Mobile Sensing Research Papers - Academia.edu (original) (raw)

This paper describes the development and evaluation of a novel effort control system for cycling, which contributes to promote the users’ mobility and physical health. This system provides automatic control of the motor assistance level of an electric bicycle in order to ensure that the cyclist’s effort remains inside the desired target zone, regardless of changes in other variables which normally affect the effort, such as the slope of the road. The system presented in this paper controls the pedaling resistance perceived by the cyclist through the use of a sensor device placed inside of the bicycle crankset, which provides the required torque signal. The data processing, effort control algorithm and user interface are implemented in a smartphone application, whereas a microcontroller on the bicycle is responsible for the data acquisition, wireless data exchange with the smartphone, and real-time control of the motor assistance level. Experimental results validate the effectiveness of the implemented effort control system.

- by
- •
- Cycling Performance, Electric Vehicles, Health and Fitness, Mobile Sensing

Urban spaces have a great impact on how people feel and behave. There are number of factors that impact our emotional responses to a space. In this paper, we propose an objective way to measure people’s emotional reactions in places by monitoring their physiological signals that are related to emotion. By integrating wearable biosensors with mobile phones, we can obtain geo-annotated data relating to emotional states in relation to our spatial surroundings. We are the able to visualize the emotional response data by creating an emotional layer over a geographical map. This can then help us to understand how individuals emotionally perceive urban spaces and help us to illustrate the interdependency between emotions and environmental surroundings.

- by eiman kanjo
- •
- Emotional intelligence, Leadership, Competency Mapping, Mobile Sensing

By 2025, when most of today’s psychology undergraduates will be in their mid-30s, more than 5 billion people on our planet
will be using ultra-broadband, sensor-rich smartphones far beyond the abilities of today’s iPhones, Androids, and Blackberries. Although smartphones were not designed for psychological research, they can collect vast amounts of ecologically valid data, easily and quickly, from large global samples. If participants download the right “psych apps,” smartphones can record where they are, what they are doing, and what they can see and hear and can run interactive surveys, tests, and experiments through touch screens and wireless connections to nearby screens, headsets, biosensors, and other peripherals. This article reviews previous behavioral research using mobile electronic devices, outlines what smartphones can do now and will be able to do in the near future, explains how a smartphone study could work practically given current technology (e.g., in studying ovulatory cycle effects on women’s sexuality), discusses some limitations and challenges of smartphone research, and compares smartphones to other research methods. Smartphone research will require new skills in app development and data analysis and will raise tough new ethical issues, but smartphones could transform psychology even more profoundly than PCs and brain imaging did.

Much of the stress and strain of student life remains hidden. The StudentLife continuous sensing app assesses the day-to- day and week-by-week impact of workload on stress, sleep, activity, mood, sociability, mental well-being and academic performance of a single class of 48 students across a 10 week term at Dartmouth College using Android phones. Results from the StudentLife study show a number of significant cor- relations between the automatic objective sensor data from smartphones and mental health and educational outcomes of the student body. We also identify a Dartmouth term lifecycle in the data that shows students start the term with high pos- itive affect and conversation levels, low stress, and healthy sleep and daily activity patterns. As the term progresses and the workload increases, stress appreciably rises while posi- tive affect, sleep, conversation and activity drops off. The StudentLife dataset is publicly available on the web.

- by Gabriella M Harari and +2
- •
- Mental Health, Health Behaviors, Academic Performance, Student Life
- by Mohit Jain
- •
- Machine Learning, Mobile Sensing

Continuous advances in sensors, semiconductors, wireless networks, mobile and cloud computing enable the development of integrated wearable computing systems for continuous health monitoring. These systems can be used as a part of diagnostic procedures, in the optimal maintenance of chronic conditions, in the monitoring of adherence to treatment guidelines, and for supervised recovery. In this paper, we describe a wearable system called Smart Button designed to assess mobility of elderly. The Smart Button is easily mounted on the chest of an individual and currently quantifies the Timed-Up-and-Go and 30-Second Chair Stand tests. These two tests are routinely used to assess mobility, balance, strength of the lower extremities, and fall risk of elderly and people with Parkinson’s disease. The paper describes the design of the Smart Button, parameters used to quantify the tests, signal processing used to extract the parameters, and integration of the Smart Button into a broader mHealth system.

