Anticipatory Mobile Computing: A Survey of the State of the Art and Research Challenges

Today's mobile phones are far from mere communication devices they were ten years ago. Equipped with sophisticated sensors and advanced computing hardware, phones can be used to infer users' location, activity, social setting and more. As devices become increasingly intelligent, their capabilities evolve beyond inferring context to predicting it, and then reasoning and acting upon the predicted context. This article provides an overview of the current state of the art in mobile sensing and context prediction paving the way for full-fledged anticipatory mobile computing. We present a survey of phenomena that mobile phones can infer and predict, and offer a description of machine learning techniques used for such predictions. We then discuss proactive decision making and decision delivery via the user-device feedback loop. Finally, we discuss the challenges and opportunities of anticipatory mobile computing.Comment: 29 pages, 5 figure

Employing Environmental Data and Machine Learning to Improve Mobile Health Receptivity

Behavioral intervention strategies can be enhanced by recognizing human activities using eHealth technologies. As we find after a thorough literature review, activity spotting and added insights may be used to detect daily routines inferring receptivity for mobile notifications similar to just-in-time support. Towards this end, this work develops a model, using machine learning, to analyze the motivation of digital mental health users that answer self-assessment questions in their everyday lives through an intelligent mobile application. A uniform and extensible sequence prediction model combining environmental data with everyday activities has been created and validated for proof of concept through an experiment. We find that the reported receptivity is not sequentially predictable on its own, the mean error and standard deviation are only slightly below by-chance comparison. Nevertheless, predicting the upcoming activity shows to cover about 39% of the day (up to 58% in the best case) and can be linked to user individual intervention preferences to indirectly find an opportune moment of receptivity. Therefore, we introduce an application comprising the influences of sensor data on activities and intervention thresholds, as well as allowing for preferred events on a weekly basis. As a result of combining those multiple approaches, promising avenues for innovative behavioral assessments are possible. Identifying and segmenting the appropriate set of activities is key. Consequently, deliberate and thoughtful design lays the foundation for further development within research projects by extending the activity weighting process or introducing a model reinforcement.BMBF, 13GW0157A, Verbundprojekt: Self-administered Psycho-TherApy-SystemS (SELFPASS) - Teilvorhaben: Data Analytics and Prescription for SELFPASSTU Berlin, Open-Access-Mittel - 201

Mobile Data Science: Towards Understanding Data-Driven Intelligent Mobile Applications

Due to the popularity of smart mobile phones and context-aware technology, various contextual data relevant to users' diverse activities with mobile phones is available around us. This enables the study on mobile phone data and context-awareness in computing, for the purpose of building data-driven intelligent mobile applications, not only on a single device but also in a distributed environment for the benefit of end users. Based on the availability of mobile phone data, and the usefulness of data-driven applications, in this paper, we discuss about mobile data science that involves in collecting the mobile phone data from various sources and building data-driven models using machine learning techniques, in order to make dynamic decisions intelligently in various day-to-day situations of the users. For this, we first discuss the fundamental concepts and the potentiality of mobile data science to build intelligent applications. We also highlight the key elements and explain various key modules involving in the process of mobile data science. This article is the first in the field to draw a big picture, and thinking about mobile data science, and it's potentiality in developing various data-driven intelligent mobile applications. We believe this study will help both the researchers and application developers for building smart data-driven mobile applications, to assist the end mobile phone users in their daily activities.Comment: Journal, 11 pages, Double Colum

Self-adaptive unobtrusive interactions of mobile computing systems

[EN] In Pervasive Computing environments, people are surrounded by a lot of embedded services. Since pervasive devices, such as mobile devices, have become a key part of our everyday life, they enable users to always be connected to the environment, making demands on one of the most valuable resources of users: human attention. A challenge of the mobile computing systems is regulating the request for users¿ attention. In other words, service interactions should behave in a considerate manner by taking into account the degree to which each service intrudes on the user¿s mind (i.e., the degree of obtrusiveness). The main goal of this paper is to introduce self-adaptive capabilities in mobile computing systems in order to provide non-disturbing interactions. We achieve this by means of an software infrastructure that automatically adapts the service interaction obtrusiveness according to the user¿s context. This infrastructure works from a set of high-level models that define the unobtrusive adaptation behavior and its implication with the interaction resources in a technology-independent way. Our infrastructure has been validated through several experiments to assess its correctness, performance, and the achieved user experience through a user study.This work has been developed with the support of MINECO under the project SMART-ADAPT TIN2013-42981-P, and co-financed by the Generalitat Valenciana under the postdoctoral fellowship APOSTD/2016/042.Gil Pascual, M.; Pelechano Ferragud, V. (2017). Self-adaptive unobtrusive interactions of mobile computing systems. Journal of Ambient Intelligence and Smart Environments. 9(6):659-688. https://doi.org/10.3233/AIS-170463S65968896Aleksy, M., Butter, T., & Schader, M. (2008). Context-Aware Loading for Mobile Applications. 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City Data Fusion: Sensor Data Fusion in the Internet of Things

