Crowdsensed Mobile Data Analytics

Mobile devices, especially smartphones, are nowadays an essential part of everyday life. They are used worldwide and across all the demographic groups - they can be utilized for multiple functionalities, including but not limited to communications, game playing, social interactions, maps and navigation, leisure, work, and education. With a large on-device sensor base, mobile devices provide a rich source of data. Understanding how these devices are used help us also to increase the knowledge of people's everyday habits, needs, and rituals. Data collection and analysis can thus be utilized in different recommendation and feedback systems that further increase usage experience of the smart devices. Crowdsensed computing describes a paradigm where multiple autonomous devices are used together to collect large-scale data. In the case of smartphones, this kind of data can include running and installed applications, different system settings, such as network connection and screen brightness, and various subsystem variables, such as CPU and memory usage. In addition to the autonomous data collection, user questionnaires can be used to provide a wider view to the user community. To understand smartphone usage as a whole, different procedures are needed for cleaning missing and misleading values and preprocessing information from various sets of variables. Analyzing large-scale data sets - rising in size to terabytes - requires understanding of different Big Data management tools, distributed computing environments, and efficient algorithms to perform suitable data analysis and machine learning tasks. Together, these procedures and methodologies aim to provide actionable feedback, such as recommendations and visualizations, for the benefit of smartphone users, researchers, and application development. This thesis provides an approach to a large-scale crowdsensed mobile analytics. First, this thesis describes procedures for cleaning and preprocessing mobile data collected from real-life conditions, such as current system settings and running applications. It shows how interdependencies between different data items are important to consider when analyzing the smartphone system state as a whole. Second, this thesis provides suitable distributed machine learning and statistical analysis methods for analyzing large-scale mobile data. The algorithms, such as the decision tree-based classification and recommendation system, and information analysis methods presented in this thesis, are implemented in the distributed cloud-computing environment Apache Spark. Third, this thesis provides approaches to generate actionable feedback, such as energy consumption and application recommendations, which can be utilized in the mobile devices themselves or when understanding large crowds of smartphone users. The application areas especially covered in this thesis are smartphone energy consumption analysis in the case of system settings and subsystem variables, trend-based application recommendation system, and analysis of demographic, geographic, and cultural factors in smartphone usage.Erilaiset älylaitteet, erityisesti älypuhelimet, ovat muodostuneet oleelliseksi osaksi arkipäivän elektroniikan käyttöä. Älypuhelinten käyttö ei rajoitu perinteisiin kommunikaatiotoimintoihin, vaan niillä on voitu korvata monia muita laitteita ja palveluita, kuten pelit, kartat, sosiaalinen media, ja monet Internetin kautta saavutettavat palvelut. Koska laitteita on saatavilla monissa eri hintaluokissa, ne ovat pääsääntöisesti lähes kaikkien saatavilla, myös maailmanlaajuisesti. Aina mukana kannettavan älypuhelimen käyttö tuottaa runsaasti henkilökohtaista tietoa, mikä tarjoaa mahdollisuuden analysoida käyttäjien päivittäistä elämää. Henkilökohtaisia suosituksia hyödyntäen käyttäjille voidaan tarjota tietoa, joka auttaa parantamaan käyttäjäkokemusta ja laajentamaan älylaitteen käyttömahdollisuuksia. Joukkoistava havainnointi tarkoittaa tiedonkeräysmenetelmää, jossa useat erilliset laitteet osallistuvat automaattisesti suuremman datajoukon kartuttamiseen. Puhelinlaitteista tällaista kerättävää dataa ovat muun muassa tieto suorituksessa olevista ja asennetuista sovelluksista, erilaiset järjestelmäasetukset, kuten verkkoyhteystiedot ja näytön kirkkaus, sekä lukuisat muut järjestelmätason parametrit, kuten suorittimen ja muistin käyttö. Automaattista datan keräystä voidaan täydentää käyttäjille lähetettävillä kyselyillä. Älypuhelimista kerättävän datan analysoinnissa on monia vaiheita, jotka tekevät koko prosessista haasteellisen. Automaattisesti kerättyyn dataan päätyy helposti virheitä ja puutteita, joiden käsittely on hallittava. Datan määrä kasvaa helposti teratavuluokkaan, jolloin analysointiin tarvitaan suurten datajoukkojen käsittelyyn sopivia hajautettuja laskenta-alustoja ja algoritmeja. Hyödyllisten suositusten generoimiseksi puhelinlaitteisiin liittyvän analyysin halutaan usein olevan reaaliaikaista, mikä asettaa lisää haasteita analyysin suorituskyvylle. Tässä väitöskirjassa esitetään menetelmiä joukkoistetusti havainnoidun älypuhelindatan käsittelemiseksi tehokkaasti ja hyödyllistä informaatiota tuottaen. Väitöskirjan alussa kuvaillaan älypuhelindatan keräämistä prosessina, datan esikäsittelyä ja siistimistä hyödylliseen ja käsiteltävään muotoon. Väitöskirja esittää, että puhelinlaitteen tila tulisi ottaa huomioon kokonaisuutena, jossa useat eri tekijät, kuten samanaikaisesti suoritettavat sovellukset ja toisiinsa liittyvät järjestelmäasetukset vaikuttavat toisiinsa. Tämän jälkeen väitöskirjassa esitetään joitakin sopivia tilastollisen analyysin ja koneoppimisen menetelmiä, joita väitöskirjan tutkimuksessa on käytetty älypuhelindatan analysointiin. Kaikki näistä menetelmistä ovat suoritettavissa hajautetussa laskentaympäristössä ja toteutettu Apache Spark -järjestelmää käyttäen. Lopuksi väitöskirja näyttää, kuinka analyysiä sovelletaan käytännössä käyttäjille suunnatun palautteen ja suositusten generointiin. Päähuomion saavat puhelinlaitteiden energiankulutuksen analysointi, puhelinsovellusten trendien havainnointi, ja erilaisten kulttuuristen ja sosioekonomisten taustatekijöiden huomiointi mobiilikäyttöä tutkittaessa

Elastic Multi-resource Network Slicing: Can Protection Lead to Improved Performance?

