Showing papers on "Participatory sensing published in 2018"

MPiLoc: Self-Calibrating Multi-Floor Indoor Localization Exploiting Participatory Sensing

Abstract: While location is one of the most important context information in mobile and pervasive computing, large-scale deployment of indoor localization system remains elusive. In this work, we propose MPiLoc, a multi-floor indoor localization system that utilizes data contributed by smartphone users through participatory sensing for automatic floor plan and radio map construction. Our system does not require manual calibration, prior knowledge, or infrastructure support. The key novelty of MPiLoc is that it clusters and merges walking trajectories annotated with sensor and signal strengths to derive a map of walking paths annotated with radio signal strengths in multi-floor indoor environments. We evaluate MPiLoc over five different indoor areas. Evaluation shows that our system can derive indoor maps for various indoor environments in multi-floor settings and achieve an average localization error of 1.82 m.

FIRST: A Framework for Optimizing Information Quality in Mobile Crowdsensing Systems

Abstract: Thanks to the collective action of participating smartphone users, mobile crowdsensing allows data collection at a scale and pace that was once impossible. The biggest challenge to overcome in mobile crowdsensing is that participants may exhibit malicious or unreliable behavior, thus compromising the accuracy of the data collection process. Therefore, it becomes imperative to design algorithms to accurately classify between reliable and unreliable sensing reports. To address this crucial issue, we propose a novel Framework for optimizing Information Reliability in Smartphone-based participaTory sensing (FIRST) that leverages mobile trusted participants (MTPs) to securely assess the reliability of sensing reports. FIRST models and solves the challenging problem of determining before deployment the minimum number of MTPs to be used to achieve desired classification accuracy. After a rigorous mathematical study of its performance, we extensively evaluate FIRST through an implementation in iOS and Android of a room occupancy monitoring system and through simulations with real-world mobility traces. Experimental results demonstrate that FIRST reduces significantly the impact of three security attacks (i.e., corruption, on/off, and collusion) by achieving a classification accuracy of almost 80% in the considered scenarios. Finally, we discuss our ongoing research efforts to test the performance of FIRST as part of the National Map Corps project.

A Geoprivacy by Design Guideline for Research Campaigns That Use Participatory Sensing Data.

Abstract: Participatory sensing applications collect personal data of monitored subjects along with their spatial or spatiotemporal stamps. The attributes of a monitored subject can be private, sensitive, or confidential information. Also, the spatial or spatiotemporal attributes are prone to inferential disclosure of private information. Although there is extensive problem-oriented literature on geoinformation disclosure, our work provides a clear guideline with practical relevance, containing the steps that a research campaign should follow to preserve the participants’ privacy. We first examine the technical aspects of geoprivacy in the context of participatory sensing data. Then, we propose privacy-preserving steps in four categories, namely, ensuring secure and safe settings, actions prior to the start of a research survey, processing and analysis of collected data, and safe disclosure of datasets and research deliverables.

QLDS: A Novel Design Scheme for Trajectory Privacy Protection with Utility Guarantee in Participatory Sensing

Abstract: Participatory sensing, leveraging on the ubiquity of cheap sensors in mobile devices, enables various promising applications of great social benefit. However, its ubiquitous sampling and openness results in serious privacy concerns. People's activity trajectories may reveal their private information such as home and work place and thus requires proper protection. In this paper, we propose a novel design scheme, called query logics detached storage (QLDS), for trajectory privacy protection. The core idea of QLDS is to extract the query logics for personal trajectory retrieval and make actual trajectory tuples not clustered to any route-identity or user-identity at server end, which introduces fine-granularity anonymity. The QLDS design scheme stores the extracted query logics at users own client devices and the un-clustered location tuples at the backend server, guaranteeing trajectory reconstruction at client-end and privacy preservation at server-end. Besides, integration of QLDS with other privacy protection approaches can further enhance the protection strength and bring flexible configurations to meet individuals’ variable privacy concerns. The theoretical analytics is provided for the privacy and utility evaluation. The data retrieval performance of QLDS is experimentally evaluated in real-world internet environment.

