Understanding Urban Human Mobility for Network Applications

Understanding urban human mobility is crucial for various mobile and network applications. This thesis addresses two key challenges presented by mobile applications, namely urban mobility modeling and its applications in Delay Tolerant Networks (DTNs). First, we model urban human mobility with transportation mode information. Our research is based on two real-life GPS datasets containing approximately 20 and 10 million GPS samples. Previous research has suggested that the trajectories in human mobility have statistically similar features as Lévy Walks. We attempt to explain the Lévy Walks behavior by decomposing them into different classes according to the different transportation modes, such as Walk/Run, Bike, Train/ Subway or Car/Taxi/Bus. We show that human mobility can be modelled as a mixture of different transportation modes, and that these single movement patterns can be approximated by a lognormal distribution rather than a power-law distribution. Then, we demonstrate that the mixture of the decomposed lognormal flight distributions associated with each modality is a power-law distribution, providing an explanation for the emergence of Lévy Walks patterns that characterize human mobility patterns. Second, we find that urban human mobility exhibits strong spatial and temporal patterns. We leverage such human mobility patterns to derive an optimal routing algorithm that minimizes the hop count while maximizing the number of needed nodes in DTNs. We propose a solution framework, called Ameba, for timely data delivery in DTNs. Simulation results with experimental traces indicate that Ameba achieves a comparable delivery ratio to a Flooding-based algorithm, but with much lower overhead. Third, we infer the functions of the sub-areas in three cities by analyzing urban mobility patterns. The analysis is based on three large taxi GPS datasets in Rome, San Francisco and Beijing containing 21, 11 and 17 million GPS points, respectively. We categorize the city regions into four categories, workplaces, entertainment places, residential places and other places. We show that the identification of these functional sub-areas can be utilized to increase the efficiency of urban DTN applications. The three topics pertaining to urban mobility examined in the thesis support the design and implementation of network applications for urban environments.Ihmisen liikkumisen ymmärtäminen on erittäin tärkeää monille mobiiliverkkojen sovelluksille. Tämä väitöskirja käsittelee mobiilikäyttäjien liikkuvuuden mallintamista ja sen soveltamista viiveitä sietävään tiedonvälitykseen urbaanissa ympäristössä. Aloitamme mallintamalla mobiilikäyttäjien liikkuvuutta ottaen huomioon kulkumuodon. Tutkimuksemme perustuu kahteen laajaan GPS-data-aineistoon. Käytetyissä data-aineisto koostuu 10 ja 20 miljoonan havaintopisteen kulkuvälineet sisältävistä GPS-tiedoista. Aikaisemmat tutkimukset ovat ehdottaneet, että liikkuvuusmalleilla on samankaltaisia tilastollisia ominaisuuksia kuin Lévy-kävelyillä. Tutkimuksemme selittää Lévy-kävelyiden käyttäytymisen jakamalla ne erilaisiin kulkumuotoihin, kuten kävely/juoksu, polkupyöräily, juna/metro tai auto/taksi/bussi. Näytämme, että ihmisten liikkuvuus voidaan mallintaa eri kulkumuotojen yhdistelminä ja että yksittäiset liikkuvuusmallit voidaan arvioida logaritmisella normaalijakaumalla paremmin kuin potenssilakia noudattavalla jakaumalla. Lisäksi osoitamme, että yhdistelmä kävelyjen lavennetusta logaritmisesta normaalijakaumasta eri kulkumuotojen kanssa on potenssilakia noudattava jakauma, joka selittää ihmisten liikkuvuusmalleja luonnehtivien Lévy-kävelymallien esiintymisen. Toiseksi osoitamme, että urbaanin ihmisen liikkuvuuteen kuuluu vahvoja aikaan ja paikkaan liittyviä malleja. Johdamme näistä ihmisten liikkuvuusmalleista optimaalisen reititysalgoritmin, joka minimoi tarvittavien hyppyjen määrän ja maksimoi tarvittavien solmujen määrän viiveitä sietävissä verkoissa. Esitämme ratkaisuksi arkkitehtuurikehyksen nimeltä Ameba, joka takaa oikea-aikaisen viestien välityksen viiveitä sietävissä verkoissa. Simulointitulosten perusteella Ameba saavuttaa tulvitukseen perustuvien algoritmien kanssa vertailukelpoisen viestien kuljetussuhteen, mutta pienemmällä resurssikustannuksella. Kolmanneksi päättelemme maantieteellisten osa-alueiden funktiot analysoimalla kolmen kaupungin urbaaneja liikkumismalleja. Analyysi perustuu kolmeen laajaan taksien GPS-paikkatiedosta. GPS-data on kerätty Roomassa, San Franciscossa, ja Pekingissä ja koostuu 21, 11, ja 17 miljoonasta havaintopisteestä. Luokittelemme kaupunkien alueet neljään luokkaan: työpaikat, viihde-, asuin-, ja muut paikat. Näytämme, että näiden luokkien tunnistamista voidaan käyttää parantamaan viiveitä sietävien verkkojen sovellusten tehokkuutta. Kaikki tässä väitöskirjassa käsitellyt mobiilikäyttäjien liikkuvuuden mallintamisen aihepiirit edesauttavat urbaanien ympäristöjen verkkojen sovellusten suunnittelua ja toteutusta

