Behavior-Based Mobility Prediction for Seamless Handoffs in Mobile Wireless Networks

The field of wireless networking has received unprecedented attention from the research community during the last decade due to its great potential to create new horizons for communicating beyond the Internet. Wireless LANs (WLANs) based on the IEEE 802.11 standard have become prevalent in public as well as residential areas, and their importance as an enabling technology will continue to grow for future pervasive computing applications. However, as their scale and complexity continue to grow, reducing handoff latency is particularly important. This paper presents the Behavior-based Mobility Prediction scheme to eliminate the scanning overhead incurred in IEEE 802.11 networks. This is achieved by considering not only location information but also group, time-of-day, and duration characteristics of mobile users. This captures short-term and periodic behavior of mobile users to provide accurate next-cell predictions. Our simulation study of a campus network and a municipal wireless network shows that the proposed method improves the next-cell prediction accuracy by 23~43% compared to location-only based schemes and reduces the average handoff delay down to 24~25 ms

Behavior-Based Mobility Prediction for Seamless Handoffs in Mobile Wireless Networks

The field of wireless networking has received unprecedented attention from the research community during the last decade due to its great potential to create new horizons for communicating beyond the Internet. Wireless LANs (WLANs) based on the IEEE 802.11 standard have become prevalent in public as well as residential areas, and their importance as an enabling technology will continue to grow for future pervasive computing applications. However, as their scale and complexity continue to grow, reducing handoff latency is particularly important. This paper presents the Behavior-based Mobility Prediction scheme to eliminate the scanning overhead incurred in IEEE 802.11 networks. This is achieved by considering not only location information but also group, time-of-day, and duration characteristics of mobile users. This captures short-term and periodic behavior of mobile users to provide accurate next-cell predictions. Our simulation study of a campus network and a municipal wireless network shows that the proposed method improves the next-cell prediction accuracy by 23~43% compared to location-only based schemes and reduces the average handoff delay down to 24~25 ms

Towards an activity-based model for pedestrian facilities

This paper develops a framework for understanding pedestrian mobility pattern from WiFi traces and other data sources. It can be used to forecast demand for pedestrian facilities such as railway stations, music festivals, campus, airports, supermarkets or even pedestrian area in city centers. Scenarios regarding the walkable infrastructure and connectors, the scheduling (trains in stations, classes on campus, concerts in festivals) or the proposed services in the facility may then be evaluated. It is inspired by activity-based approach. We assume that pedestrian demand is driven by a willingness to perform activities. Activity scheduling decision is explicitly taken into account. Activity-based approach for urban areas is adapted for pedestrian facilities, with similarities (scheduling behavior) and differences (no ``home'' in pedestrian facilities, thus no tours). This is a first attempt to define a integrated system of choice models in the context of pedestrian facilities

Mobility management in 5G heterogeneous networks

In recent years, mobile data traffic has increased exponentially as a result of widespread popularity and uptake of portable devices, such as smartphones, tablets and laptops. This growth has placed enormous stress on network service providers who are committed to offering the best quality of service to consumer groups. Consequently, telecommunication engineers are investigating innovative solutions to accommodate the additional load offered by growing numbers of mobile users. The fifth generation (5G) of wireless communication standard is expected to provide numerous innovative solutions to meet the growing demand of consumer groups. Accordingly the ultimate goal is to achieve several key technological milestones including up to 1000 times higher wireless area capacity and a significant cut in power consumption. Massive deployment of small cells is likely to be a key innovation in 5G, which enables frequent frequency reuse and higher data rates. Small cells, however, present a major challenge for nodes moving at vehicular speeds. This is because the smaller coverage areas of small cells result in frequent handover, which leads to lower throughput and longer delay. In this thesis, a new mobility management technique is introduced that reduces the number of handovers in a 5G heterogeneous network. This research also investigates techniques to accommodate low latency applications in nodes moving at vehicular speeds