Distributed Social Welfare Maximization in Urban Vehicular Participatory Sensing Systems

Abstract: We consider the crucial problem of maximizing the social welfare of a vehicular participatory sensing system, where the system's social welfare is measured by the amount of sensing data delivered to a central platform through a vehicular ad hoc network. The key to the problem is to control network stability since both network congestion and idleness will slump system social welfare. However, several great challenges exist. First, limited vehicle-to-vehicle (V2V) link capacity and vehicle buffer size will lead to heavy network congestion when each individual vehicle blindly injects too much data into the network hoping to get more rewards. Second, the highly dynamic network topology and stochastic inter-vehicle contacts have a serious impact on the performance of multi-hop data transmission. Third, vehicles need to be practically rewarded based on their sensing and transmission cost, which, however, greatly vary among vehicles. To tackle the aforementioned challenges, we propose a distributed backpressure control approach, the first work to the best of our knowledge, to maximize the social welfare while balancing network stability for a vehicular participatory sensing system. Combining vehicular network properties and Lyapunov optimization techniques, individualized strategies are developed for each participant to control its sensing rate, make its own routing decisions, and set its own price for data relaying. Formally proved by rigorous theoretical analysis, the social welfare achieved by the proposed approach is comparative to the optimum performance. In addition, extensive data-driven simulations based on real taxi GPS traces have been conducted, and the results confirm the efficacy of the proposed algorithm.

Stop the Noise! Enhancing Meaningfulness in Participatory Sensing with Community Level Indicators

Abstract: In this paper we examine ways to make data more meaningful and useful for citizens in participatory sensing. Participatory sensing has evolved as a digitally enabled grassroots approach to data collection for citizens with shared concerns. However, citizens often struggle to understand data in relation to their daily lives, and use them effectively. This paper presents a qualitative study on the development of a novel approach to Community Level Indicators (CLIs) during two participatory sensing projects focused on noise pollution. It investigates how CLIs can provide an infrastructure to address challenges in participatory sensing, specifically, making data meaningful and useful for non-experts. Furthermore, we consider how this approach moves towards an ambition of achieving change and impact through participatory sensing and discuss the challenges in this way of working and provide recommendations for future use of CLIs.

The Role of Gamification in Participatory Environmental Sensing: A Study In the Wild

Abstract: Participatory sensing (PS) and citizen science hold promises for a genuinely interactive and inclusive citizen engagement in meaningful and sustained collection of data about social and environmental phenomena. Yet the underlying motivations for public engagement in PS remain still unclear particularly regarding the role of gamification, for which HCI research findings are often inconclusive. This paper reports the findings of an experimental study specifically designed to further understand the effects of gamification on citizen engagement. Our study involved the development and implementation of two versions (gamified and non-gamified) of a mobile application designed to capture lake ice coverage data in the sub-arctic region. Emerging findings indicate a statistically significant effect of gamification on participants' engagement levels in PS. The motivation, approach and results of our study are outlined and implications of the findings for future PS design are reflected.

Security and privacy in ubiquitous sensor networks

Abstract: The availability of powerful and sensor-enabled mobile and Internet-connected devices have enabled the advent of the ubiquitous sensor network (USN) paradigm. USN provides various types of solutions to the general public in multiple sectors, including environmental monitoring, entertainment, transportation, security, and healthcare. Here, we explore and compare the features of wireless sensor networks and USN. Based on our extensive study, we classify the security- and privacy-related challenges of USNs. We identify and discuss solutions available to address these challenges. Finally, we briefly discuss open challenges for designing more secure and privacy-preserving approaches in next-generation USNs.

Making Sense: Empowering participatory sensing with transformation design

Abstract: This paper demonstrates the value of transformation design in participatory sensing and describes how design can inform awareness and develop actions for change to tackle environmental issues. Rece...