In order to meet the performance/privacy requirements of future data-intensive mobile applications, e.g., self-driving cars, mobile data analytics, and AR/VR, service providers are expected to draw on shared storage/computation/connectivity resources at the network "edge". To be cost-effective, a key functional requirement for such infrastructure is enabling the sharing of heterogeneous resources amongst tenants/service providers supporting spatially varying and dynamic user demands. This paper proposes a resource allocation criterion, namely, Share Constrained Slicing (SCS), for slices allocated predefined shares of the network's resources, which extends the traditional alpha-fairness criterion, by striking a balance among inter- and intra-slice fairness vs. overall efficiency. We show that SCS has several desirable properties including slice-level protection, envyfreeness, and load driven elasticity. In practice, mobile users' dynamics could make the cost of implementing SCS high, so we discuss the feasibility of using a simpler (dynamically) weighted max-min as a surrogate resource allocation scheme. For a setting with stochastic loads and elastic user requirements, we establish a sufficient condition for the stability of the associated coupled network system. Finally, and perhaps surprisingly, we show via extensive simulations that while SCS (and/or the surrogate weighted max-min allocation) provides inter-slice protection, they can achieve improved job delay and/or perceived throughput, as compared to other weighted max-min based allocation schemes whose intra-slice weight allocation is not share-constrained, e.g., traditional max-min or discriminatory processor sharing

Mobile Big Data Analytics in Healthcare

Mobile and ubiquitous devices are everywhere around us generating considerable amount of data. The concept of mobile computing and analytics is expanding due to the fact that we are using mobile devices day in and out without even realizing it. These mobile devices use Wi-Fi, Bluetooth or mobile data to be intermittently connected to the world, generating, sending and receiving data on the move. Latest mobile applications incorporating graphics, video and audio are main causes of loading the mobile devices by consuming battery, memory and processing power. Mobile Big data analytics includes for instance, big health data, big location data, big social media data, and big heterogeneous data. Healthcare is undoubtedly one of the most data-intensive industries nowadays and the challenge is not only in acquiring, storing, processing and accessing data, but also in engendering useful insights out of it. These insights generated from health data may reduce health monitoring cost, enrich disease diagnosis, therapy, and care and even lead to human lives saving. The challenge in mobile data and Big data analytics is how to meet the growing performance demands of these activities while minimizing mobile resource consumption. This thesis proposes a scalable architecture for mobile big data analytics implementing three new algorithms (i.e. Mobile resources optimization, Mobile analytics customization and Mobile offloading), for the effective usage of resources in performing mobile data analytics. Mobile resources optimization algorithm monitors the resources and switches off unused network connections and application services whenever resources are limited. However, analytics customization algorithm attempts to save energy by customizing the analytics process while implementing some data-aware techniques. Finally, mobile offloading algorithm decides on the fly whether to process data locally or delegate it to a Cloud back-end server. The ultimate goal of this research is to provide healthcare decision makers with the advancements in mobile Big data analytics and support them in handling large and heterogeneous health datasets effectively on the move

Latency Analysis of Coded Computation Schemes over Wireless Networks

Large-scale distributed computing systems face two major bottlenecks that limit their scalability: straggler delay caused by the variability of computation times at different worker nodes and communication bottlenecks caused by shuffling data across many nodes in the network. Recently, it has been shown that codes can provide significant gains in overcoming these bottlenecks. In particular, optimal coding schemes for minimizing latency in distributed computation of linear functions and mitigating the effect of stragglers was proposed for a wired network, where the workers can simultaneously transmit messages to a master node without interference. In this paper, we focus on the problem of coded computation over a wireless master-worker setup with straggling workers, where only one worker can transmit the result of its local computation back to the master at a time. We consider 3 asymptotic regimes (determined by how the communication and computation times are scaled with the number of workers) and precisely characterize the total run-time of the distributed algorithm and optimum coding strategy in each regime. In particular, for the regime of practical interest where the computation and communication times of the distributed computing algorithm are comparable, we show that the total run-time approaches a simple lower bound that decouples computation and communication, and demonstrate that coded schemes are $\Theta(\log(n))$ times faster than uncoded schemes

MOSDEN: A Scalable Mobile Collaborative Platform for Opportunistic Sensing Applications

Mobile smartphones along with embedded sensors have become an efficient enabler for various mobile applications including opportunistic sensing. The hi-tech advances in smartphones are opening up a world of possibilities. This paper proposes a mobile collaborative platform called MOSDEN that enables and supports opportunistic sensing at run time. MOSDEN captures and shares sensor data across multiple apps, smartphones and users. MOSDEN supports the emerging trend of separating sensors from application-specific processing, storing and sharing. MOSDEN promotes reuse and re-purposing of sensor data hence reducing the efforts in developing novel opportunistic sensing applications. MOSDEN has been implemented on Android-based smartphones and tablets. Experimental evaluations validate the scalability and energy efficiency of MOSDEN and its suitability towards real world applications. The results of evaluation and lessons learned are presented and discussed in this paper.Comment: Accepted to be published in Transactions on Collaborative Computing, 2014. arXiv admin note: substantial text overlap with arXiv:1310.405