Showing papers on "Participatory sensing published in 2017"

Mutual Privacy Preserving $k$ -Means Clustering in Social Participatory Sensing

Abstract: In this paper, we consider the problem of mutual privacy protection in social participatory sensing in which individuals contribute their private information to build a (virtual) community. Particularly, we propose a mutual privacy preserving $k$ -means clustering scheme that neither discloses an individual's private information nor leaks the community's characteristic data (clusters). Our scheme contains two privacy-preserving algorithms called at each iteration of the $k$ -means clustering. The first one is employed by each participant to find the nearest cluster while the cluster centers are kept secret to the participants; and the second one computes the cluster centers without leaking any cluster center information to the participants while preventing each participant from figuring out other members in the same cluster. An extensive performance analysis is carried out to show that our approach is effective for $k$ -means clustering, can resist collusion attacks, and can provide mutual privacy protection even when the data analyst colludes with all except one participant.

PSAllocator: Multi-Task Allocation for Participatory Sensing with Sensing Capability Constraints

Abstract: This paper proposes a novel multi-task allocation framework, named PSAllocator, for participatory sensing (PS). Different from previous single-task oriented approaches, which select an optimal set of users for each single task independently, PSAllocator attempts to coordinate the allocation of multiple tasks to maximize the overall system utility on a multi-task PS platform. Furthermore, PSAllocator takes the maximum number of sensing tasks allowed for each participant and the sensor availability of each mobile device into consideration. PSAllocator utilizes a two-phase offline multi-task allocation approach to achieve the near-optimal goal. First, it predicts the participants' connections to cell towers and locations based on historical data from the telecom operator; Then, it converts the multi-task allocation problem into the representation of a bipartite graph, and employs an iterative greedy process to optimize the task allocation. Extensive evaluations based on real-world mobility traces show that PSAllocator outperforms the baseline methods under various settings.

Toward Big Data in Green City

Abstract: Integrating sensors and cloud computing, sensor- cloud is a very powerful system for users to obtain big data in green city. In this article, toward big data in green city, we first review the latest work concerning big data and sensor-cloud, respectively. Further, we introduce three types of sensor-cloud (i.e., PSC, ASC, and SSC) for green city. Specifically, about PSC, participatory sensing is incorporated into sensor-cloud for sensing big data. In terms of ASC, an agent is incorporated into sensor-cloud for transmitting big data. For SSC, a social network is incorporated into sensor- cloud for sharing big data. Finally, the open research issues with respect to big data and sensor- cloud are discussed, respectively. We hope this article can serve as enlightening guidance for future research regarding big data in green city.

Environmental Monitoring for Smart Cities

Abstract: This paper presents an innovative, multidisciplinary, and cost-effective ecosystem of ICT solutions able to collect, process, and distribute geo-referenced information about the influence of pollution and micro-climatic conditions on the quality of life in Smart Cities. The system has been developed and experimentally evaluated in the framework of the research project Smart Healthy Environment, co-funded by the Tuscany Region (Italy). Specifically, an innovative monitoring network has been developed, constituted by fixed and mobile sensor nodes, which provided comparable measurements in stationary and mobile conditions. In addition, sensor data have been enriched with those generated by citizens through the use of a dedicated mobile application, exploiting participatory sensing and mobile social network paradigms.

Wheelmap: the wheelchair accessibility crowdsourcing platform

Abstract: Crowdsourcing (geo-) information and participatory GIS are among the current hot topics in research and industry. Various projects are implementing participatory sensing concepts within their workflow in order to benefit from the power of volunteers, and improve their product quality and efficiency. Wheelmap is a crowdsourcing platform where volunteers contribute information about wheelchair-accessible places. This article presents information about the technical framework of Wheelmap, and information on how it could be used in projects dealing with accessibility and/or multimodal transportation.

