720 research outputs found
Adaptive routing for intermittently connected mobile ad hoe networks
The vast majority of mobile ad hoc networking research makes a very large assumption: that communication can only take place between nodes that are simultaneously accessible within in the same connected cloud (i.e., that communication is synchronous). In reality, this assumption is likely to be a poor one, particularly for sparsely or irregularly populated environments.In this paper we present the Context-Aware Routing (CAR) algorithm. CAR is a novel approach to the provision of asynchronous communication in partially-connected mobile ad hoc networks, based on the intelligent placement of messages. We discuss the details of the algorithm, and then present simulation results demonstrating that it is possible for nodes to exploit context information in making local decisions that lead to good delivery ratios and latencies with small overheads.</p
Exploiting Temporal Complex Network Metrics in Mobile Malware Containment
Malicious mobile phone worms spread between devices via short-range Bluetooth
contacts, similar to the propagation of human and other biological viruses.
Recent work has employed models from epidemiology and complex networks to
analyse the spread of malware and the effect of patching specific nodes. These
approaches have adopted a static view of the mobile networks, i.e., by
aggregating all the edges that appear over time, which leads to an approximate
representation of the real interactions: instead, these networks are inherently
dynamic and the edge appearance and disappearance is highly influenced by the
ordering of the human contacts, something which is not captured at all by
existing complex network measures. In this paper we first study how the
blocking of malware propagation through immunisation of key nodes (even if
carefully chosen through static or temporal betweenness centrality metrics) is
ineffective: this is due to the richness of alternative paths in these
networks. Then we introduce a time-aware containment strategy that spreads a
patch message starting from nodes with high temporal closeness centrality and
show its effectiveness using three real-world datasets. Temporal closeness
allows the identification of nodes able to reach most nodes quickly: we show
that this scheme can reduce the cellular network resource consumption and
associated costs, achieving, at the same time, a complete containment of the
malware in a limited amount of time.Comment: 9 Pages, 13 Figures, In Proceedings of IEEE 12th International
Symposium on a World of Wireless, Mobile and Multimedia Networks (WOWMOM '11
Epcast: Controlled Dissemination in Human-based Wireless Networks by means of Epidemic Spreading Models
Epidemics-inspired techniques have received huge attention in recent years
from the distributed systems and networking communities. These algorithms and
protocols rely on probabilistic message replication and redundancy to ensure
reliable communication. Moreover, they have been successfully exploited to
support group communication in distributed systems, broadcasting, multicasting
and information dissemination in fixed and mobile networks. However, in most of
the existing work, the probability of infection is determined heuristically,
without relying on any analytical model. This often leads to unnecessarily high
transmission overheads.
In this paper we show that models of epidemic spreading in complex networks
can be applied to the problem of tuning and controlling the dissemination of
information in wireless ad hoc networks composed of devices carried by
individuals, i.e., human-based networks. The novelty of our idea resides in the
evaluation and exploitation of the structure of the underlying human network
for the automatic tuning of the dissemination process in order to improve the
protocol performance. We evaluate the results using synthetic mobility models
and real human contacts traces
Predicting the temporal activity patterns of new venues.
Estimating revenue and business demand of a newly opened venue is paramount
as these early stages often involve critical decisions such as first rounds of staffing
and resource allocation. Traditionally, this estimation has been performed through
coarse-grained measures such as observing numbers in local venues or venues at
similar places (e.g., coffee shops around another station in the same city). The
advent of crowdsourced data from devices and services carried by individuals on a
daily basis has opened up the possibility of performing better predictions of
temporal visitation patterns for locations and venues. In this paper, using mobility
data from Foursquare, a location-centric platform, we treat venue categories as
proxies for urban activities and analyze how they become popular over time. The
main contribution of this work is a prediction framework able to use characteristic
temporal signatures of places together with k-nearest neighbor metrics capturing
similarities among urban regions, to forecast weekly popularity dynamics of a new
venue establishment in a city neighborhood. We further show how we are able to
forecast the popularity of the new venue after one month following its opening by
using locality and temporal similarity as features. For the evaluation of our
approach we focus on London. We show that temporally similar areas of the city
can be successfully used as inputs of predictions of the visit patterns of new
venues, with an improvement of 41% compared to a random selection of wards as
a training set for the prediction task. We apply these concepts of temporally
similar areas and locality to the real-time predictions related to new venues and
show that these features can effectively be used to predict the future trends of a
venue. Our findings have the potential to impact the design of location-based
technologies and decisions made by new business owners
Measuring urban social diversity using interconnected geo-social networks
