Routine-based network deployment for data offloading in metropolitan area

International audienceThis paper tackles the WiFi hotspot deployment problem in a metropolitan area by leveraging mobile users' context, i.e., their trajectories and scenario interaction. The careful deployment of hotspots in such areas allow to maximize WiFi offloading, a viable solution to the recent boost up of mobile data consumption.Our proposed strategy considers the restrictions imposed by transportation modes to people trajectories and the space-time interaction between people and urban locations, key points for an efficient network planning. Using a real-life metropolitan trace, we show our strategy guarantees high coverage time with a small set of deployed hotspots

Routine-based Network Deployment

International audienceThe careful deployment of hotspots in metropolitan areas allow to maximize WiFi offloading, a viable solution to the recent boost up of mobile data consumption. Our proposed strategy considers routine characteristics present on people's daily trajectories, the space-time interaction between them urban locations, and their transportation modes. Using a real-life metropolitan trace, we show our routine-based strategy guarantees higher offload ratio than the current approach in the literature while using a realistic traffic model

De la Routine Humaine vers des Réseaux Mobiles Plus Efficaces

The proliferation of pervasive communication caused a recent boost up on the mobile data usage, which network operators are not always prepared for. The main origin of the mobile network demands are smartphone devices. From the network side those devices may be seen as villains for imposing an enormous traffic, but from the analytical point of view they provide today the best means of gathering users information about content consumption and mobility behavior on a large scale. Understanding users' mobility and network behavior is essential in the design of efficient communication systems. We are routinary beings. The routine cycles on our daily lives are an essential part of our interface with the world. Our habits define, for instance, where we are going Saturday night, or what is the typical website for the mornings of Monday. The repetitive behavior reflects on our mobility patterns and network activities. In this thesis we focus on metropolitan users generating traffic demands during their normal daily lives. We present a detailed study on both users' routinary mobility and routinary network behavior. As a study of case where such investigation can be useful, we propose a hotspot deployment strategy that takes into account the routine aspects of people's mobility.We first investigate urban mobility patterns. We analyze large-scale datasets of mobility in different cities of the world, namely Beijing, Tokyo, New York, Paris, San Francisco, London, Moscow and Mexico City. Our contribution is this area is two-fold. First, we show that there is a similarity on people's mobility behavior regardless the city. Second, we unveil three characteristics present on the mobility of typical urban population: repetitiveness, usage of shortest-paths, and confinement. Those characteristics undercover people's tendency to revisit a small portion of favorite venues using trajectories that are close to the shortest-path. Furthermore, people generally have their mobility restrict to a dozen of kilometers per day.We then investigate the users' traffic demands patterns. We analyze a large data set with 6.8 million subscribers. We have mainly two contributions in this aspect. First, a precise characterization of individual subscribers' traffic behavior clustered by their usage patterns. We see how the daily routine impacts on the network demands and the strong similarity between traffic on different days. Second, we provide a way for synthetically, still consistently, reproducing usage patterns of mobile subscribers. Synthetic traces offer positive implications for network planning and carry no privacy issues to subscribers as the original datasets.To assess the effectiveness of these findings on real-life scenario, we propose a hotspot deployment strategy that considers routine characteristics of mobility and traffic in order to improve mobile data offloading. Carefully deploying Wi-Fi hotspots can both be cheaper than upgrade the current cellular network structure and can concede significant improvement in the network capacity. Our approach increases the amount of offload when compared to other solution from the literature.L’omniprésence des communications a entraîné une récente augmentation des volumes de données mobiles, pour laquelle les opérateurs n’étaient pas toujours préparés. Les smartphones sont les plus gros consommateurs de données mobiles. Ces appareils peuvent être considérés comme méchants à cause d’un tel traffic, mais d’un point de vue analytique ils fournissent, aujourd’hui un des meilleurs moyens afin de collecter les données sur le comportement de consommation et de mobilité de grande échelle. Comprendre le comportement des utilisateurs sur leur mobilité et leur connectivité est nécessaire à la création d’un système de communication effectifs. Nous sommes routiniers. Ces cycles routiniers sont une grande partie de nos interactions avec le monde. Par exemple, nos habitudes definissent ce que l’on va faire le samedi ou les sites que nous consultons le lundi matin. Ces comportements répétés reflètent nos déplacements et activités en ligne. Dans cette thèse, nous allons nous concentrer sur les demandes de traffic générées par les usagers métropolitains durant leurs activités quotidiennes. Nous présentons une étude détaillée des usagers selon les comportements routiniers de mobilité ou d’activité sur internet. Dans une étude de cas, ou cette enquête serait utile, nous proposons une stratégies de déploiement de points de accès qui prendra en compte les aspects routiniers de la mobilités des utilisateurs.Nous étudirons en premier lieu, les modèles de mobilité en milieu urbain. Nous analyserons les données de mobilité à grande échelle dans de grandes villes comme Beijing, Tokyo, New York, Paris, San Francisco, London, Moscow, Mexico City. Cette contribution se fait en deux étapes. Premièrement, nous observerons les similitudes des déplacements peu importe la ville concernée. Ensuite, nous mettrons en évidence trois caractéristiques présentes dans les déplacements d’une population urbaine typique: Répétivité, utilisation de raccourcis, confinement. Ces caractéristiques sont dues à la tendance qu’ont les personnes à revisiter les même rues en utilisant les trajectoires proches du chemin le plus court. D’ailleurs, les personnes ont une mobilité quotidienne inférieure à dix kilomètres par jour.Nous avons ensuite étudié les modèles de demandes de traffic en utilisant une base de données comprenant les données de 6.8 millions d’utilisateurs. Pour cela nous avons principalement deux contributions. Premièrement, une caractérisation précise des comportements de consommation des utilisateurs agrégés par modèle. Nous pouvons voir comment les routines quotidiennes impactent nos demandes de connections et la similarité de ce traffic en fonction des jours. En suite, nous fournirons un moyen de reproduire artificiellement mais avec cohérence les modèles des utilisateurs de données mobiles. Ces données synthétisées ont l’avantage de permettre la planification du réseau sans information sur la vie privées de utilisateurs comme les bases de données d’origine.Afin d’évaluer l’efficacité de ces informations dans un scénario grandeur nature, nous proposerons une stratégie de deploiement de points de accès qui prend en compte les caractéristiques routinières en terme de déplacement et de demande de trafic dans le but d’améliorer la décharge de données mobile. Déployer correctement des points de accès WiFi peut être moins cher que d’améliorer l’infrastructure de réseaux mobiles, et peut permettre d’améliorer considérablement la capacité du réseau. Notre approche améliore l’évacuation de trafic comparée aux autres solutions disponibles dans la littérature

