Autonomous service robots and drones are increasingly deployed in urban spaces, yet evaluating their designs in situ is constrained by permits, logistics, and the cost of repeated user studies. We contribute SHOWTIME, a generative video-fusion framework that synthesizes realistic, controllable evaluation clips by combining structured prompts, encoding navigation behavior, proxemic policies, and task context with real urban scenes. Across two studies, participants rated SHOWTIME’s generative videos on par with physical recordings for realism, behavioral plausibility, ecological appropriateness, and comfort, and could not reliably distinguish real from generated footage; perceptual differences were driven by environmental structure and crowd density rather than modality. A follow-up experiment showed that generative prototyping elicits robust perceptual contrasts across design variants: communicative signaling (lights, functional cues) increases acceptability and comfort, whereas erratic motion reduces them. The pipeline achieves over a 10× reduction in per-clip time compared to physical trials, enabling rapid, human-in-the-loop iteration on behaviors and UI cues. Together, these results demonstrate human-level perceptual fidelity and practical viability, positioning SHOWTIME as a cost-effective, scalable method for refining autonomous systems and their interfaces in real-world urban contexts.

Human Activity Recognition (HAR) is vital for proactive health monitoring, but current systems are hindered by fixed-length time windows and the labor-intensive process of collecting annotated data which complicates effective HAR classifier development. To address these challenges, we contribute LLMSEG, a novel two-stage framework that integrates dynamic segmentation and HAR classification. Utilizing an LLM-based segmentation approach with signature activities, LLMSEG dynamically adjusts window lengths based on contextual activity, significantly enhancing accuracy over traditional methods. Additionally, it generates sensor data descriptions enriched with contextual cues, enabling effective few-shot classification without extensive labeled datasets. Systematic evaluations across various HAR datasets demonstrate that LLMSEG outperforms fixed-window methods, balances expressivity and efficiency, and generalizes well across activities. Performance can be further improved by carefully designed contextual prompts. Furthermore, LLMSEG supports robust deployment on both on-device (Raspberry Pi) and edge (laptop) devices. Overall, LLMSEG enhances the generality and reliability of HAR solutions while accommodating diverse deployment scenarios.

Large language models (LLMs) are reshaping the educational landscape, particularly in online learning environments where student supervision is often limited. Early evidence and anecdotal reports suggest that the use of AI-generated content is highly prevalent among students. However, definitive statistics remain elusive, primarily due to the challenges associated with distinguishing between AI-generated and human-generated responses. Establishing clear evidence and effective mechanisms for identifying AI-generated responses is crucial for understanding the significance of this challenge and for developing policies to address it. To tackle these issues, we present a large-scale empirical study on the prevalence of AI-generated content in online education. Our study analyzes over 4045 student responses from an introductory MOOC on the Internet of Things, employing textual analysis techniques to evaluate various metrics for identifying AI-generated responses and understanding their characteristics. Our findings reveal that a significant majority of student responses (up to 90.1%) exhibit strong similarities to AI-generated content in both wording and contextual meaning, regardless of the specific LLM or similarity metric employed. In terms LLMs usage, DeepSeek, Gemini, and Grok are the three most popular LLMs used to generate responses.

Large-scale datasets, particularly those collected from smart devices and Internet of Things sensors, usually exhibit significant temporal and spatial sparsity, resulting in high amounts of missing data. Unless addressed in the analysis, this sparsity can result in substantial gaps and biases as well as limit the generalizability of conclusions drawn from such data. To address this challenge in data quality, we contribute the Sparsity-Aware Reconstruction Framework (SARF) as a novel and unified data fusion and reconstruction framework that enhances data quality and addresses sparsity. SARF analyzes datasets, partitioning the data into segments with similar characteristics, and reconstructs the data in each segment individually by selecting a reconstruction technique that is tailored to the internal temporal-spatial characteristics of the dataset. Through extensive experiments on two representative datasets - mobile application measurements and IoT sensor data from low-cost air quality sensors - we demonstrate that the targeted adaptation of reconstruction strategies employed by SARF significantly enhances the quality of reconstructed data. Our results show the robustness of SARF's performance across spatiotemporal variations, outperforming current state-of-the-art methods by margins up to 68% on average (74% for compressive sensing, 53% for convolutional sparse coding, 78% for deep learning). These findings underscore SARF's potential to enhance datadriven insights across multiple domains, paving the way for more robust analyses of sparsity-affected datasets.

We study the impact of air pollutants on occupants' health by deploying 26 low-cost IoT air quality sensors in the UbiKampus office space at the University of Helsinki. Our data shows significant air quality variation even within meter-scale distances, highlighting the need for detailed indoor air quality monitoring and emphasizing the potential benefits low-cost IoT sensors can bring.

Air pollution represents a critical global health challenge. Traditional air quality monitoring methods, which rely on expensive stations or extensive low-cost sensor networks, often fall short in capturing the fine spatial and temporal variations of pollutants, particularly in urban environments dominated by vehicular emissions. Recent advancements in drone technology offer a novel solution to these limitations, enabling the collection of high-resolution air quality data across different altitudes and environments. We contribute by investigating the potential of drone-based air quality monitoring. Specifically, we conduct measurements in two distinct settings: an industrial site and a residential area. We present analytical findings that highlight the effectiveness of drones in capturing pollutant levels and discuss the key challenges associated with this technology. Our findings underscore the promise of drone-based monitoring in enhancing air quality assessment and inform directions for future research.

3D printing has revolutionized DIY (Do-It-Yourself) IoT prototyping, enabling cost-effective, creative custom device creation. However, this freedom also presents challenges due to the interplay between components within an IoT design, which can influence the overall utility and performance of the prototype. Optimizing these designs is difficult due to limited means of estimating their efficacy. To address this, we introduce TOAD, a novel tool for profiling IoT prototypes and gauging their performance impact. TOAD uses thermal imaging and video analysis to extract and compare design performance characteristics. Unlike existing solutions that only profile overall performance, our tool assesses component interactions and overall design effects. It offers an affordable, non-intrusive method without needing device access or code instrumentation. Extensive benchmarks show TOAD accurately extracts performance data, aiding in selecting the best design for IoT applications. Additionally, it provides insights into how casing factors like thickness and material influence thermal behavior and performance. We demonstrate practical applications by optimizing offloading decisions based on thermal behavior, highlighting casing impacts on design performance. TOAD paves the way for efficient IoT prototype designs, offering a better understanding of component interactions and significantly enhancing the utility of custom IoT designs and their effectiveness.

