16,955 research outputs found
Three applications for mobile epidemic algorithms
This paper presents a framework for the pervasive sharing of data using wireless networks. 'FarCry' uses the mobility of users to carry files between separated networks. Through a mix of ad-hoc and infrastructure-based wireless networking, files are transferred between users without their direct involvement. As users move to different locations, files are then transmitted on to other users, spreading and sharing information. We examine three applications of this framework. Each of these exploits the physically proximate nature of social gatherings. As people group together in, for example, business meetings and cafés, this can be taken as an indication of similar interests, e.g. in the same presentation or in a type of music. MediaNet affords sharing of media files between strangers or friends, MeetingNet shares business documents in meetings, and NewsNet shares RSS feeds between mobile users. NewsNet also develops the use of pre-emptive caching: collecting information from others not for oneself, but for the predicted later sharing with others. We offer observations on developing this system for a mobile, multi-user, multi-device environment
Early Warning Analysis for Social Diffusion Events
There is considerable interest in developing predictive capabilities for
social diffusion processes, for instance to permit early identification of
emerging contentious situations, rapid detection of disease outbreaks, or
accurate forecasting of the ultimate reach of potentially viral ideas or
behaviors. This paper proposes a new approach to this predictive analytics
problem, in which analysis of meso-scale network dynamics is leveraged to
generate useful predictions for complex social phenomena. We begin by deriving
a stochastic hybrid dynamical systems (S-HDS) model for diffusion processes
taking place over social networks with realistic topologies; this modeling
approach is inspired by recent work in biology demonstrating that S-HDS offer a
useful mathematical formalism with which to represent complex, multi-scale
biological network dynamics. We then perform formal stochastic reachability
analysis with this S-HDS model and conclude that the outcomes of social
diffusion processes may depend crucially upon the way the early dynamics of the
process interacts with the underlying network's community structure and
core-periphery structure. This theoretical finding provides the foundations for
developing a machine learning algorithm that enables accurate early warning
analysis for social diffusion events. The utility of the warning algorithm, and
the power of network-based predictive metrics, are demonstrated through an
empirical investigation of the propagation of political memes over social media
networks. Additionally, we illustrate the potential of the approach for
security informatics applications through case studies involving early warning
analysis of large-scale protests events and politically-motivated cyber
attacks
Effects of temporal correlations on cascades: Threshold models on temporal networks
A person's decision to adopt an idea or product is often driven by the
decisions of peers, mediated through a network of social ties. A common way of
modeling adoption dynamics is to use threshold models, where a node may become
an adopter given a high enough rate of contacts with adopted neighbors. We
study the dynamics of threshold models that take both the network topology and
the timings of contacts into account, using empirical contact sequences as
substrates. The models are designed such that adoption is driven by the number
of contacts with different adopted neighbors within a chosen time. We find that
while some networks support cascades leading to network-level adoption, some do
not: the propagation of adoption depends on several factors from the frequency
of contacts to burstiness and timing correlations of contact sequences. More
specifically, burstiness is seen to suppress cascades sizes when compared to
randomised contact timings, while timing correlations between contacts on
adjacent links facilitate cascades.Comment: 9 pages, 7 figures, Published versio
Memory-full context-aware predictive mobility management in dual connectivity 5G networks
Network densification with small cell deployment is being considered as one of the dominant themes in the fifth generation (5G) cellular system. Despite the capacity gains, such deployment scenarios raise several challenges from mobility management perspective. The small cell size, which implies a small cell residence time, will increase the handover (HO) rate dramatically. Consequently, the HO latency will become a critical consideration in the 5G era. The latter requires an intelligent, fast and light-weight HO procedure with minimal signalling overhead. In this direction, we propose a memory-full context-aware HO scheme with mobility prediction to achieve the aforementioned objectives. We consider a dual connectivity radio access network architecture with logical separation between control and data planes because it offers relaxed constraints in implementing the predictive approaches. The proposed scheme predicts future HO events along with the expected HO time by combining radio frequency performance to physical proximity along with the user context in terms of speed, direction and HO history. To minimise the processing and the storage requirements whilst improving the prediction performance, a user-specific prediction triggering threshold is proposed. The prediction outcome is utilised to perform advance HO signalling whilst suspending the periodic transmission of measurement reports. Analytical and simulation results show that the proposed scheme provides promising gains over the conventional approach
Improved Handover Through Dual Connectivity in 5G mmWave Mobile Networks
The millimeter wave (mmWave) bands offer the possibility of orders of
magnitude greater throughput for fifth generation (5G) cellular systems.
