Showing papers on "Participatory sensing published in 2020"

Participatory Sensing and Digital Twin City: Updating Virtual City Models for Enhanced Risk-Informed Decision-Making

Abstract: The benefits of a digital twin city have been assessed based on real-time data collected from preinstalled Internet of Things (IoT) sensors (e.g., traffic, energy use, air pollution, water ...

Privacy Protection Based on Stream Cipher for Spatiotemporal Data in IoT

Abstract: In the participatory sensing framework, privacy protection of the Internet of Things (IoT) is very important. In this article, cryptography-based methods are utilized to protect participants’ privacy information in unsecured network channels for dynamic and real-time sensing tasks. The edge computing paradigm is introduced in the traditional participatory sensing framework to reduce network latency. Then, the Rivest Cipher 4 stream cipher and logistic mapping are combined to deal with the problems of participants’ limited resources and untruthful third-party platforms. Finally, the product algebra and logistic mapping are combined to deal with the problems of large numbers of participants’ access and poor randomness of keystream. Through extensive performance evaluation and comparison experiments on the real-world data, the effectiveness and adaptation of the proposed privacy protection based on stream cipher are verified. It could effectively solve the problem of poor network latency and improve the privacy protection level of IoT.

JUSense: A Unified Framework for Participatory-based Urban Sensing System

Abstract: Participatory sensing has become an effective way of sensing urban dynamics due to the widespread availability of smartphones among citizens. Traditionally, separate urban sensing applications are designed to monitor different urban dynamics like environment, transportation, mobility, etc. However, combining these applications to aggregate information can lead to various new inferences. The main objective of this work is to improve urban sensing applications by overcoming their individual limitations. A unified framework called JUSense (Judicious Urban Sensing) is proposed that can derive benefits from these applications by combining their functionalities. JUSense provides the opportunity for applications to tackle the challenges associated with data collection, aggregation of data in cloud, calibration, data cleaning, and prediction. A multi-view fusion model is proposed for spatiotemporal urban air and noise pollution map generation. Further, a random forest classifier is built to classify the driving events. Here, large scale experiments are performed to evaluate the efficacy of JUSense on real-world dataset. Both the fusion model and the random forest classifier yield better accuracies compared to the baseline methods. Additionally, case studies are conducted to show the advantages that can arise out of the mutual interactions among the applications.

A Fog-Based Recommender System

Abstract: Fog computing is an emergent computing paradigm that extends the cloud paradigm. With the explosive growth of smart devices and mobile users, cloud computing no longer matches the requirements of the Internet of Things (IoT) era. Fog computing is a promising solution to satisfying these new requirements, such as low latency, uninterrupted service, and location awareness. As a typical new computing paradigm and network architecture, fog computing raises new challenges, such as privacy, data management, data analytics, information overload, and participatory sensing. In this article, we present a fog-based hybrid recommender system to address the issue of information overload in fog computing. Our proposed system not only abstracts useful information from the fog environment but can also be considered as an optimization tool due to its ability to provide suggestions to improve system performance. In particular, we demonstrate that the proposed system provides personalized and localized recommendations to users, and also advise the system itself to precache the content to optimize the storage capacity of the fog server.

Out of sight, out of mind : participatory sensing for monitoring indoor air quality

Abstract: In southern Chile, epidemiological studies have linked high levels of air pollution produced by the use of wood-burning stoves with the incidence of numerous diseases. Using a quasi-experimental design, this study explores the potential of participatory sensing strategies to transform experiences, perceptions, attitudes, and daily routine activities in 15 households equipped with wood-burning stoves in the city of Temuco, Chile. The results suggest that the experience of using a low-cost sensor improves household members’ awareness levels of air pollution. However, the information provided by the sensors does not seem to improve the participants’ self-efficacy to control air quality and protect themselves from pollution. The high degree of involvement with the participatory sensing experience indicates that the distribution of low-cost sensors could be a key element in the risk communication policies.