Urban Activity Patterns Mining in Wi-Fi Access Point Logs

RÉSUMÉ Aujourd’hui la grande majorité des données sont basée sur des enquêtes ou des études appliquées à des échantillons définis de la population. De plus les méthodes traditionnelles de collecte de données en termes de coûts ainsi que de temps tout en ne garantissant pas la représentativité des observations du fait du biais d’échantillonages et de la relative fiabilité des répondants. La disponibilité grandissantes de bases de données collectées passivements couplé à la forte pénétration des smartphones ont ouvert des perspectives intéressantes concernant la collecte et le traitement automatisé de données de mobilité.----------ABSTRACT This thesis proposes a methodology to mine valuable nformation about the usage of a facility (e.g. building), based only on Wi-Fi network connection history. Data are collected at Concordia University in Montreal, Canada, during one week in Febuary 2015. Using the Wi-Fi access log data, we characterize activities taking place within a building without any additional knowledge of the building itself. Such information can be used to monitor the use of a facility automatically, to study human mobility or as an input information for mobility models

Robustness of optimal channel reservation using handover prediction in multiservice wireless networks

The aim of our study is to obtain theoretical limits for the gain that can be expected when using handover prediction and to determine the sensitivity of the system performance against different parameters. We apply an average-reward reinforcement learning approach based on afterstates to the design of optimal admission control policies in mobile multimedia cellular networks where predictive information related to the occurrence of future handovers is available. We consider a type of predictor that labels active mobile terminals in the cell neighborhood a fixed amount of time before handovers are predicted to occur, which we call the anticipation time. The admission controller exploits this information to reserve resources efficiently. We show that there exists an optimum value for the anticipation time at which the highest performance gain is obtained. 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Estudo de implementação rádio para comunicações V2X

A constante necessidade de aumento da capacidade das redes de telecomunicações, e outras já bem conhecidas e utilizadas na nossa sociedade atual, deve-se ao fato de atender às necessidades e desafios tecnológicos crescentes com que se depara esta nova "Era Digital". De modo a dar um contributo a estas necessidades pretende-se abordar o tema da comunicação entre Veículos (V2V). Para isso há que abordar também outras redes, particularmente o 5G, e a Rede Heterogénea Veicular (HetVAN’s) possibilitada por novas arquiteturas de rede mais dinâmicas formando o Sistema de Transporte Inteligente (ITS). Deste modo, a presente dissertação consiste na apresentação das diferentes tecnologias que vão dar suporte à HetVAN’s, que vai consistir a nível de telecomunicações a 4G e 5G, assim como as Comunicações Dedicadas de Curto Alcance (DSRC- Dedicated Short-Range Communications) com o intuito de implementar a normalização do IEEE802.11p. Em seguida estuda-se, a futura Rede Veicular que tem como grande objetivo a condução automática. Sendo necessário que nessa rede a velocidade seja de gigabits por segundo e para tal ainda não há nada para além de propostas teóricas. A proposta teórica que vai ser apresentada como solução à comunicação V2V nesta dissertação é a Comunicação Sem Fio em Onda Milimétrica que pode fornecer uma eficiente troca de informações em tempo real entre veículos sem a necessidade de infraestrutura de comunicação periférica na estrada. Embora as redes celulares móveis sejam capazes de fornecer ampla cobertura para veículos, os requisitos de serviços que exigem uma rigorosa segurança em tempo real nem sempre podem ser garantidos por redes celulares.The constant need to increase the capacity of telecommunications networks, and others already well-known and used in our current society, is due to the increasing technological needs and challenges facing this new "Digital Era". In order to contribute to these needs we intend to address the theme of communication between vehicles (V2V). In order to do this, other networks, particularly 5G, and the Heterogeneous Vehicle Network (HetVAN's), made possible by new, more dynamic network architectures, form the Intelligent Transport System (ITS). Thus, the present dissertation consists of presenting the different technologies that will support HetVAN's, which will consist of 4G and 5G telecommunications, as well as the Dedicated Short-Range Communications (DSRC) for the purpose implement the IEEE802.11p standardization. Next, the future vehicular network is studied, whose main objective is automatic driving. It is necessary that in this network the speed is of gigabits per second and for that still there is nothing besides theoretical proposals. The theoretical proposal that will be presented as a solution to the V2V communication in this dissertation is the Wireless Communication in Millimeter Wave that can provide an efficient exchange of information in real time between vehicles without the need of peripheral communication infrastructure on the road. Although mobile cellular networks are capable of providing extensive vehicle coverage, service requirements that require strict real-time security cannot always be guaranteed by cellular networks