We present SNAKE, an innovative method that harnesses heat transferred from human touch interactions to estimate product quality. SNAKE offers an accessible and cost-effective solution that seamlessly integrates with existing retail practices; for example, it can be integrated with scales and cashiers already present in shops. Rigorous and systematic experiments demonstrate that SNAKE achieves a high level of accuracy (83%) and outperforms optical sensing and WiFi sensing baselines. We also provide evidence that SNAKE can capture touch interactions of different durations and maintain consistency across diverse user profiles and operating environments. To assess the potential for practical impact, we also carry out an additional user study (N = 100) which suggests that SNAKE has potential to improve consumer purchasing decisions by at least 25% and reduce food waste (or increase promotional opportunities) by 10%–15%. In summary, our contribution offers a novel solution for leveraging smart IoT solutions to support retailing and foster sustainable retail practices.

We develop a portable and affordable solution for estimating personal exposure to black carbon (BC) using low-cost sensors and machine learning. Our approach uses other pollutants and environmental variables as proxies for estimating the concentrations of BC and combines this with machine learning based sensor calibration to improve the quality of the inputs that are used as proxies in the modeling. We extensively validate the feasibility of our approach and demonstrate its benefits with benchmarks conducted on real world data from two different urban locations with different population densities and characteristics. Our results demonstrate that our approach can accurately estimate BC (R2 higher than 0.9) without relying on a dedicated sensor. The results also highlight how calibration is essential for ensuring accurate modeling on low-cost sensor measurements. Our results offer a novel affordable and portable solution that can be used to estimate personal exposure to BC and, more generally, demonstrate how low-cost sensors and proxy modeling can increase the spatiotemporal scale at which information about BC level is available.

Smart spaces, physical spaces that are integrated with sensor-enabled IoT devices, are a powerful paradigm for optimizing the operations of the space and improving its quality for the occupants. Managing the applications and services running in the space is a complex task as the operations of the devices and services are dependent on the physical characteristics of the space, the occupants of the space, and the technologies that are being integrated. Digital twinning, the combination of physical representations with a virtual counterpart, is a potential technology for facilitating the management of smart space devices and services. While digital twins are increasingly adopted in industry, their use in everyday environments remains low due to difficulties in creating and linking the virtual representation with the physical environment. In this paper, we propose our vision for the adoption of digital twinning as a pathway to improve the functions of smart spaces. We derive a generic reference architecture that comprises four layers, covering the physical space, the sensing infrastructure, the network interfaces, and the underlying computational infrastructure. Next, we identify and address key requirements for the uptake of digital twins in smart space and assess their benefits using the ascendancy model of business analytics. Finally, to demonstrate the practicality of digital twinning, we present a proof-of-concept digital twin for the TellUs smart space at the University of Oulu in Finland and use it to highlight the potential benefits of different ascendancy levels.

We study user's valuations of smartphone sensing resources and the factors mediating them through a systematic auction study with 108 bids from$N=18$participants, two resource use conditions [fixed battery (FB) and variable battery (VB)] and three sensors (camera, microphone, and GPS) with differing energy and privacy costs. We use a second-price sealed-bid reverse auction as this allows us to elicit the participants’ truthful perceived value for sharing resources. We show that most users would be willing to share even highly-privacy intrusive sensors if they are sufficiently compensated. At the FB level, participants placed much lower value for sharing GPS (€13) than camera (€30) or microphone (€32.5). The values people place on sharing access to resources generally reflect four considerations: 1) the perceived value of the sensor type; 2) the value of the data captured by the sensor; 3) the impact of sharing on the device; and 4) personal variations related to sharing motives, personal tendencies, and the broader sharing context. We address the practical impact of our results by presenting two case studies (collaborative sensing and collaborative AI). Finally, we derive design implications for sharing sensing resources on personal devices.

AI-assisted interactive annotation is a powerful way to facilitate data annotation—a prerequisite for constructing robust AI models. While AI-assisted interactive annotation has been extensively studied in static settings, less is known about its usage in dynamic scenarios where the annotators operate under time and cognitive constraints, e.g., while detecting suspicious or dangerous activities from real-time surveillance feeds. Understanding how AI can assist annotators in these tasks and facilitate consistent annotation is paramount to ensure high performance for AI models trained on these data. We address this gap in interactive machine learning (IML) research, contributing an extensive investigation of the benefits, limitations, and challenges of AI-assisted annotation in dynamic application use cases. We address both the effects of AI on annotators and the effects of (AI) annotations on the performance of AI models trained on annotated data in real-time video annotations. We conduct extensive experiments that compare annotation performance at two annotator levels (expert and non-expert) and two interactive labeling techniques (with and without AI assistance). In a controlled study with \(N=34\) annotators and a follow-up study with 51,963 images and their annotation labels being input to the AI model, we demonstrate that the benefits of AI-assisted models are greatest for non-expert users and for cases where targets are only partially or briefly visible. The expert users tend to outperform or achieve similar performance as the AI model. Labels combining AI and expert annotations result in the best overall performance as the AI reduces overflow and latency in the expert annotations. We derive guidelines for the use of AI-assisted human annotation in real-time dynamic use cases.

Unmanned Aerial Vehicles (UAVs) equipped with air quality sensors offer a powerful solution for increasing the spatial and temporal resolution of air quality data, searching and detecting emission sources, and monitoring emissions from fixed and mobile sources. Despite the numerous advantages of using UAVs, their use, however, presents several challenges that limit their broader adoption. For example, UAVs require efficient algorithms and components to minimize power consumption, the overall payload used on UAVs needs to be small to ensure optimal portability which poses limitations on the sensors that can be integrated with UAVs, and there is a need for specialized algorithms, e.g., for identifying and locating air pollution sources. Currently, most solutions for UAV-based air quality monitoring focus on specific challenges or demonstrating the potential of using UAVs, and there is a lack of comprehensive overview of the research field and its open challenges. In this paper, we contribute a systematic review of UAV-based air quality monitoring, highlighting and analyzing technical solutions and challenges, and identifying open challenges with the aim of providing a research roadmap for the path forward.

Underwater environments are emerging as a new frontier for data science thanks to an increase in deployments of underwater sensor technology. Challenges in operating computing underwater combined with a lack of high-speed communication technology covering most aquatic areas means that there is a significant delay between the collection and analysis of data. This in turn limits the scale and complexity of the applications that can operate based on these data. In this article, we develop underwater fog computing support using low-cost micro-clouds and demonstrate how they can be used to deliver cost-effective support for data-heavy underwater applications. We develop a proof-of-concept micro-cloud prototype and use it to perform extensive benchmarks that evaluate the suitability of underwater micro-clouds for diverse underwater data science scenarios. We conduct rigorous tests in both controlled and field deployments, using river and sea waters. We also address technical challenges in enabling underwater fogs, evaluating the performance of different communication interfaces and demonstrating how accelerometers can be used to detect the likelihood of communication failures and determine which communication interface to use. Our work offers a cost-effective way to increase the scale and complexity of underwater data science applications, and demonstrates how off-the-shelf devices can be adopted for this purpose.

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.

