An Open-Source Platform for Indoor Environment Monitoring with Participatory Comfort Sensing.

We present an open-source wireless network and data management system for collecting and storing indoor environmental measurements and perceived comfort via participatory sensing in commercial buildings. The system, called a personal comfort and indoor environment measurement (PCIEM) platform, consists of several devices placed in office occupants' work areas, a wireless network, and a remote database to store the data. Each device, called a PCFN (personal comfort feedback node), contains a touchscreen through which the occupant can provide feedback on their perceived comfort on-demand, and several sensors to collect environmental data. The platform is designed to be part of an indoor climate control system that can enable personalized comfort control in real-time. We describe the design, prototyping, and initial deployment of a small number of PCFNs in a commercial building. We also provide lessons learned from these steps. Application of the data collected from the PCFNs for modeling and real-time control will be reported in future work. We use hardware components that are commercial and off-the-shelf, and our software design is based on open-source tools that are freely and publicly available to enable repeatability.

Intra-Company Crowdsensing: Datafication with Human-in-the-Loop.

Every day employees learn about things happening in their company. This includes plain facts witnessed while on the job, related or not to one's job responsibilities. Many of these facts, which we call "occurrence data", are known by employees but remain unknown to the company. We suppose that some of them are valuable and may improve the company's situational awareness. In the spirit of mobile crowdsensing, we propose intra-company crowdsensing (ICC), a method of "extracting" occurrence data from employees. In ICC, an employee occasionally responds to sensing requests, each about one plain fact. We elaborate the concept of ICC, proposing a model of human-system interaction, a system architecture, and an organizational process. We position ICC with respect to related concepts from information technology, and we look at it from selected organizational and managerial viewpoints. Finally, we conducted a survey, in which we presented the concept of ICC to employees of different companies and asked for their evaluation. Respondents positive about ICC outnumbered skeptics by a wide margin. The survey also revealed some concerns, mostly related to ICC being perceived as another employee surveillance tool. However, useful and acceptable sensing requests are likely to be found in every organization.

Privacy Aware Incentivization for Participatory Sensing.

Participatory sensing is a process whereby mobile device users (or participants) collect environmental data on behalf of a service provider who can then build a service based upon these data. To attract submissions of such data, the service provider will often need to incentivize potential participants by offering a reward. However, for the privacy conscious, the attractiveness of such rewards may be offset by the fact that the receipt of a reward requires users to either divulge their real identity or provide a traceable pseudonym. An incentivization mechanism must therefore facilitate data submission and rewarding in a way that does not violate participant privacy. This paper presents Privacy-Aware Incentivization (PAI), a decentralized peer-to-peer exchange platform that enables the following: (i) Anonymous, unlinkable and protected data submission; (ii) Adaptive, tunable and incentive-compatible reward computation; (iii) Anonymous and untraceable reward allocation and spending. PAI makes rewards allocated to a participant untraceable and unlinkable and incorporates an adaptive and tunable incentivization mechanism which ensures that real-time rewards reflect current environmental conditions and the importance of the data being sought. The allocation of rewards to data submissions only if they are truthful (i.e., incentive compatibility) is also facilitated in a privacy-preserving manner. The approach is evaluated using proofs and experiments.

Data Trustworthiness Evaluation in Mobile Crowdsensing Systems with Users' Trust Dispositions' Consideration.

Mobile crowdsensing is a powerful paradigm that exploits the advanced sensing capabilities and ubiquity of smartphones in order to collect and analyze data on a scale that is impossible with fixed sensor networks. Mobile crowdsensing systems incorporate people and rely on their participation and willingness to contribute up-to-date and accurate information, meaning that such systems are prone to malicious and erroneous data. Therefore, trust and reputation are key factors that need to be addressed in order to ensure sustainability of mobile crowdsensing systems. The objective of this work is to define the conceptual trust framework that considers human involvement in mobile crowdsensing systems and takes into account that users contribute their opinions and other subjective data besides the raw sensing data generated by their smart devices. We propose a novel method to evaluate the trustworthiness of data contributed by users that also considers the subjectivity in the contributed data. The method is based on a comparison of users' trust attitudes and applies nonparametric statistic methods. We have evaluated the performance of our method with extensive simulations and compared it to the method proposed by Huang that adopts Gompertz function for rating the contributions. The simulation results showed that our method outperforms Huang's method by 28.6% on average and the method without data trustworthiness calculation by 33.6% on average in different simulation settings.