Εξελιγμένες Τεχνικές Πρόβλεψης Θέσης στον Κινητό Υπολογισμό

Η επίγνωση-πλαισίου εμφανίζεται ως μία από τις πιο σημαντικές πτυχές στο αναδυόμενο περιβάλλον του διάχυτου υπολογισμού. Απαιτούνται κινητές εφαρμογές επίγνωσης πλαισίου για την αίσθηση και την αντίδραση σε συνθήκες μεταβαλλόμενου περιβάλλοντος. Τέτοιες εφαρμογές, συχνά, χρειάζεται να αναγνωρίζουν, να ταξινομούν και να προβλέπουν το πλαίσιο με σκοπό να δρουν αποδοτικά, εκ των προτέρων, προς όφελος του χρήστη. Πρώτον, προτείνουμε έναν αποδοτικό ταξινομητή χωρικού πλαισίου και έναν βραχείας-μνήμης προγνώστη για την μελλοντική θέση ενός κινητού χρήστη σε κυψελωτά δίκτυα. Δεύτερον, προτείνουμε έναν καινοτόμο προσαρμοστικό αλγόριθμο, ο οποίος χειρίζεται το πλαίσιο αναπαράστασης θέσης και την πρόβλεψη τροχιών των κινούμενων χρηστών. Τρίτον, προτείνουμε έναν βραχείας- μνήμης προσαρμοστικό προγνώστη θέσης που χειρίζεται την πρόβλεψη υπό την απουσία ιστορικής κινητής πληροφορίας. Τέταρτον, υποθέτουμε μία βάση προτύπων και προσπαθούμε να συγκρίνουμε το πρότυπο κίνησης ενός χρήστη με την αποθηκευμένη πληροφορία με σκοπό να προβλέψουμε μελλοντικές θέσεις. Τα συμπεράσματά μας, συγκρινόμενα με άλλα σχήματα, είναι πολύ ελπιδοφόρα για το πρόβλημα της πρόβλεψης θέσης.Context-awareness is viewed as one of the most important aspects in the emerging pervasive computing paradigm. Mobile context-aware applications are required to sense and react to changing environment conditions. Such applications, usually, need to recognize, classify and predict context in order to act efficiently, beforehand, for the benefit of the user. Firstly, we propose an efficient spatial context classifier and a short-term predictor for the future location of a mobile user in cellular networks. Secondly, we propose a novel adaptive mobility prediction algorithm, which deals with location context representation and trajectory prediction of moving users. Thirdly, we propose a short-memory adaptive location predictor that realizes mobility prediction in the absence of extensive historical mobility information. Fourthly, we assume the existence of a pattern base and try to compare the movement pattern of a certain user with stored information in order to predict future locations. Our findings, compared with other schemes, are very promising for the location prediction problem and the adoption of proactive context-aware applications and services