We contribute a novel model evaluation technique that divides available measurements into training and testing sets in a way that adheres to the requirements imposed on professional monitoring stations. We perform extensive and systematic experiments with a wide range of state-of-the-art calibration models to demonstrate that our approach provides accurate insights about the performance of calibration models in real-world deployments, while at the same time highlighting issues with evaluation techniques used in previous works. Among others, our results show that although trained and tested in the same location, calibration errors can exhibit deviation up to 116% depending on the evaluation protocol that is being adopted. We also demonstrate that models trained with continuous data can suffer up to 76% greater error when tested with data coming from diverse environmental conditions. In contrast, when models are trained and tested with our method, the variability of errors is significantly reduced and the robustness of calibration models is significantly improved. The overall performance improvements depend on pollutant concentration, ranging from 10% for low concentrations to 90% for high concentrations that represent conditions that are most dangerous for human health.

The ubiquity of smart devices, combined with a lack of information about data garnered by them, make privacy a significant challenge for adopting smart devices. Ensuring users can safeguard their privacy without compromising the devices’ functionality requires effective yet intuitive ways to manage personal privacy preferences. Current solutions for privacy management are severely lacking as they are ineffective in making users aware of potential privacy risks or how to mitigate them and as they offer limited support for interaction. As our first contribution, we develop a novel AR privacy management interface (PARA) that uses AR visualization to contextualize data disclosure and improve user’s perceptions of privacy threats. Besides offering support for enhancing user’s privacy perceptions, our interface supports privacy control on compatible devices through privacy-enhancing technologies. As our second contribution, we systematically study factors affecting privacy perceptions and privacy control for two device classes (smart camera and smart speaker) through a user study with N = 32 participants. Our results show that PARA’s contextualization and visualization of privacy disclosure strongly affect the participants’ privacy perceptions. For privacy control, we demonstrate that our prototype improves the participant’s capability to identify risks and provides an effective and easy-to-use mechanism for controlling privacy disclosure, in contrast to existing state-of-the-art privacy management interfaces.

Research on smart device privacy has consistently highlighted how privacy is an important concern for users, but they fail to act on their concerns. While this discrepancy between user perceptions and actions has been consistently reported, currently there is a limited understanding of why this is the case or how the situation can be ameliorated. This paper systematically studies how visualizations in privacy assistants can improve the situation, reporting on two studies that explore the users' privacy perceptions in smart device ecosystems. The first study shows that displaying device location and data type reduces the users' privacy perceptions. Participants also weigh the use of media such as online news as a source to inform users about the possible inferences. The second study analyzes participants' preferences to visualize smart device information and privacy policies using augmented reality. Through these two studies, we derive insights and guidelines on how to design effective privacy assistants and to improve user's knowledge of risks associated with data disclosure in smart home scenarios.

Wireless sensing builds upon machine learning shows encouraging results. However, adopting wireless sensing as a large-scale solution remains challenging as experiences from deployments have shown the performance of a machine-learned model to suffer when there are changes in the environment, e.g., when furniture is moved or when other objects are added or removed from the environment. We present Rise, a novel solution for enhancing the robustness and performance of learning-based wireless sensing techniques against such changes during a deployment. Rise combines probability and statistical assessments together with anomaly detection to identify samples that are likely to be misclassified and uses feedback on these samples to update a deployed wireless sensing model. We validate Rise through extensive empirical benchmarks by considering 11 representative sensing methods covering a broad range of wireless sensing tasks. Our results show that Rise can identify 92.3% of misclassifications on average. We showcase how Rise can be combined with incremental learning to help wireless sensing models retain their performance against dynamic changes in the operating environment to reduce the maintenance cost, paving the way for learning-based wireless sensing to become capable of supporting long-term monitoring in complex everyday environments.

We contribute MIDAS as a novel sensing solution for characterizing everyday objects using thermal dissipation. MIDAS takes advantage of the fact that anytime a person touches an object, it results in heat transfer. By capturing and modeling the dissipation of the transferred heat, e.g., through the decrease in the captured thermal radiation, MIDAS can characterize the object and determine its material. We validate MIDAS through extensive empirical benchmarks and demonstrate that MIDAS offers an innovative sensing modality that can recognize a wide range of materials – with up to 83% accuracy – and generalize to variations in the people interacting with objects.

The ever-increasing ubiquity of smart devices is creating new opportunities for people to interact and engage with digital information using multiple devices. In the simplest case this can refer to choosing which device to use for a particular task (e.g., phone, laptop or smartwatch), whereas a more complex example is simultaneously taking advantage of the capabilities of different devices (e.g., laptop and smart TV). Despite these types of opportunities becoming increasing available, currently the full potential of multi-device interactions is not being realized as people struggle to take advantage of them. As our first contribution, we study people’s willingness to engage with multi-device interactions and rank the factors that mediate this response through an online survey (N = 60). Our results show that users are strongly in favour of using multiple devices, but lack the awareness or information to engage with them, or feel that establishing the interactions is too laborious and would disrupt the fluidity of the interactions. Motivated by this result, as our second contribution we design and evaluate intelligent shifting cues, visualizations that offer information about available interaction opportunities and how to establish them, and study how they influence users willingness to engage in multi-device interactions. Results of our study show that the cues can be effective in helping people to engage with multiple devices, but that the suitability of the proposed device and fit with task are important mediating factors. We end the paper by deriving design implications for intelligent systems that can support people in engaging with multi-device interactions.

Widespread sensing devices enable a world in which physical spaces become personalised in the presence of mobile users. An important example of such personalisation is the use of pervasive displays to show content that matches the requirements of proximate viewers. Despite prior work on prototype systems that use mobile devices to personalise displays, no significant attempts to trial such systems have been carried out. In this article, we report on our experiences of designing, developing and operating the world’s first comprehensive display personalisation service for mobile users. Through a set of rigorous quantitative measures and 11 potential user/stakeholder interviews, we demonstrate the success of the platform in realising display personalisation, and offer a series of reflections to inform the design of future systems.

The significance of air pollution and the problems associated with it are fueling deployments of air quality monitoring stations worldwide. The most common approach for air quality monitoring is to rely on environmental monitoring stations, which unfortunately are very expensive both to acquire and to maintain. Hence, environmental monitoring stations are typically sparsely deployed, resulting in limited spatial resolution for measurements. Recently, low-cost air quality sensors have emerged as an alternative that can improve the granularity of monitoring. The use of low-cost air quality sensors, however, presents several challenges: They suffer from cross-sensitivities between different ambient pollutants; they can be affected by external factors, such as traffic, weather changes, and human behavior; and their accuracy degrades over time. Periodic re-calibration can improve the accuracy of low-cost sensors, particularly with machine-learning-based calibration, which has shown great promise due to its capability to calibrate sensors in-field. In this article, we survey the rapidly growing research landscape of low-cost sensor technologies for air quality monitoring and their calibration using machine learning techniques. We also identify open research challenges and present directions for future research.