Fair Energy-Efficient Sensing Task Allocation in Participatory Sensing with Smartphones

Abstract: With the proliferation of smartphones, participatory sensing using smartphones provides unprecedented opportunities for collecting enormous sensing data. There are two crucial requirements in participatory sensing, fair task allocation and energy efficiency, which are particularly challenging given high combinatorial complexity, tradeoff between energy efficiency and fairness, and dynamic and unpredictable task arrivals. In this paper, we present a novel fair energy-efficient allocation framework whose objective is characterized by min-max aggregate sensing time. We rigorously prove that optimizing the min-max aggregate sensing time is NP hard even when the tasks are assumed as a priori. We consider two allocation models: offline allocation and online allocation. For the offline allocation model, we design an efficient approximation algorithm with the approximation ratio of 2 - 1/m, where m is the number of member smartphones in the system. For the online allocation model, we propose a greedy online algorithm which achieves a competitive ratio of at most m. The results demonstrate that the approximation algorithm reduces over 81% total sensing time, the greedy online algorithm reduces more than 73% total sensing time, and both algorithms achieve over 3x better min-max fairness.

A Participatory Urban Traffic Monitoring System: The Power of Bus Riders

Abstract: This paper presents a participatory sensing-based urban traffic monitoring system. Different from existing works that heavily rely on intrusive sensing or full cooperation from probe vehicles, our system exploits the power of participatory sensing and crowdsources the traffic sensing tasks to bus riders’ mobile phones. The bus riders are information source providers and, meanwhile, major consumers of the final traffic output. The system takes public buses as dummy probes to detect road traffic conditions, and collects the minimum set of cellular data together with some lightweight sensing hints from the bus riders’ mobile phones. Based on the crowdsourced data from participants, the system recovers the bus travel information and further derives the instant traffic conditions of roads covered by bus routes. The real-world experiments with a prototype implementation demonstrate the feasibility of our system, which achieves accurate and fine-grained traffic estimation with modest sensing and computation overhead at the crowd.

Participatory Sensing or Participatory Nonsense?: Mitigating the Effect of Human Error on Data Quality in Citizen Science

Abstract: Citizen Science with mobile and wearable technology holds the possibility of unprecedented observation systems. Experts and policy makers are torn between enthusiasm and scepticism regarding the value of the resulting data, as their decision making traditionally relies on high-quality instrumentation and trained personnel measuring in a standardized way. In this paper, we (1) present an empirical behavior taxonomy of errors exhibited in non-expert smartphone-based sensing, based on four small exploratory studies, and discuss measures to mitigate their effects. We then present a large summative study (N=535) that compares instructions and technical measures to address these errors, both from the perspective of improvements to error frequency and perceived usability. Our results show that (2) technical measures without explanation notably reduce the perceived usability and (3) technical measures and instructions nicely complement each other: Their combination achieves a significant reduction in observed error rates while not affecting the user experience negatively.

Participatory Sensing for Smart Cities: A Case Study on Transport Trip Quality Measurement

Abstract: Participatory sensing (PS), an emerging sensing paradigm through organizing people and their mobile devices as sensors, is regarded as a promising solution to solve large-scale urban issues. PS has been implemented in many applications such as transportation, environmental monitoring, public health, etc. These applications are of high relevance for smart cities. Smart cities rely on the infrastructures established by government and industrial agencies to sense urban dynamics. PS can complement these infrastructures by involving “human sensors” and obtaining insights into people's activities. In this paper, the key features and challenges of PS are surveyed. A prototype PS platform is designed for efficient collection and management of smartphone sensing and survey data. A transport trip quality measurement system (TQMS) has been developed using this platform. Our experience to organize PS activities through a novel collaboration driven incentive mechanism is elucidated. A comprehensive real-world PS case study on TQMS including platform promotion, project collaboration, participant recruitment, activity organization has been successfully completed. This paper is concluded by discussing the potential future applications of the PS platform.

Personalized Prediction of Vehicle Energy Consumption Based on Participatory Sensing

Abstract: The advent of abundant on-board sensors and electronic devices in vehicles populates the paradigm of participatory sensing to harness crowd-sourced data gathering for intelligent transportation applications, such as distance-to-empty prediction and eco-routing. While participatory sensing can provide diverse driving data, there lacks a systematic study of effective utilization of the data for personalized prediction. There are considerable challenges on how to interpolate the missing data from a sparse data set, which often arises from participatory sensing. This paper presents and compares various approaches for personalized vehicle energy consumption prediction, including a blackbox framework that identifies driver/vehicle/environment-dependent factors and a collaborative filtering approach based on matrix factorization. Furthermore, a case study of distance-to-empty prediction for electric vehicles by participatory sensing data is conducted and evaluated empirically, which shows that our approaches can significantly improve the prediction accuracy.