Bringing the National Security Agency into the Classroom: Ethical Reflections on Academia-Intelligence Agency Partnerships.

Academia-intelligence agency collaborations are on the rise for a variety of reasons. These can take many forms, one of which is in the classroom, using students to stand in for intelligence analysts. Classrooms, however, are ethically complex spaces, with students considered vulnerable populations, and become even more complex when layering multiple goals, activities, tools, and stakeholders over those traditionally present. This does not necessarily mean one must shy away from academia-intelligence agency partnerships in classrooms, but that these must be conducted carefully and reflexively. This paper hopes to contribute to this conversation by describing one purposeful classroom encounter that occurred between a professor, students, and intelligence practitioners in the fall of 2015 at North Carolina State University: an experiment conducted as part of a graduate-level political science class that involved students working with a prototype analytic technology, a type of participatory sensing/self-tracking device, developed by the National Security Agency. This experiment opened up the following questions that this paper will explore: What social, ethical, and pedagogical considerations arise with the deployment of a prototype intelligence technology in the college classroom, and how can they be addressed? How can academia-intelligence agency collaboration in the classroom be conducted in ways that provide benefits to all parties, while minimizing disruptions and negative consequences? This paper will discuss the experimental findings in the context of ethical perspectives involved in values in design and participatory/self-tracking data practices, and discuss lessons learned for the ethics of future academia-intelligence agency partnerships in the classroom.

Fog Architectures and Sensor Location Certification in Distributed Event-Based Systems.

Since smart cities aim at becoming self-monitoring and self-response systems, their deployment relies on close resource monitoring through large-scale urban sensing. The subsequent gathering of massive amounts of data makes essential the development of event-filtering mechanisms that enable the selection of what is relevant and trustworthy. Due to the rise of mobile event producers, location information has become a valuable filtering criterion, as it not only offers extra information on the described event, but also enhances trust in the producer. Implementing mechanisms that validate the quality of location information becomes then imperative. The lack of such strategies in cloud architectures compels the adoption of new communication schemes for Internet of Things (IoT)-based urban services. To serve the demand for location verification in urban event-based systems (DEBS), we have designed three different fog architectures that combine proximity and cloud communication. We have used network simulations with realistic urban traces to prove that the three of them can correctly identify between 73% and 100% of false location claims.

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

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

Do Monetary Incentives Influence Users' Behavior in Participatory Sensing?

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

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

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

Symbiotic Sensing for Energy-Intensive Tasks in Large-Scale Mobile Sensing Applications.

Energy consumption is a critical performance and user experience metric when developing mobile sensing applications, especially with the significantly growing number of sensing applications in recent years. As proposed a decade ago when mobile applications were still not popular and most mobile operating systems were single-tasking, conventional sensing paradigms such as opportunistic sensing and participatory sensing do not explore the relationship among concurrent applications for energy-intensive tasks. In this paper, inspired by social relationships among living creatures in nature, we propose a symbiotic sensing paradigm that can conserve energy, while maintaining equivalent performance to existing paradigms. The key idea is that sensing applications should cooperatively perform common tasks to avoid acquiring the same resources multiple times. By doing so, this sensing paradigm executes sensing tasks with very little extra resource consumption and, consequently, extends battery life. To evaluate and compare the symbiotic sensing paradigm with the existing ones, we develop mathematical models in terms of the completion probability and estimated energy consumption. The quantitative evaluation results using various parameters obtained from real datasets indicate that symbiotic sensing performs better than opportunistic sensing and participatory sensing in large-scale sensing applications, such as road condition monitoring, air pollution monitoring, and city noise monitoring.

