Enhanced stochastic mobility prediction with multi-output Gaussian processes

Outdoor robots such as planetary rovers must be able to navigate safely and reliably in order to successfully perform missions in remote or hostile environments. Mobility prediction is critical to achieving this goal due to the inherent control uncertainty faced by robots traversing natural terrain. We propose a novel algorithm for stochastic mobility prediction based on multi-output Gaussian process regression. Our algorithm considers the correlation between heading and distance uncertainty and provides a predictive model that can easily be exploited by motion planning algorithms. We evaluate our method experimentally and report results from over 30 trials in a Mars-analogue environment that demonstrate the effectiveness of our method and illustrate the importance of mobility prediction in navigating challenging terrain.

Learned stochastic mobility prediction for planning with control uncertainty on unstructured terrain

Motion planning for planetary rovers must consider control uncertainty in order to maintain the safety of the platform during navigation. Modelling such control uncertainty is difficult due to the complex interaction between the platform and its environment. In this paper, we propose a motion planning approach whereby the outcome of control actions is learned from experience and represented statistically using a Gaussian process regression model. This mobility prediction model is trained using sample executions of motion primitives on representative terrain, and predicts the future outcome of control actions on similar terrain. Using Gaussian process regression allows us to exploit its inherent measure of prediction uncertainty in planning. We integrate mobility prediction into a Markov decision process framework and use dynamic programming to construct a control policy for navigation to a goal region in a terrain map built using an on-board depth sensor. We consider both rigid terrain, consisting of uneven ground, small rocks, and non-traversable rocks, and also deformable terrain. We introduce two methods for training the mobility prediction model from either proprioceptive or exteroceptive observations, and report results from nearly 300 experimental trials using a planetary rover platform in a Mars-analogue environment. Our results validate the approach and demonstrate the value of planning under uncertainty for safe and reliable navigation.

User mobility prediction in hybrid and ad hoc wireless networks

Next Generation Networks will employ hybrid network architectures using both cellular and ad hoc networking concepts. The vision of real-time
multimedia services requires that mobility management be addressed in a proactive manner. If the user movements can be predicted accurately in a
hybrid network environment then handoff/cluster change, resource reservation and context transfer procedures can be efficiently completed as required by node mobility. In this work we propose a sectorized ad hoc mobility prediction scheme for cluster change prediction. Simulation study of the scheme shows it to be efficient in terms of prediction accuracy and prediction related control overhead despite randomness in user movement.

Mobility prediction based routing for minimizing control overhead in mobile ad hoc networks

Routing in ad hoc networks faces significant challenges due to node mobility and dynamic network topology. In this work we propose the use of mobility prediction to reduce the search space required for route discovery. A method of mobility prediction making use of a sectorized cluster structure is described with the proposal of the Prediction based Location Aided routing (P-LAR) protocol. Simulation study and analytical results of the of P-LAR find it to offer considerable saving in the amount of routing traffic generated during the route discovery phase.

A review of current mobility prediction techniques for ad hoc networks

Next Generation Networks (3G & beyond) will support real-time multimedia applications through traditional wide-area networking concepts as well as hot-spot (WLAN) and ad hoc networking concepts. In order to fulfil the vision of Next Generation Networks a method of maintaining a real-time flow despite frequent topology changes and irregularity in user movement is required. Mobility Prediction has been identified as having applications in the areas of link availability estimation and pro-active routing in ad hoc networks. In this work we present an overview of current mobility prediction schemes that have been proposed. Simulation results are also presented.

Mobility prediction for seamless mobility in wireless networks

One of the requirements for seamless mobility is efficient resource reservation and context transfer procedures during handoff. If context transfer and resource reservation can occur prior to handoff continuation of the same level of service as at the previous connection point is possible. Resource reservation is required to be non-aggressive for optimal use of limited bandwidth and a low call-blocking probability. In this work we present a method of mobility prediction that can aid in achieving seamless mobility. In order to optimise the efficiency of a resource reservation algorithm we believe accurate prediction of the future movements of the user is required.

A review on current work in mobility prediction for wireless networks

The vision of next generation networks (4G & beyond) is to make possible seamless mobility across heterogeneous networks and to support real-time multimedia services. This would require intra/inter-domain handovers and service reconfiguration procedures to be completed with minimum latency. Mobility Prediction has been identified as a key abettor to this goal. The increasing ease of coupling between the mobile user and the network requires that a mobility prediction scheme that is to be deployed in next generation networks be capable of high levels of prediction accuracy despite randomness in user movement. In this work we have presented a survey on mobility prediction schemes that have been proposed for wireless networks. The results of our simulation study focused on the robustness of different schemes to randomness in user movement are also presented.

