Landing AI on Networks: An equipment vendor viewpoint on Autonomous Driving Networks

The tremendous achievements of Artificial Intelligence (AI) in computer vision, natural language processing, games and robotics, has extended the reach of the AI hype to other fields: in telecommunication networks, the long term vision is to let AI fully manage, and autonomously drive, all aspects of network operation. In this industry vision paper, we discuss challenges and opportunities of Autonomous Driving Network (ADN) driven by AI technologies. To understand how AI can be successfully landed in current and future networks, we start by outlining challenges that are specific to the networking domain, putting them in perspective with advances that AI has achieved in other fields. We then present a system view, clarifying how AI can be fitted in the network architecture. We finally discuss current achievements as well as future promises of AI in networks, mentioning a roadmap to avoid bumps in the road that leads to true large-scale deployment of AI technologies in networks

A Cost-Effective Distributed Framework for Data Collection in Cloud-based Mobile Crowd Sensing Architectures

Mobile crowd sensing received significant attention in the recent years and has become a popular paradigm for sensing. It operates relying on the rich set of built-in sensors equipped in mobile devices, such as smartphones, tablets and wearable devices. To be effective, mobile crowd sensing systems require a large number of users to contribute data. While several studies focus on developing efficient incentive mechanisms to foster user participation, data collection policies still require investigation. In this paper, we propose a novel distributed and sustainable framework for gathering information in cloud-based mobile crowd sensing systems with opportunistic reporting. The proposed framework minimizes cost of both sensing and reporting, while maximizing the utility of data collection and, as a result, the quality of contributed information. Analytical and simulation results provide performance evaluation for the proposed framework by providing a fine-grained analysis of the energy consumed. The simulations, performed in a real urban environment and with a large number of participants, aim at verifying the performance and scalability of the proposed approach on a large scale under different user arrival patterns

Multi-objective Search-based Mobile Testing

Despite the tremendous popularity of mobile applications, mobile testing still relies heavily on manual testing. This thesis presents mobile test automation approaches based on multi-objective search. We introduce three approaches: Sapienz (for native Android app testing), Octopuz (for hybrid/web JavaScript app testing) and Polariz (for using crowdsourcing to support search-based mobile testing). These three approaches represent the primary scientific and technical contributions of the thesis. Since crowdsourcing is, itself, an emerging research area, and less well understood than search-based software engineering, the thesis also provides the first comprehensive survey on the use of crowdsourcing in software testing (in particular) and in software engineering (more generally). This survey represents a secondary contribution. Sapienz is an approach to Android testing that uses multi-objective search-based testing to automatically explore and optimise test sequences, minimising their length, while simultaneously maximising their coverage and fault revelation. The results of empirical studies demonstrate that Sapienz significantly outperforms both the state-of-the-art technique Dynodroid and the widely-used tool, Android Monkey, on all three objectives. When applied to the top 1,000 Google Play apps, Sapienz found 558 unique, previously unknown crashes. Octopuz reuses the Sapienz multi-objective search approach for automated JavaScript testing, aiming to investigate whether it replicates the Sapienz’ success on JavaScript testing. Experimental results on 10 real-world JavaScript apps provide evidence that Octopuz significantly outperforms the state of the art (and current state of practice) in automated JavaScript testing. Polariz is an approach that combines human (crowd) intelligence with machine (computational search) intelligence for mobile testing. It uses a platform that enables crowdsourced mobile testing from any source of app, via any terminal client, and by any crowd of workers. It generates replicable test scripts based on manual test traces produced by the crowd workforce, and automatically extracts from these test traces, motif events that can be used to improve search-based mobile testing approaches such as Sapienz

A Hybrid SDN-based Architecture for Wireless Networks

With new possibilities brought by the Internet of Things (IoT) and edge computing, the traffic demand of wireless networks increases dramatically. A more sophisticated network management framework is required to handle the flow routing and resource allocation for different users and services. By separating the network control and data planes, Software-defined Networking (SDN) brings flexible and programmable network control, which is considered as an appropriate solution in this scenario.Although SDN has been applied in traditional networks such as data centers with great successes, several unique challenges exist in the wireless environment. Compared with wired networks, wireless links have limited capacity. The high mobility of IoT and edge devices also leads to network topology changes and unstable link qualities. Such factors restrain the scalability and robustness of an SDN control plane. In addition, the coexistence of heterogeneous wireless and IoT protocols with distinct representations of network resources making it difficult to process traffic with state-of-the-art SDN standards such as OpenFlow. In this dissertation, we design a novel architecture for the wireless network management. We propose multiple techniques to better adopt SDN to relevant scenarios. First, while maintaining the centralized control plane logically, we deploy multiple SDN controller instances to ensure their scalability and robustness. We propose algorithms to determine the controllers\u27 locations and synchronization rates that minimize the communication costs. Then, we consider handling heterogeneous protocols in Radio Access Networks (RANs). We design a network slicing orchestrator enabling allocating resources across different RANs controlled by SDN, including LTE and Wi-Fi. Finally, we combine the centralized controller with local intelligence, including deploying another SDN control plane in edge devices locally, and offloading network functions to a programmable data plane. In all these approaches, we evaluate our solutions with both large-scale emulations and prototypes implemented in real devices, demonstrating the improvements in multiple performance metrics compared with state-of-the-art methods

