Showing papers on "Participatory sensing published in 2023"

Online Organizing Large-Scale Heterogeneous Tasks and Multi-Skilled Participants in Mobile Crowdsensing

Abstract: Online gathering large-scale heterogeneous tasks and multi-skilled participant can make the tasks and participants to be shared in real time. However, their online gathering will bring many intractable objective requirements, which makes task-participant matching become extremely complex. To cope well with the gathering, we design a hierarchy tree and time-series queue to organize tasks and participants. The data structures we designed can effectively meet all requirements that are brought due to tasks and participants gathering online. In addition, based on the designed data structures, we study online large-scale heterogeneous task allocation problem from three aspects: the computing pattern, the tree creation method, and the extension of matching strategy. Our best method (TsPY) is based on parallel computing in the computing pattern, adopts time first and then space in the tree creation method, and increases the short-distance first strategy in the matching strategy. Finally, we conducted detailed experiments under the conditions of different participant geographical distributions (i.e., uniform distribution, Gaussian distribution, and check-in empirical distribution), different sensing methods (i.e., participatory sensing and opportunistic sensing), and different recommendation methods (i.e., point recommendation and trajectory recommendation). The experimental results show that TsPY has a good performance in multiple indicators such as algorithm running time, task-participant matching rate, participant travel distance, and redundant tasks removed. Compared with serial computing, parallel computing can reduce the algorithm running time by more than 66% on average in our experimental environment. Compared with space first and then time, creating a tree based on time first and then space can increase task-participant matching rate by more than 13% on average. Increasing the short-distance first strategy can reduce the participant travel distance by more than 4% on average.

HSM-SMCS: Task Assignment Based on Hybrid Sensing Modes in Sparse Mobile Crowdsensing

Abstract: Sparse mobile crowdsensing (Sparse MCS) is an emerging paradigm for urban-scale sensing applications, which recruits suitable participants to complete sensing tasks in only a few selected cells and then infers data of unsensed cells for saving sensing costs and obtaining high-quality sensing maps. In Sparse MCS, one crucial issue is task assignment, in which the platform selects cells whose sensing data can reduce inferred sensing maps errors (i.e., cell selection) and recruits the participant set with the maximum contribution for performing tasks (i.e., participant recruitment). The research on participant recruitment mainly focuses on single participatory-based or single opportunistic-based sensing mode. Due to the complementarity of two sensing modes, recruiting participants by only one sensing mode would result in wasting sensing resources and compromising the quality of task completion. Thus, combining the advantages of two sensing modes, we propose a task assignment framework based on hybrid sensing modes in Sparse MCS (HSM-SMCS) for achieving a good tradeoff between sensing quality and cost. Specifically, we propose a heuristic two-stage search strategy that simultaneously recruits opportunistic and participatory participants to perform tasks in significant cells within the constraint of total costs, considering their contributions to sensing map inference. Thereinto, for opportunistic participants, mobility prediction greatly affects task assignment effectiveness. However, existing prediction algorithms lead to unsatisfactory outcomes when the historical trajectory data of opportunistic participants are scarce. To effectively improve the predictive accuracy, we design a mobility prediction model based on transfer learning. The experimental evaluation on real trajectory data sets and sensor data sets of corresponding areas demonstrates that our framework outperforms state-of-the-art methods with higher quality reconstructed sensing maps.

Developing and testing a validation procedure to successfully use on-the-move sensors in urban environments

Abstract: With the increasing attempt to empower citizens and civil society in promoting virtuous behaviours and relevant climate actions, novel user-friendly and low-cost tools and sensors are nowadays being developed and distributed on the market. Most of these sensors are typically easy to install with a ready-to-use system, while measured data are automatically uploaded on a mobile application or a web dashboard which also guarantees secure and open access to measurements gathered by other users. However, the quality of the datum and the calibration of these sensors are often ensured against research-grade instrumentations only in the laboratory and rarely in real-world measurement. The discrepancies arising between these low-cost sensors and research-grade instrumentations are such that the first might be impossible to use if a validation (and re-calibration if needed) under environmental conditions is not performed. Here we propose a validation procedure applied to the MeteoTracker, a recently developed portable sensor to monitor atmospheric quantities on the move. The ultimate scope is to develop and implement a general procedure to test and validate the quality of the MeteoTracker data to compile user guidelines tailored for on-the-move sensors. The result will evaluate the feasibility of MeteoTracker (and potentially other on-the-move sensors) to integrate the existing monitoring networks on the territory, improve the atmospheric data local coverage and support the informed decision by the authorities. The procedure will include multi-sensor testing of all the sensor functionalities, validation of all data simultaneously acquired by several sensors under similar conditions, methods and applications of comparisons with research-grade instruments. The first usage of the MeteoTracker will be also presented for different geographical contexts where the sensors will be used for citizen science activities and develop a monitoring network of selected Essential Variables within the HORIZON-EU project I-CHANGE (Individual Change of HAbits Needed for Green European transition).