IoT-Enabled Smart Healthcare Infrastructure Maximises Energy Efficiency

Advancements in IoT-based applications have become the cutting-edge technology among researchers due to the wide availability of the Internet. In order to make the application more user-friendly, Android-based and Web-based technologies have become increasingly important in this cutting-edge technology. Smart cities, Internet of Things(IoT), Smart health care systems are the technology of the future. A combination of numerous systems focusing on monitoring different components of the smart city (such as water, e-health, gas,  power monitoring and emergency scenario detection) can be used to make the city more sustainable and secure. In smart cities, energy consumption is particularly important for e-health. An optimization approach is provided in this paper to reduce total network energy usage. When compared to previous methods, the overall performance has improved by 57.89%

A Vision and Framework for the High Altitude Platform Station (HAPS) Networks of the Future

A High Altitude Platform Station (HAPS) is a network node that operates in the stratosphere at an of altitude around 20 km and is instrumental for providing communication services. Precipitated by technological innovations in the areas of autonomous avionics, array antennas, solar panel efficiency levels, and battery energy densities, and fueled by flourishing industry ecosystems, the HAPS has emerged as an indispensable component of next-generations of wireless networks. In this article, we provide a vision and framework for the HAPS networks of the future supported by a comprehensive and state-of-the-art literature review. We highlight the unrealized potential of HAPS systems and elaborate on their unique ability to serve metropolitan areas. The latest advancements and promising technologies in the HAPS energy and payload systems are discussed. The integration of the emerging Reconfigurable Smart Surface (RSS) technology in the communications payload of HAPS systems for providing a cost-effective deployment is proposed. A detailed overview of the radio resource management in HAPS systems is presented along with synergistic physical layer techniques, including Faster-Than-Nyquist (FTN) signaling. Numerous aspects of handoff management in HAPS systems are described. The notable contributions of Artificial Intelligence (AI) in HAPS, including machine learning in the design, topology management, handoff, and resource allocation aspects are emphasized. The extensive overview of the literature we provide is crucial for substantiating our vision that depicts the expected deployment opportunities and challenges in the next 10 years (next-generation networks), as well as in the subsequent 10 years (next-next-generation networks).Comment: To appear in IEEE Communications Surveys & Tutorial

Exploring traffic and QoS management mechanisms to support mobile cloud computing using service localisation in heterogeneous environments