High-Performance Modelling and Simulation for Big Data Applications

This open access book was prepared as a Final Publication of the COST Action IC1406 “High-Performance Modelling and Simulation for Big Data Applications (cHiPSet)“ project. Long considered important pillars of the scientific method, Modelling and Simulation have evolved from traditional discrete numerical methods to complex data-intensive continuous analytical optimisations. Resolution, scale, and accuracy have become essential to predict and analyse natural and complex systems in science and engineering. When their level of abstraction raises to have a better discernment of the domain at hand, their representation gets increasingly demanding for computational and data resources. On the other hand, High Performance Computing typically entails the effective use of parallel and distributed processing units coupled with efficient storage, communication and visualisation systems to underpin complex data-intensive applications in distinct scientific and technical domains. It is then arguably required to have a seamless interaction of High Performance Computing with Modelling and Simulation in order to store, compute, analyse, and visualise large data sets in science and engineering. Funded by the European Commission, cHiPSet has provided a dynamic trans-European forum for their members and distinguished guests to openly discuss novel perspectives and topics of interests for these two communities. This cHiPSet compendium presents a set of selected case studies related to healthcare, biological data, computational advertising, multimedia, finance, bioinformatics, and telecommunications

Automating Software Development for Mobile Computing Platforms

Mobile devices such as smartphones and tablets have become ubiquitous in today\u27s computing landscape. These devices have ushered in entirely new populations of users, and mobile operating systems are now outpacing more traditional desktop systems in terms of market share. The applications that run on these mobile devices (often referred to as apps ) have become a primary means of computing for millions of users and, as such, have garnered immense developer interest. These apps allow for unique, personal software experiences through touch-based UIs and a complex assortment of sensors. However, designing and implementing high quality mobile apps can be a difficult process. This is primarily due to challenges unique to mobile development including change-prone APIs and platform fragmentation, just to name a few. in this dissertation we develop techniques that aid developers in overcoming these challenges by automating and improving current software design and testing practices for mobile apps. More specifically, we first introduce a technique, called Gvt, that improves the quality of graphical user interfaces (GUIs) for mobile apps by automatically detecting instances where a GUI was not implemented to its intended specifications. Gvt does this by constructing hierarchal models of mobile GUIs from metadata associated with both graphical mock-ups (i.e., created by designers using photo-editing software) and running instances of the GUI from the corresponding implementation. Second, we develop an approach that completely automates prototyping of GUIs for mobile apps. This approach, called ReDraw, is able to transform an image of a mobile app GUI into runnable code by detecting discrete GUI-components using computer vision techniques, classifying these components into proper functional categories (e.g., button, dropdown menu) using a Convolutional Neural Network (CNN), and assembling these components into realistic code. Finally, we design a novel approach for automated testing of mobile apps, called CrashScope, that explores a given android app using systematic input generation with the intrinsic goal of triggering crashes. The GUI-based input generation engine is driven by a combination of static and dynamic analyses that create a model of an app\u27s GUI and targets common, empirically derived root causes of crashes in android apps. We illustrate that the techniques presented in this dissertation represent significant advancements in mobile development processes through a series of empirical investigations, user studies, and industrial case studies that demonstrate the effectiveness of these approaches and the benefit they provide developers

High-Performance Modelling and Simulation for Big Data Applications

This open access book was prepared as a Final Publication of the COST Action IC1406 “High-Performance Modelling and Simulation for Big Data Applications (cHiPSet)“ project. Long considered important pillars of the scientific method, Modelling and Simulation have evolved from traditional discrete numerical methods to complex data-intensive continuous analytical optimisations. Resolution, scale, and accuracy have become essential to predict and analyse natural and complex systems in science and engineering. When their level of abstraction raises to have a better discernment of the domain at hand, their representation gets increasingly demanding for computational and data resources. On the other hand, High Performance Computing typically entails the effective use of parallel and distributed processing units coupled with efficient storage, communication and visualisation systems to underpin complex data-intensive applications in distinct scientific and technical domains. It is then arguably required to have a seamless interaction of High Performance Computing with Modelling and Simulation in order to store, compute, analyse, and visualise large data sets in science and engineering. Funded by the European Commission, cHiPSet has provided a dynamic trans-European forum for their members and distinguished guests to openly discuss novel perspectives and topics of interests for these two communities. This cHiPSet compendium presents a set of selected case studies related to healthcare, biological data, computational advertising, multimedia, finance, bioinformatics, and telecommunications