Citizen-sensor-networks to confront government decision-makers: Two lessons from the Netherlands.

This paper presents one emerging social-technical innovation: The evolution of citizen-sensor-networks where citizens organize themselves from the 'bottom up', for the sake of confronting governance officials with measured information about environmental qualities. We have observed how citizen-sensor-networks have been initiated in the Netherlands in cases where official government monitoring and business organizations leave gaps. The formed citizen-sensor-networks collect information about issues that affect the local community in their quality-of-living. In particular, two community initiatives are described where the sensed environmental information, on noise pollution and gas-extraction induced earthquakes respectively, is published through networked geographic information methods. Both community initiatives pioneered in developing an approach that comprises the combined setting-up of sensor data flows, real-time map portals and community organization. Two particular cases are analyzed to trace the emergence and network operation of such 'networked geo-information tools' in practice: (1) The Groningen earthquake monitor, and (2) The Airplane Monitor Schiphol. In both cases, environmental 'externalities' of spatial-economic activities play an important role, having economic dimensions of national importance (e.g. gas extraction and national airport development) while simultaneously affecting the regional community with environmental consequences. The monitoring systems analyzed in this paper are established bottom-up, by citizens for citizens, to serve as 'information power' in dialogue with government institutions. The goal of this paper is to gain insight in how these citizen-sensor-networks come about: how the idea for establishing a sensor network originated, how their value gets recognized and adopted in the overall 'system of governance'; to what extent they bring countervailing power against vested interests and established discourses to the table and influence power-laden conflicts over environmental pressures; and whether or not they achieve (some form of) institutionalization and, ultimately, policy change. We find that the studied-citizen-sensor networks gain strength by uniting efforts and activities in crowdsourcing data, providing factual, 'objectivized data' or 'evidence' of the situation 'on the ground' on a matter of local community-wide concern. By filling an information need of the local community, a process of 'collective sense-making' combined with citizen empowerment could grow, which influenced societal discourse and challenged prevailing truth-claims of public institutions. In both cases similar, 'competing' web-portals were developed in response, both by the gas-extraction company and the airport. But with the citizen-sensor-networks alongside, we conclude there is a shift in power balance involved between government and affected communities, as the government no longer has information monopoly on environmental measurements.

Energy-Efficient Collaborative Outdoor Localization for Participatory Sensing.

Location information is a key element of participatory sensing. Many mobile and sensing applications require location information to provide better recommendations, object search and trip planning. However, continuous GPS positioning consumes much energy, which may drain the battery of mobile devices quickly. Although WiFi and cell tower positioning are alternatives, they provide lower accuracy compared to GPS. This paper solves the above problem by proposing a novel localization scheme through the collaboration of multiple mobile devices to reduce energy consumption and provide accurate positioning. Under our scheme, the mobile devices are divided into three groups, namely the broadcaster group, the location information receiver group and the normal participant group. Only the broadcaster group and the normal participant group use their GPS. The location information receiver group, on the other hand, makes use of the locations broadcast by the broadcaster group to estimate their locations. We formulate the broadcaster set selection problem and propose two novel algorithms to minimize the energy consumption in collaborative localization. Simulations with real traces show that our proposed solution can save up to 68% of the energy of all of the participants and provide more accurate locations than WiFi and cellular network positioning.

Mapping Urban Environmental Noise Using Smartphones.

