Showing papers on "Participatory sensing published in 2011"

Real time pothole detection using Android smartphones with accelerometers

Abstract: The importance of the road infrastructure for the society could be compared with importance of blood vessels for humans. To ensure road surface quality it should be monitored continuously and repaired as necessary. The optimal distribution of resources for road repairs is possible providing the availability of comprehensive and objective real time data about the state of the roads. Participatory sensing is a promising approach for such data collection. The paper is describing a mobile sensing system for road irregularity detection using Android OS based smart-phones. Selected data processing algorithms are discussed and their evaluation presented with true positive rate as high as 90% using real world data. The optimal parameters for the algorithms are determined as well as recommendations for their application.

Crowdsourcing, citizen sensing and sensor web technologies for public and environmental health surveillance and crisis management: trends, OGC standards and application examples

Abstract: 'Wikification of GIS by the masses' is a phrase-term first coined by Kamel Boulos in 2005, two years earlier than Goodchild's term 'Volunteered Geographic Information'. Six years later (2005-2011), OpenStreetMap and Google Earth (GE) are now full-fledged, crowdsourced 'Wikipedias of the Earth' par excellence, with millions of users contributing their own layers to GE, attaching photos, videos, notes and even 3-D (three dimensional) models to locations in GE. From using Twitter in participatory sensing and bicycle-mounted sensors in pervasive environmental sensing, to creating a 100,000-sensor geo-mashup using Semantic Web technology, to the 3-D visualisation of indoor and outdoor surveillance data in real-time and the development of next-generation, collaborative natural user interfaces that will power the spatially-enabled public health and emergency situation rooms of the future, where sensor data and citizen reports can be triaged and acted upon in real-time by distributed teams of professionals, this paper offers a comprehensive state-of-the-art review of the overlapping domains of the Sensor Web, citizen sensing and 'human-in-the-loop sensing' in the era of the Mobile and Social Web, and the roles these domains can play in environmental and public health surveillance and crisis/disaster informatics. We provide an in-depth review of the key issues and trends in these areas, the challenges faced when reasoning and making decisions with real-time crowdsourced data (such as issues of information overload, "noise", misinformation, bias and trust), the core technologies and Open Geospatial Consortium (OGC) standards involved (Sensor Web Enablement and Open GeoSMS), as well as a few outstanding project implementation examples from around the world.

Crowdsourcing, citizen sensing and Sensor Web technologies for public and environmental health surveillance and crisis management: trends, OGC standards and application examples

Abstract: 'Wikification of GIS by the masses' is a phrase-term first coined by Kamel Boulos in 2005, two years earlier than Goodchild's term 'Volunteered Geographic Information'. Six years later (2005-2011), OpenStreetMap and Google Earth (GE) are now full-fledged, crowdsourced 'Wikipedias of the Earth' par excellence, with millions of users contributing their own layers to GE, attaching photos, videos, notes and even 3-D (three dimensional) models to locations in GE. From using Twitter in participatory sensing and bicycle-mounted sensors in pervasive environmental sensing, to creating a 100,000-sensor geo-mashup using Semantic Web technology, to the 3-D visualisation of indoor and outdoor surveillance data in real-time and the development of next-generation, collaborative natural user interfaces that will power the spatially-enabled public health and emergency situation rooms of the future, where sensor data and citizen reports can be triaged and acted upon in real-time by distributed teams of professionals, this paper offers a comprehensive state-of-the-art review of the overlapping domains of the Sensor Web, citizen sensing and 'human-in-the-loop sensing' in the era of the Mobile and Social Web, and the roles these domains can play in environmental and public health surveillance and crisis/disaster informatics. We provide an in-depth review of the key issues and trends in these areas, the challenges faced when reasoning and making decisions with real-time crowdsourced data (such as issues of information overload, "noise", misinformation, bias and trust), the core technologies and Open Geospatial Consortium (OGC) standards involved (Sensor Web Enablement and Open GeoSMS), as well as a few outstanding project implementation examples from around the world.

