Identifying Meaningful Places

Place identification refers to the process of analyzing sensor data in order to detect places, i.e., spatial areas that are linked with activities and associated with meanings. Place information can be used, e.g., to provide awareness cues in applications that support social interactions, to provide personalized and location-sensitive information to the user, and to support mobile user studies by providing cues about the situations the study participant has encountered. Regularities in human movement patterns make it possible to detect personally meaningful places by analyzing location traces of a user. This thesis focuses on providing system level support for place identification, as well as on algorithmic issues related to the place identification process. The move from location to place requires interactions between location sensing technologies (e.g., GPS or GSM positioning), algorithms that identify places from location data and applications and services that utilize place information. These interactions can be facilitated using a mobile platform, i.e., an application or framework that runs on a mobile phone. For the purposes of this thesis, mobile platforms automate data capture and processing and provide means for disseminating data to applications and other system components. The first contribution of the thesis is BeTelGeuse, a freely available, open source mobile platform that supports multiple runtime environments. The actual place identification process can be understood as a data analysis task where the goal is to analyze (location) measurements and to identify areas that are meaningful to the user. The second contribution of the thesis is the Dirichlet Process Clustering (DPCluster) algorithm, a novel place identification algorithm. The performance of the DPCluster algorithm is evaluated using twelve different datasets that have been collected by different users, at different locations and over different periods of time. As part of the evaluation we compare the DPCluster algorithm against other state-of-the-art place identification algorithms. The results indicate that the DPCluster algorithm provides improved generalization performance against spatial and temporal variations in location measurements.Paikkatietoiset sovellukset hyödyntävät paikkatietoa tarjotakseen hyödyllistä ja mielenkiintoista tietoa käyttäjille. Paikkatietoiset sovellukset pohjautuvat pääsääntöisesti koordinaattipohjaiseen paikkatietoon (esimerkiksi longitudi ja latitudi), vaikkakin ihmiset arkielämän tilanteissa kommunikoivat paikkatietoa käyttäen merkityksellisiä paikkanimiä (esimerkiksi kotona tai työpaikalla). Merkityksellisten paikkojen tunnistaminen hyödyntää säännönmukaisuuksia ihmisten arkielämässä tunnistaakseen paikkatiedosta alueita, jotka ovat käyttäjille henkilökohtaisesti mielenkiintoisia. Tunnistettuja merkityksellisiä paikkoja voidaan hyödyntää esimerkiksi tarjoamalla personoitua ja paikkariippuvaista tietoa käyttäjälle, tai mobiileissa sosiaalisen verkoston sovelluksissa tarjoamaan käyttäjän ystäville tietoa käyttäjän sen hetkisestä tilanteesta. Tietoa merkityksellisistä paikoista voidaan myös hyödyntää käyttäjätutkimuksessa tarkastelemaan missä käyttäjä on käyttänyt sovellusta. Väitöskirjatyössäni tarkastelen merkityksellisten paikkojen tunnistamiseen liittyviä haasteita järjestelmä- ja algoritmitasolla. Järjestelmätasolla merkityksellisten paikkojen hyödyntäminen vaatii yhteistoimintaa paikannusjärjestelmien (esimerkiksi GPS- tai GSM-pohjainen paikannus), merkityksellisten paikkojen tunnistamisalgoritmien ja sovellusten välillä. Eri järjestelmäkomponenttien välistä yhteistoimintaa voidaan helpottaa hyödyntämällä mobiilia sovellusalustaa, joka muun muassa tarjoaa toimintoja sensoridatan keräämisen sekä tiedon levittämisen helpottamiseksi. Väitöskirjan ensimmäisenä kontribuutiona esitellään BeTelGeuse, vapaasti saatavilla oleva vapaan lähdekoodin sovellusalusta, joka tukee useita eri käyttöjärjestelmiä ja sovellusympäristöjä. Merkityksellisten paikkojen tunnistamisprosessi voidaan tulkita tiedonlouhintaongelmana, jossa analysoidaan käyttäjän paikkatietoa ja pyritään tunnistamaan siitä alueita, jotka ovat käyttäjälle mielenkiintoisia. Väitöskirjan toisena kontribuutiona esitellään uusi merkityksellisten paikkojen tunnistamisalgoritmi, joka pohjautuu Dirichlet-prosessien sekoitemalleihin. Väitöskirjassa vertaillaan algoritmin yleistyvyyskykyä aikaisemmin esitettyihin merkityksellisten paikkojen tunnistamisalgoritmeihin. Tulokset osoittavat että uusi algoritmi pystyy paremmin tunnistamaan merkityksellisiä paikkoja ja yleistyy paremmin paikkatiedossa esiintyville variaatioille