As a core component of the Internet of Things technology (IoT), Radio Frequency Identification (RFID) tagged items will add billions, perhaps trillions, of objects to the Internet. As a result, uses of Ultra High Frequency (UHF) RFID sensing become massive ranging from logistics, retail and healthcare to homes and even entire smart cities. Under this trend, mobile UHF RFID scanners also need to evolve. Consumers will interact with their surroundings via tagged RFID items taking full advantage of the advancing IoT. For mainstream consumer smartphones, unfortunately, UHF RFID connectivity has yet to be fully integrated. The major challenges are: 1) the compatibility of an RFID reader module to the host platform, 2) Radio Frequency (RF) signal coexistence interference between the RFID reader and other sensor/RF technologies, and 3) the unacceptable high current drain caused by RFID active scanning. In this paper, we present a design and implementation of a novel modular UHF RFID scanning subsystem, the UHF RFID reader module, on a Motorola Moto-Z smartphone. This module is fully integrated with an Android 7.0 Operating System (OS) and directly interconnects with the low-level smartphone hardware and software framework. With the new antenna design and the signal spectrum analysis, we guarantee the RF isolation of the Mod with the smartphone's other native wireless components and sensors. Our design and implementation also address the current drain issue and extends the battery life of Moto-Z smartphone up to 30.4 hours with IoT RFID scanning.

This work presents the implementation of a wireless network based on Bluetooth Low Energy (BLE) which enables the integration of multiple sensor nodes into a smartphone-based system in order to monitor the posture of cyclists. The developed posture monitoring system obtains the orientation in space of each body segment in which the sensor nodes are placed and calculates the trunk angle, the knee angle and the angle of inclination of the road. Raw sensor data are collected periodically from accelerometers, magnetometers and gyroscopes and sent via BLE to an Android smartphone, which plays the role of central station and performs the data processing concerning the posture calculation. We describe the development of the hardware and software of the sensor nodes, which are based on the CC2540 BLE system-on-chip, as well as the development of the Android application, and provide experimental results concerning the measurement of the posture of a cyclist in order to validate the proposed system.

- by Jose A Afonso
- •
- Health and Fitness, Wireless Body Area Network (WBAN), Motion Detection, Cycling
- by Tadashi Okoshi and +2
- •
- Machine Learning, Context-Aware Computing, Queueing theory, Middleware

In today’s ubiquitous computing environment where the number of devices, applications and web services are ever increasing, human attention is the new bottleneck in computing. To minimize user cognitive load, we propose Attelia, a novel middleware that identifies breakpoints in user interaction and delivers notifications at these moments. Attelia works in real- time and uses only the mobile devices that users naturally use and wear, without any modifications to applications, and without any dedicated psycho-physiological sensors. Our evaluation proved the effectiveness of Attelia. A controlled user study showed that notifications at detected breakpoint timing resulted in 46% lower cognitive load compared to randomly-timed notifications. Furthermore, our “in-the-wild” user study with 30 participants for 16 days further validated Attelia’s value, with a 33% decrease in cognitive load compared to randomly-timed notifications.

- by Hiroki Nozaki and +2
- •
- Human Computer Interaction, Machine Learning, Attention, Notification Systems

Does personality predict how people feel in different types of situations? The present research addressed this question using data from several thousand individuals who used a mood tracking smartphone application for several weeks. Results from our analyses indicated that people's momentary affect was linked to their location, and provided preliminary evidence that the relationship between state affect and location might be moderated by personality. The results highlight the importance of looking at person-situation relationships at both the trait-and state-levels and also demonstrate how smartphones can be used to collect person and situation information as people go about their everyday lives.

- by Gillian Sandstrom
- •
- Happiness and Well Being, Mobile Sensing

Mental health problems are on the rise globally and strain national health systems worldwide. Mental disorders are closely associated with fear of stigma, structural barriers such as financial burden, and lack of available services and resources which often prohibit the delivery of frequent clinical advice and monitoring. Technologies for mental well-being exhibit a range of attractive properties which facilitate the delivery of state of the art clinical monitoring. This review article provides an overview of traditional techniques followed by their technological alternatives, sensing devices, behaviour changing tools, and feedback interfaces. The challenges presented by these technologies are then discussed with data collection, privacy and battery life being some of the key issues which need to be carefully considered for the successful deployment of mental health tool-kits. Finally, the opportunities this growing research area presents are discussed including the use of portable tangible interfaces combining sensing and feedback technologies. Capitalising on the captured data these ubiquitous devices offer, state of the art machine learning algorithms can lead to the development of a robust clinical decision support tools towards diagnosis and improvement of mental well-being delivery in real-time.

