Participatory Sensing

The emerging Internet of Things (IoT) that effectively integrates cyber-physical space to create smart environments will undoubtedly have a plethora of applications in the near future. Meanwhile, it is also the key technological enabler to create smart cities, which will provide great benefits to our society. In this paper, four different IoT network architectures spanning various smart city applications are presented and their corresponding network Quality of Service (QoS) requirements are defined. Furthermore, as the beneficiary of smart city, we have the responsibility to actively participate in its development as well. A new network paradigm, participatory sensing, is thus discussed as a special case to highlight the way people may be involved in the information acquisition-transmissioninterpretation-action loop.

Integration of the physical world with the computerized world has led to the manifestation of Cyber-Physical Systems (CPSs) in an attempt to build a better and smarter world. In this paper, such a CPS named D&RSense has been proposed to promote smart transportation in order to make travelling more comfortable and safe. By studying driving patterns of drivers, D&RSense can get valuable insights to their braking and accelerating styles which can help to give them real-time warnings when they drive aggressively. Detection of rash driving prone areas across the city can help to recommend which areas of the city need stricter surveillance. D&RSense involves smartphones of commuters and utilizes their accelerometer and GPS sensors to detect driving events like braking and acceleration as well as poor road conditions like bumps and potholes by applying the ensemble learning method for classification, Random Forest (RF). The accuracy of the same has been compared to other supervised machine learning classifiers like Naïve Bayes, k-Nearest Neighbours (k-NN), Decision Trees (DT), Support Vector Machine (SVM) and Artificial Neural Networks (ANN). Rash-driving prone areas and poor road segments during the course of the experiment have been plotted using Density-based spatial clustering of applications with noise (DBSCAN) algorithm. Effectiveness of the proposed application has been evaluated through extensive testing.

Participatory sensing is a new research area that emerged from the need to complement our previous efforts in wireless sensor networks. It takes advantage of the emergence of rich-sensor mobile phones and their wide adoption, in order to turn people to producers of sensed data and enable new classes of collective applications. Unavoidably, this raises a lot of privacy concerns, as people are becoming sensors and give out a lot of personal information, like their location. If we choose to protect their privacy by anonymizing the data and completely hiding any identifying information, then the visibility of their contributions to others is lost. However, it is important to maintain this property, in order to support accountability on one hand and allow people gain reputation for their efforts on the other hand. In this book chapter we investigate which of the available technical solutions we need, in order to resolve this conflict and what are the research directions that emerge.

Crowdsourcing is rapidly evolving and applied in situations where ideas, labour, opinion or expertise of large groups of people are used. Crowdsourcing is now used in various policy-making initiatives; however, this use has usually focused on open collaboration platforms and specific stages of the policy process, such as agenda-setting and policy evaluations. Other forms of crowdsourcing have been neglected in policy-making, with a few exceptions. This article examines crowdsourcing as a tool for policy-making, and explores the nuances of the technology and its use and implications for different stages of the policy process. The article addresses questions surrounding the role of crowdsourcing and whether it can be considered as a policy tool or as a technological enabler and investigates the current trends and future directions of crowdsourcing.

Participatory phone sensing is a new sensing paradigm that asks volunteers to contribute their phones' sensing capabilities and gather, analyze, and share local knowledge about their surroundings. While most existing participatory phone sensing systems are standalone structures without cross-system integration, in this study, we propose a novel Comfort Measuring System (CMS) for public transportation systems. CMS exploits the GPS and 3-axis accelerometer functions of modern smart phones to measure the comfort level of vehicle rides. Then, it mashes up the sensed data with the authorized data of the public transportation system, and provides a detailed comfort statistics as a value added service. Using real data collected from a CMS deployed in Taipei City, we show that the system can achieve a high hit rate in trajectory matching of phone sensed data and the authorized bus data. Moreover, based on the statistics, we demonstrate that the system is capable of ranking the comfort levels of the bus services provided by different agencies, and monitoring the comfort levels of the transportation system overall. The system is also highly scalable without the cost of deploying a sensing infrastructure. We believe that it has the potential to provide a durable and large-scale comfort measuring service for public transportation systems.

Noise pollution in urban areas is a subject of grave concern and it is being recognized globally in different countries and cities. People are facing many health-related problems because of this. Therefore, in the proposed work, we envisioned to tackle the challenge of acquiring real time and spatially fine-grained noise pollution data with a community-driven sensing infrastructure. Mobile crowdsourcing over smartphones presents a new paradigm for collecting context aware sensing data of a vast area like a city. Thus, the proposed system exploits the power of mobile crowdsourcing. The proposed system monitors the present noise level in the surroundings of the user and also generates city's noise pollution footprints. The noise map reflects the real-time pollution scenario of the city which changes with time. The prototype of the system has been evaluated with extensive experiments based on crowdsourced sensing data collected by volunteers in Kolkata city.