Smartphone-Based Participatory Soundscape Mapping for a More Sustainable Acoustic Environment

Abstract: The urban environmental planning, a fundamental dynamic process for cities’ sustainability, could benefit from the soundscape approach, dealing with the perception of the acoustic environment in which sound is considered as a resource rather than a waste (noise). Noise and soundscape maps are useful tools for planning mitigation actions and for communication with citizens. Both mappings can benefit from crowdsourcing and participatory sound monitoring that has been made possible due to the large use of internet connections and mobile devices with dedicated apps. This paper is a “scoping review” to provide an overview of the potential, benefits, and drawbacks of participatory noise monitoring in noise and soundscape mapping applications, while also referring to metrological aspects. Gathering perceptual data on soundscapes by using digital questionnaires will likely be more commonly used than printed questionnaires; thus, the main differences between the experimental protocols concern the measurement of acoustic data. The authors propose to classify experimental protocols for in-field soundscape surveys into three types (GUIDE, MONITOR, and SMART) to be selected according to the survey’s objectives and the territorial extension. The main future developments are expected to be related to progress in smartphone hardware and software, to the growth of social networks data analysis, as well as to the implementation of machine learning techniques.

Gamified Participatory Sensing in Tourism: An Experimental Study of the Effects on Tourist Behavior and Satisfaction

Abstract: In the tourism sector, user-generated information and communication among tourists are perceived to be more effective and reliable contents. In addition, the collection of dynamic tourism information with high spatio-temporal resolution is required to provide comfortable tourism in response to the changing tourism style with the advancement of information technology. Participatory sensing, which can collect various types of information is a useful method by which to collect these contents. However, continuous participation of users is essential in participatory sensing, and it is one of the most important points to stimulate participation motivation. In the tourism situation, we also need to pay attention to the total tourist satisfaction of participants. In this paper, we adopt gamification, i.e., the implementation of game design elements in real-world contexts for non-gaming purposes, for participatory sensing as an incentive mechanism to motivate participants with active participation and collect the necessary information efficiently. Within the framework, where points are given when completing the requested sensing task (=mission), two sensing missions with different burdens; Area Mission and Check-in Mission, and three different types of rewarding mechanisms; Fixed, Variable and Dynamic Variable, are designed as a gamification mechanism. We implemented these elements in the proposed participatory sensing platform application and conducted an experimental case study with 33 participants at an actual tourist spot: Kyoto, Japan. Then, we investigate the effects on tourist behavior and satisfaction by analyzing collected sensor data, mission logs, and post-survey answers. As a result, we can conclude the following: (1) the tourist behavior is changed due to the proposed gamification design and the necessary information was collected efficiently; (2) the participants tend to prioritize Check-in Mission over the sightseeing, which can induce a behavior change but might impact sightseeing enjoyment.

Accuracy Enhancement of Anomaly Localization with Participatory Sensing Vehicles.

Abstract: Transportation agencies cannot afford to scale existing methods of roadway and railway condition monitoring to more frequently detect, localize, and fix anomalies throughout networks. Consequently, anomalies such as potholes and cracks develop between maintenance cycles and cause severe vehicle damage and safety issues. The need for a lower-cost and more-scalable solution spurred the idea of using sensors on board vehicles for a continuous and network-wide monitoring approach. However, the timing of the full adoption of connected vehicles is uncertain. Therefore, researchers used smartphones to evaluate a variety of methods to implement the application using regular vehicles. However, the poor accuracy of standard positioning services with low-cost geospatial positioning system (GPS) receivers presents a significant challenge. The experiments conducted in this research found that the error spread can exceed 32 m, and the mean localization error can exceed 27 m at highway speeds. Such large errors can make the application impractical for widespread use. This work used statistical techniques to inform a model that can provide more accurate localization. The proposed method can achieve sub-meter accuracy from participatory vehicle sensors by knowing only the mean GPS update rate, the mean traversal speed, and the mean latency of tagging accelerometer samples with GPS coordinates.

Mobile shared resources in the context of IoT low power lossy networks

Abstract: Mobile wireless sensors are becoming an increasingly important component of many IoT initiatives that, ranging from smart city to clean agriculture, heavily rely on participatory sensing. Most of these scenarios and technologies are deployed as Wireless Personal Area Networks (WPANs) where devices exhibit low power consumption in order to extend battery life. Low power consumption implies that sensors depend on duty cycles to periodically enable or disable functionality for a predefined amount of time. Although duty cycles affect connectivity, under participatory sensing, mobility enables multiple sensors to observe a single asset. In this context, it is advantageous to control the transmission of redundant traffic in order to prevent inefficient use of device energy as well as computational and memory resources. This paper addresses this as well as other relevant and related issues by introducing a mechanism that based on well known state-of-the-art IoT protocols extends the battery life of the devices in the network.