Noise mapping is an effective method of visualizing and accessing noise pollution. In this paper, a noise-mapping method based on smartphones to effectively and easily measure environmental noise is proposed. By using this method, a noise map of an entire area can be created using limited measurement data. To achieve the measurement with certain precision, a set of methods was designed to calibrate the smartphones. Measuring noise with mobile phones is different from the traditional static observations. The users may be moving at any time. Therefore, a method of attaching an additional microphone with a windscreen is proposed to reduce the wind effect. However, covering an entire area is impossible. Therefore, an interpolation method is needed to achieve full coverage of the area. To reduce the influence of spatial heterogeneity and improve the precision of noise mapping, a region-based noise-mapping method is proposed in this paper, which is based on the distribution of noise in different region types tagged by volunteers, to interpolate and combine them to create a noise map. To validate the effect of the method, a comparison of the interpolation results was made to analyse our method and the ordinary Kriging method. The result shows that our method is more accurate in reflecting the local distribution of noise and has better interpolation precision. We believe that the proposed noise-mapping method is a feasible and low-cost noise-mapping solution.

Task and Participant Scheduling of Trading Platforms in Vehicular Participatory Sensing Networks.

The vehicular participatory sensing network (VPSN) is now becoming more and more prevalent, and additionally has shown its great potential in various applications. A general VPSN consists of many tasks from task, publishers, trading platforms and a crowd of participants. Some literature treats publishers and the trading platform as a whole, which is impractical since they are two independent economic entities with respective purposes. For a trading platform in markets, its purpose is to maximize the profit by selecting tasks and recruiting participants who satisfy the requirements of accepted tasks, rather than to improve the quality of each task. This scheduling problem for a trading platform consists of two parts: which tasks should be selected and which participants to be recruited? In this paper, we investigate the scheduling problem in vehicular participatory sensing with the predictable mobility of each vehicle. A genetic-based trading scheduling algorithm (GTSA) is proposed to solve the scheduling problem. Experiments with a realistic dataset of taxi trajectories demonstrate that GTSA algorithm is efficient for trading platforms to gain considerable profit in VPSN.

Evaluation of Smartphone Inertial Sensor Performance for Cross-Platform Mobile Applications.

Smartphone sensors are being increasingly used in mobile applications. The performance of sensors varies considerably among different smartphone models and the development of a cross-platform mobile application might be a very complex and demanding task. A publicly accessible resource containing real-life-situation smartphone sensor parameters could be of great help for cross-platform developers. To address this issue we have designed and implemented a pilot participatory sensing application for measuring, gathering, and analyzing smartphone sensor parameters. We start with smartphone accelerometer and gyroscope bias and noise parameters. The application database presently includes sensor parameters of more than 60 different smartphone models of different platforms. It is a modest, but important start, offering information on several statistical parameters of the measured smartphone sensors and insights into their performance. The next step, a large-scale cloud-based version of the application, is already planned. The large database of smartphone sensor parameters may prove particularly useful for cross-platform developers. It may also be interesting for individual participants who would be able to check-up and compare their smartphone sensors against a large number of similar or identical models.

A Self-Adaptive Behavior-Aware Recruitment Scheme for Participatory Sensing.

Participatory sensing services utilizing the abundant social participants with sensor-enabled handheld smart device resources are gaining high interest nowadays. One of the challenges faced is the recruitment of participants by fully utilizing their daily activity behavior with self-adaptiveness toward the realistic application scenarios. In the paper, we propose a self-adaptive behavior-aware recruitment scheme for participatory sensing. People are assumed to join the sensing tasks along with their daily activity without pre-defined ground truth or any instructions. The scheme is proposed to model the tempo-spatial behavior and data quality rating to select participants for participatory sensing campaign. Based on this, the recruitment is formulated as a linear programming problem by considering tempo-spatial coverage, data quality, and budget. The scheme enables one to check and adjust the recruitment strategy adaptively according to application scenarios. The evaluations show that our scheme provides efficient sensing performance as stability, low-cost, tempo-spatial correlation and self-adaptiveness.

A low-cost sensing system for cooperative air quality monitoring in urban areas.