Do Monetary Incentives Influence Users' Behavior in Participatory Sensing?

Abstract: Participatory sensing combines the powerful sensing capabilities of current mobile devices with the mobility and intelligence of human beings, and as such has to potential to collect various types of information at a high spatial and temporal resolution. Success, however, entirely relies on the willingness and motivation of the users to carry out sensing tasks, and thus it is essential to incentivize the users’ active participation. In this article, we first present an open, generic participatory sensing framework (Citizense) which aims to make participatory sensing more accessible, flexible and transparent. Within the context of this framework we adopt three monetary incentive mechanisms which prioritize the fairness for the users while maintaining their simplicity and portability: fixed micro-payment, variable micro-payment and lottery. This incentive-enabled framework is then deployed on a large scale, real-world case study, where 230 participants were exposed to 44 different sensing campaigns. By randomly distributing incentive mechanisms among participants and a subset of campaigns, we study the behaviors of the overall population as well as the behaviors of different subgroups divided by demographic information with respect to the various incentive mechanisms. As a result of our study, we can conclude that (1) in general, monetary incentives work to improve participation rate; (2) for the overall population, a general descending order in terms of effectiveness of the incentive mechanisms can be established: fixed micro-payment first, then lottery-style payout and finally variable micro-payment. These two conclusions hold for all the demographic subgroups, even though different different internal distances between the incentive mechanisms are observed for different subgroups. Finally, a negative correlation between age and participation rate was found: older participants contribute less compared to their younger peers.

Quality Utilization Aware Based Data Gathering for Vehicular Communication Networks

Abstract: The vehicular communication networks, which can employ mobile, intelligent sensing devices with participatory sensing to gather data, could be an efficient and economical way to build various applications based on big data. However, high quality data gathering for vehicular communication networks which is urgently needed faces a lot of challenges. So, in this paper, a fine-grained data collection framework is proposed to cope with these new challenges. Different from classical data gathering which concentrates on how to collect enough data to satisfy the requirements of applications, a Quality Utilization Aware Data Gathering (QUADG) scheme is proposed for vehicular communication networks to collect the most appropriate data and to best satisfy the multidimensional requirements (mainly including data gathering quantity, quality, and cost) of application. In QUADG scheme, the data sensing is fine-grained in which the data gathering time and data gathering area are divided into very fine granularity. A metric named “Quality Utilization” (QU) is to quantify the ratio of quality of the collected sensing data to the cost of the system. Three data collection algorithms are proposed. The first algorithm is to ensure that the application which has obtained the specified quantity of sensing data can minimize the cost and maximize data quality by maximizing QU. The second algorithm is to ensure that the application which has obtained two requests of application (the quantity and quality of data collection, or the quantity and cost of data collection) could maximize the QU. The third algorithm is to ensure that the application which aims to satisfy the requirements of quantity, quality, and cost of collected data simultaneously could maximize the QU. Finally, we compare our proposed scheme with the existing schemes via extensive simulations which well justify the effectiveness of our scheme.

Towards a Smart Campus Through Participatory Sensing

Abstract: In recent years, the percentage of the population owning a smartphone has increased significantly. These devices provide users with more and more functions that make them real sensing platforms. Exploiting the capabilities offered by smartphones, users can collect data from the surrounding environment and share them with other entities in the network thanks to existing communication infrastructures, i.e., 3G/4G/5G or WiFi. In this work, we present a system based on participatory sensing paradigm using smartphones to collect and share local data in order to monitor make a campus "smart". In particular, our system infers the activities performed by users (e.g., students) in a campus in order to identify trends and behavioral patterns. This information allows the system to decide in real-time which actions are needed to provide the best possible services to users, according to their needs and preferences.