Participatory Sensing: Crowdsourcing Data from Mobile Smartphones in Urban Spaces

Abstract: The recent wave of sensor-rich, Internet-enabled, smart mobile devices such as the Apple iPhone has opened the door for a novel paradigm for monitoring the urban landscape known as participatory sensing. Using this paradigm, ordinary citizens can collect multi-modal data streams from the surrounding environment using their mobile devices and share the same using existing communication infrastructure (e.g., 3G service or WiFi access points). The data contributed from multiple participants can be combined to build a spatiotemporal view of the phenomenon of interest and also to extract important community statistics. Given the ubiquity of mobile phones and the high density of people in metropolitan areas, participatory sensing can achieve an unprecedented level of coverage in both space and time for observing events of interest in urban spaces. Several exciting participatory sensing applications have emerged in recent years. For example, GPS traces uploaded by drivers and passengers can be used to generate real time traffic statistics. Similarly, street-level audio samples collected by pedestrians can be aggregated to create a citywide noise map. In this advanced seminar, we will provide a comprehensive overview of this new and exciting paradigm and outline the major research challenges.

P-Sense: A participatory sensing system for air pollution monitoring and control

Abstract: This article presents P-Sense, a participatory sensing application for air pollution monitoring and control. The paper describes in detail the system architecture and individual components of a successfully implemented application. In addition, the paper points out several other research-oriented problems that need to be addressed before these applications can be effectively implemented in practice, in a large-scale deployment. Security, privacy, data visualization and validation, and incentives are part of our work-in-progress activities

A privacy-aware framework for participatory sensing

Abstract: With the abundance and ubiquity of mobile devices, a new class of applications is emerging, called participatory sensing (PS), where people can contribute data (e.g., images, video) collected by their mobile devices to central data servers. However, privacy concerns are becoming a major impediment in the success of many participatory sensing systems. While several privacy preserving techniques exist in the context of conventional location-based services, they are not directly applicable to the PS systems because of the extra information that the PS systems can collect from their participants. In this paper, we formally define the problem of privacy in PS systems and identify its unique challenges assuming an un-trusted central data server model. We propose PiRi, a privacy-aware framework for PS systems, which enables participation of the users without compromising their privacy. Our extensive experiments verify the efficiency of our approach.

NoiseMap - Real-time participatory noise maps

Abstract: Noise pollution is a problem increasingly acknowledged by authorities and governments around the globe. However, creating noise maps with conventional methods is either inaccurate or very expensive. To increase the spatial and temporal data resolution a high number of sensors must be deployed. In this paper we present results and prototypes based on participatory sensing leading to accurate, real-time noise maps. First we present NoiseMap a application currently released for Android phones. NoiseMap gathers data on loudness and transfers it to the open urban sensing platform da sense. da sense allows users to access and control their data, generate real-time noise maps and data graphs. Public data is made available using either a web service or a JavaScript API.

Short paper: PEPSI---privacy-enhanced participatory sensing infrastructure

Abstract: Participatory Sensing combines the ubiquity of mobile phones with the sensing capabilities of Wireless Sensor Networks. It targets the pervasive collection of information, e.g., temperature, traffic conditions, or medical data. Users produce measurements from their mobile devices, thus, a number of privacy concerns -- due to the personal information conveyed by reports -- may hinder the large-scale deployment of participatory sensing applications. Prior work has attempted to protect privacy in participatory sensing, but it relied on unrealistic assumptions and achieved no provably-secure guarantees. In this paper, we introduce PEPSI: Privacy-Enhanced Participatory Sensing Infrastructure. We explore realistic architectural assumptions and a minimal set of formal requirements aiming at protecting privacy of both data producers and consumers. We also present an instantiation that attains privacy guarantees with provable security at very low additional computational cost and almost no extra communication overhead. Finally, we highlight some problems that call for further research in this developing area.

