Mobile Sensing Systems

[EN] Rich-sensor smart phones have made possible the recent birth of the mobile sensing research area as part of ubiquitous sensing which integrates other areas such as wireless sensor networks and web sensing. There are several types of mobile sensing: individual, participatory, opportunistic, crowd, social, etc. The object of sensing can be people-centered or environment-centered. The sensing domain can be home, urban, vehicular Currently there are barriers that limit the social acceptance of mobile sensing systems. Examples of social barriers are privacy concerns, restrictive laws in some countries and the absence of economic incentives that might encourage people to participate in a sensing campaign. Several technical barriers are phone energy savings and the variety of sensors and software for their management. Some existing surveys partially tackle the topic of mobile sensing systems. Published papers theoretically or partially solve the above barriers. We complete the above surveys with new works, review the barriers of mobile sensing systems and propose some ideas for efficiently implementing sensing, fusion, learning, security, privacy and energy saving for any type of mobile sensing system, and propose several realistic research challenges. The main objective is to reduce the learning curve in mobile sensing systems where the complexity is very high.This work has been partially supported by the "Ministerio de Ciencia e Innovacion", through the "Plan Nacional de I+D+i 2008-2011" in the "Subprograma de Proyectos de Investigacion Fundamental", project TEC2011-27516, and by the Polytechnic University of Valencia, through the PAID-05-12 multidisciplinary projects.Macias Lopez, EM.; Suarez Sarmiento, A.; Lloret, J. (2013). Mobile Sensing Systems. Sensors. 13(12):17292-17321. https://doi.org/10.3390/s131217292S1729217321131

A User Study of a Wearable System to Enhance Bystanders’ Facial Privacy

The privacy of users and information are becoming increasingly important with the growth and pervasive use of mobile devices such as wearables, mobile phones, drones, and Internet of Things (IoT) devices. Today many of these mobile devices are equipped with cameras which enable users to take pictures and record videos anytime they need to do so. In many such cases, bystanders’ privacy is not a concern, and as a result, audio and video of bystanders are often captured without their consent. We present results from a user study in which 21 participants were asked to use a wearable system called FacePET developed to enhance bystanders’ facial privacy by providing a way for bystanders to protect their own privacy rather than relying on external systems for protection. While past works in the literature focused on privacy perceptions of bystanders when photographed in public/shared spaces, there has not been research with a focus on user perceptions of bystander-based wearable devices to enhance privacy. Thus, in this work, we focus on user perceptions of the FacePET device and/or similar wearables to enhance bystanders’ facial privacy. In our study, we found that 16 participants would use FacePET or similar devices to enhance their facial privacy, and 17 participants agreed that if smart glasses had features to conceal users’ identities, it would allow them to become more popular

QoE-Based Low-Delay Live Streaming Using Throughput Predictions

Recently, HTTP-based adaptive streaming has become the de facto standard for video streaming over the Internet. It allows clients to dynamically adapt media characteristics to network conditions in order to ensure a high quality of experience, that is, minimize playback interruptions, while maximizing video quality at a reasonable level of quality changes. In the case of live streaming, this task becomes particularly challenging due to the latency constraints. The challenge further increases if a client uses a wireless network, where the throughput is subject to considerable fluctuations. Consequently, live streams often exhibit latencies of up to 30 seconds. In the present work, we introduce an adaptation algorithm for HTTP-based live streaming called LOLYPOP (Low-Latency Prediction-Based Adaptation) that is designed to operate with a transport latency of few seconds. To reach this goal, LOLYPOP leverages TCP throughput predictions on multiple time scales, from 1 to 10 seconds, along with an estimate of the prediction error distribution. In addition to satisfying the latency constraint, the algorithm heuristically maximizes the quality of experience by maximizing the average video quality as a function of the number of skipped segments and quality transitions. In order to select an efficient prediction method, we studied the performance of several time series prediction methods in IEEE 802.11 wireless access networks. We evaluated LOLYPOP under a large set of experimental conditions limiting the transport latency to 3 seconds, against a state-of-the-art adaptation algorithm from the literature, called FESTIVE. We observed that the average video quality is by up to a factor of 3 higher than with FESTIVE. We also observed that LOLYPOP is able to reach a broader region in the quality of experience space, and thus it is better adjustable to the user profile or service provider requirements.Comment: Technical Report TKN-16-001, Telecommunication Networks Group, Technische Universitaet Berlin. This TR updated TR TKN-15-00

Toward Sensor-Based Random Number Generation for Mobile and IoT Devices

The importance of random number generators (RNGs) to various computing applications is well understood. To ensure a quality level of output, high-entropy sources should be utilized as input. However, the algorithms used have not yet fully evolved to utilize newer technology. Even the Android pseudo RNG (APRNG) merely builds atop the Linux RNG to produce random numbers. This paper presents an exploratory study into methods of generating random numbers on sensor-equipped mobile and Internet of Things devices. We first perform a data collection study across 37 Android devices to determine two things-how much random data is consumed by modern devices, and which sensors are capable of producing sufficiently random data. We use the results of our analysis to create an experimental framework called SensoRNG, which serves as a prototype to test the efficacy of a sensor-based RNG. SensoRNG employs collection of data from on-board sensors and combines them via a lightweight mixing algorithm to produce random numbers. We evaluate SensoRNG with the National Institute of Standards and Technology statistical testing suite and demonstrate that a sensor-based RNG can provide high quality random numbers with only little additional overhead