ClusterRep: A cluster-based reputation framework for balancing privacy and trust in vehicular participatory sensing:

Abstract: In vehicular participatory sensing, vehicles may provide false data or low-quality data. Building trust in vehicular ad hoc networks is an efficient way to deal with this issue. On one hand, vehicles need to disclose necessary information to demonstrate their trustworthiness. On the other hand, vehicles tend to hide their sensitive information to preserve user privacy. Therefore, privacy and trust are conflict in vehicular ad hoc networks. A cluster-based reputation framework named ClusterRep is proposed to balance privacy and trust in vehicular ad hoc networks. In this framework, the cluster head collaborates with cluster members to change pseudonyms and reputation values. The experiments show the scalability and the effectiveness of the ClusterRep compared with Beta strategy and IncogniSense-floor strategy.

Context-Aware Vehicle Route Recommendation Platform: Exploring Open and Crowdsourced Data

Abstract: An increasing number of users have been adopting route recommendation systems, mostly motivated by the convenience that those systems bring to their traffic experiences. Usually, those systems observe the historical and current traffic conditions in order to evaluate and recommend the fastest routes. However, besides mobility aspects, more contextual information such as unplanned street events and neighborhood safety, are not taken into account in the recommendation process. With this in mind, we propose a platform to support context-aware route recommendation systems. The proposed platform aims to improve existing recommendation algorithms or enable the proposal of new ones. To assess it, we use datasets of routes suggested by Google Maps in the city of Curitiba, Brazil, official open data provided by the city and also data generated voluntarily by citizens in a participatory sensing fashion. Our results show the existence of an opportunity for route planners to provide personalized services to users, which is an important step towards the development of context- aware vehicular networks. Besides, these results illustrate how publicly available big data can be explored to improve context-aware route recommendations.

A Generation Method of Filtering Rules of Twitter Via Smartphone Based Participatory Sensing System for Tourist by Interactive GHSOM and C4.5

Abstract: Mobile Phone based Participatory Sensing (MPPS) systems involve a community of users sending personal information and participating in autonomous sensing through their mobile phones. Sensed data can also be obtained from external sensing devices that can communicate wirelessly to the phone. We have developed the tourist subjective data collection system with Android smartphone. The tourist can tweet the information of sightseeing spots by using the application. The application can determine the filtering rules to provide the important information of sightseeing spot. The rules are automatically generated by Interactive Growing Hierarchical SOM and C4.5.

Edge-Based Anomalous Sensor Placement Detection for Participatory Sensing of Urban Heat Islands

Abstract: Crowdsensing temperature data have enabled a paradigm shift in the ways we collect data and analyze the heat exposure effects on individuals and communities. The use of low-cost sensors has helped in gathering granular spatiotemporal temperature data and capturing ever-changing ambient environmental conditions. However, this practice poses challenges such as sensor failures and data integrity. One of the main concerns of the participatory sensing approach is the misplacement of temperature sensors in a way that they are not exposed to the natural outdoor environment. We propose a novel approach to detect anomalous sensor placement in a semi-real-time manner at the edge of the Internet. We introduce a sliding window technique in conjunction with supervised learning classifiers to detect anomalously-placed sensors effectively. This approach is based on the empirical observation that temperature readings show more frequent fluctuations while exposed to the outdoor environment. We also conduct a series of comparative performance analysis of different classifiers including SVM, Logistic Regression, and Random Forest.

Advanced Smartphone-Based Sensing with Open-Source Task Automation.

Abstract: Smartphone-based sensing is becoming a convenient way to collect data in science, especially in environmental research. Recent studies that use smartphone sensing methods focus predominantly on single sensors that provide quantitative measurements. However, interdisciplinary projects call for study designs that connect both, quantitative and qualitative data gathered by smartphone sensors. Therefore, we present a novel open-source task automation solution and its evaluation in a personal exposure study with cyclists. We designed an automation script that advances the sensing process with regard to data collection, management and storage of acoustic noise, geolocation, light level, timestamp, and qualitative user perception. The benefits of this approach are highlighted based on data visualization and user handling evaluation. Even though the automation script is limited by the technical features of the smartphone and the quality of the sensor data, we conclude that task automation is a reliable and smart solution to integrate passive and active smartphone sensing methods that involve data processing and transfer. Such an application is a smart tool gathering data in population studies.

