Contributions to modeling, structural analysis, and routing performance in dynamic networks

Cette thèse apporte des contributions à la modélisation, compréhension ainsi qu’à la communication efficace d’information dans les réseaux dynamiques peuplant la périphérie de l’Internet. Par réseaux dynamiques, nous signifions les réseaux pouvant être modélisés par des graphes dynamiques dans lesquels noeuds et liens évoluent temporellement. Dans la première partie de la thèse, nous proposons un nouveau modèle de mobilité - STEPS - qui permet de capturer un large spectre de comportement de mobilité humains. STEPS mets en oeuvre deux principes fondamentaux de la mobilité humaine : l’attachement préférentiel à une zone de prédilection et l’attraction vers une zone de prédilection. Nous proposons une modélisation markovienne de ce modèle de mobilité. Nous montrons que ce simple modèle paramétrique est capable de capturer les caractéristiques statistiques saillantes de la mobilité humaine comme la distribution des temps d’inter-contacts et de contacts. Dans la deuxième partie, en utilisant STEPS, nous analysons les propriétés comportementales et structurelles fondamentales des réseaux opportunistes. Nous redéfinissons dans le contexte des réseaux dynamiques la notion de structure petit monde et montrons comment une telle structure peut émerger. En particulier, nous montrons que les noeuds fortement dynamiques peuvent jouer le rôle de ponts entre les composants déconnectés, aident à réduire significativement la longueur du chemin caractéristique du réseau et contribuent à l’émergence du phénomène petit-monde dans les réseaux dynamiques. Nous proposons une façon de modéliser ce phénomène sous STEPS. À partir d’un réseau dynamique régulier dans lequel les noeuds limitent leur mobilité à leurs zones préférentielles respectives. Nous recablons ce réseau en injectant progressivement des noeuds nomades se déplaçant entre plusieurs zones. Nous montrons que le pourcentage de tels nœuds nomades est de 10%, le réseau possède une structure petit monde avec un fort taux de clusterisation et un faible longueur du chemin caractéristique. La troisième contribution de cette thèse porte sur l’étude de l’impact du désordre et de l’irrégularité des contacts sur la capacité de communication d’un réseau dynamique. Nous analysons le degré de désordre de réseaux opportunistes réels et montrons que si exploité correctement, celui-ci peut améliorer significativement les performances du routage. Nous introduisons ensuite un modèle permettant de capturer le niveau de désordre d’un réseau dynamique. Nous proposons deux algorithmes simples et efficaces qui exploitent la structure temporelle d’un réseau dynamique pour délivrer les messages avec un bon compromis entre l’usage des ressources et les performances. Les résultats de simulations et analytiques montrent que ce type d’algorithme est plus performant que les approches classiques. Nous mettons également en évidence aussi la structure de réseau pour laquelle ce type d’algorithme atteint ses performances optimum. Basé sur ce résultat théorique nous proposons un nouveau protocole de routage efficace pour les réseaux opportunistes centré sur le contenu. Dans ce protocole, les noeuds maintiennent, via leurs contacts opportunistes, une fonction d’utilité qui résume leur proximité spatio-temporelle par rapport aux autres noeuds. En conséquence, router dans un tel contexte se résume à suivre le gradient de plus grande pente conduisant vers le noeud destination. Cette propriété induit un algorithme de routage simple et efficace qui peut être utilisé aussi bien dans un contexte d’adressage IP que de réseau centré sur les contenus. Les résultats de simulation montrent que ce protocole superforme les protocoles de routage classiques déjà définis pour les réseaux opportunistes. La dernière contribution de cette thèse consiste à mettre en évidence une application potentielle des réseaux dynamiques dans le contexte du « mobile cloud computing ». En utilisant les techniques d’optimisation particulaires, nous montrons que la mobilité peut augmenter considérablement la capacité de calcul des réseaux dynamiques. De plus, nous montrons que la structure dynamique du réseau a un fort impact sur sa capacité de calcul. ABSTRACT : This thesis contributes to the modeling, understanding and efficient communication in dynamic networks populating the periphery of the Internet. By dynamic networks, we refer to networks that can be modeled by dynamic graphs in which nodes and links change temporally. In the first part of the thesis, we propose a new mobility model - STEPS - which captures a wide spectrum of human mobility behavior. STEPS implements two fundamental principles of human mobility: preferential attachment and attractor. We show that this simple parametric model is able to capture the salient statistical properties of human mobility such as the distribution of inter-contact/contact time. In the second part, using STEPS, we analyze the fundamental behavioral and structural properties of opportunistic networks. We redefine in the context of dynamic networks the concept of small world structure and show how such a structure can emerge. In particular, we show that highly dynamic nodes can play the role of bridges between disconnected components, helping to significantly reduce the length of network path and contribute to the emergence of small-world phenomenon in dynamic networks. We propose a way to model this phenomenon in STEPS. From a regular dynamic network in which nodes limit their mobility to their respective preferential areas. We rewire this network by gradually injecting highly nomadic nodes moving between different areas. We show that when the ratio of such nomadic nodes is around 10%, the network has small world structure with a high degree of clustering and a low characteristic path length. The third contribution of this thesis is the study of the impact of disorder and contact irregularity on the communication capacity of a dynamic network. We analyze the degree of disorder of real opportunistic networks and show that if used correctly, it can significantly improve routing performances. We then introduce a model to capture the degree of disorder in a dynamic network. We propose two simple and efficient algorithms that exploit the temporal structure of a dynamic network to deliver messages with a good tradeoff between resource usage and performance. The simulation and analytical results show that this type of algorithm is more efficient than conventional approaches. We also highlight also the network structure for which this type of algorithm achieves its optimum performance. Based on this theoretical result, we propose a new efficient routing protocol for content centric opportunistic networks. In this protocol, nodes maintain, through their opportunistic contacts, an utility function that summarizes their spatio-temporal proximity to other nodes. As a result, routing in this context consists in following the steepest slopes of the gradient field leading to the destination node. This property leads to a simple and effective algorithm routing that can be used both in the context of IP networks and content centric networks. The simulation results show that this protocol outperforms traditional routing protocols already defined for opportunistic networks. The last contribution of this thesis is to highlight the potential application of dynamic networks in the context of "mobile cloud computing." Using the particle optimization techniques, we show that mobility can significantly increase the processing capacity of dynamic networks. In addition, we show that the dynamic structure of the network has a strong impact on its processing capacity