Large metropolitan cities bring together diverse individuals, creating opportunities for cultural and intellectual exchanges, which can ultimately lead to social and economic enrichment. In this work, we present a novel network perspective on the interconnected nature of people and places, allowing us to capture the social diversity of urban locations through the social network and mobility patterns of their visitors. We use a dataset of approximately 37K users and 42K venues in London to build a network of Foursquare places and the parallel Twitter social network of visitors through check-ins. We define four metrics of the social diversity of places which relate to their social brokerage role, their entropy, the homogeneity of their visitors and the amount of serendipitous encounters they are able to induce. This allows us to distinguish between places that bring together strangers versus those which tend to bring together friends, as well as places that attract diverse individuals as opposed to those which attract regulars. We correlate these properties with wellbeing indicators for London neighbourhoods and discover signals of gentrification in deprived areas with high entropy and brokerage, where an influx of more affluent and diverse visitors points to an overall improvement of their rank according to the UK Index of Multiple Deprivation for the area over the five-year census period. Our analysis sheds light on the relationship between the prosperity of people and places, distinguishing between different categories and urban geographies of consequence to the development of urban policy and the next generation of socially-aware location-based applications.This work was supported by the Project LASAGNE, Contract No. 318132 (STREP), funded by the European Commission and EPSRC through Grant GALE (EP/K019392).This is the author accepted manuscript. The final version is available from the Association for Computing Machinery via http://dx.doi.org/10.1145/2872427.288306
Small-world behavior in time-varying graphs
Connections in complex networks are inherently fluctuating over time and
exhibit more dimensionality than analysis based on standard static graph
measures can capture. Here, we introduce the concepts of temporal paths and
distance in time-varying graphs. We define as temporal small world a
time-varying graph in which the links are highly clustered in time, yet the
nodes are at small average temporal distances. We explore the small-world
behavior in synthetic time-varying networks of mobile agents, and in real
social and biological time-varying systems.Comment: 5 pages, 2 figure
Protective role of vitamin B6 (PLP) against DNA damage in Drosophila models of type 2 diabetes
Growing evidence shows that improper intake of vitamin B6 increases cancer risk and several studies indicate that diabetic patients have a higher risk of developing tumors. We previously demonstrated that in Drosophila the deficiency of Pyridoxal 5\u2032 phosphate (PLP), the active form of vitamin B6, causes chromosome aberrations (CABs), one of cancer prerequisites, and increases hemolymph glucose content. Starting from these data we asked if it was possible to provide a link between the aforementioned studies. Thus, we tested the effect of low PLP levels on DNA integrity in diabetic cells. To this aim we generated two Drosophila models of type 2 diabetes, the first by impairing insulin signaling and the second by rearing flies in high sugar diet. We showed that glucose treatment induced CABs in diabetic individuals but not in controls. More interestingly, PLP deficiency caused high frequencies of CABs in both diabetic models demonstrating that hyperglycemia, combined to reduced PLP level, impairs DNA integrity. PLP-depleted diabetic cells accumulated Advanced Glycation End products (AGEs) that largely contribute to CABs as \u3b1-lipoic acid, an AGE inhibitor, rescued not only AGEs but also CABs. These data, extrapolated to humans, indicate that low PLP levels, impacting on DNA integrity, may be considered one of the possible links between diabetes and cancer
Predicting the temporal activity patterns of new venues
Estimating revenue and business demand of a newly opened venue is paramount as these early stages often involve critical decisions such as first rounds of staffing and resource allocation. Traditionally, this estimation has been performed through coarse-grained measures such as observing numbers in local venues or venues at similar places (e.g., coffee shops around another station in the same city). The advent of crowdsourced data from devices and services carried by individuals on a daily basis has opened up the possibility of performing better predictions of temporal visitation patterns for locations and venues. In this paper, using mobility data from Foursquare, a location-centric platform, we treat venue categories as proxies for urban activities and analyze how they become popular over time. The main contribution of this work is a prediction framework able to use characteristic temporal signatures of places together with k-nearest neighbor metrics capturing similarities among urban regions, to forecast weekly popularity dynamics of a new venue establishment in a city neighborhood. We further show how we are able to forecast the popularity of the new venue after one month following its opening by using locality and temporal similarity as features. For the evaluation of our approach we focus on London. We show that temporally similar areas of the city can be successfully used as inputs of predictions of the visit patterns of new venues, with an improvement of 41% compared to a random selection of wards as a training set for the prediction task. We apply these concepts of temporally similar areas and locality to the real-time predictions related to new venues and show that these features can effectively be used to predict the future trends of a venue. Our findings have the potential to impact the design of location-based technologies and decisions made by new business owners
Keep Your Friends Close and Your Facebook Friends Closer: A Multiplex Network Approach to the Analysis of Offline and Online Social Ties
Social media allow for an unprecedented amount of interaction between people online. A fundamental aspect of human social behavior, however, is the tendency of people to
associate themselves with like-minded individuals, forming
homogeneous social circles both online and offline. In this
work, we apply a new model that allows us to distinguish
between social ties of varying strength, and to observe evidence of homophily with regards to politics, music, health,
residential sector & year in college, within the online and
offline social network of 74 college students. We present a
multiplex network approach to social tie strength, here applied to mobile communication data - calls, text messages,
and co-location, allowing us to dimensionally identify relationships by considering the number of communication channels utilized between students. We find that strong social ties
are characterized by maximal use of communication channels, while weak ties by minimal use. We are able to identify
75% of close friendships, 90% of weaker ties, and 90% of
Facebook friendships as compared to reported ground truth.
We then show that stronger ties exhibit greater profile similarity than weaker ones. Apart from high homogeneity in social
circles with respect to political and health aspects, we observe
strong homophily driven by music, residential sector and year
in college. Despite Facebook friendship being highly dependent on residence and year, exposure to less homogeneous
content can be found in the online rather than the offline social circles of students, most notably in political and music
aspects
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