WiFi networks are increasingly subjected to cross-technology interference with emerging IoT and even mobile communication solutions all crowding the 2.4 GHz ISM band where WiFi networks conventionally operate. Due to the diversity of interference sources, maintaining high level of network performance is becoming increasing difficult. Recently, deep learning based interference detection has been proposed as a potentially powerful way to identify sources of interference and to provide feedback on how to mitigate their effects. The performance of such approaches has been shown to be impressive in controlled evaluations. However, little information exists on how they generalize to the complexity of everyday environments. In this paper, we contribute by conducting a comprehensive performance evaluation of deep learning based interference detection. In our evaluation, we consider five orthogonal but complementary metrics: correctness, overfitting, robustness, efficiency, and interpretability. Our results show that, while deep learning indeed has excellent correctness (i.e., detection accuracy), it can be prone to noise in measurements (e.g., struggle when transmission power is dynamically adjusted) and suffers from poor interpretability. Deep learning is also highly sensitive to the quality and quantity of training data, with performance decreasing rapidly when the training and testing measurements come from environments with different characteristics. To compensate for weaknesses of deep learning, as our second contribution we propose a novel signal modeling approach for interference detection and compare it against deep learning. Our results demonstrate that, in terms of errors, there are some differences across the two approaches, with signal modeling being better at identifying technologies that rely on frequency hopping or that have dynamic spectrum signatures but suffering in other cases. Based on our results, we draw guidelines for improving interference detection performance.

Web technology underpins many interactive mobile applications. However, energy-efficient mobile web interactions is an outstanding challenge. Given the increasing diversity and complexity of mobile hardware, any practical optimization scheme must work for a wide range of users, mobile platforms and web workloads. This paper presents CAMEL, a novel energy optimization system for mobile web interactions. CAMEL leverages machine learning techniques to develop a smart, adaptive scheme to judiciously trade performance for reduced power consumption. Unlike prior work, CAMEL directly models how a given web content affects the user expectation and uses this to guide energy optimization. It goes further by employing transfer learning and conformal predictions to tune a previously learned model in the end-user environment and improve it over time. We apply CAMEL to Chromium and evaluate it on four distinct mobile systems involving 1,000 testing webpages and 30 users. Compared to four state-of-the-art web-event optimizers, CAMEL delivers 22% more energy savings, but with 49% fewer violations on the quality of user experience, and exhibits orders of magnitudes less overhead when targeting a new computing environment.

Human conversation is characterized by brief pauses and so-called turn-taking behavior between the speakers. In the context of VoIP, this means that there are frequent periods where the microphone captures only background noise - or even silence whenever the microphone is muted. The bits transmitted from such silence periods introduce overhead in terms of data usage, energy consumption, and network infrastructure costs. In this paper, we contribute by shedding light on these costs for VoIP applications. We systematically measure the performance of six popular mobile VoIP applications with controlled human conversation and acoustic setup. Our analysis demonstrates that significant savings can indeed be achieved - with the best performing silence suppression technique being effective on 75% of silent pauses in the conversation in a quiet place. This results in 2-5 times data savings, and 50-90% lower energy consumption compared to the next best alternative. Even then, the effectiveness of silence suppression can be sensitive to the amount of background noise, underlying speech codec, and the device being used. The codec characteristics and performance do not depend on the network type. However, silence suppression makes VoIP traffic network friendly as much as VoLTE traffic. Our results provide new insights into VoIP performance and offer a motivation for further enhancements to a wide variety of voice assisted applications, such as home assistants and other IoT devices.

Pervasive availability of programmable smart de-vices is giving rise to sensing and computing scenarios that involve collaboration between multiple devices. Maximizing the benefits of collaboration requires careful selection of devices with whom to collaborate as otherwise collaboration may be interrupted prematurely or be sub-optimal for the characteristics of the task at hand. Existing research on collaborative scenarios has mostly focused on providing mechanisms that can establish and harness collaboration, without considering how to maximally benefit from it. In this paper, we contribute by developing COSINE as a novel approach for selecting collaborators in multi-device computing scenarios. COSINE identifies and recommends collaborators based on a novel information theoretic measure based on Markov trajectory entropy. Rigorous experimental benchmarks carried out using a large-scale dataset of device-to-device encounters demonstrate that COSINE can significantly improve collaboration benefits compared to current state-of-the-art solutions, increasing expected duration of collaboration and reducing variability of collaborations.

Air pollution is a major problem in urban areas, where high population density is accompanied with excess anthropomorphic emissions impacting the environment and increasing health effects. Highly accurate air quality monitoring stations have been used to monitor the severity of the problem and warn citizens. However, air quality can vary sharply even within the same city block, and pollution exposure can vary even 30% between individuals living in the same residence. Therefore, a dense deployment of air quality sensors is needed to detect these variations, and protect citizens from overexposure. Low-cost air quality sensors make it possible to densely instrument a city and detect hot spots as they happen. However, thus far limited information exists on their accuracy and practicability. In this paper, we conduct a 44-day measurement campaign to assess performance of low-cost air quality monitors under different environmental conditions. As practical use case, we consider pollution hot spot detection. Our results show that the mean error of low-cost sensors is small, but the variation in error is significantly larger than with reference sensors. We also show that the accuracy is sufficient for applications relying on variations in air quality index values, such as hot spot detection.

Poor indoor air quality is a significant burden to society that can cause health issues and decrease productivity. According to research, indoor air quality is intrinsically linked with human activity and mobility. Indeed, mobility is directly linked with transfer of small particles (e.g. PM2.5) and extent of activity affects production of CO2. Currently, however, estimation of indoor quality is difficult, requiring deployment of highly specialized sensing devices which need to be carefully placed and maintained. In this paper, we contribute by examining the suitability of infrastructure-based motion detectors for indoor air quality estimation. Such sensors are increasingly being deployed into smart environments, e.g., to control lighting and ventilation for energy management purposes. Being able to take advantage of these sensors would thus provide a cost-effective solution for indoor quality monitoring without need for deploying additional sensors. We perform a feasibility study considering measurements collected from a smart office environment having a dense deployment of motion detectors and correlating measurements obtained from motion detectors against air quality values. We consider two main pollutants, PM2.5and CO2, and demonstrate that there indeed is a connection between extent of movement and PM2.5concentration. However, for CO2, no relationship can be established, mostly due to difficulties in separating between people passing by and those residing long-term in the environment.

Crowdsourcing is a powerful technique for bootstrapping sensing systems that are based on wireless signals. For example, wireless sensing systems can ask users to contribute training data and localization systems (such as WiFi fingerprinting) can take advantage of wireless measurements provided by users of the system. Indeed, previous research has demonstrated that crowdsourcing can reduce labor efforts needed to deploy and initialize wireless sensing systems by several orders of magnitude, without compromising on system performance. Despite the many benefits of crowdsourcing, current methods suffer from one significant drawback, namely that they are highly sensitive to variations in devices capturing the measurements. Indeed, as we demonstrate in this paper, cross-device variations can decrease performance of crowdsourced bootstrapping approaches up to 70of radio maps used for localization and to make them robust against cross-device variations in wireless signals. We evaluate our framework by considering WiFi fingerprinting based localization as a representative example of applications that benefit from our approach. Our results demonstrate up to 1.8m localization error, and 18.7.