Noise pollution in urban areas is a subject of grave concern and it is being recognized globally in different countries and cities. People are facing many health-related problems because of this. Therefore, in the proposed work, we envisioned to tackle the challenge of acquiring real time and spatially fine-grained noise pollution data with a community-driven sensing infrastructure. Mobile crowdsourcing over smartphones presents a new paradigm for collecting context aware sensing data of a vast area like a city. Thus, the proposed system exploits the power of mobile crowdsourcing. The proposed system monitors the present noise level in the surroundings of the user and also generates city's noise pollution footprints. The noise map reflects the real-time pollution scenario of the city which changes with time. The prototype of the system has been evaluated with extensive experiments based on crowdsourced sensing data collected by volunteers in Kolkata city.

Full paper available at http://goo.gl/0mzbrl.
This paper presents the project of a Mobile Cockpit System (MCS) for smartphones, which provides assistance to Electric Bicycle (EB) cyclists in Smart Cities’ environment. The presented system introduces a mobile application (MCS App) with the goal to provide useful personalized information to the cyclist related with the EB’s use, including EB range prediction considering the intended path, management of the cycling effort performed by the cyclist, handling of the battery charging process and the provisioning of information regarding available public transport. This work also introduces the EB cyclist profile concept, which is based on historical data analysis previously stored in a database and collected from mobile devices sensors. From the tests performed, the results show the importance of route guidance, taking into account the energy savings. The results also show significant changes on range prediction based on user and route taken. It is important to say that the proposed system can be used for all bicycle in general.

We design an incentive mechanism based on all-pay
auctions for participatory sensing. The organizer (principal) aims
to attract a high amount of contribution from participating users
(agents) while at the same time lowering his payout, which we
formulate as a profit-maximization problem. We use a contribution-
dependent prize function in an environment that is specifically
tailored to participatory sensing, namely incomplete information
(with information asymmetry), risk-averse agents, and stochastic
population. We derive the optimal prize function that induces
the maximum profit for the principal, while satisfying strict
individual rationality (i.e., strictly have incentive to participate at
equilibrium) for both risk-neutral and weakly risk-averse agents.
The thus induced profit is demonstrated to be higher than the
maximum profit induced by constant (yet optimized) prize. We
also show that our results are readily extensible to cases of
risk-neutral agents and deterministic populations.

Many crowdsourcing scenarios are heterogeneous in the sense that, not only the workers' types (e.g., abilities or costs) are different, but the beliefs (probabilistic knowledge) about their respective types are also different. In this paper, we design an incentive mechanism for such scenarios using an asymmetric all-pay contest (or auction) model. Our design objective is an optimal mechanism, i.e., one that maximizes the crowdsourcing revenue minus cost. To achieve this, we furnish the contest with a prize tuple which is an array of reward functions each for a potential winner. We prove and characterize the unique equilibrium of this contest, and solve the optimal prize tuple. In addition, this study discovers a counter-intuitive property, called strategy autonomy (SA), which means that heterogeneous workers behave independently of one another as if they were in a homogeneous setting. In game-theoretical terms, it says that an asymmetric auction admits a symmetric equilibrium. Not only theoretically interesting, but SA also has important practical implications on mechanism complexity, energy efficiency, crowdsourcing revenue, and system scalability. By scrutinizing seven mechanisms, our extensive performance evaluation demonstrates the superior performance of our mechanism as well as offers insights into the SA property.

Crowdsourcing is both a new paradigm of collaboration and a new frontier for CSCWD. Since its introduction in 2006, the notion of Crowdsourcing has been the object of a number of studies. Crowdsourcing has a multidisciplinary nature with a huge diversity of applications that encompasses many practices. The concepts that shape crowdsourcing are still under construction and it is difficult to reach a consensus. Based on this scenario, this paper proposes a meta-model which aims to fit generic solutions related to a specific subset of crowdsourcing designed to accomplish a specific task: Data Collection and Participatory Sensing to leverage the engagement of volunteers with Science and Environmental issues.

