HUMAN MOBILITY IN URBAN SPACE

Nowadays we witness a rapid increase of people mobility as the world population has become more interconnected and is relying on faster transportation methods, simplified connections and shorter commuting times. Unveiling and understanding human mobility patterns have become a crucial issue to support decisions and prediction activities when managing the complexity of the today's social organization. The strict connections between human mobility patterns, the planning, deployment and management of a variety of public and commercial services have fueled the rise of a vast research activity. Throughout this work, we are more interested and mainly focusing on urban mobility because here most of the human interactions take place and mobility has the greatest impact on management and optimization of public and commercial services. In this thesis, we provided a general framework for dealing with the modeling importance of locations from a per-user perspective and identified a few novel properties of human mobility. Also through characterizing the transition patterns driving user movement among visited places, we pave the way to propose a new mobility model in urban spaces. Meanwhile relying on the relevance of visited places, we propose a new algorithm for detecting and distinguishing Home and Workplaces. And finally, we suggest a framework for predicting the different aspects of Encounter/Colocation events. By exploiting the weighted Bayesian predictor we could enhance the accuracy of prediction w.r.t. the standard naive Bayesian and also to some other state-of-the-art predictors

Characterizing Communicability of Networks formed on Mobile Node

Smartphones have become extremely popular by launching wide ubiquitous networks. Nowadays studying of DTN Delay Tolerant Networks (DTN) and Opportunistic Networks where formed over these mobile nodes, is one of the interesting topics in the research community. In this paper, we measure communicability capacity of DTN Network formed over the mobile nodes at a university campus and also an area in Montreal city through exploiting static and temporal graphs. We observed a significant difference between communicability measures in static and temporal cases, especially for short snapshot windows. It implies that analyzing dynamic networks by considering a static model for them may lead to an unrealistic and even mislead results

Predicting encounter and colocation events

Although an extensive literature has been devoted to mine and model mobility features, forecasting where, when and whom people will encounter/colocate still deserve further research effort s. Forecasting people\u2019s encounter and colocation features is the key point for the success of many applications rang- ing from epidemiology to the design of new networking paradigms and services such as delay tolerant and opportunistic networks. While many algorithms which rely on both mobility and social informa- tion have been proposed, we propose a novel encounter and colocation predictive model which predicts user\u2019s encounter and colocation events and their features by exploiting the spatio-temporal regularity in the history of these events. We adopt a weighted features Bayesian predictor and evaluate its accuracy on two large scales WiFi and cellular datasets. Results show that our approach could improve prediction accuracy with respect to standard na\uefve Bayesian and some of the state of the art predictors

ROBUST congestion control in networks with multiple congested nodes

In this paper, an analytical method for design of a congestion control scheme in packet switching network is presented.This scheme is particularly suitable for implementation in ATM Switch Systems, for the support of the available bit rate(ABR) service in ATM networks.The control method is rate based with a local feedback controller associated with each switch node. The controller is a generalization of the standard proportional-plus-derivative controller, with the difference that extra higher-order derivative terms are involved to accommodate the delay in networks. It is shown that there exist a set of control gain that results in applying asymptotic stability of the linearized network model.The use of optimal control theory for finding the state feedback gains for full utilization of bandwidth and low packet loss is our main purposes. Since the proposed approach considers the case of multiple congested nodes, it stabilizes the entire links in the network

Adaptive congestion control in networks with multiple congested nodes

In this paper, an analytical method for a design of a congestion control scheme in packet switching network is presented This scheme is particularly suitable for implementation in ATM Switch Systems, for the support of the available bit rate (ABR) service in ATM networks The control method is rate based with a local feedback controller associated with each switch node. The controller is a generalization of the standard proportional-plus-derivative controller, with the difference that extra higher order derivative terms are involved to accommodate for delay in networks. It is shown that there exist a set of control gain that result in applying asymptotic stability of the linearized network model. The use of adaptive control for finding the state feedback gains for asymptotically stability is our main purposes. Since the proposed approach considers the case of multiple congested nodes, it stabilizes the entire links in the network