Wearable thermal imaging is emerging as a powerful and increasingly affordable sensing technology. Current thermal imaging solutions are mostly based on uncooled forward looking infrared (FLIR), which is susceptible to errors resulting from warming of the camera and the device casing it. To mitigate these errors, a blackbody calibration technique where a shutter whose thermal parameters are known is periodically used to calibrate the measurements. This technique, however, is only accurate when the shutter's temperature remains constant over time, which rarely is the case. In this paper, we contribute by developing a novel deep learning based calibration technique that uses battery temperature measurements to learn a model that allows adapting to changes in the internal thermal calibration parameters. Our method is particularly effective in continuous sensing where the device casing the camera is prone to heating. We demonstrate the effectiveness of our technique through controlled benchmark experiments which show significant improvements in thermal monitoring accuracy and robustness.

We contribute by quantifying the effect of network latency and battery consumption on mobile app performance and retention, i.e., user's decisions to continue or stop using apps. We perform our analysis by fusing two large-scale crowdsensed datasets collected by piggybacking on information captured by mobile apps. We find that app performance has an impact in its retention rate. Our results demonstrate that high energy consumption and high latency decrease the likelihood of retaining an app. Conversely, we show that reducing latency or energy consumption does not guarantee higher likelihood of retention as long as they are within reasonable standards of performance. However, we also demonstrate that what is considered reasonable depends on what users have been accustomed to, with device and network characteristics, and app category playing a role. As our second contribution, we develop a model for predicting retention based on performance metrics. We demonstrate the benefits of our model through empirical benchmarks which show that our model not only predicts retention accurately, but generalizes well across application categories, locations and other factors moderating the effect of performance.

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.

We contribute by developing GeoMatch as a novel, scalable, and efficient big-data pipeline for large-scale map matching on Apache Spark. GeoMatch improves existing spatial big data solutions by utilizing a novel spatial partitioning scheme inspired by Hilbert space-filling curves. Thanks to the partitioning scheme, GeoMatch can effectively balance operations across different processing units and achieve significant performance gains. We demonstrate the effectiveness of GeoMatch through rigorous and extensive benchmarks that consider data sets containing large-scale urban spatial data sets ranging from 166, 253 to 3.78 billion location measurements. Our results show over 17-fold performance improvements compared to previous works while achieving better processing accuracy than current solutions (97.48%).

While mobile apps have become an integral part of everyday life, little is known about the factors that govern their usage. Particularly the role of geographic and cultural factors has been understudied. This article contributes by carrying out a large-scale analysis of geographic, cultural, and demographic factors in mobile usage. We consider app usage gathered from 25,323 Android users from 44 countries and 54,776 apps in 55 categories, and demographics information collected through a user survey. Our analysis reveals significant differences in app category usage across countries and we show that these differences, to large degree, reflect geographic boundaries. We also demonstrate that country gives more information about application usage than any demographic, but that there also are geographic and socio-economic subgroups in the data. Finally, we demonstrate that app usage correlates with cultural values using the Value Survey Model of Hofstede as a reference of cross-cultural differences.

We present CrossSense, a novel system for scaling up WiFi sensing to new environments and larger problems. To reduce the cost of sensing model training data collection, CrossSense employs machine learning to train, off-line, a roaming model that generates from one set of measurements synthetic training samples for each target environment. To scale up to a larger problem size, CrossSense adopts a mixture-of-experts approach where multiple specialized sensing models, or experts, are used to capture the mapping from diverse WiFi inputs to the desired outputs. The experts are trained offline and at runtime the appropriate expert for a given input is automatically chosen. We evaluate CrossSense by applying it to two representative WiFi sensing applications, gait identification and gesture recognition, in controlled single-link environments. We show that CrossSense boosts the accuracy of state-of-the-art WiFi sensing techniques from 20% to over 80% and 90% for gait identification and gesture recognition respectively, delivering consistently good performance - particularly when the problem size is significantly greater than that current approaches can effectively handle.

The use of intelligent information technologies has the means to provide ecological information just-in-time, thus alleviating consumers' cognitive burden at the time of purchase. We propose a computational framework for supporting consumer awareness of the ecological impact of products they consider purchasing at point of sale. The proposed framework permits consulting multiple information sources through diverse access interfaces, combined with a recommendation engine to score product greenness. We evaluate our approach in terms of usability, performance, and user-influence tests through two conceptual prototypes: an online store and an augmented reality interface to use at physical stores. Our findings suggest that providing ecological information at the time of purchase is able to direct consumers' preference towards products that are ecological and away from products that are not; consumers also express willingness to pay slightly more for ecological products. The experimental results obtained with the interface prototypes are statistically significant.

Computational offloading can improve user experience of mobile apps through improved responsiveness and reduced energy footprint. A fundamental challenge in offloading is to distinguish situations where offloading is beneficial from those where it is counterproductive. Currently, offloading decisions are predominantly based on profiling performed on individual devices. While significant gains have been shown in benchmarks, these gains rarely translate to real-world use due to the complexity of contexts and parameters that affect offloading. We contribute by proposing crowdsensed evidence traces as a novel mechanism for improving the performance of offloading systems. Instead of limiting to profiling individual devices, crowdsensing enables characterizing execution contexts across a community of users, providing better generalisation and coverage of contexts. We demonstrate the feasibility of using crowdsensing to characterize offloading contexts through an analysis of two crowdsensing datasets. Motivated by our results, we present the design and development of the EMCO toolkit and platform as a novel solution for computational offloading. Experiments carried out on a testbed deployment in Amazon EC2 Ireland demonstrate that EMCO can consistently accelerate app execution while at the same time reduce energy footprint. We also demonstrate that EMCO provides better scalability than current cloud platforms, being able to serve a larger number of clients without variations in performance. Ourframework, use cases, and tools are available as open source from GitHub.

The Carat project started in 2012 has collected over 1.5 TB of data from over 850,000 mobile users all over the world. The project uses Apache Thrift to transmit data, and Apache Spark to run data analysis tasks, and the gist of the Carat analysis method has been published. While the Carat application code is open source, the data is much harder to share because of its size and privacy concerns. This paper outlines the challenges in sharing such a large-scale dataset with detailed information about smart devices, applications, and their users, and presents some solutions to these challenges.

We propose crowd replication as a low-effort, easy to implement and cost-effective mechanism for quantifying the uses, activities, and sociability of public spaces. Crowd replication combines mobile sensing, direct observation, and mathematical modeling to enable resource efficient and accurate quantification of public spaces. The core idea behind crowd replication is to instrument the researcher investigating a public space with sensors embedded on commodity devices and to engage him/her into imitation of people using the space. By combining the collected sensor data with a direct observations and population model, individual sensor traces can be generalized to capture the behavior of a larger population. We validate the use of crowd replication as a data collection mechanism through a field study conducted within an exemplary metropolitan urban space. Results of our evaluation show that crowd replication accurately captures real human dynamics (0.914 correlation between indicators estimated from crowd replication and visual surveillance) and captures data that is representative of the behavior of people within the public space.