Application of Modern Web Technologies to the Citizen Science Project BAYSICS on Climate Research and Science Communication

Abstract: Participatory sensing has become an important element in citizen science projects. Information and communication technologies (ICTs) such as web platforms and mobile phones can generate high-resolution data for science and progress assessment of the United Nations Sustainable Development Goals (e.g., SDGs 11, 13, and 15). This paper gives an overview of web technologies in citizen science and illustrates how these technologies were applied in the citizen science project BAYSICS (Bavarian Citizen Science Information Platform for Climate Research and Science Communication) in Bavaria, in the south-eastern part of Germany. For the project, three digital platforms were developed: a website, web portal, and mobile application, each of which fulfills different tasks based on the project’s needs. The website informs visitors about the project structure, makes the project known to the community, and advertises the latest activities. The web portal is the main interface for citizens who want to join and actively participate in the project. The mobile application of the web portal was realized in the form of a progressive web application, which allows installation on a mobile phone and is connected with offline access to the content. The provision of an IT service for participatory sensing-based research which covers a development package, including a database, website/web application, and smartphone application, is further discussed.

Differentially Private Mobile Crowd Sensing Considering Sensing Errors.

Abstract: An increasingly popular class of software known as participatory sensing, or mobile crowdsensing, is a means of collecting people's surrounding information via mobile sensing devices. To avoid potential undesired side effects of this data analysis method, such as privacy violations, considerable research has been conducted over the last decade to develop participatory sensing that looks to preserve privacy while analyzing participants' surrounding information. To protect privacy, each participant perturbs the sensed data in his or her device, then the perturbed data is reported to the data collector. The data collector estimates the true data distribution from the reported data. As long as the data contains no sensing errors, current methods can accurately evaluate the data distribution. However, there has so far been little analysis of data that contains sensing errors. A more precise analysis that maintains privacy levels can only be achieved when a variety of sensing errors are considered.

On Physical-Social-Aware Localness Inference by Exploring Big Data from Location-Based Services

Abstract: A user's localness (i.e., whether a user is a local resident in a city or not) and a venue's local attractiveness (i.e., the likelihood of a venue to attract local people) are important information for many location-based applications related with Cyber-Physical Systems (CPS), such as participatory sensing, urban planning, traffic control and localized travel recommendations. Previous effort has been devoted to geo-locating users in a city using supervised learning approaches, which depend on the availability of high quality training datasets. However, it is difficult to obtain such training datasets in the real-world CPS applications due to the issue of privacy. In this work, we develop an unsupervised approach, called a Physical-Social-Aware Inference (PSAI) scheme, to jointly infer a user's localness and a venue's local attractiveness by exploring both the physical and social information embedded in the location-based social networks (LBSN). We further implement a parallel PSAI framework on the platform of a Graphic Processing Unit (GPU) to enhance its ability to process large-scale data. Our extensive experiments on the real-world LBSN datasets demonstrate the effectiveness and efficiency of the PSAI scheme compared to the state-of-the-art baselines.

Engaging the Commons in Participatory Sensing: Practice, Problems, and Promise in the Context of Dockless Bikesharing

Abstract: Participatory sensing refers to the sensing paradigm where human participants use personal mobile devices to generate and share data from their surroundings. It holds the promise of providing information that is otherwise challenging to access, which sets the stage for understanding and resolving various social issues. However, difficulties in engaging participants often hinder the fulfillment of this promise. The current paper presents a qualitative study in the context of dockless bikesharing, where participatory sensing constitutes a backbone of the bike status monitoring system. We conducted in-depth interviews with 30 participants. These participants came from different emergent groups who took part in filing status reports for shared bikes. Our analysis indicated close associations among participants' models of engagement, their perceived (dis)connections with the sensing data, and their situated interpretation of the incentives. Based on these findings, we propose ways to engage the commons in participatory sensing for dockless bikesharing and beyond.

Design and evaluation on task allocation interfaces in gamified participatory sensing for tourism

Abstract: In the tourism sector, user-generated information and communication among tourists are perceived to be more effective and reliable contents. In addition, the collection of dynamic tourism information with high spatio-temporal resolution is required to provide comfortable tourism in response to the changing tourism style with the advancement of information technology. Participatory sensing, which can collect various types of information, is a useful method by which to collect these contents. However, continuous participation of users is essential in participatory sensing, and it is one of the most important points to stimulate participation motivation. In the tourism situation, we also need to pay attention to the total tourist satisfaction of participants. In this study, we investigate the effects of task allocation interfaces and user types on the efficiency of tourism information collection, tourism behavior and satisfaction in gamified participatory sensing. Two types of task allocation interfaces (free selection and agent interaction) were designed and implemented, and a sightseeing experiment was conducted with 10 participants at an actual sightseeing spot (Nara, Japan). As a result, we found that there was no difference in the effect of each interface on sightseeing satisfaction, but the characteristics of the collected data that, free selection allows for the collection of quantitative data and agent interaction allows for the efficient collection of data needed by the system, were different. In addition, we found that different user types had different tendencies for their contribution to sensing and their interface preferences.