Enhancing Motivation in a Mobile Participatory Sensing Project through Gaming

Abstract: Bud Burst Mobile is a smart phone application for an environmental Participatory Sensing project that focuses on observing plants and collecting plant life stage data. The app was initially designed for record-keeping and motivation to participate in this project has been based on improving scientific knowledge. To test other methods for motivating data collection and increasing user retention, we added an outdoor game activity, similar to geocaching, called flora caching. Players gain points and levels within the game by finding and making qualitative observations on plants. Location-based information is included in the game with the display of local lists of plant species occurring in a user's area derived from governmental data sources. Additionally, user-collected data and the occurrence of species on the local lists obtained from the photo-sharing website Flickr are displayed on an interactive map. Administrator targeting of individual plants facilitates expert control over crowd-sourced data collection for species of interest. We evaluated these additional features with the help of 50 volunteers playing on the UCLA campus as a case study. Results indicated that participants were highly motivated by the flora caching game, and next-most by the knowledge that environmental scientists will use the data collected for studying the effects of global climate change. Other motivating features included sharing plant observations with other users and the information contained in the local lists of plants.

Ceci n'est pas une pipe bombe: authoring urban landscapes with air quality sensors

Abstract: Our work explores the convergence between participatory sensing, political activism and public expressions Unlike prior research, which focuses on personal sensing, we present low-cost, networked air quality sensors, designed to be repositioned across public landscapes by communities of citizen stakeholders Our GPS-enabled sensors report dust, exhaust, or VOC's (volatile organic compounds), along with temperature, humidity and light levels to a website that visualizes this data in real time The sensors can be attached to a variety of surfaces serving as research probes to demarcate ('tag') public spaces with environmental concerns We deploy our fully functional system with four urban communities - parents, bicyclists, homeless and activists, positioning our system as a tool for studying and supporting community togetherness and public activism Our findings highlight community sharing of the physical sensors and dialogues surrounding the collected data

Efficient network management for context-aware participatory sensing

Abstract: Participatory sensing is becoming more popular with the help of sensor-embedded smartphones to retrieve context-aware information for users. However, new challenges arise for the maintenance of the energy supply, the support of the quality-of-information (QoI) requirements, and the generation of maximum revenue for network operator, but with sparsely research exposure. This paper proposes a novel efficient network management framework to tackle the above challenges, where four key design elements are introduced. First is the QoI satisfaction index, where the QoI benefit the queries receive is quantified in relation to the level they require. Second is the credit satisfaction index, where the credits are used by the network operator to motivate the user participation, and this index is to quantify its degree of satisfaction. Third is the Gur Game-based distributed energy control, where the above two indexes are used as inputs to the mathematical framework of the Gur Game for distributed decision-making. Fourth is the dynamic pricing scheme, based on a constrained optimization problem to allocate credits to the participants while minimizing the necessary adaptation of the pricing scheme from the network operator. We finally evaluate the proposed scheme under an event occurrence detection scenario, where the proposed scheme successfully guarantees less than 7% detection outage, saves 80% of the energy reserve if compared with the lower bound solution, and achieves the suboptimum with only 4% gap if compared with optimal solution.

Continuous Sensing of Occupant Perception of Indoor Ambient Factors

Abstract: Ambient factors such as temperature, lighting, and air quality influence occupants’ productivity and behavior. Although these factors are regulated by industry standards and monitored by the facilities management groups, occupants’ perceptions vary from actual values due to various factors such as building schedules and occupancy, occupant activity and preferences, weather and climate, and the placement of sensors. While occupant comfort surveys are sometimes conducted, they are generally limited to one-time or periodic assessments that do not fully represent occupant experiences throughout building operations. This study proposes a new methodology for gathering real time data on a continuous basis through participatory sensing of occupant ambient comfort in indoor environments based on a smart phone application. The developed application is presented and validated by a pilot study in a university building. Occupant perceptions of temperature are compared to actual temperature records. No correlation is found between perceived and actual room temperatures demonstrating the potential of a participatory sensing tool for adaptively controlling building temperature ranges.

Ikarus: large-scale participatory sensing at high altitudes

Abstract: Sensor networks proved to be a useful research tool in the field of environmental monitoring. While first sensor deployments consisted of a relatively small number of static nodes, mobile sensor devices have attracted growing interest for large-scale sensing applications in recent years. In this paper, we present Ikarus, a novel participatory sensing application having orders of magnitude more users than existing approaches. The Ikarus system exploits sensor data collected during cross-country flights by paraglider pilots to study thermal effects in the atmosphere. Based on first experiences gained from this approach, we identify three key aspects that are crucial for the success of participatory sensing applications: incentives for participation, the ability to deal with faulty data, and concise data representation.