- by eiman kanjo
- •
- Artificial Intelligence, Mental Health, The Internet of Things, Affective Computing

As the availability and use of wearables increases, they are becoming a promising platform for context sensing and context analysis. Smartwatches are a particularly interesting platform for this purpose, as they offer salient advantages, such as their proximity to the human body. However, they also have limitations associated with their small form factor, such as processing power and battery life, which makes it difficult to simply transfer smartphone-based context sensing and prediction models to smartwatches. In this paper, we introduce an energy-efficient, generic, integrated framework for continuous context sensing and prediction on smartwatches. Our work extends previous approaches for context sensing and prediction on wrist-mounted wearables that perform predictive analytics outside the device. We offer a generic sensing module and a novel energy-efficient, on-device prediction module that is based on a semantic abstraction approach to convert sensor data into meaningful information objects, similar to human perception of a behavior. Through six evaluations, we analyze the energy efficiency of our framework modules, identify the optimal file structure for data access and demonstrate an increase in accuracy of prediction through our semantic abstraction method. The proposed framework is hardware independent and can serve as a reference model for implementing context sensing and prediction on small wearable devices beyond smartwatches, such as body-mounted cameras.

- by Martin Tomitsch and +5
- •
- Wearable Computing, Energy efficiency, Lifelogging, Mobile Sensing
- by Sanjit Kaul
- •
- Machine Learning, Mobile Sensing

Urban spaces have a great impact on how people's emotion and behaviour. There are number of factors that impact our brain responses to a space. This paper presents a novel urban place recommendation approach, that is based on modelling in-situ EEG data. The research investigations leverages on newly affordable Electroencephalogram (EEG) headsets, which has the capability to sense mental states such as meditation and attention levels. These emerging devices have been utilized in understanding how human brains are affected by the surrounding built environments and natural spaces. In this paper, mobile EEG headsets have been used to detect mental states at different types of urban places. By analysing and modelling brain activity data, we were able to classify three different places according to the mental state signature of the users, and create an association map to guide and recommend people to therapeutic places that lessen brain fatigue and increase mental rejuvenation. Our mental states classifier has achieved accuracy of (%90.8). NeuroPlace breaks new ground not only as a mobile ubiquitous brain monitoring system for urban computing, but also as a system that can advise urban planners on the impact of specific urban planning policies and structures. We present and discuss the challenges in making our initial prototype more practical , robust, and reliable as part of our ongoing research. In addition, we present some enabling applications using the proposed architecture.

- by eiman kanjo
- •
- Spatial Analysis, Emotional intelligence, Data Science, Smart Cities

Can we predict which conversations are enjoyable without hearing the words that are spo-ken? A total of 36 participants used a mobile app, My Social Ties, which collected data about 473 conversations that the participants engaged in as they went about their daily lives. We tested whether conversational properties (conversation length, rate of turn taking, proportion of speaking time) and acoustical properties (volume, pitch) could predict enjoyment of a conversation. Surprisingly, people enjoyed their conversations more when they spoke a smaller proportion of the time. This pilot study demonstrates how conversational properties of social interactions can predict psychologically meaningful outcomes, such as how much a person enjoys the conversation. It also illustrates how mobile phones can provide a window into everyday social experiences and well-being.

- by Gillian Sandstrom and +2
- •
- Acoustics, Social Interaction, Happiness and Well Being, Mobile Sensing

Mobile Ad hoc Networks (MANET) is an autonomous or independent system of mobile nodes connected by wireless communication links. Every node operates not only as an end station and also a base station to forward data packets. In random topology, the nodes are free to move and frequently change their positions. Ad hoc On-demand Distance Vector (AODV) is a reactive routing protocol; which is all routes are discovered only when needed and to find the shortest route between communication nodes. Link failure is a major issue of the current ad hoc wireless network due to node mobility, node energy loss or drain to battery power. In this paperwork has been made to compare the performance of three prominent methods support of AODV routing protocol for MANET: Proposed AODV Routing (PRO-AODV), Divert Failure Route Recovery (DFRR), Check Point Route Recovery (CPRR) Methods. PRO-AODV and DFRR methods were designed to avoid a link failure route recovery process based on node sequence number and in advance node signal strength connection in the highly dynamic ad hoc network. CPRR method conquers of node low energy, node monitoring and blocking kind of process to rectification in active communication. In this method sensor activities on actor nodes and maintain routes, link failure route recovery process to measure help of static, dynamic sensor nodes and Network Topology Management (NTM) for optimal connection in Wireless ad hoc sensor Networks (WASN). The performance comparison between different three methods is analyzed using varying time intervals in NS-2 Network Simulator carefully evaluating and implementing efficient routing establishment process.