We propose the use of a single continuous gesture as a novel, intuitive, and efficient mechanism to authenticate a secure communication channel. Our approach builds on a novel algorithm for encoding (at least 20-bits) authentication information as a single continuous gesture, referred to as a checksum gesture. By asking the user to perform the generated gesture, a secure channel can be authenticated. Results from a controlled user experiment (N = 13 participants, 1022 trials) demonstrate the feasibility of our technique, showing over 90% success rate in establishing a secure communication channel despite relying on complex gesture patterns. The authentication times of our method are over three-folds faster than with previous gesture-based solutions. The average execution time of a gesture is 5:7 seconds in our study, which is comparable to the input time of conventional text input based PIN authentication. Our approach is particularly well-suited for scenarios involving wearable devices that lack conventional input capabilities, e.g., pairing a smartwatch with an interactive display.

Template-based approaches are currently the most popular gesture recognition solution for interactive systems as they provide accurate and runtime efficient performance in a wide range of applications. The basic idea in these approaches is to measure similarity between a user gesture and a set of pre-recorded templates, and to determine the appropriate gesture type using a nearest neighbor classifier. While simple and elegant, this approach performs well only when the gestures are relatively simple and unambiguous. In increasingly many scenarios, such as authentication, interactive learning, and health care applications, the gestures of interest are complex, consist of multiple sub-strokes, and closely resemble other gestures. Merely considering the shape of the gesture is not sufficient for these scenarios, and robust identification of the constituent sequence of sub-strokes is also required. The present paper contributes by introducing Gestimator, a novel gesture recognizer that combines shape and stroke-based similarity into a sequential classification framework for robust gesture recognition. Experiments carried out using three datasets demonstrate significant performance gains compared to current state-of-the-art techniques. The performance improvements are highest for complex gestures, but consistent improvements are achieved even for simple and widely studied gesture types.

Synthetic fingerprint generation using propagation models has been proposed as a cost-effective way to reduce the deployment cost of WiFi positioning systems. Contrary to traditional WiFi positioning systems, which require recording WiFi fingerprints together with ground truth locations, fingerprint generation attempts to automatically populate the radio map using theoretical properties of radio signals. Current solutions for fingerprint generation, however, are extremely complex, requiring complicated modeling of both the signal characteristics and the environment. The present paper contributes by demonstrating that simpler modeling, where only the path-loss exponent is learned from empirical measurements, is sufficient for practical purposes reaching accuracy comparable to carrying out a detailed survey.

The question “Where has my battery life gone?” remains a common source of frustration for many smartphone users. With the increased complexity of smartphone applications, and the increasing number of system settings affecting them, understanding and optimizing battery use has become a difficult chore. The present paper develops a novel approach for constructing energy models from crowdsourced measurements. In contrast to previous approaches, which have focused on the effect of a specific sensor, system setting or application, our approach can simultaneously capture relationships between multiple factors, and provide a unified view of the energy state of the mobile device. We demonstrate the validity of using crowdsourced measurements for constructing battery models through a combination of large-scale analysis of a dataset containing battery discharge and system state measurements and hardware power measurements. The results indicate that the models captured by our approach are both in line with previous studies on battery consumption and empirical measurements, providing a cost-effective way to construct energy models during normal operations of the device. The analysis also provides several new insights about battery consumption. For example, our analysis shows the energy use of high CPU activity with automatic screen brightness is actually higher (resulting in around 9 minutes shorter battery lifetime on average) than with a medium CPU load and manual screen brightness; a Wi-Fi signal strength drop of one bar can result in a battery life loss of over 13%; and a smartphone sitting in the sun can experience over 50% worse battery life than one indoors in cool conditions.

Many mobile location-aware applications require the sampling of trajectory data accurately over an extended period of time. However, continuous trajectory tracking poses new challenges to the overall battery life of the device, and thus novel energy-efficient sensor management strategies are necessary for improving the lifetime of such applications. Additionally, such sensor management strategies are required to provide a high and application-adjustable level of robustness regardless of the user’s transportation mode. In this article, we extend and further analyze the sensor management strategies of the EnTracked$_{T}$system that intelligently determines when to sample different on-device sensors (e.g., accelerometer, compass and GPS) for trajectory tracking. Specifically, we propose the concept of situational bounding to improve and parameterize the robustness of sensor management strategies for trajectory tracking. We demonstrate the effectiveness of our proposed approach by performing a series of emulation experiments on real world data sets collected from different modes of transportation (including walking, running, biking and commuting by car) on mobile devices from two different platforms. Thorough experimental analyses indicate that our system can save significant amounts of battery power compared to the state-of-the-art position tracking systems, while simultaneously maintaining robustness and accuracy bounds as required by diverse location-aware applications.

Linear acceleration is an important enabler for many applications of mobile and wearable activity recognition. The most common approach for estimating linear acceleration is to estimate the gravity component of accelerometer measurements and to project gravity-eliminated accelerometer measurements o

Zero-Interaction Authentication (ZIA) refers to approaches that authenticate a user to a verifier (terminal) without any user interaction. Currently deployed ZIA solutions are predominantly based on the terminal detecting the proximity of the user's personal device, or a security token, by running an authentication protocol over a short-range wireless communication channel. Unfortunately, this simple approach is highly vulnerable to low-cost and practical relay attacks which completely offset the usability benefits of ZIA. The use of contextual information, gathered via on-board sensors, to detect the co-presence of the user and the verifier is a recently proposed mechanism to resist relay attacks. In this paper, we systematically investigate the performance of different sensor modalities for co-presence detection with respect to a standard Dolev-Yao adversary. First, using a common data collection framework run in realistic everyday settings, we compare the performance of four commonly available sensor modalities (WiFi, Bluetooth, GPS, and Audio) in resisting ZIA relay attacks, and find that WiFi is better than the rest. Second, we show that, compared to any single modality, fusing multiple modalities improves resilience against ZIA relay attacks while retaining a high level of usability. Third, we motivate the need for a stronger adversarial model to characterize an attacker who can compromise the integrity of context sensing itself. We show that in the presence of such a powerful attacker, each individual sensor modality offers very low security. Positively, the use of multiple sensor modalities improves security against such an attacker if the attacker cannot compromise multiple modalities simultaneously.