Privacy-Preserving Collaborative Path Hiding for Participatory Sensing Applications

Abstract: The presence of multimodal sensors on current mobile phones enables a broad range of novel mobile applications including, e.g., monitoring noise pollution or traffic and road conditions in urban environments. Data of unprecedented quantity and quality can be collected and reported by a possible user base of billions of mobile phone subscribers worldwide. The collection of detailed sensor and location data may however compromise user privacy. In this paper, we present a decentralized mechanism to preserve location privacy during the collection of sensor readings. As most sensor readings are geotagged, we propose to exchange them between users in physical proximity in order to jumble the paths followed by the users. We evaluate different strategies to exchange and report the sensor readings to the application using real-world GPS traces of mobile users. The results demonstrate the feasibility and efficacy of our proposed scheme, which can obfuscate up to 100% of the visited locations in the best instances.

Participatory Cyber Physical System in Public Transport Application

Abstract: Population in the urban areas around the world is increasing yet energy and natural resources which are non-renewable, have continued to deplete. This scenario leads us to the many challenges faced by public transportation since it is directly linked to urbanization. From a civilian point of view, proper travel planning and knowledge of others' travel experiences are useful in easing public transportation woes. We apply Cyber Physical System (CPS) within cyber-physical-socio space to make those capabilities possible. In this approach, we regard data as a service, data are contributed and queried by the public masses. Location and time represent the physical domain and data with information derived from them represent the digital domain in our CPS design. We designed and implemented a prototypical system named ContriSenseCloud that works based on client-server model. The system has two main distinctive platforms, namely Service Exchange Platform (SEP) and Application-specific Exchange Platform (AEP). The SEP is designed to be as generalizable as possible whereas AEP is where our current public transportation applications reside. Such architecture enables extensibility of ContriSenseCloud to other application domains. Other contributions include Restful Application Programming Interface (API) in CPS, near real-time sensing, and mapping of GPS readings to correct sequence of bus stops.

Sensorsafe: a framework for privacy-preserving management of personal sensory information

Abstract: The widespread use of smartphones and body-worn sensors has made continuous and unobtrusive collection of personal data feasible. This has led to the emergence of useful applications in diverse areas such as medical behavioral studies, personal health-care and participatory sensing. However, the nature of highly personal information shared with these applications, together with the additional inferences that could be possibly drawn using the same data leads to a variety of privacy concerns. This paper proposes SensorSafe, an architecture for managing personal sensory information in a privacy-preserving way. Our architecture consists of multiple remote data stores and a broker so users can retain the ownership of their data and management of multiple users can be well supported. SensorSafe also provides a context-aware ne-grained access control mechanism by which users can dene their own sharing rules based on various conditions including context and behavioral status. We discuss our design of the SensorSafe architecture and provide application examples to show how our system can support user privacy.

People-centric sensing in assistive healthcare: Privacy challenges and directions

Abstract: As the domains of pervasive computing and sensor networking are expanding, there is an ongoing trend towards assistive living and healthcare support environments that can effectively assimilate these technologies according to human needs. Most of the existing research in assistive healthcare follows a more passive approach and has focused on collecting and processing data using a static-topology and an application-aware infrastructure. However, with the technological advances in sensing, computation, storage, and communications, a new era is about to emerge changing the traditional view of sensor-based assistive environments where people are passive data consumers, with one where people carry mobile sensing elements involving large volumes of data related to everyday human activities. This evolution will be driven by people-centric sensing and will turn mobile phones into global mobile sensing devices enabling thousands new personal, social, and public sensing applications. In this paper, we discuss our vision for people-centric sensing in assistive healthcare environments and study the security challenges it brings. This highly dynamic and mobile setting presents new challenges for information security, data privacy and ethics, caused by the ubiquitous nature of data traces originating from sensors carried by people. We aim to instigate discussion on these critical issues because people-centric sensing will never succeed without adequate provisions on security and privacy. To that end, we discuss the latest advances in security and privacy protection strategies that hold promise in this new exciting paradigm. We hope this work will better highlight the need for privacy in people-centric sensing applications and spawn further research in this area. Copyright © 2011 John Wiley & Sons, Ltd.