The comfort of rides has been identified as one of the top criteria that affects passengers' satisfaction with public transportation systems. Conventional comfort measurement approaches rely on professional measure tools or interviews from passengers, which are costly, time-consuming, and not yet feasible. The concept of Internet of Things (IoT) is a new solution to answer this problem. The idea of IoT is to interconnect state-of-the-art digital products in physical world to provide more powerful applications. Vehicles equipped with GPS devices and wireless access technologies are parts of the IoT elements in traffic networks. We use the GPS data to measure the comfort level of vehicle rides, and provide a detailed comfort statistics as a value added service. Using real data collected from one of the Taipei taxi service providers, we show that over 95% taxi trajectories are viewed as comfortable. In addition, rides without passengers get higher comfort scores than with passengers. We also give the rankings of all taxi drivers according to a number of criteria, such as the comfort score and the number of loads. With the ranking results, we can track back to the trajectories and infer drives' driving behaviors, road conditions, and traffic conditions. We believe that the proposed solution has the potential to provide a representative comfort measurement service for taxi services and additional value-added services for public transportation systems.

With the development of mobile sensing and mobile social networking techniques, Mobile Crowd Sensing and Computing (MCSC), which leverages heterogeneous crowdsourced data for large-scale sensing, has become a leading paradigm. Built on top of the participatory sensing vision, MCSC has two characterizing features: (1) it leverages heterogeneous crowdsourced data from two data sources: participatory sensing and participatory social media; and (2) it presents the fusion of human and machine intelligence (HMI) in both the sensing and computing process. This paper characterizes the unique features and challenges of MCSC. We further present early efforts on MCSC to demonstrate the benefits of aggregating heterogeneous crowdsourced data.

Participatory sensing is a promising new low-cost approach for collecting environmental data. However, current large-scale environmental participatory sensing campaigns typically do not coordinate the measurements of participants, which can lead to gaps or redundancy in the collected data. While some work has considered this problem, it has made several unrealistic assumptions. In particular, it assumes that complete and accurate knowledge about the participants future movements is available and it does not consider constraints on the number of measurements a user is willing to take. To address these shortcomings, we develop a computationally-efficient coordination algorithm (Best-match) to suggest to users where and when to take measurements. Our algorithm exploits human mobility patterns, but explicitly considers the inherent uncertainty of these patterns. We empirically evaluate our algorithm on a real-world human mobility and air quality dataset and show that it outperforms the ...

This demonstration presents Apollo, a new sensor information processing tool for uncovering likely facts in noisy participatory sensing data. 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 ...

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 su...

Due to the high cost of traditional forest plot measurements, the availability of up-to-date in situ forest inventory data has been a bottleneck for remote sensing image analysis in support of the important global forest biomass mapping. Capitalizing on the proliferation of smartphones, citizen science is a promising approach to increase spatial and temporal coverages of in situ forest observations in a cost-effective way. Digital cameras can be used as a relascope device to measure basal area, a forest density variable that is closely related to biomass. In this paper, we present the Relasphone mobile application with extensive accuracy assessment in two mixed forest sites from different biomes. Basal area measurements in Finland (boreal zone) were in good agreement with reference forest inventory plot data on pine (R 2 = 0.75, RMSE = 5.33 m 2 /ha), spruce (R 2 = 0.75, RMSE = 6.73 m 2 /ha) and birch (R 2 = 0.71, RMSE = 4.98 m 2 /ha), with total relative RMSE(%) = 29.66%. In Durango, Mexico (temperate zone), Relasphone stem volume measurements were best for pine (R 2 = 0.88, RMSE = 32.46 m 3 /ha) and total stem volume (R 2 = 0.87, RMSE = 35.21 m 3 /ha). Relasphone data were then successfully utilized as the only reference data in combination with optical satellite images to produce biomass maps. The Relasphone concept has been validated for future use by citizens in other locations.

As mobile crowdsensing techniques are steering many smart-city applications, an incentive scheme that motivates the crowd to actively participate becomes a key to the success of such city-scale applications. This paper presents a crowdsensing WiFi advisory system called WiFiScout, which helps smartphone users to find good quality WiFi hotspots. The quality information is defined in terms of user experience and hence the system requires users to contribute information of their experience with WiFi hotspots. To motivate people to contribute such information, we design and implement a gamification-based incentive scheme in WiFiScout. It allows a user to " conquer WiFi territories " by becoming the top contributor for WiFi hotspots at different locations. The contribution is based on the diversity and amount of data a user submits, for which he will be rewarded accordingly. WiFiScout has been implemented on Android and it facilitates the collection of city-wide WiFi advisory information provided by real users according to their actual experience.