Internet of Things (IoT) has gained substantial attention recently and play a significant role in smart city application deployments. A number of such smart city applications depend on sensor fusion capabilities in the cloud from diverse data sources. We introduce the concept of IoT and present in detail ten different parameters that govern our sensor data fusion evaluation framework. We then evaluate the current state-of-the art in sensor data fusion against our sensor data fusion framework. Our main goal is to examine and survey different sensor data fusion research efforts based on our evaluation framework. The major open research issues related to sensor data fusion are also presented.Comment: Accepted to be published in International Journal of Distributed Systems and Technologies (IJDST), 201

Cyclist-aware intelligent transportation system

Abstract. Rapidly developing cities make cycling popular way of traveling around and with enhanced smart traffic light infrastructure cycling can be safer and smoother. Smartphones with an internet connectivity and advanced positioning sensors can be used to build a cost-effective infrastructure to enable cyclist-aware traffic lights system. However, such systems depends on proper time of arrival estimation which can be affected by the GPS errors which works poorly in area with tall buildings and driver behaviour. In this paper we discuss how presence of feedback from smart traffic system influence the driver awareness of the cyclist and affects the negative impact of time of arrival estimation errors. This paper gives an analysis of the existing approaches to build smart cyclist-aware traffic systems and different sources of errors that affects their performance. With designed computer appliance we evaluated the effectiveness of cyclist-aware system with and without a presence of additional haptic and audio feedback. The results show that the presence of feedback positively affects the driver awareness of cyclist and allow them to react earlier. Experiment shows that just introduction of feedback can increase the accuracy of time of arrival estimation up to 34% without any other modification to the system.Pyöräilijät tiedostava älykäs liikennejärjestelmä. Tiivistelmä. Pyöräily on suosittu tapa liikkua nopeasti kasvavissa kaupungeissa. Parannetuilla älyliikennevaloilla pyöräilystä voisi tulla turvallisempaa ja sujuvampaa. Huokean infrastruktuurin rakentamisessa pyöräilijät tiedostavaan liikennevalojärjestelmään voidaan hyödyntää älypuhelinten verkkoyhteyttä sekä pitkälle kehitettynyttä paikannusmahdollisuutta. Paikannuksen haasteena kuitenkin ovat epätarkkuus korkeiden rakennusten katveessa sekä pyöräilijöiden ja autoilijoiden käyttäytyminen. Kyseisen kaltainen järjestelmä vaatii toimivan kulunaika-arvioinnin, mikä on haastavaa GPS-paikannuksen epätarkkuuden vuoksi. Tässä julkaisussa keskustelemme siitä, kuinka älykkäästä liikennejärjestelmästä saatu palaute vaikuttaa autoilijoiden tiedostavuuteen ja sitä kautta saapumisaika-arvioiden epätarkkuuteen. Analysoimme olemassa olevia älykkäitä pyöräiljät tiedostavia liikennejärjestelmiä ja niihin vaikuttavia epätarkkuus- sekä virhelähteitä. Käytämme kehittämäämme tietokone ohjelmaa arvioimaan pyöräilijät tiedostavan järjestelmän tehokkuutta käyttäen koemuuttujina haptista ja auditiivista palautetta. Tulokset paljastavat, että saatu palaute vaikuttaa positiivisesti parantaen autoilijoiden reaktioaikaa sekä sitä kuinka he tiedostavat pyöräiljät. Kokeet osoittavat, että pelkästään esittelyn ja palautteen olemassaolo lisäävät saapumisaika-arvioiden tarkkuutta jopa 34%