Motivated by Smart City applications and services, this article presents a novel approach to identifyinguser communitiesin communication networks. We define user communities as groups of users that share common mobility features over spatio-temporal scales of arbitrary length, such as time spent in certain locales, mobility speed, and time between consecutive movements. We describe our user community identification framework in detail including how mobility features can be extracted from real mobility traces (as examples of unlabelled data) and synthetic mobility records (as examples of labeled data). We present results obtained when using our approach in four distinct mobility scenarios represented by both unlabeled and labeled datasets. We also introduce a new validation methodology that uses image-based similarity metrics in order to assess the quality of identified communities. Our results show that the proposed approach significantly increases similarity between users within the same community as well as dissimilarity between users in different communities. We also demonstrate that the proposed user community identification approach yields significant increase in contact time amongst users belonging to the same community when compared to the average contact time when not considering community structures.

Characterizing and modeling node mobility is of critical importance in building intelligent transportation systems and their applications. In this paper, we discuss the scale-free properties of some important human mobility characteristics, namely spatial node density and mobility degree, and show that they exhibit behavior that can be described by a power-law. Based on their power-law characteristics, we derive analytical models for the spatial node density and mobility degree and show that the data generated by the proposed analytical models closely approach the empirical data extracted from the real mobility traces. Another contribution of our work is to use the proposed analytical models to build a synthetic mobility regime that is suitable for simulations of intelligent transportation systems. Finally, through network simulations, we show that the ad-hoc network routing behavior under our mobility regime closely approximates routing behavior when the corresponding real trace is used.

Motivated by the internets of the future, which will likely be considerably larger in size as well as highly heterogeneous and decentralized, we propose Decentralize-SDN, D-SDN, a framework that enables not only physical- but also logical distribution of the Software-Defined Networking (SDN) control plane. D-SDN accomplishes network control distribution by defining a hierarchy of controllers that can “match” an internet's organizational- and administrative structure. By delegating control between main controllers and secondary controllers, D-SDN is able to accommodate administrative decentralization and autonomy.It incorporates security as an integral part of the framework. This paper describes D-SDN and presents two use cases, namely network capacity sharing and public safety network services.

In this paper, we show empirically that the spatial node density resulting from human mobility follows a power law. We also show that the number of locales visited by users also exhibit heavy-tail behavior. We develop a stochastic model that confirms our empirical observations by showing that node mobility resulting from our model closely approximates mobility recorded in real traces collected from a variety of scenarios. Besides corroborating our empirical observations, we showcase another application of our model by using it to generate mobility regimes whose spatial node density exhibit heavy-tail behavior. We validate the resulting mobility generator by comparing its output against real traces.

This paper proposes a novel approach to capacity sharing in hybrid networked environments, i.e., environments that consist of infrastructure-based as well as infrastructureless networks. The proposed framework is based on Software-Defined Networking (SDN) and provides flexible, efficient, and secure capacity sharing solutions in a variety of hybrid network scenarios. In this paper, we describe the challenges raised by capacity sharing in hybrid networks, describe our framework in detail and how it addresses these challenges, and discuss implementation issues. To the best of our knowledge, this is the first SDN-based capacity sharing solution that targets hybrid networks and that incorporates security as an integral part of the proposed approach.

This paper introduces a modeling framework to analyze spatial node density in mobile networks under “waypoint”-like mobility regimes. The proposed framework is based on a set of first order ordinary differential equations (ODEs) that take as parameters (1) the probability of going from one subregion of the mobility domain to another and (2) the rate at which a node decides to leave a given subregion. We validate our model by using it to describe the steady-state behavior of real user mobility recorded by GPS traces in different scenarios. To the best of our knowledge, this is the first node density modeling framework generic enough that can be applied to any “waypoint”-based mobility regime.

In this paper, we introduce a user mobility modeling framework that accounts for both the users' social structure as well as the geographic diversity of the region of interest. SAGA, or Socially- and Geography-Aware mobility model, captures social features through the use of communities which cluster users with similar features such as average time in a cell, average speed, and pause time. SAGA accounts for geographic diversity by considering that different communities exhibit different interests for different locales; therefore, different communities are attracted to certain physical locations with different intensities. Besides introducing SAGA, the contributions of this work include: a model calibration approach based on formal statistical procedures to extract social structures and geographical diversity from real traces and set SAGA's parameters; and validation of SAGA by applying it to real mobility traces. Our experimental results show that, when compared to existing mobility regimes such as Random-Waypoint and Preferential-Attachment based mobility, SAGA is able to preserve the desired non-uniform node spatial density present in real user mobility, creating and maintaining clusters and accounting for differential node popularity and transitivity.

In this paper, we explore a novel approach to end-to-end round-trip time (RTT) estimation using a machine-learning technique known as the Experts Framework. In our proposal, each of several "experts" guesses a fixed value. The weighted average of these guesses estimates the RTT, with the weights updated after every RTT measurement based on the difference between the estimated and actual RTT. Through extensive simulations we show that the proposed machine-learning algorithm adapts very quickly to changes in the RTT. Our results show a considerable reduction in the number of retransmitted packets and a increase in goodput, in particular on more heavily congested scenarios. We corroborate our results through "live" experiments using an implementation of the proposed algorithm in the Linux kernel. These experiments confirm the higher accuracy of the machine learning approach with more than 40% improvement, not only over the standard TCP, but also over the well known Eifel RTT estimator.

In this paper we show that human mobility exhibits "persistent" behavior in terms of the spatial density distribution of the mobile nodes over time. Using real mobility traces, we observe that the original non-homogeneous node spatial density distribution, where some regions may be quite dense while others may be completely deserted, is maintained at different instants of time. We also show that mobility models that select the next node position based on the position of other nodes, a la "preferential attachment", do not preserve the original spatial node density distribution and lead to behavior similar to random mobility as exemplified by the Random Waypoint model. To the best of our knowledge, this is the first time that these phenomena have been reported. Based on these observations, we propose a simple mobility model that preserves the desired spatial density distribution. Moreover, when simulating the operation of a network moving according to the proposed model, we found that performance results expressed by a number of network metrics also match closely results obtained under mobility governed by real traces. We also compare our results to models whose steady-state do not preserve the original non-homogeneous density distribution and show that network performance under such regimes deviates from performance under real trace mobility.

In this paper analyzed WLAN-, GPS-, and synthetic traces that record mobility in a variety of network environments. We observe that from a macroscopic level, human mobility is symmetric. In other words, the number of users that move from point A to point B approximates the number of users that go in the opposite direction, i.e., from B to A. We show that this type of symmetry is more accentuated in synthetic mobility models, in particular, in random way-point mobility. We also study the direction of movement which also exhibits symmetric behavior in both real- as well as synthetic mobility. Additional contributions of our work include metrics to quantify mobility symmetry. We conclude the paper with a discussion of possible applications of our results in mobile networking.