Temporal GIS Design of an Extended Time-geographic Framework for Physical and Virtual Activities

Recent rapid developments of information and communication technologies (ICT) enable a virtual space, which allows people to conduct activities remotely through tele-presence rather than through conventional physical presence in physical space. ICT offer people additional freedom in space and time to carry out their activities; this freedom leads to changes in the spatio-temporal distributions of activities. Given that activities are the reasons for travel, these changes will impact transportation systems. Therefore, a better understanding of the spatial and temporal characteristics of human activities in today’s society will help researchers study the impact of ICT on transportation. Using an integrated space-time system, Hägerstrand’s time geography provides an effective framework for studying the relationships of various constraints and human activities in physical space, but it does not support activities in virtual space. The present study provides a conceptual model to describe the relationships of physical space and virtual space, extending Hägerstrand’s time geography to handle both physical and virtual activities. This extended framework is used to support investigations of spatial and temporal characteristics of human activities and their interactions in physical and virtual spaces. Using a 3D environment (i.e., 2D space + 1D time), a temporal GIS design is developed to accommodate the extended time-geographic framework. This GIS design supports representations of time-geographic objects (e.g., space-time paths, networkbased space-time prisms, and space-time life paths) and a selected set of analysis functions applied to these objects (e.g., temporal dynamic segmentation and spatiotemporal intersection). A prototype system, with customized functions developed in Visual Basic for Applications (VBA) programs with ArcObjects, is implemented in ArcGIS according to the design. Using a hypothetical activity dataset, the system demonstrates the feasibility of the extended framework and the temporal GIS design to explore physical and virtual activities. This system offers useful tools with which to tackle various real problems related to physical and virtual activities

On the use of multi-sensor digital traces to discover spatio-temporal human behavioral patterns

134 p.La tecnología ya es parte de nuestras vidas y cada vez que interactuamos con ella, ya sea en una llamada telefónica, al realizar un pago con tarjeta de crédito o nuestra actividad en redes sociales, se almacenan trazas digitales. En esta tesis nos interesan aquellas trazas digitales que también registran la geolocalización de las personas al momento de realizar sus actividades diarias. Esta información nos permite conocer cómo las personas interactúan con la ciudad, algo muy valioso en planificación urbana,gestión de tráfico, políticas publicas e incluso para tomar acciones preventivas frente a desastres naturales.Esta tesis tiene por objetivo estudiar patrones de comportamiento humano a partir de trazas digitales. Para ello se utilizan tres conjuntos de datos masivos que registran la actividad de usuarios anonimizados en cuanto a llamados telefónicos, compras en tarjetas de crédito y actividad en redes sociales (check-ins,imágenes, comentarios y tweets). Se propone una metodología que permite extraer patrones de comportamiento humano usando modelos de semántica latente, Latent Dirichlet Allocation y DynamicTopis Models. El primero para detectar patrones espaciales y el segundo para detectar patrones espaciotemporales. Adicionalmente, se propone un conjunto de métricas para contar con un métodoobjetivo de evaluación de patrones obtenidos