Towards Citizen-Centric Marketplaces for Urban Sensed Data

Abstract: Due to increasing urbanization, the competition for cities’ finite resources is intensifying. To maintain a high quality of life for citizens, more efficient use of these resources can be targeted. One way to achieve this goal is to use sensor networks that collect real-time information about the conditions in a city and improve citizens’ understanding of their environment. Nevertheless, many existing sensor networks make their data available only locally, are not interconnected, and target companies and experts instead of average citizens.

Participatory Sensing in the Speculative Smart City

Abstract: This paper explores a bottom-up way to speculate towards future smart cities by inviting residents of Copenhagen, Denmark to participate in sensing activities. It illustrates how the idea of “design things” could support bottom-up citizen participation in a smart city. It uses a research through design approach, deploying a wearable air quality sensor to three Copenhageners. By investigating citizens’ perception of the city through this prototype, we illustrate a possible path for engagement in the development of future smart city technologies that offer a greater sense of influence and relevance for residents. Further, citizen participation in sensing activities provides a route to different understandings of smart cities: as a place for people and participation instead of for data and rationalisation.

Understanding the Potential of Edge-Based Participatory Sensing: an Experimental Study

Abstract: Participatory sensing uses both local devices for data collection and cloud-based servers for processing. However, transferring the collected data to the cloud can lead to draining device battery power and cause network bandwidth bottlenecks, especially for large multimedia files. In this paper, we investigate how the processing resources at the edge of the network can be leveraged to enable efficient participatory sensing that avoids heavy network traffic. In particular, we report on the experiences of designing, implementing, and evaluating a sensing system that constructs indoor maps by recognizing door signs. A distinguishing characteristic of our system is an almost exclusive use of edge-based processing for tasks that include ML-based image recognition, human-assisted data verification, data model retraining, and administrative data flow aggregation. Our evaluation shows that our system architecture effectively leverages the available edge resources, while greatly reducing network traffic. Based on our experiences of implementing and evaluating our system prototype, we identify several open research directions for further advancing edge-based participatory sensing.

Participatory Sensing or Sensing of Participation: Awareness and Privacy Concerns With Smart Device Applications

Abstract: Smart devices have become a basic necessity in this technically advanced era. Many smart device applications, when installed, collect personal data and track users' online behavior for marketing or other purposes. This study aims to explore whether users are aware of related privacy issues and whether their knowledge influences the usage of their apps. Cognate-based views of privacy indicate that privacy concern is dynamic and varies depending on an individual's characteristics and the context. Adopting this view creates the attempt to understand the effect of privacy awareness on user's behavior, considering the moderating effects of individual characteristics and application categories. The study was conducted by surveying smart devices' users and analyzing their responses to the privacy awareness treatment across eight groups of applications. Results indicate that privacy awareness impacts smart device user preferences for app usage. This influence varies depending on individual user characteristics and different application categories.

Provisioning Privacy and Accountability in Participatory Sensing

Abstract: Mobile Crowd Sensing (MCS) is a cost-effective and innovative paradigm that exploits the power of the crowd by facilitating individuals with sensing and computing devices to collectively sense the physical world and share the sensed data. The goal is to extract information from the collected data to measure and map phenomena of common interest. For an MCS campaign to be successful, privacy of the participants should be preserved. At the same time, the platform should be able to fix responsibility when a dishonest participant behaves maliciously (for instance, shares falsified data). Hence, privacy and accountability are important issues which need to be provisioned in the MCS architecture. This work proposes an extension to an existing MCS architecture which takes care of both. Security analysis of the architecture is also presented.