Gestion des ressources dans les réseaux cellulaires sans fil

L’émergence de nouvelles applications et de nouveaux services (tels que les applications multimédias, la voix-sur-IP, la télévision-sur-IP, la vidéo-sur-demande, etc.) et le besoin croissant de mobilité des utilisateurs entrainent une demande de bande passante de plus en plus croissante et une difficulté dans sa gestion dans les réseaux cellulaires sans fil (WCNs), causant une dégradation de la qualité de service. Ainsi, dans cette thèse, nous nous intéressons à la gestion des ressources, plus précisément à la bande passante, dans les WCNs. Dans une première partie de la thèse, nous nous concentrons sur la prédiction de la mobilité des utilisateurs des WCNs. Dans ce contexte, nous proposons un modèle de prédiction de la mobilité, relativement précis qui permet de prédire la destination finale ou intermédiaire et, par la suite, les chemins des utilisateurs mobiles vers leur destination prédite. Ce modèle se base sur : (a) les habitudes de l’utilisateur en terme de déplacements (filtrées selon le type de jour et le moment de la journée) ; (b) le déplacement courant de l’utilisateur ; (c) la connaissance de l’utilisateur ; (d) la direction vers une destination estimée ; et (e) la structure spatiale de la zone de déplacement. Les résultats de simulation montrent que ce modèle donne une précision largement meilleure aux approches existantes. Dans la deuxième partie de cette thèse, nous nous intéressons au contrôle d’admission et à la gestion de la bande passante dans les WCNs. En effet, nous proposons une approche de gestion de la bande passante comprenant : (1) une approche d’estimation du temps de transfert intercellulaire prenant en compte la densité de la zone de déplacement en terme d’utilisateurs, les caractéristiques de mobilité des utilisateurs et les feux tricolores ; (2) une approche d’estimation de la bande passante disponible à l’avance dans les cellules prenant en compte les exigences en bande passante et la durée de vie des sessions en cours ; et (3) une approche de réservation passive de bande passante dans les cellules qui seront visitées pour les sessions en cours et de contrôle d’admission des demandes de nouvelles sessions prenant en compte la mobilité des utilisateurs et le comportement des cellules. Les résultats de simulation indiquent que cette approche réduit largement les ruptures abruptes de sessions en cours, offre un taux de refus de nouvelles demandes de connexion acceptable et un taux élevé d’utilisation de la bande passante. Dans la troisième partie de la thèse, nous nous penchons sur la principale limite de la première et deuxième parties de la thèse, à savoir l’évolutivité (selon le nombre d’utilisateurs) et proposons une plateforme qui intègre des modèles de prédiction de mobilité avec des modèles de prédiction de la bande passante disponible. En effet, dans les deux parties précédentes de la thèse, les prédictions de la mobilité sont effectuées pour chaque utilisateur. Ainsi, pour rendre notre proposition de plateforme évolutive, nous proposons des modèles de prédiction de mobilité par groupe d’utilisateurs en nous basant sur : (a) les profils des utilisateurs (c’est-à-dire leur préférence en termes de caractéristiques de route) ; (b) l’état du trafic routier et le comportement des utilisateurs ; et (c) la structure spatiale de la zone de déplacement. Les résultats de simulation montrent que la plateforme proposée améliore la performance du réseau comparée aux plateformes existantes qui proposent des modèles de prédiction de la mobilité par groupe d’utilisateurs pour la réservation de bande passante.The emergence of new applications and services (e.g., multimedia applications, voice over IP and IPTV) and the growing need for mobility of users cause more and more growth of bandwidth demand and a difficulty of its management in Wireless Cellular Networks (WCNs). In this thesis, we are interested in resources management, specifically the bandwidth, in WCNs. In the first part of the thesis, we study the user mobility prediction that is one of key to guarantee efficient management of available bandwidth. In this context, we propose a relatively accurate mobility prediction model that allows predicting final or intermediate destinations and subsequently mobility paths of mobile users to reach these predicted destinations. This model takes into account (a) user’s habits in terms of movements (filtered according to the type of day and the time of the day); (b) user's current movement; (c) user’s contextual knowledge; (d) direction from current location to estimated destination; and (e) spatial conceptual maps. Simulation results show that the proposed model provides good accuracy compared to existing models in the literature. In the second part of the thesis, we focus on call admission control and bandwidth management in WCNs. Indeed, we propose an efficient bandwidth utilization scheme that consists of three schemes: (1) handoff time estimation scheme that considers navigation zone density in term of users, users’ mobility characteristics and traffic light scheduling; (2) available bandwidth estimation scheme that estimates bandwidth available in the cells that considers required bandwidth and lifetime of ongoing sessions; and (3) passive bandwidth reservation scheme that passively reserves bandwidth in cells expected to be visited by ongoing sessions and call admission control scheme for new call requests that considers the behavior of an individual user and the behavior of cells. Simulation results show that the proposed scheme reduces considerably the handoff call dropping rate while maintaining acceptable new call blocking rate and provides high bandwidth utilization rate. In the third part of the thesis, we focus on the main limitation of the first and second part of the thesis which is the scalability (with the number of users) and propose a framework, together with schemes, that integrates mobility prediction models with bandwidth availability prediction models. Indeed, in the two first contributions of the thesis, mobility prediction schemes process individual user requests. Thus, to make the proposed framework scalable, we propose group-based mobility prediction schemes that predict mobility for a group of users (not only for a single user) based on users’ profiles (i.e., their preference in terms of road characteristics), state of road traffic and users behaviors on roads and spatial conceptual maps. Simulation results show that the proposed framework improves the network performance compared to existing schemes which propose aggregate mobility prediction bandwidth reservation models