Provably Secure Anonymous-yet-Accountable Crowdsensing with Scalable Sublinear Revocation

Abstract: Abstract Group signature schemes enable anonymous-yet-accountable communications. Such a capability is extremely useful for applications, such as smartphone-based crowdsensing and citizen science. However, the performance of modern group signature schemes is still inadequate to manage large dynamic groups. In this paper, we design the first provably secure verifier-local revocation (VLR) - based group signature scheme that supports sublinear revocation, named Sublinear Revocation with Backward unlinkability and Exculpability (SRBE). To achieve this performance gain, SRBE introduces time bound pseudonyms for the signer. By introducing low-cost short-lived pseudonyms with sublinear revocation checking, SRBE drastically improves the efficiency of the group-signature primitive. The backward-unlinkable anonymity of SRBE guarantees that even after the revocation of a signer, her previously generated signatures remain unlinkable across epochs. This behavior favors the dynamic nature of real-world crowdsensing settings. We prove its security and discuss parameters that influence its scalability. Using SRBE, we also implement a prototype named GroupSense for anonymous-yet-accountable crowdsensing, where our experimental findings confirm GroupSense’s scalability. We point out the open problems remaining in this space.

Simultaneous Power-Based Localization of Transmitters for Crowdsourced Spectrum Monitoring

Abstract: The current mechanisms for locating spectrum offenders are time consuming, human-intensive, and expensive. In this paper, we propose a novel approach to locate spectrum offenders using crowdsourcing. In such a participatory sensing system, privacy and bandwidth concerns preclude distributed sensing devices from reporting raw signal samples to a central agency; instead, devices would be limited to measurements of received power. However, this limitation enables a smart attacker to evade localization by simultaneously transmitting from multiple infected devices. Existing localization methods are insufficient or incapable of locating multiple sources when the powers from each source cannot be separated at the receivers. In this paper, we first propose a simple and efficient method that simultaneously locates multiple transmitters using the received power measurements from the selected devices. Second, we build sampling approaches to select sensing devices required for localization. Next, we enhance our sampling to also take into account incentives for participation in crowdsourcing. We experimentally evaluate our localization framework under a variety of settings and find that we are able to localize multiple sources transmitting simultaneously with reasonably high accuracy in a timely manner.

Energy-Efficient Event Detection by Participatory Sensing Under Budget Constraints

Abstract: Dynamic event detection by using participatory sensing paradigms has received growing interests recently, where detection tasks are assigned to smart-device users who can potentially collect needed sensory data from device-equipped sensors. Typical applications include, but are not limited to, noise and air pollution detections, people gathering, even disaster prediction. Given this problem, although many existing centralized solutions are effective and widely used, they usually cause heavy communication overhead. Thus, it is strongly desired to design distributed solutions to reduce energy consumption, while achieving a high level of detection accuracy with limited sensing task budget. In this paper, we first present two novel centralized detection algorithms as the performance benchmark, which make use of the Minimum Cut theory and support vector machine (SVM)-based pattern recognition techniques. Then, we introduce a novel distributed and energy-efficient event detection framework under task budget constraint, where we formulate an optimization problem and derive an optimal utility function. Finally, based on a real trace-driven data set in an urban area of Beijing, extensive simulation results demonstrate the effectiveness of our proposed algorithms.

Understanding the potential of humanmachine crowdsourcing for weather data

Abstract: Reliable weather estimation traditionally requires a dense network of meteorological measurement stations. The concept of participatory sensing promises to alleviate this requirement by crowdsourcing weather data from an ideally very large set of participating users instead. Participation may involve nothing more than downloading a corresponding app to enable the collection of such data, given that modern smartphones contain a plethora of weather-related sensors. To understand the potential of participatory sensing for weather estimation, and how humans can be put in the loop to further improve such sensing, we created Atmos a crowdsourcing weather app that not only periodically samples smartphones sensors for weather measurements, but also allows users to enter their own estimates of both current and future weather conditions. We present the results of a 32-month public deployment of Atmos on the Google Play Store, showing that a combination of both types of sensing results in accurate temperature estimates, featuring an average error rate of 2.7C, whereas when using only user inputs, the average error rate drops to 1.86C. Sensor readings revealed significant variations during users commuting times.User inputs were more accurate in estimating actual temperature than sensor inputs.Bagged decision trees with user reported temperature achieved the lowest error rate.A 32-month public deployment on Google Play Store.

