Participatory Sensing Research Articles

Non-monetary Incentives for Participatory Sensing Data Collection: A Sequential Explanatory Design

Non-monetary Incentives for Participatory Sensing Data Collection: A Sequential Explanatory Design

Participatory Sensing Based Urban Road Condition Classification using Transfer Learning

Participatory Sensing Based Urban Road Condition Classification using Transfer Learning

Hydraulic Analysis of Lekhnath Small Town Water Supply Distribution Network, Pokhara Metropolitan City, Nepal

Water Distribution Network earlier will have challenge in fulfilling the growing need of water supply. Forecasting and monitoring the flow, pressure, demand, and quality of water in water distribution network are key requirement for sustainable and reliable water supply. Lekhnath city, due to the recent merger with the biggest metropolitan city of Nepal, Pokhara, has created the rapid growth of population resulting to haphazard pipeline extension in this area. For hydraulic analysis of Lekhnath Small Town Water Supply Distribution Network, one of the networks i.e., Shishuwakhudi distribution network was faced with problem analysis. This hydraulic analysis is a part of on-going doctoral research and Youth-led Initiative ‘drinkPani’ on ‘Water Supply and Quality Monitoring using Emerging Technologies’ where capacity building of Youth and Women through data-driven engagements (Water-informatics) is one of the core targets in the overall project-work package. The preliminary analysis of water quality was also derived from Young Water Volunteers (YWVs) under the research work on Youth-led Participatory Sensing (YPS) Model to enhance drinking water security of the study area. The Shishuwakhudi Water Distribution Network (WDN) supplies water to 1138 household at present and consisting of 41 junction nodes and 11.2km distribution pipeline distributing water from a reservoir. With field survey, households at each node of the network were determined. Demand driven analysis using EPANET was done to determine the pressure deficit at various nodes. Demand pattern based on design guideline was adopted for analysis. At present scenario, four nodes at end of the network showed negative pressure at peak hours (5 am- 7am). At other times, the pressure was above 10m head. Demand was forecasted for 10 years from now by projecting the household population based on growth rate of census 2011. When projected to future scenario, the existing pipeline showed only 17% reliability with a minimum pressure requirement of 10 m head as per norms of urban water supply project in Nepal. Genetic algorithm was used for optimization of the Shishuwakhudi Network with total cost of replacing pipes as an objective function with pressure constraint of minimum 10m head. With 500 generation of run, the optimal diameter for each pipe was obtained that gave optimal cost of NRs 4,21,50,917 (USD 359,690.64). This optimization helps in selecting the proper pipeline network for future expansion to fulfil the growing need of water demand.

Participatory sensors. A device between data production and involvement of the citizens

On observe actuellement une multiplication des dispositifs participatifs de collecte des données. L’objectif de cet article est d’analyser les enjeux de ces pratiques en interrogeant la capacité d’un dispositif à orienter l’action collective de collecte. Celle-ci est étudiée en fonction des caractéristiques de la structure de gouvernance du dispositif, analysée à travers le protocole de mesure, et ses finalités. Trois dispositifs participatifs reposant sur l’utilisation de microcapteurs sont comparés. Nous montrons que cette capacité de guidage du dispositif est variable selon les situations et s’exprime à travers le processus de validation des données et l’objet de la surveillance réalisée par les volontaires distinguant, en particulier, les dispositifs de production de données de ceux destinés à impliquer des publics.

City Wide Participatory Sensing of Air Quality

Air pollution is a contributor to approximately one in every nine deaths annually. Air quality monitoring is being carried out extensively in urban environments. Currently, however, city air quality stations are expensive to maintain resulting in sparse coverage and data is not readily available to citizens. This can be resolved by city-wide participatory sensing of air quality fluctuations using low-cost sensors. We introduce new concepts for participatory sensing: a voluntary community-based monitoring data forum for stakeholders to manage air pollution interventions; an automated system (cyber-physical system) for monitoring outdoor air quality and indoor air quality; programmable platform for calibration and generating virtual sensors using data from low-cost sensors and city monitoring stations. To test our concepts, we developed a low-cost sensor to measure particulate matter (PM2.5), nitrogen dioxide (NO2), carbon monoxide (CO), and ozone (O3) with GPS. We validated our approach in Helsinki, Finland, with participants carrying the sensor for 3 months during six data campaigns between 2019 and 2021. We demonstrate good correspondence between the calibrated low-cost sensor data and city’s monitoring station measurements. Data analysis of their personal exposure was made available to the participants and stored as historical data for later use. Combining the location of low cost sensor data with participants public profile, we generate proxy concentrations for black carbon and lung deposition of particles between districts, by age groups and by the weekday.