Route maintenance using mobility prediction for mobile ad hoc networks

Next generation networks (3G & beyond) will support real-time multimedia applications through traditional wide-area networking concepts as well as hot-spot (WLAN) and ad hoc networking concepts. In order to fulfill the vision of next generation networks a method of maintaining a real-time flow despite frequent topology changes and irregularity in user movement is required. Mobility prediction has been identified as having applications in the areas of link availability estimation and pro-active routing in ad hoc networks. In this work we present the mobility prediction based algorithm for route maintenance in mobile ad hoc networks. Simulation study of the algorithm proves it to offer significant benefits to dynamic source routing (DSR)

A Demonstration of Mobility Prediction as a Service in Cloudified LTE Networks

Location prediction has attracted a significant amount of research effort. Being able to predict users’ movement benefits a wide range of communication systems, including location-based service/applications, mobile access control, mobile QoS provision, and resource management for mobile computation and storage management. In this demo, we present MOBaaS, which is a cloudified Mobility and Bandwidth prediction services that can be instantiated, deployed, and disposed on-demand. Mobility prediction of MOBaaS provides location predictions of a single/group user equipments (UEs) in a future moment. This information can be used for self-adaptation procedures and optimal network function configuration during run-time operations. We demonstrate an example of real-time mobility prediction service deployment running on OpenStack platform, and the potential benefits it bring to other invoking services.

Edge caching with mobility prediction in virtualized LTE mobile networks

Abstract Mobile Edge Computing enables the deployment of services, applications, content storage and processing in close proximity to mobile end users. This highly distributed computing environment can be used to provide ultra-low latency, precise positional awareness and agile applications, which could significantly improve user experience. In order to achieve this, it is necessary to consider next-generation paradigms such as Information-Centric Networking and Cloud Computing, integrated with the upcoming 5th Generation networking access. A cohesive end-to-end architecture is proposed, fully exploiting Information-Centric Networking together with the Mobile Follow-Me Cloud approach, for enhancing the migration of content-caches located at the edge of cloudified mobile networks. The chosen content-relocation algorithm attains content-availability improvements of up to 500 when a mobile user performs a request and compared against other existing solutions. The performed evaluation considers a realistic core-network, with functional and non-functional measurements, including the deployment of the entire system, computation and allocation/migration of resources. The achieved results reveal that the proposed architecture is beneficial not only from the users’ perspective but also from the providers point-of-view, which may be able to optimize their resources and reach significant bandwidth savings.

A probabilistic approach to user mobility prediction for wireless services.

Mobile and wireless networks have long exploited mobility predictions, focused on predicting the future location of given users, to perform more efficient network resource management. In this paper, we present a new approach in which we provide predictions as a probability distribution of the likelihood of moving to a set of future locations. This approach provides wireless services a greater amount of knowledge and enables them to perform more effectively. We present a framework for the evaluation of this new type of predictor, and develop 2 new predictors, HEM and G-Stat. We evaluate our predictors accuracy in predicting future cells for mobile users, using two large geolocation data sets, from MDC [11], [12] and Crawdad [13]. We show that our predictors can successfully predict with as low as an average 2.2% inaccuracy in certain scenarios.

Prediction based location aided routing for mobile ad hoc networks

Routing in ad hoc networks faces significant challenges due to node mobility and dynamic network topology. In this work we propose the use of mobility prediction to reduce the search space required for route discovery. A method of mobility prediction making use of a sectorized cluster structure is described with the proposal of the Prediction based Location Aided Routing (P-LAR) protocol. Simulation study and analytical results of P-LAR find it to offer considerable saving in the amount of routing traffic generated during the route discovery phase.

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.

Mobility modelling and trajectory prediction for cellular networks with mobile base stations

This paper provides mobility estimation and prediction for a variant of GSM network which resembles an adhoc wireless mobile network where base stations and users are both mobile. We propose using Robust Extended Kalman Filter (REKF)as a location heading altitude estimator of mobile user for next node (mobile-base station)in order to improve the connection reliability and bandwidth efficiency of the underlying system. Through analysis we demonstrate that our algorithm can successfully track the mobile users with less system complexity as it requires either one or two closest mobile-basestation measurements. Further, the technique is robust against system uncertainties due to inherent deterministic nature in the mobility model. Through simulation, we show the accuracy and simplicity in implementation of our prediction algorithm.

Mobility and bandwidth prediction in virtualized LTE systems: Architecture and challenges

Long Term Evolution (LTE) represents the fourth generation (4G) technology which is capable of providing high data rates as well as support of high speed mobility. The EU FP7 Mobile Cloud Networking (MCN) project integrates the use of cloud computing concepts in LTE mobile networks in order to increase LTE's performance. In this way a shared distributed virtualized LTE mobile network is built that can optimize the utilization of virtualized computing, storage and network resources and minimize communication delays. Two important features that can be used in such a virtualized system to improve its performance are the user mobility and bandwidth prediction. This paper introduces the architecture and challenges that are associated with user mobility and bandwidth prediction approaches in virtualized LTE systems.