Do users care about ad's performance costs? Exploring the effects of the performance costs of in-app ads on user experience

Context: In-app advertising is the primary source of revenue for many mobile apps. The cost of advertising (ad cost) is non-negligible for app developers to ensure a good user experience and continuous profits. Previous studies mainly focus on addressing the hidden performance costs generated by ads, including consumption of memory, CPU, data traffic, and battery. However, there is no research on analyzing users’ perceptions of ads’ performance costs to our knowledge. / Objective: To fill this gap and better understand the effects of performance costs of in-app ads on user experience, we conduct a study on analyzing user concerns about ads’ performance costs. / Method: First, we propose RankMiner, an approach to quantify user concerns about specific app issues, including performance costs. Then, based on the usage traces of 20 subject apps, we measure the performance costs of ads. Finally, we conduct correlation analysis on the performance costs and quantified user concerns to explore whether users complain more for higher performance costs. / Results: Our findings include the following: (1) RankMiner can quantify users’ concerns better than baselines by an improvement of 214% and 2.5% in terms of Pearson correlation coefficient (a metric for computing correlations between two variables) and NDCG score (a metric for computing accuracy in prioritizing issues), respectively. (2) The performance costs of the with-ads versions are statistically significantly larger than those of no-ads versions with negligible effect size; (3) Users are more concerned about the battery costs of ads, and tend to be insensitive to ads’ data traffic costs. / Conclusion: Our study is complementary to previous work on in-app ads, and can encourage developers to pay more attention to alleviating the most user-concerned performance costs, such as battery cost

Annual Report, 2013-2014

Beginning in 2004/2005- issued in online format onl

Geo-locating Drivers: A Study of Sensitive Data Leakage in Ride-Hailing Services.

Increasingly, mobile application-based ride-hailing services have become a very popular means of transportation. Due to the handling of business logic, these services also contain a wealth of privacy-sensitive information such as GPS locations, car plates, driver licenses, and payment data. Unlike many of the mobile applications in which there is only one type of users, ride-hailing services face two types of users: riders and drivers. While most of the efforts had focused on the rider’s privacy, unfortunately, we notice little has been done to protect drivers. To raise the awareness of the privacy issues with drivers, in this paper we perform the first systematic study of the drivers’ sensitive data leakage in ride-hailing services. More specifically, we select 20 popular ride-hailing apps including Uber and Lyft and focus on one particular feature, namely the nearby cars feature. Surprisingly, our experimental results show that largescale data harvesting of drivers is possible for all of the ridehailing services we studied. In particular, attackers can determine with high-precision the driver’s privacy-sensitive information including mostly visited address (e.g., home) and daily driving behaviors. Meanwhile, attackers can also infer sensitive information about the business operations and performances of ride-hailing services such as the number of rides, utilization of cars, and presence on the territory. In addition to presenting the attacks, we also shed light on the countermeasures the service providers could take to protect the driver’s sensitive information

FacePET: Enhancing Bystanders\u27 Facial Privacy with Smart Wearables/Internet of Things

Given the availability of cameras in mobile phones, drones and Internet-connected devices, facial privacy has become an area of major interest in the last few years, especially when photos are captured and can be used to identify bystanders’ faces who may have not given consent for these photos to be taken and be identified. Some solutions to protect facial privacy in photos currently exist. However, many of these solutions do not give a choice to bystanders because they rely on algorithms that de-identify photos or protocols to deactivate devices and systems not controlled by bystanders, thereby being dependent on the bystanders’ trust in these systems to protect his/her facial privacy. To address these limitations, we propose FacePET (Facial Privacy Enhancing Technology), a wearable system worn by bystanders and designed to enhance facial privacy. We present the design, implementation, and evaluation of the FacePET and discuss some open research issues

Reputation and Reward : Two Sides of the Same Bitcoin

In Mobile Crowd Sensing (MCS), the power of the crowd, jointly with the sensing capabilities of the smartphones they wear, provides a new paradigm for data sensing. Scenarios involving user behavior or those that rely on user mobility are examples where standard sensor networks may not be suitable, and MCS provides an interesting solution. However, including human participation in sensing tasks presents numerous and unique research challenges. In this paper, we analyze three of the most important: user participation, data sensing quality and user anonymity. We tackle the three as a whole, since all of them are strongly correlated. As a result, we present PaySense, a general framework that incentivizes user participation and provides a mechanism to validate the quality of collected data based on the users' reputation. All such features are performed in a privacy-preserving way by using the Bitcoin cryptocurrency. Rather than a theoretical one, our framework has been implemented, and it is ready to be deployed and complement any existint MCS system