Towards preserving privacy in participatory sensing

Abstract: With the abundance and ubiquity of mobile devices, a new class of applications is emerging, called participatory sensing (PS), where people can contribute data (e.g., images, video) collected by their mobile devices to central data servers. However, privacy concerns are becoming a major impediment in the success of many participatory sensing systems. While several privacy preserving techniques exist in the context of conventional location-based services, they are not directly applicable to the PS systems because of the extra information that the PS systems can collect from their participants. In this paper, we formally define the problem of privacy in PS systems and identify its unique challenges assuming an un-trusted central data server model. We propose PiRi, a privacy-aware framework for PS systems, which enables participation of the users without compromising their privacy.

How personal fitness data can be re-used by smart cities

Abstract: The growing trend to use mobile devices and applications to collect data relating to fitness activities has resulted in large amounts of sensor data being generated. Further, in some cases the fitness data is shared over social networks. This collected data has potential uses in a number of fields including: public health and population health data, urban planning, fitness trends analysis, social network analysis and personalization of health information. As the motivation for creating this sensor data already exists for the individual in relation to fitness benefits and health monitoring, this type of participatory sensing approach has a lower barrier to entry. However, there is currently no structured approach to collect and re-use this sensor data. This paper describes the distinct types of fitness sensor applications; conceptual architecture for data collection and aggregation and the steps and developments that would improve the quality and usability of data collected. Further, the types of secondary uses for smart cities of this collected data are explored.

Sensors, empowerment, and accountability: a Digital Earth view from East Africa

Abstract: Several innovative ‘participatory sensing’ initiatives are under way in East Africa. They can be seen as local manifestations of the global notion of Digital Earth. The initiatives aim to amplify t...

A Novel Approach for Selecting the Participants to Collect Data in Participatory Sensing

Abstract: This paper works on the participatory sensing in the area of sensor network. One of the most important challenges in participatory sensing is selecting the participants to collect their sensed data to monitor the environment. The goal here is to obtain the data of high quality with low payment from a predefined number of participants. To achieve the goal, this paper proposes a novel approach using the techniques of evolutionary algorithm. It models the goal as a multi-objective Knapsack problem, which is resolved by the hybrid scheme of Univariate Model Distribution Algorithm (UDMA) and the enhanced Third Evolution step of Generalized Differential Evolution (EGDE3). The experiment results show the effectiveness of the proposed approach in terms of the ratio of the payment and data quality, and takeover time compared with the Genetic Algorithm (GA) and Reverse Auction based Dynamic Price (RADP) scheme.

Participatory sensing algorithms for mobile object discovery in urban areas

Abstract: This paper introduces mechanisms for the automated detection of mobile objects in urban areas. Widely available devices such as mobile phones with integrated proximity sensors such as RFID readers or Bluetooth cooperatively perform sensing operations to discover mobile objects. In this paper, we propose a coverage metric for assessing the completeness of sensing that considers spatial and temporal aspects. To maximize coverage while minimizing energy consumption of mobile nodes, we propose both a centralized and a distributed coordination algorithm for selecting nodes that need to sense. Moreover, we present strategies that allow selected nodes to perform efficient sense operations. By extensive simulations, we show that distributed coordination achieves drastic energy savings of up to 63%, while limiting the coverage loss to 13%. Moreover, we show that the centralized algorithm loses less than 1% coverage compared to the maximum possible coverage.

Demystifying privacy in sensory data: A QoI based approach

Abstract: There is a growing consensus regarding the emergence of privacy concerns as a major deterrent towards the widespread adoption of emerging technologies such as mobile healthcare, participatory sensing and other social network based applications. In this paper, we motivate the need for privacy awareness, present a taxonomy of the privacy problems, and the various existing solutions. We highlight the tension that exists between quality of service at the receiver and the privacy requirement at the source and present a linear program formalization to model the tradeoff between the two objectives. We further present the design and architecture of SensorSafe, a framework which allows privacy-aware sharing of sensory information.

PEPSI: Privacy-Enhanced Participatory Sensing Infrastructure.