Anonymous Data Reporting Strategy with Dynamic Incentive Mechanism for Participatory Sensing

Participatory sensing is often used in environmental or personal data monitoring, wherein a number of participants collect data using their mobile intelligent devices for earning the incentives. However, a lot of additional information is submitted along with the data, such as the participant’s location, IP and incentives. This multimodal information implicitly links to the participant’s identity and exposes the participant’s privacy. In order to solve the issue of these multimodal information associating with participants’ identities, this paper proposes a protocol to ensure anonymous data reporting while providing a dynamic incentive mechanism simultaneously. The proposed protocol first establishes a submission schedule by anonymously selecting a slot in a vector by each member where every member and system entities are oblivious of other members’ slots and then uses this schedule to submit the all members’ data in an encoded vector through bulk transfer and multiplayer dining cryptographers networks (DC-nets) . Hence, the link between the data and the member’s identity is broken. The incentive mechanism uses blind signature to anonymously mark the price and complete the micropayments transfer. Finally, the theoretical analysis of the protocol proves the anonymity, integrity, and efficiency of this protocol. We implemented and tested the protocol on Android phones. The experiment results show that the protocol is efficient for low latency tolerable applications, which is the cases with most participatory sensing applications, and they also show the advantage of our optimization over similar anonymous data reporting protocols.

Mobile Participatory Sensing Systems: A Comprehensive Review

Mobile Participatory Sensing Systems have the potential to improve different services through monitoring of the urban landscape using mobile devices based on the collaboration of thousands of mobile users. Many articles have been recently published related to mobile participatory syst

An egalitarian approach of scheduling time restricted tasks in mobile crowdsourcing for double auction environment

Crowdsourcing with intelligent agent felicitated with portable smart devices is popularly known as mobile crowdsourcing (MCS) or participatory sensing (PS). To motivate the task executors for performing the available tasks has been a challenge in an MCS environment. In this paper we have addressed this issue in a double auction environment when the tasks are time restricted (each task has start time and finish time) and may be overlapped. Here, we have taken an egalitarian approach so that a balanced allocation of tasks can be established to the task executors. In this, first the tasks are partitioned into several slots in a non-overlapping manner and then allocated to the task executors through double auction. Our proposed mechanism satisfies several economic properties such as truthfulness, individual rationality, and budget balanced. It is also exhibited via simulation that our proposed mechanism performs better when the agents (task executors and task providers) misreport their valuations.

From Do-It-Yourself (DIY) to Do-It-Together (DIT): Reflections on designing a citizen-driven air quality monitoring framework in Taiwan

Air pollution is a serious problem and has caused public health concerns all over the world. Despite the evidence, the preparedness and response of citizens has been limited. This underlines the importance of having sustainable air quality monitoring solutions that foster inclusion and multi-stakeholder partnerships for social-scientific interventions. This study illustrates how AirBox project has emerged in Taiwan, where makers and citizens use the sensors to sense air quality and provide the public with actionable data about their environments. The AirBox project includes elements of technology-innovation and citizen science: (1) Participatory Sensing – Static and mobile air quality sensing, (2) Open Data – Open hardware, software and access to data, (3) Co-creation Citizen Science – Citizen-led campaigns and forums, and (4) Outreach – Knowledge sharing, trust building and multi-stakeholder collaboration. The project uses a wide range of sensors to provide extendable solutions and data at fine spatio-temporal resolution. The results are highlighted using five cases studies that show how integrating social dimensions in an air quality monitoring framework can lead to public awareness, data-driven applications and environmentally sustainable cities. The multi-faceted approach highlights the effects of a bottom-up citizen science approach that considers local culture, practices and problems at grassroots.

Increasing Reliability of Participatory Sensing for Utility Pole Condition Assessment Using Fuzzy Inference

AbstractAging infrastructure has become a safety issue for local communities. For example, aging and deteriorating utility poles in strong winds have a high risk of falling onto roads, adjacent hou...

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

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.

Towards participatory sensing of regions of interest with adaptive sampling rate

Participatory Sensing (PS) is a known paradigm of collaborative networks which provides incentives for users to participate in sensing tasks of Regions of Interest (RoIs). A challenge in wireless networking, however, is to balance the amount of data collected by users without imposing excessive load to the network. In this direction, this paper proposes a centralized system to adapt the sampling rate assigned to each crowdsourcing participant sensor. The sampling rate is computed based on the standard deviation of samples collected from a given RoI. The results obtained via simulations show a tradeoff between the sampling rate and the number of crowdsourcing participants. The more crowdsourcing participants, the lower must be the individual sampling rate and the amount of data transferred. This strategy can increase the data delivery rate taking into account the available short contact times, even though it requires a larger number of sensors.

Privacy Preserving Participatory Sensor Network on Named Data Networking Infrastructure

The world of Internet of Things (IoT) and ubiquitous computing lead the computing systems integrate sensors and handheld devices into a common platform to offer new services. Participatory Sensor Network (PSN) is one of such a network which is formed in an ad-hoc basis. The success of such network always depends on the quality of data shared by the participants. Privacy concern is one of the main reasons why an individual may not prefer to share their sensitive data. Not many research works have been performed to preserve the privacy of individual data in a PSN. On the other hand, Named Data Network (NDN), an instance of Information-Centric Network (ICN), is an alternative of TCP/IP that inherently considers the concern of security as opposed to TCP/IP. By default, NDN ensures the privacy of the data consumer but it fails to ensure the same for data provider. In this paper, we propose a ring signature based NDN to ensure the privacy of the data provider. Our proposed solution seems to be effective based on the performance and security analysis..