Design and Implementation of a Crowdsensing-Based Air Quality Monitoring Open and FAIR Data Infrastructure

Abstract: This work reports on the development of a real-time vehicle sensor network (VSN) system and infrastructure devised to monitor particulate matter (PM) in urban areas within a participatory paradigm. The approach is based on the use of multiple vehicles where sensors, acquisition and transmission devices are installed. PM values are measured and transmitted using standard mobile phone networks. Given the large number of acquisition platforms needed in crowdsensing, sensors need to be low-cost (LCS). This sets limitations in the precision and accuracy of measurements that can be mitigated using statistical methods on redundant data. Once data are received, they are automatically quality controlled, processed and mapped geographically to produce easy-to-understand visualizations that are made available in almost real time through a dedicated web portal. There, end users can access current and historic data and data products. The system has been operational since 2021 and has collected over 50 billion measurements, highlighting several hotspots and trends of air pollution in the city of Trieste (north-east Italy). The study concludes that (i) this perspective allows for drastically reduced costs and considerably improves the coverage of measurements; (ii) for an urban area of approximately 100,000 square meters and 200,000 inhabitants, a large quantity of measurements can be obtained with a relatively low number (5) of public buses; (iii) a small number of private cars, although less easy to organize, can be very important to provide infills in areas where buses are not available; (iv) appropriate corrections for LCS limitations in accuracy can be calculated and applied using reference measurements taken with high-quality standardized devices and methods; and that (v) analyzing the dispersion of measurements in the designated area, it is possible to highlight trends of air pollution and possibly associate them with traffic directions. Crowdsensing and open access to air quality data can provide very useful data to the scientific community but also have great potential in fostering environmental awareness and the adoption of correct practices by the general public.

Using Mobile Phones for Participatory Detection and Localization of a GNSS Jammer

Abstract: It is well known that GNSS receivers are vulnerable to jamming and spoofing attacks, and numerous such incidents have been reported in the last decade all over the world. The notion of participatory sensing, or crowdsensing, is that a large ensemble of voluntary contributors provides measurements, rather than relying on a dedicated sensing infrastructure. The participatory sensing network under consideration in this work is based on GNSS receivers embedded in, for example, mobile phones. The provided measurements refer to the receiver-reported carrier-to-noise-density ratio ($C/N_0$) estimates or automatic gain control (AGC) values. In this work, we exploit $C/N_0$ measurements to locate a GNSS jammer, using multiple receivers in a crowdsourcing manner. We extend a previous jammer position estimator by only including data that is received during parts of the sensing period where jamming is detected by the sensor. In addition, we perform hardware testing for verification and evaluation of the proposed and compared state-of-the-art algorithms. Evaluations are performed using a Samsung S20+ mobile phone as participatory sensor and a Spirent GSS9000 GNSS simulator to generate GNSS and jamming signals. The proposed algorithm is shown to work well when using $C/N_0$ measurements and outperform the alternative algorithms in the evaluated scenarios, producing a median error of 50 meters when the pathloss exponent is 2. With higher pathloss exponents the error gets higher. The AGC output from the phone was too noisy and needs further processing to be useful for position estimation.

Sound environments and citizen action: what place for a participatory sciences tool? The case of the city of Rezé using NoiseCapture

Abstract: Local authorities are increasingly interested in implementing participatory processes, associating inhabitants in decision-making. The Sonorezé project, involving researchers from the Gustave Eiffel University and the City of Rezé, evaluates the interest of a smartphone application for participatory noise measurement, namely Noisecapture, as a vehicle for this citizen participation in the noise context. The project includes the recruitment of participants, the creation of participatory noise maps integrating different indicators, and the constitution of discussion groups that aim to elaborate concerted proposals regarding noise mitigation. In parallel, one will evaluate how access to this tool modifies the perception that inhabitants have of their soundscape, and facilitates their empowerment and the valorization of their inhabitant knowledge. This communication will present the whole workflow, highlighting how this framework helps to raise awareness of urban noise environments among inhabitants. Then, one will present in detail the dynamics of the recruitment, which amounts to more than 100 participants that performed almost 1000 measurements, at the stage of the first 4 months. The diversity of the participants' profiles, the temporal and spatial heterogeneity in the measurements, are however possibly an obstacle to the production of representative noise maps, which will be discussed in the communication.