Comparison of CNN models for application in crop health assessment with participatory sensing

Abstract: Timely and robust diagnosis of plant diseases and nutrient deficiencies play a major role in management of crop yield. Automation is a low cost alternative to human experts and can help to detect early onset of crop diseases which aids faster decision making and in giving recommendations to farmers to curb yield loss. We have developed a smart-phone based participatory sensing application for agriculture which is used by farmers to scout their fields for events of interest, especially those related to crop health. Recently, deep convolutional neural networks (CNN) have emerged as a prominent technique in computer vision related challenges and such deep-learning based models could prove as an important tool to do just-in-time assessment of crop health. With a view to building state-of-the-art diagnostic capabilities on the phone, we present analysis of CNN models in terms of accuracy, memory, and inference time. Effects of change in hyperparameters have been evaluated in terms of accuracy. The trained model gives 99.7% classification accuracy with satisfactory inference time and model size which assures the application of CNN architectures for real-time crop state diagnosis on a large scale with limited hardware capabilities.

SmartAQnet: remote and in-situ sensing of urban air quality

Abstract: Air quality and the associated subjective and health-related quality of life are among the important topics of urban life in our time. However, it is very difficult for many cities to take measures to accommodate today’s needs concerning e.g. mobility, housing and work, because a consistent fine-granular data and information on causal chains is largely missing. This has the potential to change, as today, both large-scale basic data as well as new promising measuring approaches are becoming available. The project “SmartAQnet”, funded by the German Federal Ministry of Transport and Digital Infrastructure (BMVI), is based on a pragmatic, data driven approach, which for the first time combines existing data sets with a networked mobile measurement strategy in the urban space. By connecting open data, such as weather data or development plans, remote sensing of influencing factors, and new mobile measurement approaches, such as participatory sensing with low-cost sensor technology, “scientific scouts” (autonomous, mobile smart dust measurement device that is auto-calibrated to a high-quality reference instrument within an intelligent monitoring network) and demand-oriented measurements by light-weight UAVs, a novel measuring and analysis concept is created within the model region of Augsburg, Germany. In addition to novel analytics, a prototypical technology stack is planned which, through modern analytics methods and Big Data and IoT technologies, enables application in a scalable way.

Device Studies of Participatory Sensing: Ontological Politics and Design Interventions

Abstract: This study investigates how ubiquitous sensing technologies are being used to engage the public in environmental monitoring. The academic literature and mainstream media claim participatory sensing is contributing to science, improving the environment and creating new forms of democratic citizenship. Yet there have been few studies that examine its material practices and impacts. This study addresses this gap via three ethnographic ‘device studies’ and an experimental design intervention. The methodology is based on post actor-network theory with a material-semiotic focus on the notion of the ‘device’ (Law & Ruppert 2013), in order to follow the sensing objects over their lifetime from design, usage with participants and later outputs. The design intervention uses the notion of the device as a research method to materially intervene in one of the study sites as a public controversy. The findings show that despite claims in the literature to be an empirical knowledge practice, the subjects and objects of participatory sensing are continually shifting and blurring. Instead, participatory sensing involves a ‘stringing together’ of hardware, participants and rhetorics to form new ontological entities and create publicity. However, this creates conflicts with actors for whom environmental pollution is a health concern, who want to organise collectively and want to engage with decision-making. Yet these studies have shown that it is possible to reconfigure sensing devices with situated ontologies. This led to the building of experimental design prototypes that show that participatory sensing can support pluralistic ontologies and build new connections towards decision-making. The contribution of this study is to identify the ontological politics (Mol 1999) of participatory sensing and demonstrate a ‘device study’ method that combines ethnography with material design to intervene and transform public controversies.

Smartphone Data Analysis for Human Activity Recognition

Abstract: In recent years, the percentage of the population owning a smartphone has increased significantly. These devices provide the user with more and more functions, so that anyone is encouraged to carry one during the day, implicitly producing that can be analysed to infer knowledge of the user’s context. In this work we present a novel framework for Human Activity Recognition (HAR) using smartphone data captured by means of embedded triaxial accelerometer and gyroscope sensors. Some statistics over the captured sensor data are computed to model each activity, then real-time classification is performed by means of an efficient supervised learning technique. The system we propose also adopts a participatory sensing paradigm where user’s feedbacks on recognised activities are exploited to update the inner models of the system. Experimental results show the effectiveness of our solution as compared to other state-of-the-art techniques.