Location Tracing and Potential Risks in Interaction Data Sets

Location-aware mobile phone handsets have become increasingly common in recent years, giving rise to a wide variety of location based services that rely on a person’s mobile phone reporting its current location to a remote service provider. Previous research has demonstrated that services that geo-code status updates may permit the estimation of both the rough location of users’ home locations and those of their workplaces. The paper investigates the disclosure risks of a priori knowledge of a person’s home and workplace locations, or of their current and previous home locations. Detailed interaction data sets published from censuses or other sources are characterised by the sparsity of the contained data, such that unique combinations of two locations may often be observed. In the most detailed 2011 migration data 37% of migrants had a unique combination of origin and destination, whilst in the most detailed journey to work data, 58% of workers had a unique combination of home and workplace. The amount of additional attribute data that might be disclosed is limited. When more coarse geographies are used their still remain a non-trivial number of persons with unique location combinations, with considerably more attributes potentially disclosable

都市の持続可能性に向けた旅行行動と知的移動データ統合に関する包括的研究

過去数十年にわたり世界中で都市の持続可能性がトレンドとなり研究対象となっている．人々は，非効率な天然資源の消費や社会経済活動による環境破壊など，地球環境に有害な活動を行い，これには都市計画や交通計画を始め，多くの分野が密接に関係している．現在では，これらを解決する新技術の開発や応用が広範囲な研究分野で日々取り組まれている．本研究では観光に関する問題を，交通と都市の研究の観点からさまざまなビックデータを使用し，持続可能な都市開発を目標とした具体的な解決策を示した．本研究では都市や地域の持続可能性に資するデータの活用方法として，Wi-Fiパケットセンサーを使用した旅行者にとって魅力的な観光目的地マネジメントに関する研究，およびETCプローブデータを使用した旅行時間の信頼性の観測における天候の影響に関する分析を組み合わせて示した．本論文では，都市の移動性の認知に対して以下に示す3つの研究から，特徴的な結果と有効な分析手法を確立した．1）Wi-Fiパッケージセンシング調査を使用した，広域観光エリアでの周遊パターンのマイニングベースの関連法則の調査，2）Wi-Fi追跡データでの大規模な観光地の持続可能な開発に向けた魅力的な目的地の抽出，3）ETC2.0プローブデータを使用して，様々な道路タイプを考慮した旅行の信頼性に対する降雪の影響の評価．以上の研究から，複数視点の考察を積み重ね，包括的な評価と提案を行い，いくつかの重要な結果が得られた．この論文の貢献は，より良い社会への問題解決への糸口となり，今後の政策立案者にとって有意義な内容となるだろう．According to sustainability, the trend is spreading out around the world for past decades. There are many area subjects involved, such as city planning, transportation planning, and so on, because people realized human activities harmful to the environment by consuming natural resources with less efficiency process or damage environment by social and economic movements. Currently, emerging technologies considered for the proactive procedure in extensive study areas regarding new technology application and knowledge based. In term of transport and urban study, including tourism concerns, we used intelligent data from deferent sources to be demonstrating the possible solutions which involve sustainable urban development concept. In this study, as a method of utilizing data that contributes to the sustainability of cities and regions, consideration of attractive destination management for tourists by using wireless probe data, and the weather impact on travel time reliability observation by using electronic toll collection probe data, it represented as combination experiments throughout comprehensive study. This dissertation addressed three contribution studies to the composed acknowledgment of urban mobility, and it obtained the intelligent data and specific method of research-based. It consists of; 1) an association rule mining-based exploration of travel patterns in wide tourism areas using a Wi-Fi package sensing survey, 2) Attractive destinations mining towards massive tourism area sustainable development on Wi-Fi tracking data, and 3) Assessment of the impact of snowfall on travel reliability considering different road types using ETC2.0 probe data. Hence, a stack of varying viewpoints researches provided a comprehensive review and suggestion throughout significant results. The contribution of this dissertation could be an advantage substance for strategy and policies planner to recognize alternative solutions leading to a better society.室蘭工業大学 (Muroran Institute of Technology)博士（工学

Smart Sustainable Mobility: Analytics and Algorithms for Next-Generation Mobility Systems