There is little information from independent sources in the public domain about mobile malware infection rates. The only previous independent estimate (0.0009%) [11], was based on indirect measurements obtained from domain-name resolution traces. In this paper, we present the first independent study of malware infection rates and associated risk factors using data collected directly from over 55,000 Android devices. We find that the malware infection rates in Android devices estimated using two malware datasets (0.28% and 0.26%), though small, are significantly higher than the previous independent estimate. Based on the hypothesis that some application stores have a greater density of malicious applications and that advertising within applications and cross-promotional deals may act as infection vectors, we investigate whether the set of applications used on a device can serve as an indicator for infection of that device. Our analysis indicates that, while not an accurate indicator of infection by itself, the application set does serve as an inexpensive method for identifying the pool of devices on which more expensive monitoring and analysis mechanisms should be deployed. Using our two malware datasets we show that this indicator performs up to about five times better at identifying infected devices than the baseline of random checks. Such indicators can be used, for example, in the search for new or previously undetected malware. It is therefore a technique that can complement standard malware scanning. Our analysis also demonstrates a marginally significant difference in battery use between infected and clean devices.

We present PromotionRank, a technique for generating a personalized ranking of grocery product promotions based on the contents of the customer’s personal shopping list. PromotionRank consists of four phases. First, information retrieval techniques are used to map shopping list items onto potentially relevant product categories. Second, since customers typically buy more items than what appear on their shopping lists, the set of potentially relevant categories is expanded using collaborative filtering. Third, we calculate a rank score for each category using a statistical interest criterion. Finally, the available promotions are ranked using the newly computed rank scores. To validate the different phases, we consider 12 months of anonymized shopping basket data from a large national supermarket. To demonstrate the effectiveness of PromotionRank, we also present results from two user studies. The first user study was conducted in a controlled setting using shopping lists of different lengths, whereas the second study was conducted within a large national supermarket using real customers and their personal shopping lists. The results of the two studies demonstrate that PromotionRank is able to identify promotions that are considered both relevant and interesting. As part of the second study, we used PromotionRank to identify relevant promotions to advertise and measure the influence of the advertisements on purchases. The results of this evaluation indicate that PromotionRank is also capable of targeting advertisements, improving sales compared to a baseline that selects random advertisements.

We present novel accelerometer-based techniques for accurate and fine-grained detection of transportation modes on smartphones. The primary contributions of our work are an improved algorithm for estimating the gravity component of accelerometer measurements, a novel set of accelerometer features that are able to capture key characteristics of vehicular movement patterns, and a hierarchical decomposition of the detection task. We evaluate our approach using over 150 hours of transportation data, which has been collected from 4 different countries and 16 individuals. Results of the evaluation demonstrate that our approach is able to improve transportation mode detection by over 20% compared to current accelerometer-based systems, while at the same time improving generalization and robustness of the detection. The main performance improvements are obtained for motorised transportation modalities, which currently represent the main challenge for smartphone-based transportation mode detection.

We introduce CoSense, a collaborative sensing platform for mobile devices that opportunistically distributes sensing tasks between familiar devices in close proximity. We use empirical energy measurements together with data collected from everyday transportation behaviour to demonstrate that our solution can significantly reduce power consumption while maintaining the best possible sensing accuracy.

We present Ma$$iv€, an intelligent mobile grocery assistant that provides support for the customer during the entire shopping process. To guide the design of Ma$$iv€, we conducted a user study that explored customer preferences regarding features in a mobile grocery aid. We first describe the study and its results, after which we introduce the design principles and design of Ma$$iv€. We also describe the features that Ma$$iv€ supports and discuss functionalities that we are integrating into Ma$$iv€. As part of the discussion, we describe technical challenges that we have encountered during our development efforts.

Advances in positioning technologies have resulted in a surge of location-based recommendation systems for mobile devices. A central challenge in these systems is to avoid the so-called filter bubble effect, i.e., that people are not only exposed to information that is in line with their personal ecosystem, but that they can also discover novel and otherwise interesting content. We present results from a field study of a mobile recommendation system that has been aimed to support serendipitous discovery of events at an urban culture festival. Results from the study indicate that suitably designed recommendations together with access to relevant external information sources can lead to serendipitous discovery of new content, such as new artists, bands or individual songs. Our results also indicate that proximity has little effect on the effectiveness of serendipitous recommendations.

The workshop explores the interactions between location awareness and Dual/Mixed Reality in smart environments and the impact on culture and society. The main scope of this workshop is: How can the Dual Reality paradigm be used to improve applications in smart environments and which new possibilities are opened up by these paradigms? This includes positioning methods and location-based services using the DR paradigm, such as navigation services and group interaction services (location-based social signal processing). The workshop is also open to discuss sensor and actuator technologies that may help to realize the synchronization of the virtual and real world.

Visual search in large real-world scenes is both time consuming and frustrating, because the search becomes serial when items are visually similar. Tactile guidance techniques can facilitate search by allowing visual attention to focus on a subregion of the scene. We present a technique for dynamic tactile cueing that couples hand position with a scene position and uses tactile feedback to guide the hand actively toward the target. We demonstrate substantial improvements in task performance over a baseline of visual search only, when the scene's complexity increases. Analyzing task performance, we demonstrate that the effect of visual complexity can be practically eliminated through improved spatial precision of the guidance.

The LAMDa workshop aims at discussing the impact of Dual Reality (DR) and Mixed Reality (MR) on location awareness and other applications in smart environments. Virtual environments - which are an essential part of dual and mixed realities - can be used to create new applications and to enhance already existing applications in the real world. On the other hand, existing sensors in the real world can be used to enhance the virtual world as well. The combination of both worlds can be well illustrated by location-based services, such as location-based advertising.

Using landmark-based navigation instructions is widely considered to be the most effective strategy for presenting navigation instructions. Among other things, landmark-based instructions can reduce the user's cognitive load, increase confidence in navigation decisions and reduce the number of navigational errors. Their main disadvantage is that the user typically focuses considerable amount of attention on searching for landmark points, which easily results in poor awareness of the user's surroundings. In indoor spaces, this implies that landmark-based instructions can reduce the attention the user pays on advertisements and commercial displays, thus rendering the assistance commercially inviable. To better understand how landmark-based instructions influence the user's awareness of her surroundings, we conducted a user study with $20$ participants in a large national supermarket that investigated how the attention the user pays on her surroundings varies across two types of landmark-based instructions that vary in terms of their visual demand. The results indicate that an increase in the visual demand of landmark-based instructions does not necessarily improve the participant's recall of their surrounding environment and that this increase can cause a decrease in navigation efficiency. The results also indicate that participants generally pay little attention to their surroundings and are more likely to rationalize than to actually remember much from their surroundings. Implications of the findings on navigation assistants are discussed.

The workshop on mobile interaction in retail environments (MIRE) brings together researchers and practitioners from academy and industry to explore how mobile phones and mobile interaction can be embedded in retail environments to create new shopping experiences and mobile enhanced services.