Volunteers in the Smart City: Comparison of Contribution Strategies on Human-Centered Measures.

Abstract: Provision of smart city services often relies on users contribution, e.g., of data, which can be costly for the users in terms of privacy. Privacy risks, as well as unfair distribution of benefits to the users, should be minimized as they undermine user participation, which is crucial for the success of smart city applications. This paper investigates privacy, fairness, and social welfare in smart city applications by means of computer simulations grounded on real-world data, i.e., smart meter readings and participatory sensing. We generalize the use of public good theory as a model for resource management in smart city applications, by proposing a design principle that is applicable across application scenarios, where provision of a service depends on user contributions. We verify its applicability by showing its implementation in two scenarios: smart grid and traffic congestion information system. Following this design principle, we evaluate different classes of algorithms for resource management, with respect to human-centered measures, i.e., privacy, fairness and social welfare, and identify algorithm-specific trade-offs that are scenario independent. These results could be of interest to smart city application designers to choose a suitable algorithm given a scenario-specific set of requirements, and to users to choose a service based on an algorithm that matches their privacy preferences.

Personal Context Recognition Via Reliable Human-Machine Collaboration

Abstract: An effective context recognition system cannot rely only on sensor data but requires the user to collaborate with the system in providing her own knowledge. In approaches such as participatory sensing, which leverages on users to annotate and collect their own data, user-generated data is usually assumed to be accurate; however, in real life situations, this is not the case. Research in social sciences and psychology shows that humans are unreliable due to several behavioral biases when describing their everyday life. In this paper, we propose to parametrize two biases, i.e., cognitive bias and carelessness, in order to identify and evaluate their impact on the users’ reliability when recognizing users’ context. The parameters are part of an architecture for context modelling and recognition from previous work, which combines sensors and users as a source of information. We evaluate our approach on a dataset of location points from the SmartUnitn One experiment.

IoT Solution for Data Sensing in a Smart Campus using Smartphone Sensors

Abstract: This paper explores the Android Operative System (OS) as a medium to manage and operate different sensors embedded within smartphones, which are commonly used in participatory sensing oriented applications such as traffic navigation in cities and mobility in Smart Campus. Several operations are tested to determine the possible uses of the Android OS in an Internet of Things (IoT)’ architecture, where participative sensing is being done. In this architecture, the transmission of data is essential for analysis and visualization in a cloud platform. Hence, different types of protocols exist for the above-mentioned such as HTTPS, AMQP, and MQTT. Additionally, this paper presents the design and implementation of a participatory sensing IoT based system solution for Smart Campus applications. Our solution employs smartphone sensors, as sensors embedded into IoT devices, and the Android API to extract the data from them (e.g., GPS data). The data sensing is stored in a local database and then, transferred via HTTPS to Ubidots (a cloud platform) for further analysis and visualization.

Energy-Efficient GPS Usage in Location-Based Applications

Abstract: GPS is one of the most used services in any location-based app in our smartphone, and almost a quarter of all Android apps available in the Google Play store are using this GPS. There are many apps which require monitoring your locations in a continuous fashion because of the application’s nature, and those kinds of apps consume the highest power from the smartphones. Because of the high-power draining nature of this GPS, we hesitate to take part in different crowd-sourced applications which are very much important for the smart city realization as maximum of these applications use GPS in real time or in a very frequent manner for the realization of participatory sensing in a smart city scenario. To resolve this, we have introduced an energy-efficient context-aware approach which utilizes user’s mobility information from the user’s context and as well smartphone’s sensing values from the inbuilt accelerometer, magnetometer, and gyroscope of the smartphone to provide us a very close estimation of the present location of the user without using continuous GPS. It is an energy-efficient solution without sacrificing the accuracy compared to energy saving which will boost the crowd to take part in the smartphone-based crowd-sourced applications that depend on participatory sensing for the smart city environment.

