2,918 research outputs found
Living on the Edge: The Role of Proactive Caching in 5G Wireless Networks
This article explores one of the key enablers of beyond G wireless
networks leveraging small cell network deployments, namely proactive caching.
Endowed with predictive capabilities and harnessing recent developments in
storage, context-awareness and social networks, peak traffic demands can be
substantially reduced by proactively serving predictable user demands, via
caching at base stations and users' devices. In order to show the effectiveness
of proactive caching, we examine two case studies which exploit the spatial and
social structure of the network, where proactive caching plays a crucial role.
Firstly, in order to alleviate backhaul congestion, we propose a mechanism
whereby files are proactively cached during off-peak demands based on file
popularity and correlations among users and files patterns. Secondly,
leveraging social networks and device-to-device (D2D) communications, we
propose a procedure that exploits the social structure of the network by
predicting the set of influential users to (proactively) cache strategic
contents and disseminate them to their social ties via D2D communications.
Exploiting this proactive caching paradigm, numerical results show that
important gains can be obtained for each case study, with backhaul savings and
a higher ratio of satisfied users of up to and , respectively.
Higher gains can be further obtained by increasing the storage capability at
the network edge.Comment: accepted for publication in IEEE Communications Magazin
Analyzing First-Person Stories Based on Socializing, Eating and Sedentary Patterns
First-person stories can be analyzed by means of egocentric pictures acquired
throughout the whole active day with wearable cameras. This manuscript presents
an egocentric dataset with more than 45,000 pictures from four people in
different environments such as working or studying. All the images were
manually labeled to identify three patterns of interest regarding people's
lifestyle: socializing, eating and sedentary. Additionally, two different
approaches are proposed to classify egocentric images into one of the 12 target
categories defined to characterize these three patterns. The approaches are
based on machine learning and deep learning techniques, including traditional
classifiers and state-of-art convolutional neural networks. The experimental
results obtained when applying these methods to the egocentric dataset
demonstrated their adequacy for the problem at hand.Comment: Accepted at First International Workshop on Social Signal Processing
and Beyond, 19th International Conference on Image Analysis and Processing
(ICIAP), September 201
Analyzing First-Person Stories Based on Socializing, Eating and Sedentary Patterns
First-person stories can be analyzed by means of egocentric pictures acquired
throughout the whole active day with wearable cameras. This manuscript presents
an egocentric dataset with more than 45,000 pictures from four people in
different environments such as working or studying. All the images were
manually labeled to identify three patterns of interest regarding people's
lifestyle: socializing, eating and sedentary. Additionally, two different
approaches are proposed to classify egocentric images into one of the 12 target
categories defined to characterize these three patterns. The approaches are
based on machine learning and deep learning techniques, including traditional
classifiers and state-of-art convolutional neural networks. The experimental
results obtained when applying these methods to the egocentric dataset
demonstrated their adequacy for the problem at hand.Comment: Accepted at First International Workshop on Social Signal Processing
and Beyond, 19th International Conference on Image Analysis and Processing
(ICIAP), September 201
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