Showing papers on "Participatory sensing published in 2021"

Public Participation Consortium Blockchain for Smart City Governance

Abstract: Smart cities have become a trend with improved efficiency, resilience, and sustainability, providing citizens with high quality of life. With the increasing demand for a more participatory and bottom-up governance approach, citizens play an active role in the process of policy-making, revolutionizing the management of smart cities. In the example of urban infrastructure maintenance, the public participation demand is more remarkable as the infrastructure condition is closely related to their daily life. Although blockchain has been widely explored to benefit data collection and processing in smart city governance, public engagement remains a challenge. In this paper, we propose a novel public participation consortium blockchain system for infrastructure maintenance that is expected to encourage citizens to actively participate in the decision-making process and enable them to witness all administrative procedures in a real-time manner. To that aim, we introduced a hybrid blockchain architecture to involve a verifier group, which is randomly and dynamically selected from the public citizens, to verify the transaction. In particular, we devised a private-prior peer-prediction-based truthful verification mechanism to tackle the collusion attacks from public verifiers. Then, we specified a Stackelberg-game-based incentive mechanism for encouraging public participation. Finally, we conducted extensive simulations to reveal the properties and performances of our proposed blockchain system, which indicates its superiority over other variations.

Participatory Air Quality and Urban Heat Islands Monitoring System

Abstract: The widespread use of low-cost environmental monitoring systems, together with recent developments in the design of Internet of Things architectures and protocols, has given new impetus to smart city applications. Such progress should, in particular, considerably improve the fine characterization of a wide range of physical quantities within our cities. Indeed, the cost-effectiveness of these emerging sensors combined with their reduced size allows for high-density deployments resulting in higher spatial granularity. In this article, we briefly present the 3M’Air project that aims to explore the potential of participatory citizen measures using low-cost sensors in order to improve the local knowledge of air quality and temperature and then bridge the gap between individual exposure and regional measurements. We then present the design, implementation, and evaluation of our low-cost, small-size wireless sensor network (WSN)-based participatory monitoring system. This system is composed of mobile sensing nodes measuring temperature, humidity, and a number of pollutants (NO2, PM1, PM2.5, and PM10). The collected data are sent to a server for analysis and building temperature and air quality maps. To validate our platform, we have carried out multiple tests to compare our sensor nodes to reference stations and to each other. We have also evaluated the energy consumption of our nodes under different configurations. The results are satisfactory and show that our nodes can be used in environmental participatory monitoring.

Distributed Data Collection in Age-Aware Vehicular Participatory Sensing Networks

Abstract: The advent of vehicle-to-everything communication facilitates the emergence of vehicular sensing networks, where vehicles equipped with advanced sensors continuously sample informative status updates of its surroundings and forward the sampled data to roadside infrastructure based on a certain routing strategy. The collected data is analyzed to obtain real-time situational awareness to impose certain behaviors on the vehicles. In such networked control systems, the timeliness of collected data is of critical importance to system performance, which can be quantified by the concept of Age of Information. Note that to obtain timely perception of its surroundings, each vehicle tends to sample status updates at the maximum frequency, which may congest the network due to limited communication resource. Moreover, the highly dynamic nature of vehicular network poses a great challenge in finding a reliable route for timely data forwarding. Therefore, the data collection scheme should be carefully designed to balance the timeliness of collected information and network stability. In this article, we study an age optimization problem by jointly considering the data sampling at source vehicles and the data forwarding process for multiple information flows across the network. We employ the Lyapunov optimization technique to develop a distributed age-aware data collection scheme consists of a threshold-based sampling strategy at source vehicles and a learning-based data forwarding strategy. Simulation results show that our proposed scheme outperforms existing strategies in collecting status updates in a timely manner.