In recent years, mobile devices have evolved to support an amalgam of multimedia applications and content. However, the small size of these devices poses a limit the amount of local computing resources. The emergence of Cloud technology has set the ground for an era of task offloading for mobile devices and we are now seeing the deployment of applications that make more extensive use of Cloud processing as a means of augmenting the capabilities of mobiles. Mobile Cloud Computing is the term used to describe the convergence of these technologies towards applications and mechanisms that offload tasks from mobile devices to the Cloud. In order for mobile devices to access Cloud resources and successfully offload tasks there, a solution for constant and reliable connectivity is required. The proliferation of wireless technology ensures that networks are available almost everywhere in an urban environment and mobile devices can stay connected to a network at all times. However, user mobility is often the cause of intermittent connectivity that affects the performance of applications and ultimately degrades the user experience. 5th Generation Networks are introducing mechanisms that enable constant and reliable connectivity through seamless handovers between networks and provide the foundation for a tighter coupling between Cloud resources and mobiles. This convergence of technologies creates new challenges in the areas of traffic management and QoS provisioning. The constant connectivity to and reliance of mobile devices on Cloud resources have the potential of creating large traffic flows between networks. Furthermore, depending on the type of application generating the traffic flow, very strict QoS may be required from the networks as suboptimal performance may severely degrade an application’s functionality. In this thesis, I propose a new service delivery framework, centred on the convergence of Mobile Cloud Computing and 5G networks for the purpose of optimising service delivery in a mobile environment. The framework is used as a guideline for identifying different aspects of service delivery in a mobile environment and for providing a path for future research in this field. The focus of the thesis is placed on the service delivery mechanisms that are responsible for optimising the QoS and managing network traffic. I present a solution for managing traffic through dynamic service localisation according to user mobility and device connectivity. I implement a prototype of the solution in a virtualised environment as a proof of concept and demonstrate the functionality and results gathered from experimentation. Finally, I present a new approach to modelling network performance by taking into account user mobility. The model considers the overall performance of a persistent connection as the mobile node switches between different networks. Results from the model can be used to determine which networks will negatively affect application performance and what impact they will have for the duration of the user's movement. The proposed model is evaluated using an analytical approac

From Routine to Network Deployment for Data Offloading in Metropolitan Areas

International audienceThis paper tackles the WiFi hotspot deployment problem in a metropolitan area by leveraging mobile users' context and content, i.e., their trajectories, scenario interactions, and traffic demands. The careful deployment of hotspots in such areas allow to maximize WiFi offloading, a viable solution to the recent boost up of mobile data consumption. Our proposed strategy considers the restrictions imposed by transportation modes to people trajectories and the space-time interaction between people and urban locations, key points for an efficient network planning. Using a real-life metropolitan trace, we show our routine-based strategy guarantees higher offload ratio than the current approach in the literature while using a realistic traffic model.Ce document aborde le problème de déploiement des hotspots WiFi dans une région métropolitaine en s'appuyant sur le contexte et le contenu des utilisateurs mobiles, c'est-à-dire, leurs trajectoires, leurs interactions avec l' environnement, et leurs demande de trafic. Le déploiement planifié de hotspots permet de maximiser le déchargement de données WiFi, une solution viable à la récente augmentation de trafic de données mobiles. Notre stratégie proposée tient compte des restrictions imposées par les modes de transport aux trajectoires de personnes et de l'interaction espace-temps entre les personnes et les zones urbaines, les deux points clés pour une planification efficace du réseau. En utilisant une trace réaliste métropolitaine, nous montrons que notre stratégie garantie un plus haut taux de déchargement de données par rapport à l'approche actuelle dans la littérature, tout en utilisant un modèle de trafic réaliste

Tactful Networking: Humans in the Communication Loop

International audienceThis survey discusses the human-perspective into networking through the Tactful Networking paradigm, whose goal is to add perceptive senses to the network by assigning it with human-like capabilities of observation, interpretation, and reaction to daily-life features and associated entities. To achieve this, knowledge extracted from inherent human behavior in terms of routines, personality, interactions, and others is leveraged, empowering the learning and prediction of user needs to improve QoE and system performance while respecting privacy and fostering new applications and services. Tactful Networking groups solutions from literature and innovative interdisciplinary human aspects studied in other areas. The paradigm is motivated by mobile devices' pervasiveness and increasing presence as a sensor in our daily social activities. With the human element in the foreground, it is essential: (i) to center big data analytics around individuals; (ii) to create suitable incentive mechanisms for user participation; (iii) to design and evaluate both humanaware and system-aware networking solutions; and (iv) to apply prior and innovative techniques to deal with human-behavior sensing and learning. This survey reviews the human aspect in networking solutions through over a decade, followed by discussing the tactful networking impact through literature in behavior analysis and representative examples. This paper also discusses a framework comprising data management, analytics, and privacy for enhancing human raw-data to assist Tactful Networking solutions. Finally, challenges and opportunities for future research are presented