Abstract: Participatory Sensing combines the ubiquity of mobile phones with sensing capabilities of Wireless Sensor Networks. It targets pervasive collection of information, e.g., temperature, traffic conditions, or health-related data. As users produce measurements from their mobile devices, voluntary participation becomes essential. However, a number of privacy concerns -- due to the personal information conveyed by data reports -- hinder large-scale deployment of participatory sensing applications. Prior work on privacy protection, for participatory sensing, has often relayed on unrealistic assumptions and with no provably-secure guarantees. The goal of this project is to introduce PEPSI: a Privacy-Enhanced Participatory Sensing Infrastructure. We explore realistic architectural assumptions and a minimal set of (formal) privacy requirements, aiming at protecting privacy of both data producers and consumers. We design a solution that attains privacy guarantees with provable security at very low additional computational cost and almost no extra communication overhead.

Preserving privacy while reducing power consumption and information loss in LBS and participatory sensing applications

Abstract: Participatory sensing systems rely on the willingness of mobile users to participate in the collection and reporting of data using a variety of sensors either embedded or integrated in their cellular phones. Users agree to use their cellular phone resources to sense and transmit the data of interest because these data will be used to address a collective problem that otherwise would have been very difficult to solve. However, this new data collection paradigm has not been very successful yet mainly because of privacy concerns. Without adequate privacy-preserving mechanisms most users are not willing to participate. Although several schemes have been proposed in the literature, none of them offers a complete solution, and instead, trade offs exist. For example, anonymization-based schemes change the real location of the users, and therefore preserve their privacy, but they might not be precise enough for certain applications. On the other hand, encryption-based schemes, since they do not modify the real location of the user, are very accurate and serve well all applications; however, they are very costly in terms of energy consumption. In this paper we present a scheme that combines the good properties of both approaches to reduce the energy consumption of encryption-based schemes as well as the noise added by anonymization-based schemes. Our simulation results show that the proposed scheme in fact achieves the desired objectives of reducing the energy consumption and information loss while allowing the application to track the users accurately.

Apollo: Towards factfinding in participatory sensing

Abstract: This demonstration presents Apollo, a new sensor information processing tool for uncovering likely facts in noisy participatory sensing data1. Participatory sensing, where users proactively document and share their observations, has received significant attention in recent years as a paradigm for crowd-sourcing observation tasks. However, it poses interesting challenges in assessing confidence in the information received. By borrowing clustering and ranking tools from data mining literature, we show how to group data into sets (or claims), corroborating specific events or observations, then iteratively assess both claim and source credibility, ultimately leading to a ranking of described claims by their like-lihoold of occurrence. Apollo belongs to a category of tools called fact-finders. It is the first fact-finder designed and implemented specifically for participatory sensing. Apollo uses Twitter as the underlying engine for sharing participatory sensing data. Twitter is widely popular, can be interfaced to cell-phones that share sensor data, and comes with a powerful search API, as well as a publish-subscribe mechanism. We evaluate it using a participatory sensing application that collects and posts noisy vehicular traffic data on Twitter, as well as a set of 60,000 (human) tweets collected during the Haiti tsunami and a set of 500,000 tweets collected about Cairo during its recent unrest. Viewers of the demonstration will interact with Apollo for various fact-finding tasks.

Automatic image capturing and processing for PetrolWatch

Abstract: In our previous work [1], we proposed a Participatory Sensing (PS) architecture called PetrolWatch to collect and share fuel prices from camera images of road-side price board (billboard) of service (or gas) stations. A key part of the PetrolWatch architecture, and the main focus of this paper, is the automatic billboard image capture from a moving car without user intervention. We develop the system design and implementation of the automatic image collection for PetrolWatch. Capturing a clear image by an unassisted mobile phone from a moving car is proved to be a challenge by our street driving experiments. We design the camera control and image pre-selection schemes to address this challenge. In particular, we leverage the advanced capabilities of modern mobile phones to design an acceptable camera triggering range and set the camera focus accordingly. Experiment results show that our design improve fuel price extraction rate by more than 40%. To deal with blurred images caused by vehicle vibrations, we design a set of pre-selection thresholds based on the measures from embedded accelerometer of the mobile phone. Our experiments show that our pre-selection improves the system efficiency by eliminating 78.57% of the blurred images.