However, since mmWave signals are highly susceptible to blockage, channel
quality on any one mmWave link can be extremely intermittent. This paper
implements a novel dual connectivity protocol that enables mobile user
equipment (UE) devices to maintain physical layer connections to 4G and 5G
cells simultaneously. A novel uplink control signaling system combined with a
local coordinator enables rapid path switching in the event of failures on any
one link. This paper provides the first comprehensive end-to-end evaluation of
handover mechanisms in mmWave cellular systems. The simulation framework
includes detailed measurement-based channel models to realistically capture
spatial dynamics of blocking events, as well as the full details of MAC, RLC
and transport protocols. Compared to conventional handover mechanisms, the
study reveals significant benefits of the proposed method under several
metrics.Comment: 16 pages, 13 figures, to appear on the 2017 IEEE JSAC Special Issue
on Millimeter Wave Communications for Future Mobile Network
A Fast and Efficient Incremental Approach toward Dynamic Community Detection
Community detection is a discovery tool used by network scientists to analyze
the structure of real-world networks. It seeks to identify natural divisions
that may exist in the input networks that partition the vertices into coherent
modules (or communities). While this problem space is rich with efficient
algorithms and software, most of this literature caters to the static use-case
where the underlying network does not change. However, many emerging real-world
use-cases give rise to a need to incorporate dynamic graphs as inputs.
In this paper, we present a fast and efficient incremental approach toward
dynamic community detection. The key contribution is a generic technique called
, which examines the most recent batch of changes made to an
input graph and selects a subset of vertices to reevaluate for potential
community (re)assignment. This technique can be incorporated into any of the
community detection methods that use modularity as its objective function for
clustering. For demonstration purposes, we incorporated the technique into two
well-known community detection tools. Our experiments demonstrate that our new
incremental approach is able to generate performance speedups without
compromising on the output quality (despite its heuristic nature). For
instance, on a real-world network with 63M temporal edges (over 12 time steps),
our approach was able to complete in 1056 seconds, yielding a 3x speedup over a
baseline implementation. In addition to demonstrating the performance benefits,
we also show how to use our approach to delineate appropriate intervals of
temporal resolutions at which to analyze an input network
Event-based Vision: A Survey
Event cameras are bio-inspired sensors that differ from conventional frame
cameras: Instead of capturing images at a fixed rate, they asynchronously
measure per-pixel brightness changes, and output a stream of events that encode
the time, location and sign of the brightness changes. Event cameras offer
attractive properties compared to traditional cameras: high temporal resolution
(in the order of microseconds), very high dynamic range (140 dB vs. 60 dB), low
power consumption, and high pixel bandwidth (on the order of kHz) resulting in
reduced motion blur. Hence, event cameras have a large potential for robotics
and computer vision in challenging scenarios for traditional cameras, such as
low-latency, high speed, and high dynamic range. However, novel methods are
required to process the unconventional output of these sensors in order to
unlock their potential. This paper provides a comprehensive overview of the
emerging field of event-based vision, with a focus on the applications and the
algorithms developed to unlock the outstanding properties of event cameras. We
present event cameras from their working principle, the actual sensors that are
available and the tasks that they have been used for, from low-level vision
(feature detection and tracking, optic flow, etc.) to high-level vision
(reconstruction, segmentation, recognition). We also discuss the techniques
developed to process events, including learning-based techniques, as well as
specialized processors for these novel sensors, such as spiking neural
networks. Additionally, we highlight the challenges that remain to be tackled
and the opportunities that lie ahead in the search for a more efficient,
bio-inspired way for machines to perceive and interact with the world
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