Investigations of outdoor mobility patterns of taxicabs in urban scenarios

This thesis investigates various outdoor mobility patterns of taxicabs in urban environments based on open-data real traces and it proposes a suitable outdoor mobility model to fit the provided measurement data. This thesis is processing user traces of taxicabs of two major cities: Rome and San Francisco downloaded from CRAWDAD open-source repository, which is responsible for sharing data from real networks and real mobile users across the various research communities around the world. There are numerous sources of collecting traces of users in a city, such as mobile devices, vehicles, smart cards, floating sensors etc. This thesis presents a comparative analysis of the mobility patterns of various taxicabs from Rome and San Francisco cities based on data collected via GPS-enabled mobile devices. Finding suitable mobility models of taxicabs to represent the travelling patterns of users moving from one location to another with respect to their varying time, location and speed can be quite helpful for the advanced researches in the diverse fields of wireless communications, such as better network planning, more efficient smart city design, improved traffic flows in cities. Also other applications such as weather forecasting, cellular coverage planning, e-health services, prediction of tourist areas, intelligent transport systems can benefit from the information hidden in user traces and from being able to find out statistically valid mobility models. The work here focused on extracting various mobility parameters from the crowdsourced open-source data and trying to model them according to various mobility models existing in the literature. The measurement analysis of this thesis work was completed in Matlab

Effective and Efficient Communication and Collaboration in Participatory Environments

Participatory environments pose significant challenges to deploying real applications. This dissertation investigates exploitation of opportunistic contacts to enable effective and efficient data transfers in challenged participatory environments. There are three main contributions in this dissertation: 1. A novel scheme for predicting contact volume during an opportunistic contact (PCV); 2. A method for computing paths with combined optimal stability and capacity (COSC) in opportunistic networks; and 3. An algorithm for mobility and orientation estimation in mobile environments (MOEME). The proposed novel scheme called PCV predicts contact volume in soft real-time. The scheme employs initial position and velocity vectors of nodes along with the data rate profile of the environment. PCV enables efficient and reliable data transfers between opportunistically meeting nodes. The scheme that exploits capacity and path stability of opportunistic networks is based on PCV for estimating individual link costs on a path. The total path cost is merged with a stability cost to strike a tradeoff for maximizing data transfers in the entire participatory environment. A polynomial time dynamic programming algorithm is proposed to compute paths of optimum cost. We propose another novel scheme for Real-time Mobility and Orientation Estimation for Mobile Environments (MOEME), as prediction of user movement paves way for efficient data transfers, resource allocation and event scheduling in participatory environments. MOEME employs the concept of temporal distances and uses logistic regression to make real time estimations about user movement. MOEME relies only on opportunistic message exchange and is fully distributed, scalable, and requires neither a central infrastructure nor Global Positioning System. Indeed, accurate prediction of contact volume, path capacity and stability and user movement can improve performance of deployments. However, existing schemes for such estimations make use of preconceived patterns or contact time distributions that may not be applicable in uncertain environments. Such patterns may not exist, or are difficult to recognize in soft-real time, in open environments such as parks, malls, or streets