Participatory Systems for Personalized Prediction

Abstract: Machine learning models are often personalized based on information that is protected, sensitive, self-reported, or costly to acquire. These models use information about people, but do not facilitate nor inform their \emph{consent}. Individuals cannot opt out of reporting information that a model needs to personalize their predictions, nor tell if they would benefit from personalization in the first place. In this work, we introduce a new family of prediction models, called \emph{participatory systems}, that allow individuals to opt into personalization at prediction time. We present a model-agnostic algorithm to learn participatory systems for supervised learning tasks where models are personalized with categorical group attributes. We conduct a comprehensive empirical study of participatory systems in clinical prediction tasks, comparing them to common approaches for personalization and imputation. Our results demonstrate that participatory systems can facilitate and inform consent in a way that improves performance and privacy across all groups who report personal data.

Untapped Potential of Participatory Design - Citizen Centered Social Data Utilization for Smart Cities

Abstract: Our society has shifted to digital society, where generates tremendous amount of city activity data or Social Data. This potentially rich city resource has attracted attentions of diverse city players, and often described as “the new oil”. Accumulated social data could potentially open to create better society, generate competitive industries, and improve citizen’s well-being. However, majority of societies do not know how to utilize them for future cities, yet. Challenges to social data utilization are profound. Not only technical and political challenges are there also privacy and ethical challenges. Considering these social challenges, we explored potentials of social data utilization through a series of participatory design workshops. In the workshops, participants collected their own social data and used them for collective concerns. Through the case, we found an interesting potential solution of social data utilization for designing future city, by combining concepts of data donation and citizen as co-designer. Because the combination of data donation and citizens as co-designers could provide one of the optimal solutions for utilizing social data the most without violating the data ownership. By participating in the design process, the data owner can reuse their own donated data for their own purposes, holding a right to control and interpret in the design process. This article, by introducing a smart city workshop case, proposes and discuss a potential of data donation and participatory design, which could be one way to promote social data utilization for the greater good.

Research and the City: An Experience Report on Launching the Smart City Research Lab Bamberg

Abstract: The idea of a “Smart City” has been inspiring researchers from different disciplines for decades now. Many definitions of the term exist [1]; this makes it easy to argue that a smart city can benefit from research ideas like cloud, fog and edge computing, applications of device-to-device coordination, mixed reality, smart vehicles, IoT and sensor systems or participatory and social sensing, just to name a few keywords from the PerCom 2023 Call for Papers. Hence, it makes sense to see “Cities and urban areas [as] an ideal platform for researchers and practitioners to test new technologies, concepts and applications” (PerAwareCity'23 CfP).

Hybrid Recruitment Scheme Based on Deep Learning in Vehicular Crowdsensing

Abstract: Vehicular Crowdsensing (VCS) aims to collect sensing data over a range of areas using a large number of on-board sensors and resources in intelligent vehicles. The mobility of vehicles allows for large-scale mobile sensing data, but it remains a challenging problem to recruit the right participating vehicles and to actively maximize the sensing benefits. In this paper, we formulate the vehicle recruitment problem as the maximizing completion rate with limited budget problem (MCRLB) and prove that it is NP-complete. A hybrid recruitment scheme based on deep learning in vehicular crowdsensing (HR-DLVCS) is proposed in this paper, which consists of two phases: an opportunistic vehicle recruitment phase and a participatory vehicle recruitment phase. In the first phase, a deep learning-based opportunistic vehicle recruitment algorithm (DL-OVR) is proposed to maximize the sensing task completion rate within a limited budget. It aims to recruit the most suitable vehicles to collect sensing data according to their daily movement patterns. In the second phase, a sensing task density-based participatory vehicle recruitment algorithm (STD-PVR) is proposed to reduce the computational complexity of matching vehicles with uncompleted sensing tasks. It is designed to recruit vehicles to arrive at designated locations to complete the sensing tasks within a given budget. Extensive evaluations based on a real-world dataset show that HR-DLVCS achieves higher sensing task completion rate than other baseline approaches in a variety of settings.

Hybrid Worker Selection for Task Coverage Maximization in Mobile Crowdsensing

Abstract: Mobile crowdsensing (MCS) has become an attractive issue in recent years. Most existing researches either select opportunistic sensing or participatory sensing for task execution, which will lead to the problem of restricted task locations or high cost. In this work, we propose a complementary hybrid worker selection method for MCS, where workers complete tasks in different sensing modes, namely opportunistic and participatory sensing. The proposed worker selection method contains two phases. In the opportunistic worker selection phase, an updated iterative algorithm is designed to select a low-cost and high-coverage opportunistic worker set. Specifically, when an opportunistic worker is selected, the algorithm will update the coverage of the remaining candidate opportunistic workers on the sensing task. In the participatory worker selection phase, we design an algorithm that combines group and match to solve the problem of restricted task locations. Specifically, we group the sensing tasks that opportunistic workers have failed to cover and recruit participatory workers to complete the sensing tasks in the groups. Experiments on a real dataset prove that the proposed method outperforms other benchmark methods.