Emergent location-aware applications often require tracking trajectories of mobile devices over a long period of time. To be useful, the tracking has to be energy-efficient to avoid having a major impact on the battery life of the mobile device. Furthermore, when trajectory information needs to be sent to a remote server, on-device simplification of the trajectories is needed to reduce the amount of data transmission. While there has recently been a lot of work on energy-efficient position tracking, the energy-efficient tracking of trajectories has not been addressed in previous work. In this paper we propose a novel on-device sensor management strategy and a set of trajectory updating protocols which intelligently determine when to sample different sensors (accelerometer, compass and GPS) and when data should be simplified and sent to a remote server. The system is configurable with regards to accuracy requirements and provides a unified framework for both position and trajectory tracking. We demonstrate the effectiveness of our approach by emulation experiments on real world data sets collected from different modes of transportation (walking, running, biking and commuting by car) as well as by validating with a real-world deployment. The results demonstrate that our approach is able to provide considerable savings in the battery consumption compared to a state-of-the-art position tracking system while at the same time maintaining the accuracy of the resulting trajectory, i.e., support of specific accuracy requirements and different types of applications can be ensured.

We propose a grid-based GSM positioning algorithm that can be deployed entirely on mobile devices. The algorithm uses Gaussian distributions to model signal intensity variations within each grid cell. Position estimates are calculated by combining a probabilistic centroid algorithm with particle filtering. In addition to presenting the positioning algorithm, we describe methods that can be used to create, update and maintain radio maps on a mobile device. We have implemented the positioning algorithm on Nokia S60 and Nokia N900 devices and we evaluate the algorithm using a combination of offline and real world tests. The results indicate that the accuracy of our method is comparable to state-of-the-art methods, while at the same time having significantly smaller storage requirements.

The fundamental nature of grocery shopping makes it an interesting domain for intelligent mobile assistants. Even though the central role of shopping lists is widely recognized, relatively little attention has been paid to facilitating shopping list creation and management. In this paper we introduce a predictive text input technique that is based on association rules and item frequencies. We also describe an interface design for integrating the predictive text input with a web-based mobile shopping assistant. In a user study we compared two interfaces, one with text input support and one without. Our results indicate that, even though shopping list entries are typically short, our technique makes text input significantly faster, decreases typing error rates and increases overall user satisfaction.

In a Mobile Living Lab, mobile devices are used to evaluate concepts and prototypes in real-life settings. In other words, the lab is brought to the people. This workshop provides a forum for researchers and practitioners to share experiences and issues with methods and tools for Mobile Living Labs. In particular, we seek to bring together people who have applied methods for Mobile Living Labs and people who build tools for those methods.

In this paper we describe modifications to a natural language grocery retrieval system, introduced in our earlier work. We also compare our system against an off-the-shelf retrieval tool, and show that our system is significantly better for top-ranked retrieval results.

Widgets are embeddable objects that provide easy and ubiquitous access to dynamic information sources, for example weather, news or TV program information. Widgets are typically rather static - they provide the information regardless of whether the information is relevant to the user's current information needs. In this paper we introduce Capricorn, which is an intelligent interface for mobile widgets. The interface uses various adaptive web techniques for facilitating navigation. For example, we use collaborative filtering to recommend suitable widgets and we dim infrequently used widgets. The demonstration presents the Capricorn interface focusing on the adaptive parts of the interface. The user interface is web-based, and as such platform independent. However, our target environment is mobile phones, and thus the interface has been optimized for mobile phones.

Widgets are embeddable objects that provide easy and ubiquitous access to dynamic information sources, e.g., weather, news or TV program information. Interactions with widgets take place through a so-called widget engine, which is a specialized client-side runtime component that also provides functionalities for managing widgets. As the number of supported widgets increases, managing widgets becomes increasingly complex. For example, finding relevant or interesting widgets becomes difficult and the user interface easily gets cluttered with irrelevant widgets. In addition, interacting with information sources can be cumbersome, especially on mobile platforms. In order to facilitate widget management and interactions, we have developed Capricorn, an intelligent user interface that integrates adaptive navigation techniques into a widget engine. This paper describes the main functionalities of Capricorn and presents the results of a usability evaluation that measured user satisfaction and compared how user satisfaction varies between desktop and mobile platforms.

We introduce a grocery retrieval system that maps shopping lists written in natural language into actual products in a grocery store. We have developed the system using nine months of shopping basket data from a large Finnish supermarket. To evaluate the system, we used 70 real shopping lists gathered from customers of the supermarket. Our system achieves over 80% precision for products at rank one, and the precision is around 70% for products at rank 5.

We propose Perseus, a personalized reputation system. In Perseus, reputations comprise of three aspects: how much I personally trust another individual, how trustworthy others think the individual is, and how much I trust the opinions of others. Perseus is adaptive in the sense that user feedback is used to modify the way the different aspects are considered. We also present simulation experiments, which indicate that Perseus is robust and able to survive under extreme conditions of misbehavior. In addition, Perseus encourages individuals to rate the other party and give fair ratings. We also compare Perseus against other well-known reputation systems.

The complexity associated to gathering and processing contextual data makes testing mobile context-aware applications and services difficult. Furthermore, the lack of standard data sets and simulation tools makes the evaluation of machine learning algorithms in context-aware settings an even harder task. To ease the situation, we introduce a generic simulator that has been designed with the above mentioned purposes in mind. The simulator has also proven to be a good demonstration tool for mobile services and applications that are aimed at groups. The simulator is highly customizable and it can output context information of individual entities both through an interactive GUI and as data streams consisting of comma separated values. To support a wide range of tasks and scenarios, we have separated the three main information sources: behavior of agents, the scenario being simulated and the used context variable. The simulator has been implemented using Java, and the data streams have been made available through a Web service interface

Existing context-aware mobile applications often rely on location information. However, raw location data such as GPS coordinates or GSM cell identifiers are usually meaningless to the user and, as a consequence, researchers have proposed different methods for inferring so-called places from raw data. The places are locations that carry some meaning to user and to which the user can potentially attach some (meaningful) semantics. Examples of places include home, work and airport. A lack in existing work is that the labeling has been done in an ad hoc fashion and no motivation has been given for why places would be interesting to the user. As our first contribution we use social identity theory to motivate why some locations really are significant to the user. We also discuss what potential uses for location information social identity theory implies. Another flaw in the existing work is that most of the proposed methods are not suited to realistic mobile settings as they rely on the availability of GPS information. As our second contribution we consider a more realistic setting where the information consists of GSM cell transitions that are enriched with GPS information whenever a GPS device is available. We present four different algorithms for this problem and compare them using real data gathered throughout Europe. In addition, we analyze the suitability of our algorithms for mobile devices

Mobile agents acting in wireless ad hoc networks are energy constrained, which leads to potential selfishness as nodes are not necessarily willing to forward packets for other nodes. Situations like this are traditionally analyzed using game theory and recently also the ad hoc networking community has witnessed game-theoretic approaches to especially routing. However, from a theoretical point-of-view the contemporary game-theoretic approaches have mainly ignored two important aspects: non-simultaneous decision making and incorporating history information into the decision making process. In this article we propose a new model that fills these gaps and allows to analyze routing theoretically.