The broad capabilities of current mobile devices have paved the way for Mobile Crowd Sensing (MCS) applications. The success of this emerging paradigm strongly depends on the quality of received data which, in turn, is contingent to mass user participation; the broader the participation, the more useful these systems become. However, there is an ongoing trend that tries to integrate MCS applications with emerging computing paradigms such as cloud computing. The intuition is that such a transition can significantly improve the overall efficiency while at the same time it offers stronger security and privacy-preserving mechanisms for the end-user. In this position paper, we dwell on the underpinnings of incorporating cloud computing techniques to facilitate the vast amount of data collected in MCS applications. That is, we present a list of core system, security and privacy requirements that must be met if such a transition is to be successful. To this end, we first address several competing challenges not previously considered in the literature such as the scarce energy resources of battery-powered mobile devices as well as their limited computational resources that they often prevent the use of computationally heavy cryptographic operations and thus offering limited security services to the end-user. Finally, we present a use case scenario as a comprehensive example. Based on our findings, we posit open issues and challenges, and discuss possible ways to address them, so that security and privacy do not hinder the migration of MCS systems to the cloud.

Recent advances in ubiquitous computing and availability of low cost sensors have led to the wide spread use of sensor networks in civilian applications. These networks along with multisensory personal devices generate lot of data in digital domain. Harnessing this data for urban sensing applications reduces the cost of implementation. This is possible when people share their data as a community service. However, people hesitate to participate because of trust deficit. Instilling trust among the participants will enhance people"s participation and make a way for newer applications to share data among people. This paper describes a model for data sharing by computing confidence among networked peers. The social interactions in digital domain and reputation in community establish goodwill among peers. This goodwill and the trust on various control factors that influence a peer are used to evaluate its behaviour. However, trusting on peer"s behaviour may involve risk otherwise there is an opportunity. More of opportunity than risk induces confidence on a peer. Finally, this confidence in peer decides whether to share data or not.

Crowd-sensing applications are based on the contribution of user-related context information and as such, they are particularly vulnerable to privacy-compromising attacks. In this paper we focus on the problem of information discovery by data consumers who can pose queries to mobile users providing sensed data. The way to protect the privacy of these mobile users is through the use of cloud-based agents, which obfuscate user location and enforce the sharing practices of their owners. The cloud agents organise themselves in a structure, namely a quadtree, that enables queriers to contact directly the mobile users in the area of interest and, based on their own criteria, select the ones to get sensing data from. The tree is kept in a decentralized manner, stored and maintained by the mobile agents themselves, thus avoiding the privacy implications of previous, centralized techniques. Our proposed solution complements and expands upon prior work in the area while it is shown experimentally to be both scalable, efficient and easy to maintain.

We present the design of a Quantified Workplace system which has been deployed in two European offices of a research organization since October 2014. So far, the system has collected more than 680,000 samples of various environment metrics in the workplace (e.g., noise, air quality,...) and 57,340 data points on the indoor location of employees. In addition, the system has received 7504 participatory inputs from the users about their moods and physical activities in the workplace. We present the system and its differ-ent services, discuss our initial findings on the user engagement, and highlight the challenges of device heterogeneity, privacy and trust. We conclude by discussing potential applications of work-place quantification that can be developed using the data we are collecting.

A noise map facilitates monitoring of environmental noise pollution in urban areas. It can raise citizen awareness of noise pollution levels, and aid in the development of mitigation strategies to cope with the adverse effects. However, state-of-the-art techniques for rendering noise maps in urban areas are expensive and rarely updated (months or even years), as they rely on population and traffic models rather than on real data. Participatory urban sensing can be leveraged to create an open and inexpensive platform for rendering up-to-date noise maps. In this paper, we present the design, implementation and performance evaluation of an end-to-end participatory urban noise mapping system called Ear-Phone. Ear-Phone, for the first time, leverages Compressive Sensing to address the fundamental problem of recovering the noise map from incomplete and random samples obtained by crowdsourcing data collection. Ear-Phone, implemented on Nokia N95 and HP iPAQ mobile devices, also addresses t...

Due to the high cost of traditional forest plot measurements, the availability of up-to-date in situ forest inventory data has been a bottleneck for remote sensing image analysis in support of the important global forest biomass mapping. Capitalizing on the proliferation of smartphones, citizen science is a promising approach to increase spatial and temporal coverages of in situ forest observations in a cost-effective way. Digital cameras can be used as a relascope device to measure basal area, a forest density variable that is closely related to biomass. In this paper, we present the Relasphone mobile application with extensive accuracy assessment in two mixed forest sites from different biomes. Basal area measurements in Finland (boreal zone) were in good agreement with reference forest inventory plot data on pine (R 2 = 0.75, RMSE = 5.33 m 2 /ha), spruce (R 2 = 0.75, RMSE = 6.73 m 2 /ha) and birch (R 2 = 0.71, RMSE = 4.98 m 2 /ha), with total relative RMSE(%) = 29.66%. In Durango, Mexico (temperate zone), Relasphone stem volume measurements were best for pine (R 2 = 0.88, RMSE = 32.46 m 3 /ha) and total stem volume (R 2 = 0.87, RMSE = 35.21 m 3 /ha). Relasphone data were then successfully utilized as the only reference data in combination with optical satellite images to produce biomass maps. The Relasphone concept has been validated for future use by citizens in other locations.