Mining Behavioral Patterns from Mobile Big Data

Mobile devices connected to the Internet are a ubiquitous platform that can easily record a large amount of data describing human behavior. Specifically, the data collected from mobile devices --- referred to as mobile big data reveal important social and economic information. Therefore, analyzing mobile big data is valuable for several stakeholders, ranging from smartphone manufacturers to network operators and app developers. This thesis aims to discover and understand behavioral patterns from mobile big data based on large real-world datasets. Specifically, this thesis reveals patterns from three domains: people, time, and location. First, we explore mobile big data from the people domain and propose a framework to discover users' daily activity patterns from their mobile app usage. By applying the framework to a real-world dataset consisting of 653,092 users, we successfully extract five common patterns among millions of people, including commuting, pervasive socializing, nightly entertainment, afternoon reading, and nightly socializing. Second, still from the people domain, we derive group health conditions by using their smartphone usage data. In particular, we collect mobile usage records of 452 users in North America. We then demonstrate the potential for inferring group health conditions (i.e., COVID-19 outbreak stages) by leveraging less privacy-sensitive smartphone data, including CPU usage, memory usage, and network connections. Third, we mine the behavior patterns from the time domain. We reveal the evolution of mobile app usage by conducting a longitudinal study on 1,465 users from 2012 to 2017. The results show that users' app usage significantly changes over time. However, the evolution in app-category usage and individual app usage are different in terms of popularity distribution, usage diversity, and correlations. Last, with respect to the location domain, we leverage city-scale spatiotemporal mobile app usage data to reveal urban land usage patterns. We prove the strong correlation between mobile usage behavior and location features, which brings a new angle to urban analytics.Internetiin kytketyt mobiililaitteet ovat kaikkialla läsnä oleva alusta, joka voi helposti tallentaa suuren määrän tietoja, jotka kuvaavat ihmisen käyttäytymistä. Erityisesti mobiililaitteista kerätyt tiedot, joita kutsutaan mobiiliksi massadataksi (big data), paljastavat tärkeitä sosiaalisia ja taloudellisia tietoja. Siksi mobiilin massadatan analysointi on arvokasta useille sidosryhmille älypuhelinvalmistajista verkko-operaattoreihin ja sovelluskehittäjiin. Tämän väitöskirjan tavoitteena on löytää ja ymmärtää käyttäytymismalleja mobiilista massadatasta, joka perustuu suuriin reaalimaailman tietojoukkoihin. Erityisesti tämä väitöskirja tuottaa malleja kolmelta eri alueelta: ihmisiin, aikaan ja sijaintiin liittyen. Ensinnäkin tutkimme mobiilia massadataa ihmisiin liittyen ja ehdotamme viitekehystä, jonka avulla voidaan löytää käyttäjien päivittäisiä toimintamalleja heidän mobiilisovellustensa käytön perusteella. Soveltamalla tätä viitekehystä tosielämän tietojoukkoon, joka koostuu 653 092 käyttäjästä, löysimme onnistuneesti viisi yleistä mallia miljoonien ihmisten tiedoista, joihin kuuluivat mm. tiedot työmatkoista, sosiaalisista kontakteista, yöllisestä viihteestä, iltapäivän lukemisesta ja yöllisestä seurustelusta. Toiseksi, edelleen ihmisiin liittyen, johdamme tietoja ryhmien terveysolosuhteista käyttämällä heidän älypuhelintensa käyttötietoja. Keräsimme erityisesti 452 käyttäjän mobiilikäyttötietoja Pohjois-Amerikassa. Sitten osoitamme, että on mahdollista päätellä ryhmän terveysolosuhteet (eli COVID-19-epidemiavaiheet) hyödyntämällä vähemmän yksityisyyden kannalta arkoja älypuhelintietoja, mukaan lukien suorittimen käyttö, muistin käyttö ja verkkoyhteydet. Kolmanneksi louhimme käyttäytymismalleja aikaan liittyen. Paljastamme mobiilisovellusten käytön kehityksen tekemällä pitkittäistutkimuksen 1 465 käyttäjälle vuosina 2012–2017. Tulokset osoittavat, että käyttäjien sovellusten käyttö muuttuu merkittävästi ajan myötä. Sovellusluokan käytön ja yksittäisten sovellusten käytön kehitys on kuitenkin erilainen niiden suosion jakautumisen, käytön moninaisuuden ja korrelaatioiden suhteen. Lopuksi liittyen sijaintitietoihin hyödynnämme spatiotemporaalisten mobiilisovellusten käyttötietoja suurkaupunkitasolla paljastaaksemme kaupunkien maankäyttömallit. Todistamme vahvan korrelaation mobiililaitteiden käyttöön liittyvän käyttäytymisen ja sijaintiominaisuuksien välillä, mikä tuottaa uuden näkökulman kaupunkianalytiikkaan