A Vehicle Sensor Network for Real-Time Air Pollution Analysis

Abstract: Air Pollution (AP) is one of the main threats to global health. Real-time dynamic mapping of pollution distribution is of a crucial importance to the AP reduction and management. Conventional air quality monitoring relies on expensive and cumbersome monitoring stations. Such stations are sparsely deployed over a region – typically one to a few per city. The extrapolation of the dynamic spatiotemporal data away from these stations might be inaccurate. In this paper, we present a participatory Vehicle Sensor Network (VSN) based on low-cost mobile nodes deployed on public (taxi) vehicles. The system enables continuous real-time data acquisition, transmission, and utilization. As compared to the conventional approaches, our system greatly improves sensing coverage. The proposed platform enables the acquisition of a large amount of georeferenced and time-stamped data. It provides real time pollution mapping and historical data view. The system’s operational stability and continuity are examined and confirmed through the analysis of background data collected during 15 days of experimental implementation.

A Qualitative Framework for Evaluating Participation on the Geoweb

Abstract: The participatory Geoweb emerges from the synthesis of map-based online applications and Web 2.0 concepts such as user-generated content, enhanced interactivity, and cloud computing. The result is a wide range of tools and projects using these tools to communicate, collaborate, deliberate, and inform spatial decision making. This article draws upon the literature in participatory geographic information systems to propose the “3E Framework,” which provides both a structured conceptual model and a practical tool for the evaluation of projects on the participatory Geoweb. The framework deconstructs participation on the Geoweb into the provider and public realms and represents the engagement, empowerment, and enactment processes. It includes 20 evaluation questions that are derived from themes in the literature.

How Citizens Participation Begin and Continue in Developing Smart Cities

Abstract: “Smart Cities” where utilize city data and ICT technology to improve urban living, have attracted attention in the world. Externalized characteristics of smart cities around the world are diverse, while smart cities in Nordic countries are often introduced as future city design with citizen participation. However, how exactly citizens participation begins and continues smart city projects has not fully been explored, yet. In this paper, we analyzed Danish smart cities from the three axes of the subject of action, the applied technologies, and the instruments for realization, and derived three potential devices that bring citizens’ autonomous participation. The investigation identifies constant citizens’ participation rooted in society as a mechanism, which is enabled with joint citizens decision-making by accessing to resources and commending ways of collaborating. The finding of this paper contributes to the participatory researchers on smart cities, who values citizens’ autonomous participation in design.

Multimodality Fusion for Smart Healthcare: a Journey from Data, Information, Knowledge to Wisdom

Abstract: Multimodal medical data fusion has emerged as a transformative approach in smart healthcare, enabling a comprehensive understanding of patient health and personalized treatment plans. In this paper, a journey from data, information, and knowledge to wisdom (DIKW) is explored through multimodal fusion for smart healthcare. A comprehensive review of multimodal medical data fusion focuses on the integration of various data modalities are presented. It explores different approaches such as Feature selection, Rule-based systems, Machine learning, Deep learning, and Natural Language Processing for fusing and analyzing multimodal data. The paper also highlights the challenges associated with multimodal fusion in healthcare. By synthesizing the reviewed frameworks and insights, a generic framework for multimodal medical data fusion is proposed while aligning with the DIKW mechanism. Moreover, it discusses future directions aligned with the four pillars of healthcare: Predictive, Preventive, Personalized, and Participatory approaches based on the DIKW and the generic framework. The components from this comprehensive survey form the foundation for the successful implementation of multimodal fusion in smart healthcare. The findings of this survey can guide researchers and practitioners in leveraging the power of multimodal fusion with the approaches to revolutionize healthcare and improve patient outcomes.

Mapping the Smart City: Participatory approaches to XAI

Abstract: How can we explain the broad and uneven spatial effects of Machine Learning (ML) algorithms that mediate the everyday lives of smart city residents? The discriminatory impacts of civic algorithms remain opaque to city inhabitants and experts alike. Current Explainable AI (XAI) approaches, while influential, are limited in their ability to explain the inequitable algorithmic spatial effects in an accessible, critical, and grounded manner. My thesis explores the potential of participatory mapping as a critical and collaborative technique to address these limits. My work draws on (1) scholarship on critical data and algorithmic studies, (2) qualitative research with domain experts from history and criminology, and (3) participatory mapping sessions with city residents and ML practitioners. Ultimately, my research will inform the design of a toolkit to help people in classrooms and community centers collaboratively reflect on how city residents may unevenly experience the impact of artificially intelligent systems guiding contemporary urban life.