AI on the Move From : From On-Device to On-Multi-Device

On-Device AI is an emerging paradigm that aims to make devices more intelligent, autonomous and proactive by equipping them with machine and deep learning routines for robust decision making and optimal execution in devices' operations. On-Device intelligence promises the possibility of computing huge amounts of data close to its source, e.g., sensor and multimedia data. By doing so, devices can complement their counterpart cloud services with more sophisticated functionality to provide better applications and services. However, increased computational capabilities of smart devices, wearables and IoT devices along with the emergence of services at the Edge of the network are driving the trend of migrating and distributing computation between devices. Indeed, devices can reduce the burden of executing resource intensive tasks via collaborations in the wild. While several work has shown the benefits of an opportunistic collaboration of a device with others, not much is known regarding how devices can be organized as a group as they move together. In this paper, we contribute by analyzing how dynamic group organization of devices can be utilized to distribute intelligence on the moving Edge. The key insight is that instead of On-Device solutions complementing with cloud, dynamic groups can be formed to complement each other in an On-Multi-Device manner. Thus, we highlight the challenges and opportunities from extending the scope of On-Device AI from an egocentric view to a collaborative, multi-device view.Peer reviewe

Ripe or Rotten? Low-Cost Produce Quality Estimation using Reflective Green Light Sensing

We develop an innovative low-cost approach for characterizing fresh produce by repurposing inexpensive commercial-off-the-shelf green light sensors for quality estimation. Our approach has been designed to support all stages of the supply chain while being inexpensive and easy to deploy. We validate our approach through extensive empirical benchmarks, showing that it can correctly distinguish organic produce from nonorganic items, establish unique fingerprints for different produce, and estimate the quality or ripeness of produce. Specifically, we demonstrate that changes in the reflected green light values correlate with the so-called transpiration coefficients of the produce. We also discuss the practicability of our approach and present application use cases that can benefit from our solution.Peer reviewe

Evaluation of Low-Cost Air Quality Sensor Calibration Models

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IoT Maps : Charting the Internet of Things

Internet of Things (IoT) devices are becoming increasingly ubiquitous in our everyday environments. While the number of devices and the degree of connectivity is growing, it is striking that as a society we are increasingly unaware of the locations and purposes of such devices. Indeed, much of the IoT technology being deployed is invisible and does not communicate its presence or purpose to the inhabitants of the spaces within which it is deployed. In this paper, we explore the potential benefits and challenges of constructing IoT maps that record the location of IoT devices. To illustrate the need for such maps, we draw on our experiences from multiple deployments of IoT systems.Peer reviewe

Smart Plants: Low-Cost Solution for Monitoring Indoor Environments

Humans tend to spend most of their life indoors, making the quality of indoor environments essential for human health and wellbeing. While several solutions for monitoring the indoor environment have been proposed, ranging from infrastructure-based monitoring solutions to cameras, these tend to require separate installation, making the sensors difficult to maintain and upgrade. In this article, we introduce the idea of using smart plants as an easy-to-deploy and affordable solution for monitoring the indoor environment. Plants are typically deployed close to humans and they increasingly are placed in containers that integrate sensors, such as soil moisture, temperature, humidity, and CO2 sensors. We demonstrate how these sensors can be used as an alternative technology for monitoring-and enriching-indoor spaces without needing to install proprietary sensors or other technology. Specifically, we show how smart plants can be used to estimate overall CO2 accumulation, occupancy information, and whether people use protective face masks or not. We also establish a research roadmap for the use of smart plants to monitor indoor environments.Peer reviewe

Exploiting Usage to Predict Instantaneous App Popularity : Trend Filters and Retention Rates

Popularity of mobile apps is traditionally measured by metrics such as the number of downloads, installations, or user ratings. A problem with these measures is that they reflect usage only indirectly. Indeed, retention rates, i.e., the number of days users continue to interact with an installed app, have been suggested to predict successful app lifecycles. We conduct the first independent and large-scale study of retention rates and usage trends on a dataset of app-usage data from a community of 339,842 users and more than 213,667 apps. Our analysis shows that, on average, applications lose 65% of their users in the first week, while very popular applications (top 100) lose only 35%. It also reveals, however, that many applications have more complex usage behaviour patterns due to seasonality, marketing, or other factors. To capture such effects, we develop a novel app-usage trend measure which provides instantaneous information about the popularity of an application. Analysis of our data using this trend filter shows that roughly 40% of all apps never gain more than a handful of users (Marginal apps). Less than 0.1% of the remaining 60% are constantly popular (Dominant apps), 1% have a quick drain of usage after an initial steep rise (Expired apps), and 6% continuously rise in popularity (Hot apps). From these, we can distinguish, for instance, trendsetters from copycat apps. We conclude by demonstrating that usage behaviour trend information can be used to develop better mobile app recommendations.Peer reviewe

A Survey of COVID-19 in Public Transportation: Transmission Risk, Mitigation and Prevention

The COVID-19 pandemic is posing significant challenges to public transport operators by drastically reducing demand while also requiring them to implement measures that minimize risks to the health of the passengers. While the collective scientific understanding of the SARS-CoV-2 virus and COVID-19 pandemic are rapidly increasing, currently there is a lack of understanding of how the COVID-19 relates to public transport operations. This article presents a comprehensive survey of the current research on COVID-19 transmission mechanisms and how they relate to public transport. We critically assess literature through a lens of disaster management and survey the main transmission mechanisms, forecasting, risks, mitigation, and prevention mechanisms. Social distancing and control on passenger density are found to be the most effective mechanisms. Computing and digital technology can support risk control. Based on our survey, we draw guidelines for public transport operators and highlight open research challenges to establish a research roadmap for the path forward.Peer reviewe

GeoMatch: Efficient Large-Scale Map Matching on Apache Spark

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