No abstract

Understanding human mobility plays a vital role in urban and environmental planning as cities continue to grow. Ubiquitous geo-location, localization technology, and availability of big-data-ready computing infrastructure have enabled the development of more sophisticated models to characterize human mobility in urban areas. In this work, our main goal is to extract spatio-temporal features that characterize user mobility and, based on the similarity of these features, identify user communities . To this end, we propose a novel approach that leverages image processing techniques to represent user geographical preferences as images and then apply deep convolutional autoencoders to extract latent spatio-temporal mobility features from these images. These features are then fed to a clustering algorithm that identifies the underlying community structures. We use a diverse urban mobility dataset to validate the proposed framework. Our results show that the proposed framework is able to significantly increase the similarity between intra-community nodes (by up to 107%) as well as dissimilarity between inter-community nodes (up to 54%) when compared against no pre-processing of the datasets, i.e without pre-processing the datasets through any feature fusion method. Moreover, it was also able to reach up to 100% improvement when compared against community identification using Principal Component Analysis (PCA). Our results also show that the proposed approach yields significant increase in contact time amongst users belonging to the same community, by up to 80% when compared to the average contact time when not considering community structures, and by up to 150% when compared to the baseline. To the best of our knowledge, our proposal is the first to consider deep convolutional autoencoding to perform automatic extraction of non-linear spatio-temporal mobility features characterizing individual users from raw mobility datasets with the goal of identifying user communities.

No abstract

No abstract

The paradigm of Content-Centric Networks (CCN) has been used in several networking application contexts in order to enhance the performance of data forwarding and retrieval. By disassociating device addressing from the packet routing process, CCN offers new message routing possibilities in network scenarios that are limited in this respect. An example of such scenarios concerns Delay/Disruption Tolerant Networks (DTNs), in which requesting and retrieving data and services may be even more unpredictable than simply sending any message from a source to a destination. With this in mind, we propose a DIscovery and REtrieval protocol based on Social Cooperation (DIRESC), designed specifically to perform data request and retrieval by applying the concept of CCN in the DTN scenario, an environment also known as Content-Centric Delay-Tolerant Networks (CCDTN). Our forwarding mechanism proposes the distributed sending of data based on the profile of each node, which is defined by its social interactions with other nodes. To evaluate our protocol, we used the Opportunistic Network Environment (ONE) simulator to compare DIRESC to other related CCDTN protocols, including our conceptual implementation of content-centric forwarding dubbed CCN broadcast. Results show that social cooperation has positive performance impact (i.e., improved delivery rate, delay, overhead, packet retransmission, and energy consumption) when compared to other social-oblivious CCDTN protocols.