Task Allocation in Semi-Opportunistic Mobile Crowdsensing: Paradigm and Algorithms

Abstract: Mobile crowdsensing paradigms can be categorized into two classes: opportunistic sensing and participatory sensing, each of which has its advantage and disadvantage. The high flexibility of worker mobility in participatory sensing leads to high task coverage but also high worker employment fee. The little human involvement in opportunistic sensing results in low worker employment fee but also low task coverage. In this paper, we propose a new mobile crowdsensing paradigm, named semi-opportunistic sensing, aiming to achieve both high task coverage and low worker employment fee. In this paradigm, each worker can provide multiple candidate moving paths for his/her trip, among which the service platform chooses one for the worker to undertake task(s). The platform selects workers and assigns tasks to them with an objective to optimize total task quality under the platform’s incentive budget and workers’ task performing time constraints. In this paper, we formulate the task allocation problem, prove its NP-hardness, and present two efficient heuristic algorithms. The first heuristic, named Best Path/Task first algorithm (BPT), always chooses the best path and task in a greedy manner. The second heuristic, named LP-Relaxation based algorithm (LPR), assigns paths and tasks with the largest values according to the LP-relaxation. We conduct extensive experiments on synthetic dataset and real-life traces. Experiment results show that the proposed semi-opportunistic sensing paradigm can significantly improve total task quality compared with opportunistic sensing. Moreover, the experiment results also validate the high efficiency of our proposed task allocation algorithms.

TMSA: Participatory Sensing Based on Mobile Phones in Urban Spaces

Abstract: A design for a novel mobile sensing system, called Temperature Measurement System Architecture (TMSA), that uses people as mobile sensing nodes in a network to capture spatiotemporal properties of pedestrians in urban environments is presented in this paper. In this dynamic, microservices approach, real-time data and an open-source IoT platform are combined to provide weather conditions based on information generated by a fleet of mobile sensing platforms. TMSA also offers several advantages over traditional methods using participatory sensing or more recently crowd-sourced data from mobile devices, as it provides a framework in which citizens can bring to light data relevant to urban planning services or learn human behaviour patterns, aiming to change users’ attitudes or behaviors through social influence. In this paper, we motivate the need for and demonstrate the potential of such a sensing paradigm, which supports a host of new research and application developments, and illustrate this with a practical urban sensing example.

Participatory sensing framework

Abstract: Data collection in smart cities requires some sensing infrastructure. The deployment of traditional sensors is costly, and their operation and maintenance requires a continuous effort from the city administration. Alternatively, the crowd of inhabitants can be involved in data collection via their mobile devices because with the proliferation of smartphones more and more, computing and sensing power becomes available at the hands of urbanites. This emerging paradigm is called participatory sensing or mobile crowdsensing that is a viable alternative if the community finds incentives (good services) for urbanites to participate in context sharing. In this chapter, we present a generic framework built upon the eXtensible Messaging and Presence Protocol for mobile participatory sensing–based smart city applications. Moreover, we illustrate its usage via two use-case application scenarios, namely a live transit-feed service and a smart campus scenario, which have been implemented on top of this framework.

Crowdsensed QoE for the community - a concept to make QoE assessment accessible

Abstract: In recent years several community testbeds as well as participatory sensing platforms have successfully established themselves to provide open data to everyone interested. Each of them with a specific goal in mind, ranging from collecting radio coverage data up to environmental and radiation data. Such data can be used by the community in their decision making, whether to subscribe to a specific mobile phone service that provides good coverage in an area or in finding a sunny and warm region for the summer holidays. However, the existing platforms are usually limiting themselves to directly measurable network QoS. If such a crowdsourced data set provides more in-depth derived measures, this would enable an even better decision making. A community-driven crowdsensing platform that derives spatial application-layer user experience from resource-friendly bandwidth estimates would be such a case, video streaming services come to mind as a prime example. In this paper we present a concept for such a system based on an initial prototype that eases the collection of data necessary to determine mobile-specific QoE at large scale. In addition we reason why the simple quality metric proposed here can hold its own.

SmileCityReport: Emotion-aware Participatory Sensing for Smart Cities with Double-sided Photo Shooting

Abstract: Collection of information of the events taking place in local neighborhoods along with the emotional statuses of the people involved can enable us to realize an “affective smart city map”, with which, for example, the local authority can review the measures adopted for the local areas and whether their these measure have actually contributed to the quality of life (QoL) and well-being of the people. To realize such an information system having easy-deployability, real-time and secure protection of user's sensitive data, we propose SmileCityReport, a smartphone app-based participatory sensing that can easily capture both the city events and the reporter's emotion-related status based on a novel technique that uses two cameras simultaneously. For our evaluation, we evaluated 15 users over one week and confirmed that the proposed methodology contributes to more activity and (estimated) more positive emotional status of the users, and also that the emotion-related facial expression values constitute valuable data that can be publicly shared.