- by Hanumanthu Korra
- •
- Mobile Sensing

Urban spaces have a great impact on how people feel and behave. There are number of factors that impact our emotional responses to a space. In this paper, we propose an objective way to measure people
’s
emotional reactions in places by monitoring their physiological signals that are related to emotion. By integrating wearable biosensors with mobile phones, we can obtain geo-annotated data relating to emotional states in relation to our spatial surroundings. We are the able to visualize the emotional response data by creating an
emotional layer
over a geographical map. This can then help us to understand how individuals emotionally perceive urban spaces and help us to illustrate the interdependency between emotions and environmental surroundings.

- by Alan Chamberlain
- •
- Psychology, Emotion, Geography, Urban Geography
- by mehul shah
- •
- Wireless Communications, Network Security, Mobile Sensing

Users of mass transit systems such as those of buses and trains normally rely on accurate route maps, stop locations, and service schedules when traveling. If the route map, service schedule, or stop location has errors it can reduce the transit agency’s ridership. In this paper, the problem of deriving transit systems by mining raw GPS data is studied. Specifically, we propose and evaluate novel classification features with spatial and temporal clustering techniques that derive bus stop locations, route geometries, and service schedules from GPS data. Subsequently, manual and expensive field visits to record and annotate the initial or updated route geometries, transit stop locations, or service schedules is no longer required by transit agencies. This facilitates a massive reduction in cost for transit agencies. The effectiveness of the proposed algorithms is validated on the third largest public transit system in the United States.

The effects of environmental exposure on human health have been widely explored by scholars in health geography for decades. However, recent advances in geospatial technologies, especially the development of mobile approaches to collecting real-time and high-resolution individual data, have enabled sophisticated methods for assessing people’s environmental exposure. This study proposes an individual environmental exposure assessment system (IEEAS) that integrates objective real-time monitoring devices and subjective sensing tools to provide a composite way for individualbased environmental exposure data collection. With field test data collected in Chicago and Beijing,
we illustrate and discuss the advantages of the proposed IEEAS and the composite analysis that could be applied. Data collected with the proposed IEEAS yield relatively accurate measurements of individual exposure in a composite way, and offer new opportunities for developing more sophisticated ways to measure individual environmental exposure. With the capability to consider both the variations in environmental risks and human mobility in high spatial and temporal resolutions, the IEEAS also helps mitigate some uncertainties in environmental exposure assessment and thus enables a better understanding of the relationship between individual environmental exposure and health outcomes.

Air quality measurements were conducted at rural and urban environment with a low-cost particulate matter (PM) sensor and GPS receiver based portable device, which was developed to determine the atmospheric PM concentration and distribution. Using the PM sensors and GPS receiver data, hotspots can be identified, the air quality characteristics of crawled areas and routes can be determined. Suggestions was made to improve the accuracy of the measurement.

—Traditional personality assessment techniques often rely on subjective report obtained from questionnaires. This work complements traditional techniques by exploring objective measures of traits at the behavior level. We explored behavior features extracted from smartphone sensing data, and used selected features to predict the traits of the Five Factor Model. The specific dataset we explored was the StudentLife dataset. We found behavior features corresponding to each trait, and were able to predict the traits with varying degrees of accuracy. The best result of each trait are: Extraversion (91.2%), Agreeableness (67.6%), Conscientiousness (70.6%), Neuroticism (79.4%), Openness(73.5%). Our results suggest that behavioral measures extracted from smartphone sensing data has potential in the assessment of personality.

- by Hui S Wang and +1
- •
- Data Mining, Health Informatics, Personality Assessment, Mobile Sensing

Full paper available at http://goo.gl/0mzbrl.
This paper presents the project of a Mobile Cockpit System (MCS) for smartphones, which provides assistance to Electric Bicycle (EB) cyclists in Smart Cities’ environment. The presented system introduces a mobile application (MCS App) with the goal to provide useful personalized information to the cyclist related with the EB’s use, including EB range prediction considering the intended path, management of the cycling effort performed by the cyclist, handling of the battery charging process and the provisioning of information regarding available public transport. This work also introduces the EB cyclist profile concept, which is based on historical data analysis previously stored in a database and collected from mobile devices sensors. From the tests performed, the results show the importance of route guidance, taking into account the energy savings. The results also show significant changes on range prediction based on user and route taken. It is important to say that the proposed system can be used for all bicycle in general.