Analyzing Spatial and Temporal User Behavior in Participatory Sensing

Abstract: The large number of mobile devices and their increasingly powerful computing and sensing capabilities have enabled the participatory sensing concept. Participatory sensing applications are now able to effectively collect a variety of information types with high accuracy. Success, nevertheless, depends largely on the active participation of the users. In this article, we seek to understand spatial and temporal user behaviors in participatory sensing. To do so, we conduct a large-scale deployment of Citizense, a multi-purpose participatory sensing framework, in which 359 participants of demographically different backgrounds were simultaneously exposed to 44 participatory sensing campaigns of various types and contents. This deployment has successfully gathered various types of urban information and at the same time portrayed the participants’ different spatial, temporal and behavioral patterns. From this deployment, we can conclude that (i) the Citizense framework can effectively help participants to design data collecting processes and collect the required data, (ii) data collectors primarily contribute in their free time during the working week; much fewer submissions are done during the weekend, (iii) the decision to respond and complete a particular participatory sensing campaign seems to be correlated to the campaign’s geographical context and/or the recency of the data collectors’ activities, and (iv) data collectors can be divided into two groups according to their behaviors: a smaller group of active data collectors who frequently perform participatory sensing activities and a larger group of regular data collectors who exhibit more intermittent behaviors. These identified user behaviors open avenues to improve the design and operation of future participatory sensing applications.

DISCOPAR: a visual reactive programming language for generating cloud-based participatory sensing platforms

Abstract: Participatory sensing (PS) platforms enable stakeholders to collect, analyse and visualise data for a particular interest. Despite high societal demand, developing a new PS platform remains a labour-intensive, nonreusable process that requires high technical expertise. We present DISCOPAR, a visual reactive flow-based domain-specific language geared towards the construction of reusable citizen observatories. With DISCOPAR, users interact with visual components to implement the various elements of a PS platforms without having to worry about its underlying technological complexities. We validate our approach through experiments using real-world empirical usability studies of ICT-agnostic users. The results show that DISCOPAR allows users with limited technological knowledge to create their own PS platform.

Monitoring air pollution by combining a static infrastructure with a participatory sensing approach: design and performance evaluation

Abstract: Participatory Sensing (PS) is a new fast-growing sensing approach that involves the participation of mobile phone users, and the corresponding communication infrastructure, to create a large-scale monitoring system. Using PS-based system makes it possible to measure and detect variables and events with an improvement in spatial and time resolution over traditional monitoring system. Pollution-Spots proposes an air pollution monitoring solution by means of using an infrastructure of fixed low-cost sensing devices, and reporting the measurements using a PS approach. The sensing devices acquire the variables and the pedestrian forwards this information, completing the cycle with no extra cost of data transport and/or human resources. However, including humans to the sensing loop, rises new challenges, such as protecting user private data, motivating user’s participation, and reducing mobile phone’s power consumption, all while maintaining the quality of the collected data. Pollution-Spots proposes a combined algorithm that protects the participant’s private information and also implements a gamification technique to encourage the participation without any monetary reward. The proposed system has proven to be energy efficient when compared to similar approaches, with the additional benefit of considering the quality of the collected information, which is normally affected by privacy protection algorithms.

Understanding Civic Participation in Environmental Sensing: A Values Driven Approach

Abstract: Engaging citizens is a major goal for governments, scientists and businesses, as their participation or lack of it can have a big impact on issues of common interest. This is true in particular for civic technologies such as participatory sensing (PS), which fully relies on citizens' active participation for monitoring tasks. Current research is largely focused on providing incentives to people to increase their participation. Yet, this approach has not proven to be fully effective. Hence, there is a current need to understand the underlying motivations of people to join, participate and abandon PS. I propose to study the dynamics of motivation from a value driven perspective. This research will develop novel civic technology design approaches aimed at enhancing engagement, as well as to environmental data representation, analysis and curation. I aim to advance the understanding of what motivates people to engage in PS in environmental monitoring contexts.