Delay tolerant networking in a shopping mall environment

The increasing popularity of computing devices with short-range wireless offers new communication service opportunities. These devices are small and may be mobile or embedded in almost any type of object imaginable, including cars, tools, appliances, clothing and various consumer goods. The majority of them can store data and transmit it when a wireless, or wired, transmitting medium is available. The mobility of the individuals carrying such short-range wireless devices is important because varying distances creates connection opportunities and disconnections. It is likely that successful forwarding algorithms will be based, at least in part, on the patterns of mobility that are seen in real settings. For this reason, studying human mobility in different environments for extended periods of time is essential. Thus we need to use measurements from realistic settings to drive the development and evaluation of appropriate forwarding algorithms. Recently, several significant efforts have been made to collect data reflecting human mobility. However, these traces are from specific scenarios and their validity is difficult to generalize. In this thesis we contribute to this effort by studying human mobility in shopping malls. We ran a field trial to collect real-world Bluetooth contact data from shop employees and clerks in a shopping mall over six days. This data will allow the informed design of forwarding policies and algorithms for such settings and scenarios, and determine the effects of users' mobility patterns on the prevalence of networking opportunities. Using this data set we have analysed human mobility and interaction patterns in this shopping mall environment. We present evidence of distinct classes of mobility in this situation and characterize them in terms of power law coefficients which approximate inter-contact time distributions. These results are quite different from previous studies in other environments. We have developed a software tool which implements a mobility model for "structured" scenarios such as shopping malls, trade fairs, music festivals, stadiums and museums. In this thesis we define as structured environment, a scenario having definite and highly organised structure, where people are organised by characteristic patterns of relationship and mobility. We analysed the contact traces collected on the field to guide the design of this mobility model. We show that our synthetic mobility model produces inter-contact time and contact duration distributions which approximate well to those of the real traces. Our scenario generator also implements several random mobility models. We compared our Shopping Mall mobility model to three other random mobility models by comparing the performances of two benchmark delay tolerant routing protocols, Epidemic and Prophet, when simulated with movement traces from each model. Thus, we demonstrate that the choice of a mobility model is a significant consideration when designing and evaluating delay-tolerant mobile ad-hoc network protocols. Finally, we have also conducted an initial study to evaluate the effect of delivering messages in shopping mall environments by exclusively forwarding them to customers or sellers, each of which has distinctive mobility patterns

Improving Security and Privacy in Online Social Networks

Online social networks (OSNs) have gained soaring popularity and are among the most popular sites on the Web. With OSNs, users around the world establish and strengthen connections by sharing thoughts, activities, photos, locations, and other personal information. However, the immense popularity of OSNs also raises significant security and privacy concerns. Storing millions of users\u27 private information and their social connections, OSNs are susceptible to becoming the target of various attacks. In addition, user privacy will be compromised if the private data collected by OSNs are abused, inadvertently leaked, or under the control of adversaries. as a result, the tension between the value of joining OSNs and the security and privacy risks is rising.;To make OSNs more secure and privacy-preserving, our work follow a bottom-up approach. OSNs are composed of three components, the infrastructure layer, the function layer, and the user data stored on OSNs. For each component of OSNs, in this dissertation, we analyze and address a representative security/privacy issue. Starting from the infrastructure layer of OSNs, we first consider how to improve the reliability of OSN infrastructures, and we propose Fast Mencius, a crash-fault tolerant state machine replication protocol that has low latency and high throughput in wide-area networks. For the function layer of OSNs, we investigate how to prevent the functioning of OSNs from being disturbed by adversaries, and we propose SybilDefender, a centralized sybil defense scheme that can effectively detect sybil nodes by analyzing social network topologies. Finally, we study how to protect user privacy on OSNs, and we propose two schemes. MobiShare is a privacy-preserving location-sharing scheme designed for location-based OSNs (LBSNs), which supports sharing locations between both friends and strangers. LBSNSim is a trace-driven LBSN model that can generate synthetic LBSN datasets used in place of real datasets. Combining our work contributes to improving security and privacy in OSNs