- by Jose A Afonso
- •
- Data Mining, Participatory Sensing, Mobile Sensing, Electric Bicycles

Mobile sensing enabled by GPS or smart phones has become an increasingly important source of traffic data. For sufficient coverage of the traffic stream, it is important to maintain a reasonable penetration rate of probe vehicles. From the standpoint of capturing higher-order traffic quantities such as acceleration/deceleration, emission and fuel consumption rates, it is desirable to examine the impact on the estimation accuracy of sampling frequency on vehicle position. Of the two issues raised above, the latter is rarely studied in the literature. This paper addresses the impact of both sampling frequency and penetration rate on mobile sensing of highway traffic. To capture inhomogeneous driving conditions and deviation of traffic from the equilibrium state, we employ the second-order phase transition model (PTM). Several data fusion schemes that incorporate vehicle trajectory data into the PTM are proposed. And, a case study of the NGSIM dataset is presented which shows the estimation results of various Eulerian and Lagrangian traffic quantities. The findings show that while first-order traffic quantities can be accurately estimated even with a low sampling frequency, higher-order traffic quantities, such as acceleration, deviation, and emission rate, tend to be misinterpreted due to insufficiently sampled vehicle locations. We also show that a correction factor approach has the potential to reduce the sensing error arising from low sampling frequency and penetration rate, making the estimation of higher-order quantities more robust against insufficient data coverage of the highway traffic.

- by Terry L Friesz and +1
- •
- Partial Differential Equations, Sustainable Transportation, Traffic Engineering, Environmental Sustainability
- by Lulwah Y. Al-Barrak
- •
- Mobile Sensing

"As truly ubiquitous wearable computers, mobile phones are quickly becoming the primary source for social, behavioral, and environmental sensing and data collection. Today’s smartphones are equipped with increasingly more sensors and accessible data types that enable the collection of literally dozens of signals related to the phone, its user, and its environment. A great deal of research effort in academia and industry is put into mining this raw data for higher level sense-making, such as understanding user context, inferring social networks, learning individual features, predicting outcomes, and so on. In many cases, this analysis work is the result of exploratory forays and trial-and-error. Adding to the challenge, the devices themselves are a limited platform, and any data collection campaign must be carefully designed in order to collect the right signals, in the appropriate frequency, and at the same time not exhausting the device’s limited battery and processing power. There is need for a more structured methodology and tools to help with designing mobile data collection and analysis initiative.
In this work we investigate the properties of learning and inference of real world data collected via mobile phones over time. In particular, we look at the dynamic learning process over time, and how the ability to predict individual parameters and social links is incrementally enhanced with the accumulation of additional data. To do this, we use the Friends and Family dataset, which contains rich data signals gathered from the smartphones of 140 adult members of a young-family residential community for over a year, and is one of the most comprehensive mobile phone datasets gathered in academia to date.
We develop several models that predict social and individual properties from sensed mobile phone data, including detection of life-partners, ethnicity, and whether a person is a student or not. Then, for this set of diverse learning tasks, we investigate how the prediction accuracy evolves over time, as new data is collected. Finally, based on gained insights, we propose a method for advance prediction of the maximal learning accuracy possible for the learning task at hand, based on an initial set of measurements. This has practical implications, like informing the design of mobile data collection campaigns, or evaluating analysis strategies."

- by Michael Fire
- •
- Bioinformatics, Artificial Intelligence, Social Networks, Machine Learning

The health and fitness data traffic originating on mobile devices has been continually increasing, with an exponential increase in the number of personal wearable devices and mobile health monitoring applications. Lossless data compression can increase throughput, reduce latency, and achieve energy-efficient communication between personal devices and the cloud. This paper experimentally explores the effectiveness of common compression utilities on mobile devices when uploading and downloading a representative mHealth data set. Based on the results of our study, we develop recommendations for effective data transfers that can assist mHealth application developers.

- by Armen A Dzhagaryan
- •
- Communication, Wireless Communications, Wearable Computing, Data Compression

Today’s mobile phones are smarter than ever: they now take and process pictures and videos, issue messages and email, access the Web, allow games on demand, and play music. More people around the world take their phones everywhere they go, using them in a variety of environments and situations to perform a whole range of different tasks. In India, for example, more people access the Internet from their phones than from a PC, a scenario that will certainly play out across the globe in the years to come.1
Most mobile phones include a variety of sensing components. By expanding this capability, we can derive some interesting sensing modalities—for example, scrutinizing local environments to detect and reduce pollution or using medical applications to tackle other problems on a societal scale.
In this article, we discuss experiences and lessons learned from deploying four mobile sensing applications on off-the-shelf mobile phones within a recreational framework called MobSens that contains elements of health, social, and environmental sensing at both individual and community levels. We describe the main components of our applications, which facilitate logging and external communications. We also outline the challenges faced when building and testing these applications and describe our strategies for overcoming them.