Leveraging on the pervasiveness of mobile phones and their rich built-in sensors, partici-patory sensing recently emerged as a promising approach to large-scale data collection. Whilst some contributors may be altruistic, many contributors are motivated by receiving something in return for their contributions, proportional to their level of contributions. In this paper, we adopt a service allocation approach that motivates users by allocating a determined amount of compelling services to contributors, as an alternative to other credit or reputation based incentive approaches. To address two major concerns that would arise from this approach, namely fairness and social welfare, we propose two service allocation schemes called Allocation with Demand Fairness (ADF) and Iterative Tank Filling (ITF), which is an optimization-based approach. We show that: (i) ADF is max–min fair and scores close to 1 on the Jain's fairness index, and (ii) ITF maximizes social welfare and achieves the unique Nash equilibrium, which is also Pareto and globally optimal. In addition , we use stochastic programming to extend ITF to handle uncertainty in service demands that is often encountered in real-life situations.

Participatory sensing is a promising new low-cost approach for collecting environmental data. However, current large-scale environmental participatory sensing campaigns typically do not coordinate the measurements of participants, which can lead to gaps or redundancy in the collected data. While some work has considered this problem, it has made several unrealis-tic assumptions. In particular, it assumes that complete and accurate knowledge about the participants future movements is available and it does not consider constraints on the number of measurements a user is willing to take. To address these shortcomings, we develop a computationally-efficient coordination algorithm (Best-match) to suggest to users where and when to take measurements. Our algorithm exploits human mobility patterns , but explicitly considers the inherent uncertainty of these patterns. We empirically evaluate our algorithm on a real-world human mobility and air quality dataset and show that it outperforms the state-of-the-art greedy and pull-based proximity algorithms in dynamic environments .

User-contributed or crowd-sourced information is becoming
increasingly common. In this paper, we consider the specific case of
participatory sensing whereby people contribute information captured by
sensors, typically those on a smartphone, and share the information with
others. We propose a new metric called Quality of Contributed Service
(QCS) which characterizes the information quality and timeliness of a
specific real-time sensed quantity achieved in a participatory manner.
Participatory sensing has the problem that contributions are sporadic and
infrequent. To overcome this, we formulate a market-based framework
for participatory sensing with plausible models of the market participants
comprising data contributors, service consumers and a service provider.
We analyze the market equilibrium and obtain a closed form expression
for the resulting QCS at market equilibrium. Next, we examine the effects
of realistic behaviors of the market participants and the nature of the
market equilibrium that emerges through extensive simulations. Our
results show that, starting from purely random behavior, the market and
its participants can converge to the market equilibrium with good QCS
within a short period of time.

ABSTRACT - In Press for 2014 -
Our natural environment is complex and sensitive, and is home to a number of species on the verge of extinction. Surveying is one approach to their preservation, and can be supported by technology. This paper presents the deployment of a smartphone-based citizen science biodiversity application. Our findings from interviews with members of the biodiversity community revealed a tension between the technology and their established working practices. From our experience, we present a series of general guidelines for those designing citizen science apps.

Author Keywords
Bioacoustics; biodiversity; citizen science; community practices; mobile; participatory sensing; tradition; tension.

ACM Classification Keywords
H.5.2 [Information Interfaces and Presentation]: User Interfaces - Interaction styles

Recent convergence between low-cost technology, artform and political discourse presents a new design space for enabling public participation and expression. We explore non-experts' use of place-based, modular sensors to activate, author and provoke urban landscapes. Our work with communities of bicyclists, students, parents, and homeless people suggests design opportunities for merging grassroots data collection with public expressions and activism. Members of each community were given probes that represent the measurement of exhaust, smog, pathogens, chemicals, noise or dust, and asked to engage with them as fully functional sensors over the course of one week. Our findings offer insights into participation, environmental sensing, and data sharing within and across four different communities, revealing design implications for future sensing systems as instruments of social currency and political change. Figure 1. Pathogen sensor on toilet placed by parent (top right), all 6 sensors attached to bike while passing a bridge (top left), dust sensor on construction site fence placed by homeless (bottom right), and dust and noise sensors placed in computer lab by student (bottom left).