To this date, mobility ecosystems around the world operate on an uncoordinated, inefficient and unsustainable basis. Yet, many technology-enabled solutions that have the potential to remedy these societal negatives are already at our disposal or just around the corner. Innovations in vehicle technology, IoT devices, mobile connectivity and AI-powered information systems are expected to bring about a mobility system that is connected, autonomous, shared and electric (CASE). In order to fully leverage the sustainability opportunities afforded by CASE, system-level coordination and management approaches are needed. This Thesis sets out an agenda for Information Systems research to shape the future of CASE mobility through data, analytics and algorithms (Chapter 1). Drawing on causal inference, (spatial) machine learning, mathematical programming and reinforcement learning, three concrete contributions toward this agenda are developed. Chapter 2 demonstrates the potential of pervasive and inexpensive sensor technology for policy analysis. Connected sensing devices have significantly reduced the cost and complexity of acquiring high-resolution, high-frequency data in the physical world. This affords researchers the opportunity to track temporal and spatial patterns of offline phenomena. Drawing on a case from the bikesharing sector, we demonstrate how geo-tagged IoT data streams can be used for tracing out highly localized causal effects of large-scale mobility policy interventions while offering actionable insights for policy makers and practitioners. Chapter 3 sets out a solution approach to a novel decision problem faced by operators of shared mobility fleets: allocating vehicle inventory optimally across a network when competition is present. The proposed three-stage model combines real-time data analytics, machine learning and mixed integer non-linear programming into an integrated framework. It provides operational decision support for fleet managers in contested shared mobility markets by generating optimal vehicle re-positioning schedules in real time. Chapter 4 proposes a method for leveraging data-driven digital twin (DT) frameworks for large multi-stage stochastic design problems. Such problem classes are notoriously difficult to solve with traditional stochastic optimization. Drawing on the case of Electric Vehicle Charging Hubs (EVCHs), we show how high-fidelity, data-driven DT simulation environments fused with reinforcement learning (DT-RL) can achieve (close-to) arbitrary scalability and high modeling flexibility. In benchmark experiments we demonstrate that DT-RL-derived designs result in superior cost and service-level performance under real-world operating conditions

Contributions to modeling, structural analysis, and routing performance in dynamic networks

This thesis contributes to the modeling, understanding and efficient communication in dynamic networks populating the periphery of the Internet. By dynamic networks, we refer to networks that can be modeled by dynamic graphs in which nodes and links change temporally. In the first part of the thesis, we propose a new mobility model - STEPS - which captures a wide spectrum of human mobility behavior. STEPS implements two fundamental principles of human mobility: preferential attachment and attractor. We show that this simple parametric model is able to capture the salient statistical properties of human mobility such as the distribution of inter-contact/contact time. In the second part, using STEPS, we analyze the fundamental behavioral and structural properties of opportunistic networks. We redefine in the context of dynamic networks the concept of small world structure and show how such a structure can emerge. In particular, we show that highly dynamic nodes can play the role of bridges between disconnected components, helping to significantly reduce the length of network path and contribute to the emergence of small-world phenomenon in dynamic networks. We propose a way to model this phenomenon in STEPS. From a regular dynamic network in which nodes limit their mobility to their respective preferential areas. We rewire this network by gradually injecting highly nomadic nodes moving between different areas. We show that when the ratio of such nomadic nodes is around 10%, the network has small world structure with a high degree of clustering and a low characteristic path length. The third contribution of this thesis is the study of the impact of disorder and contact irregularity on the communication capacity of a dynamic network. We analyze the degree of disorder of real opportunistic networks and show that if used correctly, it can significantly improve routing performances. We then introduce a model to capture the degree of disorder in a dynamic network. We propose two simple and efficient algorithms that exploit the temporal structure of a dynamic network to deliver messages with a good tradeoff between resource usage and performance. The simulation and analytical results show that this type of algorithm is more efficient than conventional approaches. We also highlight also the network structure for which this type of algorithm achieves its optimum performance. Based on this theoretical result, we propose a new efficient routing protocol for content centric opportunistic networks. In this protocol, nodes maintain, through their opportunistic contacts, an utility function that summarizes their spatio-temporal proximity to other nodes. As a result, routing in this context consists in following the steepest slopes of the gradient field leading to the destination node. This property leads to a simple and effective algorithm routing that can be used both in the context of IP networks and content centric networks. The simulation results show that this protocol outperforms traditional routing protocols already defined for opportunistic networks. The last contribution of this thesis is to highlight the potential application of dynamic networks in the context of "mobile cloud computing." Using the particle optimization techniques, we show that mobility can significantly increase the processing capacity of dynamic networks. In addition, we show that the dynamic structure of the network has a strong impact on its processing capacity