Review and analysis of networking challenges in cloud computing

Cloud Computing offers virtualized computing, storage, and networking resources, over the Internet, to organizations and individual users in a completely dynamic way. These cloud resources are cheaper, easier to manage, and more elastic than sets of local, physical, ones. This encourages customers to outsource their applications and services to the cloud. The migration of both data and applications outside the administrative domain of customers into a shared environment imposes transversal, functional problems across distinct platforms and technologies. This article provides a contemporary discussion of the most relevant functional problems associated with the current evolution of Cloud Computing, mainly from the network perspective. The paper also gives a concise description of Cloud Computing concepts and technologies. It starts with a brief history about cloud computing, tracing its roots. Then, architectural models of cloud services are described, and the most relevant products for Cloud Computing are briefly discussed along with a comprehensive literature review. The paper highlights and analyzes the most pertinent and practical network issues of relevance to the provision of high-assurance cloud services through the Internet, including security. Finally, trends and future research directions are also presented

Performance Enhancing of Heterogeneous Network through Optimisation and Machine Learning Techniques

In the last two decades, by the benefit of advanced wireless technology, growing data service cause the explosive traffic demand, and it brings many new challenges to the network operators. In order to match the growing traffic demand, operators shall deploy new base stations to increase the total cellular network capacity. Meanwhile, a new type of low-power base stations are frequently deployed within the network, providing extra access points to subscribers. However, even the new base station can be operated in low power, the total network energy consumption is still increased proportional to the total number of base station, and considerable network energy consumption will become one of the main issues to the network operators. The way of reducing network energy consumption become crucial, especially in 5G when multiple antennas are deployed within one site. However, the base station cannot be always operated in low power because it will damage the network performance, and power can be only reduced in light-traffic period. Therefore, the way of balancing traffic demand and energy consumption will be come the main investigation direction in this thesis, and how to link the operated power of base station to the current traffic demand is investigated. In this thesis, algorithms and optimisations are utilised to reduce the network energy consumption and improve the network performance. To reduce the energy consumption in light-traffic period, base stations switch-off strategy is proposed in the first chapter. However, the network performance should be carefully estimated before the switch-off strategy is applied. The NP-hard energy efficiency optimisation problem is summarised, and it proposes the method that some of the base stations can be grouped together due to the limited interference from other Pico cells, reducing the complexity of the optimisation problem. Meanwhile, simulated annealing is proposed to obtain the optimal base stations combination to achieve optimal energy efficiency. By the optimisation algorithm, it can obtain the optimal PCs combination without scarifying the overall network throughput. The simulation results show that not only the energy consumption can be reduced but also the significant energy efficiency improvement can achieve by the switched-off strategy. The average energy efficiency improvement over thirty simulation is 17.06%. The second chapter will tackle the issue of how to raise the power of base stations after they are switched off. These base stations shall back to regular power level to prepare the incoming traffic. However, not all base stations shall be back to normal power due to the uneven traffic distribution. By analysing the information within the collected subscriber data, such as moving speed, direction, downlink and time, Naive Bayesian classifier will be utilised to obtain the user movement pattern and predict the future traffic distribution, and the system can know which base station will become the user's destination. The load adaptive power control is utilised to inform the corresponding base stations to increased the transmission power, base stations can prepare for the incoming traffic, avoiding the performance degradation. The simulation results show that the machine learning can accurately predict the destination of the subscriber, achieving average 90.8% accuracy among thirty simulation. The network energy can be saved without damage the network performance after the load adaptive function is applied, the average energy efficiency improvement among three scenarios is 4.3%, the improvement is significant. The significant improvement prove that the proposed machine learning and load adaptive power modification method can help the network reduce the energy consumption. In the last chapter, it will utilise cell range expansion to tackle the resources issue in cooperative base station in joint transmission, improving downlink performance and tackle the cell-edge problem. Due to the uneven traffic distribution, it will cause the insufficient resources problem in cooperative base station in joint transmission, and the system throughput will be influenced if cooperative base station executes joint transmission in high load. Therefore, the cell range expansion is utilised to solve the problem of unbalanced traffic between base station tier, and flow water algorithm is utilised to tackle the resources distribution issue during the traffic offloading. The simulation shows the NP-hard problem can be sufficiently solved by the flow water algorithm, and the downlink throughput gain can be obtained, it can obtain 26% gain in the M-P scenario, and the gain in P-M scenario is 24%. The result prove that the proposed method can provide significant gain to the subscriber without losing any total network throughput