- by Eman O Younis
- •
- Physiology, Computer Science, Machine Learning, Pervasive Computing

Full paper available at http://goo.gl/0mzbrl. In recent years there has been a significant evolution regarding applications for mobile devices that provide location-based services. The mobile devices available on the market already provide a set of integrated sensors and it is also possible to acquire data from external sensors. This paper presents the development and results concerning a mobile sensing system applied to cycling which performs data collection using both sensors integrated in the smartphone and multiple wireless sensor nodes, which are used to acquire relevant performance parameters. The data collected by the developed mobile app is stored in a local database and also uploaded to a remote database, where it can be accessed later using the mobile app or a web browser. This mobile app allows users to share data with friends, join or create events, locate friends, consult graphs and access past routes in a map. Based on these functionalities, this system aims to provide detailed feedback regarding the user performance and enhance the enjoyment of the cyclists.

- by Diogo Oliveira and +1
- •
- Cycling Performance, Health and Fitness, Mobile apps, Smartphones

Eettiset normit eivät ole olleet juuri esillä tietoteknisessä tutkimuksessa Suomessa. Tekniikan kehitys ja tietotekniikan soveltaminen yhä laajemmin yhteiskunnassa ja ihmisten arjessa tuo kuitenkin väistämättä tämän puolen myös alan tutkijoiden arkeen. Monin eri tietoteknisin tavoin kerättävät tutkimusaineistot sisältävät yhä enemmän informaatiota, jonka avulla ihmisistä voidaan päätellä hyvinkin arkaluonteisina pidettäviä ja väärinkäytettyinä heille haitallisia asioita. Tällaisia ovat esimerkiksi terveydentila, poliittinen ja seksuaalinen suuntautuneisuus tai jopa itsemurhariski.
Käymme läpi mahdollisuuksia, joita tietotekninen tutkimus ja yhä monipuolisempi tiedonkeruu avaavat. Esittelemme rajoitteet, jotka koituvat tietotekniselle tutkimukselle asetetusta sääntelystä. Lainsäädännössä määritellään tiukat rajat, joiden puitteissa henkilötietojen kerääminen on ylipäätään sallittua. Käsittelemme myös EU:n henkilötietolainsäädännön uudistamista koskevaa pakettia, jossa ollaan uudistamassa
muun muassa tutkimustoimintaa koskevaa poikkeusta.
Tietoaineistojen tallentamista koskevat erikoiskysymykset käymme läpi artikkelin kolmannessa osassa.
Neljännessä osassa esittelemme tietoteknistä tutkimusta koskevien lausuntopyyntöjen käsittelyä nykyisissä eettisissä lautakunnissa. Suomessa ei ole tällä hetkellä erityisiä
tietotekniseen tutkimukseen erikoistuneita eettisiä lautakuntia tai neuvottelukuntia. Tästä syystä teimme kyselyn eri yliopistojen, tutkimuslaitosten, sekä merkittävimpien
alueellisten hallintoyksiköiden eettisiin lautakuntiin. Yritimme selvittää, kuinka usein ja miten lautakunnat ovat ottaneet kantaa tutkimuksiin, jotka sisältävät merkittävästi
tietoteknistä tutkimusta. Lopputuloksen perusteella pyyntöjä ei juuri tehdä. Ihmistieteissä ja lääketieteessä tilanne on toisenlainen.
Lopuksi esitämme suositukset tietoteknisen tutkimuksen eettisen arvioinnin kehittämiseksi Suomessa. Ensimmäinen ja merkittävin askel tässä olisi oman kansallisen tutkimuseettisen neuvottelukunnan perustaminen myös tietotekniselle tutkimukselle.

- by Mikko Heiskala
- •
- Research Ethics, ICT, Mobile Sensing

Abstract. Many mobile phones have a GPS sensor that reports accurate location. Thus, if these location data are not protected adequately, they may cause privacy breeches. Several reports are available where people have been stalked through GPS. The contributions of this paper are in two folds. First, we examine privacy issues in snapshot queries and propose a method that guarantees that all queries are protected. Previously proposed algorithms achieve a low success rate in some situations.

