. Nevertheless, this architecture suffers from a highly unstable topology since links between nodes break constantly due to users’ movement. Mobility has a paramount influence on the network topology. Therefore, it is of utmost importance to understand the impact of mobility in MANETs. In this work, we perform a thorough analysis on how mobility shape the behavior of MANETs. Our range of observation varies from general MANETs composed of walking users to a next generation of MANETs formed by vehicles moving either in a city environment or in a highway scenario, namely Vehicular Ad Hoc Networks (VANETs). Our analyses are performed observing the networks through the perspective of complex networks. We were able to identify underlying characteristics of these networks and showed how these observations can be used to improve the performance of MANETs

SecLEACH - On the security of clustered sensor networks

Clustered sensor networks have recently been shown to increase system throughput, decrease system delay, and save energy while performing data aggregation. Whereas those with rotating cluster heads, such as LEACH (low-energy adaptive clustering hierarchy), have also advantages in terms of security, the dynamic nature of their communication makes most existing security solutions inadequate for them. In this paper, we investigate the problem of adding security to hierarchical (cluster-based) sensor networks where clusters are formed dynamically and periodically, such as LEACH. For this purpose, we show how random key predistribution, widely studied in the context of flat networks, and mu TESLA, a building block from SPINS, can be both used to secure communications in this type of network. We present our solution, and provide a detailed analysis of how different values for the various parameters in such a system impact a hierarchical network in terms of security and energy efficiency. To the best of our knowledge, ours is the first that investigates security in hierarchical WSNs with dynamic cluster formation. (C) 2007 Elsevier B.V. All rights reserved.87122882289