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

AbstractThe 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 ...

Accuracy Enhancement of Anomaly Localization with Participatory Sensing Vehicles

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.

An Innovative Smartphone-based Solution for Traffic Rule Violation Detection

This paper introduces a novel smartphone-based solution to detect different traffic rule violations using a variety of computer vision and networking technologies. We propose the use of smartphones as participatory sensors via their cameras to detect the moving and stationary objects (e.g., cars and lane markers) and understand the resulting driving and traffic violation of each object. We propose novel framework which uses a fast in-mobile traffic violation detector for rapid detection of traffic rule violation. After that, the smartphone transmits the data to the cloud where more powerful computer vision and machine learning operations are used to detect the traffic violation with a higher accuracy. We show that the proposed framework detection is very accurate by combining a) a Haar-like feature cascade detector at the in-mobile level, and b) a deep learning-based classifier, and support-vector machine-based classifiers in the cloud. The accuracy of the deep convolutional network is about 92% for true positive and 95% for true negative. The proposed framework demonstrates a potential for mobile-based traffic violation detection by especially by combining the information of accurate relative position and relative speed. Finally, we propose a real-time scheduling scheme in order to optimize the use of battery and real-time bandwidth of the users given partially known navigation information among the different users in the network, which us the real case. We show that the navigation information is very important in order to better utilize the battery and bandwidth for each user for a small number of users compared to the navigation trajectory length. That is, the utilization of the resources is directly related to the number of available participants, and the accuracy of navigation information.

Multi-Dimensional Anonymization for Participatory Sensing Systems

Participatory sensing systems are designed to enable community people to collect, analyze, and share information for their mutual benefit in a cost-effective way. The apparently insensitive information transmitted in plaintext through the inexpensive infrastructure can be used by an eavesdrop-per to infer some sensitive information and threaten the privacy of the partic-ipating users. Participation of users cannot be ensured without assuring the privacy of the participants. Existing techniques add some uncertainty to the actual observation to achieve anonymity which, however, diminishes data quality/utility to an unacceptable extent. The subset-coding based anonymiza-tion technique, DGAS [LCN 16] provides the desired level of privacy. In this research, our objective is to overcome this limitation and design a scheme with broader applicability. We have developed a computationally efficient sub-set-coding scheme and also present a multi-dimensional anonymization tech-nique that anonymizes multiple properties of user observation, e.g. both loca-tion and product association of an observer in the context of consumer price sharing application. To the best of our knowledge, it is the first work which supports multi-dimensional anonymization in PSS. This paper also presents an in-depth analysis of adversary threats considering collusion of adversaries and different report interception patterns. Theoretical analysis, comprehensive simulation, and Android prototype based experiments are carried out to estab-lish the applicability of the proposed scheme. Also, the adversary capability is simulated to prove our scheme’s effectiveness against privacy risk.

4X: A Hybrid Approach for Scaffolding Data Collection and Interest in Low-Effort Participatory Sensing

Participatory sensing systems in which people actively participate in the data collection process must account for both the needs of data contributors and the data collection goals. Existing approaches tend to emphasize one or the other, with opportunistic and directed approaches making opposing tradeoffs between providing convenient opportunities for contributors and collecting high-fidelity data. This paper explores a new, hybrid approach, in which collected data-even if low-fidelity initially-can provide useful information to data contributors and inspire further contributions. We realize this approach with 4X, a multi-stage data collection framework that first collects data opportunistically by requesting contributions at specific locations along users' routes and then uses collected data to direct users to locations of interest to make additional contributions that build data fidelity and coverage. To study the efficacy of 4X, we implemented 4X into LES, an application for collecting information about campus locations and events. Results from two field deployments (N = 95, N = 18) show that the 4X framework created 34% more opportunities for contributing data without increasing disruption, and yielded 49% more data by directing users to locations of interest. Our results demonstrate the value and potential of multi-stage, dynamic data collection processes that draw on multiple sources of motivation for data, and how they can be used to better meet data collection goals as data becomes available while avoiding unnecessary disruption.

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.

Allocating Heterogeneous Tasks in Participatory Sensing with Diverse Participant-Side Factors

This paper proposes a novel task allocation framework, PSTasker, for participatory sensing (PS), which aims to maximize the overall system utility on PS platform by coordinating the allocation of multiple tasks. While existing studies mainly optimize the task allocation from the perspective of the task organizer (e.g., maximizing coverage or minimizing incentive cost), PSTasker further considers diverse factors on the participants’ side, including user work bandwidth, user availability, devices’ sensor configuration, task completion likelihood, and mobility pattern. Furthermore, by considering the heterogeneity in three dimensions (i.e., task, time, and space), it adopts a novel model to measure task sensing quality and overall system utility. In PSTasker, it first calculates the utlity of a given task allocation plan by jointly fusing different participant-side factors into one unified estimation function, and then employs an iterative greedy process to optimize the task allocation. Extensive evaluations based on real-world mobility traces demonstrate that PSTasker outperforms the baseline methods under various settings.