- by Leon Stenneth
- •
- Privacy, Mobile Sensing, Location Privacy, Mobile System

The Internet of Things (IoT) envisions billions of sensors deployed around us and connected to the Internet, where the mobile crowd sensing technologies are widely used to collect data in different contexts of the IoT paradigm. Due to the popularity of Big Data technologies, processing and storing large volumes of data have become easier than ever. However, large-scale data management tasks still require significant amounts of resources that can be expensive regardless of whether they are purchased or rented (e.g., pay-as-you-go infrastructure). Further, not everyone is interested in such large-scale data collection and analysis. More importantly, not everyone has the financial and computational resources to deal with such large volumes of data. Therefore, a timely need exists for a cloud-integrated mobile crowd sensing platform that is capable of capturing sensors data, on-demand, based on conditions enforced by the data consumers. In this paper, we propose a context-aware, specifically, location and activity-aware mobile sensing platform called context-aware mobile sensor data engine (C-MOSDEN) for the IoT domain. We evaluated the proposed platform using three real-world scenarios that highlight the importance of selective sensing. The computational effectiveness and efficiency of the proposed platform are investigated and are used to highlight the advantages of context-aware selective sensing.

- by Kyriaki Kalimeri
- •
- Social Networks, Social Computing, Face-to-face Interaction, Activity

Abstract. Bluetooth, WiFi, and NFC are considered to be low power, affordable
and available on most mobile handsets. However, these types of
wireless mediums are classified as short links since their communication
ranges are limited to ~100meters for WiFi and ~10 meters in the case of Bluetooth
which seems to stifle the full usefulness of the service. In this paper, we
propose a new wireless network concept called ViralNet, solely dependent on
the mobile devices in the vicinity using principles of opportunistic networking.
ViralNet allow new type of communications beyond the short-range limit
which can be used to connect to other phones and sensors distributed in the environment.
A message or sensor reading can be turned instantly viral. An authentic
user or a monitoring device sends a message to others nearby and they
do the same without internet connection. This can open the door to completely
new type of applications ranging from emergency evacuation in
crowded areas and animal monitoring to citizen reportage, if the authorities
want to keep an image from escaping the scene, they must confiscate hundreds
or thousands of mobile phones.

- by eiman kanjo
- •
- Crisis Management, Wireless Sensor Networks, AdHoc Networks, Crowd Sourcing

Real-time parking availability information is important in urban areas, and if available could reduce congestion, pollution, and gas consumption. In this paper, we present a software solution called PhonePark for detecting the availability of on-street parking spaces. The solution uses the GPS and/or accelerometer sensors in a traveler’s mobile phone to automatically detect when and where the traveler parked her car, and when she released a parking slot. PhonePark can also utilize the mobile phone’s Bluetooth sensor or piggyback on street parking payment transactions for parking activity detection. Thus, the solution considers only mobile phones and does not rely on any external sensors such as cameras, wireless sensors embedded in the pavements, or ultrasonic sensors on vehicles. Further contributions include an algorithm to compute the historical parking availability profile for an arbitrary street block and algorithms to estimate the parking availability in real-time for a given street block. The algorithms are evaluated using real-time and real world street parking data.

- by Leon Stenneth
- •
- Kalman Filter, Parking, Mobile Sensing, GPS , Accelerometer, Bluetooth

"Abstract. As truly ubiquitous wearable computers, mobile phones are quickly becom-ing the primary source for social, behavioral and environmental sensing and data col-lection. Today’s smartphones are equipped with increasingly more sensors and accessi-ble data types that enable the collection of literally dozens of signals related to the
phone, its user, and its environment. A great deal of research effort in academia and
industry is put into mining this raw data for higher level sense-making, such as under-standing user context, inferring social networks, learning individual features, and so on.
In many cases, this analysis work is the result of exploratory forays and trial-and-error.
In this work we investigate the properties of learning and inferences of real world data
collected via mobile phones for different sizes of analyzed networks. In particular, we
examine how the ability to predict individual features and social links is incrementally
enhanced with the accumulation of additional data. To accomplish this, we use the
Friends and Family dataset, which contains rich data signals gathered from the
smartphones of 130 adult members of a young-family residential community over the
course of a year and consequently has become one of the most comprehensive mobile
phone datasets gathered in academia to date. Our results show that features such as
ethnicity, age and marital status can be detected by analyzing social and behavioral
signals. We then investigate how the prediction accuracy is increased when the users
sample set grows. Finally, we propose a method for advanced prediction of the maxi-mal learning accuracy possible for the learning task at hand, based on an initial set of
measurements. These predictions have practical implications, such as influencing the
design of mobile data collection campaigns or evaluating analysis strategies."

- by Michael Fire
- •
- Social Networks, Social Network Analysis (SNA), Sample Size, Mobile Sensing

In this paper we present the design, implementation,
evaluation, and user experiences of the NoiseSpy
application, our sound sensing system that turns the mobile
phone into a low-cost data logger for monitoring environmental
noise. It allows users to explore a city area while
collaboratively visualizing noise levels in real-time. The
software combines the sound levels with GPS data in order
to generate a map of sound levels that were encountered
during a journey. We report early findings from the trials
which have been carried out by cycling couriers who were
given Nokia mobile phones equipped with the NoiseSpy
software to collect noise data around Cambridge city.
Indications are that, not only is the functionality of this
personal environmental sensing tool engaging for users, but
aspects such as personalization of data, contextual information,
and reflection upon both the data and its collection, are
important factors in obtaining and retaining their interest.