An Analysis of Secondary Teachers' Reasoning with Participatory Sensing Data.

Abstract: Participatory sensing is a data collection method in which communities of people collect and share data to investigate large-scale processes. These data have many features often associated with the big data paradigm: they are rich and multivariate, include non-numeric data, and are collected as determined by an algorithm rather than by traditional experimental designs. While not often found in classrooms, arguably they should be since data with these features are commonly encountered in daily life. Because of this, it is of interest to examine how teachers reason with and about such data. We propose methods for describing progress through a statistical investigation. These methods are demonstrated on two groups of secondary mathematics teachers engaged in a model-eliciting activity centered around participatory sensing data. We employ graphical depictions of discrete Markov chains to describe the strategic decisions the teachers follow while analyzing data, and find that this descriptive technique reveals some suggestive patterns, particularly emphasizing the importance of frequent questioning and crafting productive statistical questions. First published November 2017 at Statistics Education Research Journal Archives

CityCare: Crowdsourcing daily life issue reports in smart cities

Abstract: The various problems regularly encountered in urban residential environments can have significant impact on citizen's quality of life, while simultaneously their identification, reporting and management by the responsible authorities entail a very demanding process. Providing citizens with mobile applications for participatory reporting (i.e. crowdsourcing) of problems may help streamline the work of relevant municipal services; such citizen-centric services will make the city smarter through enhanced problem management and request processing. In this article, we introduce two new mobile applications, CityCare and CityCareW, which are complementary with respect to each other and cover all phases of a problem report lifecycle. They incorporate innovative mechanisms that allow the detection of the most critical problems, as well as duplicate problem reports while gamification mechanisms are applied to motivate citizen participation. Trial evaluations have shown that the two applications are user-friendly and satisfy their end-users to a very considerable extent.

Crowdsensing Multimedia Data: Security and Privacy Issues

Abstract: Recent smartphones are equipped with various sensors, such as an accelerometer, GPS, and a gravity sensor, and have high-performance wireless communication capabilities. Through the ubiquitous presence of powerful mobile devices, crowdsensing lets ordinary people collectively gather and share real-time multimedia data. Multimedia crowdsensing has made large-scale participatory sensing viable in a speedy and cost-efficient manner, but it also introduces some security and privacy concerns. Personally identifiable information of participants can be exposed while sharing individually owned sensor data. This article identifies security and privacy issues in multimedia crowdsensing and describes existing solutions that are designed to protect both data producers and consumers in multimedia crowdsensing. This article is part of a special issue on cybersecurity.

Adaptive Sampling for Urban Air Quality through Participatory Sensing

Abstract: Air pollution is one of the major problems of the modern world. The popularization and powerful functions of smartphone applications enable people to participate in urban sensing to better know about the air problems surrounding them. Data sampling is one of the most important problems that affect the sensing performance. In this paper, we propose an Adaptive Sampling Scheme for Urban Air Quality (AS-air) through participatory sensing. Firstly, we propose to find the pattern rules of air quality according to the historical data contributed by participants based on Apriori algorithm. Based on it, we predict the on-line air quality and use it to accelerate the learning process to choose and adapt the sampling parameter based on Q-learning. The evaluation results show that AS-air provides an energy-efficient sampling strategy, which is adaptive toward the varied outside air environment with good sampling efficiency.

Towards an observatory for mobile participatory sensing applications

Abstract: This paper presents an observatory for registering applications that use participatory sensing to collect data. Cataloging these applications will aid the scientific community to exchange more information, facilitating the comparison between different initiatives. Through an initial research, the applications are categorized in areas usually considered in the literature. We propose a survey to validate the platform and also discuss the taxonomies created as a result of this survey. The main contributions of this paper include the classification of crowdsensing applications in different ontological categories, as well as the proposal of a technology platform that enables the distributed and collaborative cataloging of crowdsensing initiatives.

Traffic Management for Smart Cities

Abstract: Smart cities, participatory sensing as well as location data available in communication systems and social networks generates a vast amount of heterogeneous mobility data that can be used for traffic management . This chapter gives an overview of the different data sources and their characteristics and describes a framework for utilizing the various sources efficiently in the context of traffic management. Furthermore, different types of traffic models and algorithms are related to both the different data sources as well as some key functionalities of active traffic management, for example, short-term prediction and control.