Air quality in urban areas is a very important topic as it closely affects the health of citizens. Recent studies highlight that the exposure to polluted air can increase the incidence of diseases and deteriorate the quality of life. Hence, it is necessary to develop tools for real-time air quality monitoring, so as to allow appropriate and timely decisions. In this paper, we present uSense, a low-cost cooperative monitoring tool that allows knowing, in real-time, the concentrations of polluting gases in various areas of the city. Specifically, users monitor the areas of their interest by deploying low-cost and low-power sensor nodes. In addition, they can share the collected data following a social networking approach. uSense has been tested through an in-field experimentation performed in different areas of a city. The obtained results are in line with those provided by the local environmental control authority and show that uSense can be profitably used for air quality monitoring.

A deep learning and image-based model for air quality estimation.

The serious threat of air pollution to human health makes air quality a focus of public attention, and effective, timely air quality monitoring is critical to pollution control and human health. This paper proposes a deep learning and image-based model for air quality estimation. The model extracts feature information from scene images captured by camera equipment and then classifies them to estimate air quality levels. A self-supervision module (SCA) is added to the model and the global context information of the feature map is used to reconstruct the features by using the interdependence between the channel maps to enhance the interdependent channel maps and improve the ability of feature representation. In addition, a high-quality outdoor air quality data set (NWNU-AQI) was compiled to facilitate the training and evaluation of the model's performance. This paper compares and analyzes AQC-Net, Support Vector Machine (SVM), and Deep Residual Network (ResNet) on NWNU-AQI. The experimental results show that AQC-Net yields more accurate results for air quality classification than other methods.

The geo-observer network: A proof of concept on participatory sensing of disasters in a remote setting.

Effective disaster risk reduction is often hampered by a general scarcity of reliable data collected on disastrous events, particularly in the Global South. Novel approaches are therefore necessary to alleviate this constraint, particularly with regard to reducing extensive risks. A geo-observer network, consisting of 21 reporters, was established in the Rwenzori region (Uganda) in February 2017 to collect data on eight different disasters using smartphone technology. Within the first 15 months of operation, a total of 319 disaster reports were submitted. A large majority of the reported disasters were reached by the geo-observers within 2 days after their occurrence. The analysis of reporting activity shows a large divergence, with one third of the most active geo-observers accounting for nearly 75% of all reports. By using an existing landslide susceptibility map as a proxy of expected landslide prevalence, this reporting divergence is demonstrated to be at least partially driven by a difference in disaster occurrences. This is confirmed by the results of a survey held among the geo-observers. Survey results also showed that the participants are more driven by non-pecuniary benefits rather than financial compensation. The data collected during the first 15 months of operation indicates that extensive risks in the region are underestimated and demonstrates the added value of participatory sensing to compensate for the current lack of well-functioning official data collection mechanisms. This pilot project is a proof of concept for participatory sensing to collect high quality data even in remote contexts where smartphone technology is not generally adopted. It can serve as a precedent or example for other regions where extensive risks are poorly understood but pose significant threat to the population.

Miniaturized Monitors for Assessment of Exposure to Air Pollutants: A Review.

Air quality has a huge impact on different aspects of life quality, and for this reason, air quality monitoring is required by national and international regulations. Technical and procedural limitations of traditional fixed-site stations for monitoring or sampling of air pollutants are also well-known. Recently, a different type of miniaturized monitors has been developed. These monitors, due to their characteristics (e.g., low cost, small size, high portability) are becoming increasingly important for individual exposure assessment, especially since this kind of instrument can provide measurements at high spatial and temporal resolution, which is a notable advantage when approaching assessment of exposure to environmental contaminants. The aim of this study is indeed to provide information regarding current knowledge regarding the use of miniaturized air pollutant sensors. A systematic review was performed to identify original articles: a literature search was carried out using an appropriate query for the search of papers across three different databases, and the papers were selected using inclusion/exclusion criteria. The reviewed articles showed that miniaturized sensors are particularly versatile and could be applied in studies with different experimental designs, helping to provide a significant enhancement to exposure assessment, even though studies regarding their performance are still sparse.