- by eiman kanjo
- •
- Biopolitics, Citizen Sensing, Smart City, Sustainable City

In this article, we discuss experiences and lessons learned from deploying four mobile sensing applications on off-the-shelf mobile phones within a recreational framework called MobSens that contains elements of health, social, and environmental sensing at both individual and community levels. We describe the main components of our applications, which facilitate logging and external communications. We also outline the challenges faced when building and testing these applications and describe our strategies for overcoming ...

This paper addresses the issue of monitoring spatial environmental phenomena of interest utilizing information collected by a network of mobile, wireless and noisy sensors that can take discrete measurements as they navigate through the environment. It is proposed to employ Gaussian Markov random field (GMRF) represented on an irregular discrete lattice by using the stochastic partial differential equations method to model the physical spatial field. It then derives an GMRF based approach to effectively predict the field at unmeasured locations, given available observations, in both centralized and distributed manners. Furthermore, a novel but efficient optimality criterion is then proposed to design centralized and distributed adaptive sampling strategies for the mobile robotic sensors to find the most informative sampling paths in taking future measurements. By taking advantage of conditional independence property in the GMRF, the adaptive sampling optimization problem is proven to be resolved in a deterministic time. The effectiveness of the proposed approach are compared and demonstrated using pre-published data sets with appealing results.

- by Linh Nguyen
- •
- Robotics, Mobile Robotics, Autonomous Robotics, Wireless Sensor Networks

This paper presents Sentio, a distributed middle-ware designed to provide mobile apps with seamless connectivity to remote sensors when the sensing code and the sensors are not physically on the same device, e.g., when the sensing code is offloaded to the cloud. Sentio presents the apps with virtual sensors that are mapped to remote physical sensors. Virtual sensors can be composed into higher-level sensors, which fuse sensing data from multiple physical sensors. Furthermore, they are mapped to the best available physical sensors when the app starts and re-mapped transparently to other physical sensors at runtime in response to context changes. Sentio was designed to work without modifications to the operating system and to provide low-latency access to remote sensors, which is beneficial to apps with real time-requirements such as mobile games. We have built a prototype of Sentio on Android. We have also developed four apps based on Sentio to understand the programming effort and evaluate the performance. The development of the apps shows that complex sensing tasks can be implemented quickly, benefiting from Sentio's high-level API. The experimental results show that Sentio achieves good real-time performance.

- by Hillol Debnath
- •
- Mobile Cloud Computing, Distributed Sensing, Mobile Sensing
- by eiman kanjo
- •
- Psychology, Emotion, Geography, Urban Geography

Many of researches in controlling smart home system have been proposed. Most of previous approaches in controlling smart home system requires interventions and commands from user. This paper propose a system about smart home based on mobile sensing that does not requires interventions and commands from the user. Mobile Sensing is used to records daily routine activities of the user. Then the system automatically gives a response to user based on his/her daily routine activities. We have implemented our approach to demonstrate the feasibility and effectiveness of using mobile sensing for controlling smart home system. Furthermore, we evaluate our approach and present the details in this paper.

- by Rischan Mafrur
- •
- Sensors and Sensing, Ubiquitous Computing, Smart Home, Mobile Sensing

Structural health monitoring (SHM) and damage detection have attracted great interest in recent decades, in meeting the challenges of assessing the safety condition of large-scale civil structures. By wiring remote sensors directly to a centralized data acquisition system, traditional structural health monitoring systems are usually costly and the installation is time-consuming. Recent advances in wireless sensing technology have made it feasible for structural health monitoring; furthermore, the computational core in a wireless sensing unit offers onboard data interrogation. In addition to wireless sensing, the authors have recently developed a mobile sensing system for providing high spatial resolution and flexible sensor deployment in structural health monitoring. In this study, transmissibility function analysis is embedded in the mobile sensing node to perform onboard and in-network structural damage detection. The system implementation is validated using a laboratory 2D steel portal frame. Simulated damage is applied to the frame structure, and the damage is successfully identified by two mobile sensing nodes that autonomously navigate through the structure.

- by Xiaohua Yi
- •
- Embedded Systems, Structural Health Monitoring, Damage detection, Mobile Sensing