Privacy protected user identification using deep learning for smartphone-based participatory sensing applications

Abstract: In smartphone-based crowd/participatory sensing systems, it is necessary to identify the actual sensor data provider. In this context, this paper attempts to recognize the users’ identity based on their gait patterns (i.e. unique walking patterns). More specifically, a deep convolution neural network (CNN) model is proposed for the user identification with accelerometer data generated from users smartphone sensors. The proposed model is evaluated based on the real-world benchmark dataset (accelerometer biometric competition data) having a total of 387 users accelerometer sensor readings (60 million data samples). The performance of the proposed CNN-based approach is also compared with five baseline methods namely Support Vector Machine (SVM), Decision Tree (DT), Random Forest (RF), Logistic Regression (LR), and K-Nearest Neighbours (KNN). It is observed that the proposed model achieves better results (accuracy = 98.8%, precision = 0.94, recall = 0.97, and F1-score = 0.95) as compared to the baseline methods.

Spatial Interpolation Techniques on Participatory Sensing Data

Abstract: Spatial distributions of data of natural phenomena can be estimated by using different spatial interpolation techniques. These techniques can be used for the purpose of developing urban noise pollu...

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

Abstract: Aging 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...

Venue-Popularity Prediction Using Social Data Participatory Sensing Systems and RNNs

Abstract: Analyzing social data as a participatory sensing system (PSS) provides a deep understanding of city dynamics, such as people’s mobility patterns, social patterns, and events detection. In a PSS, individuals with mobile devices sense their environment, collect, and share data. For smart cities, intelligent city dynamics analysis has many applications such as for urban planning, transportation systems, city environment, energy consumption, public safety, and city economy. This study aimed to develop an intelligent application to predict the potential number of visitors for specific venues based on the analysis of mobility patterns of individuals. The ability to accurately predict the number of visitors to a venue allows authorities to better understand the behavior of the people and allocate recourses accordingly. We formulated the venue-popularity problem as a sequence-based regression and classification problem. We employed three recurrent neural network (RNN)-based models to predict the locations of popular venues on a city map. The proposed models include basic RNN, long short-term memory (LSTM), and gated recurrent unit (GRU). We constructed several social datasets for Riyadh city using Twitter and Foursquare as the PSS. Our results revealed that modeling venue-popularity prediction as a sequence regression problem yields better results than modeling it as a sequence classification problem. For the city-popularity map prediction problem, the vector autoregression baseline model achieved better performance than the RNN-family models.

Spatio-Temporal Prediction of Noise Pollution Using Participatory Sensing

Abstract: The physiological and psychological well-being of humans depends on an extensive set of factors, of which pollution is the most ubiquitous. Its effects are often latent but extremely detrimental, especially in the case of noise pollution. In pollution monitoring and smart city systems, predicting noise from urban road traffic is of utmost significance. It is a difficult task due to the complexity of spatial correlations among the locations and temporal correlations among the timestamps, coupled with the diverse nature of these spatio-temporal correlations which are also dependent on the type of area in which the locations are situated. As a solution, we propose a participatory sensing-based solution to assess the road traffic noise by attaining a high level of granularity. We subsequently predict the noise values with a hybrid predictor that uses a regression model and ordinary kriging. Our solution outperforms the baseline regression and kriging methods and thus provides a novel method to gain a deeper insight into the levels of road traffic noise pollution. The effectiveness and strength of the proposed method are validated by extensive experiments with a real-world participatory sensing-based road traffic noise dataset.

Spatiotemporal variability analysis of air pollution data from IoT based participatory sensing

Abstract: Air pollution has become a major environmental risk of the new civilized world due to its severe influence on public health and the environment. Eventually, understanding the spatiotemporal variability of air pollution at high granularity is necessary to make relevant public policies. To explore spatiotemporal variability of air pollution at high granularity we have utilized the power of IoT based participatory sensing and data science. In this paper, we propose a predictive model for spatiotemporal air pollution estimation technique called Multiview data Fusion model (MVDF) that can consider spatial as well as temporal dependencies of air pollutants. The proposed technique is evaluated based on real-world air pollution dataset collected by participants over a period of 1 year in an urban area of city Kolkata. The results show that MVDF dominates over some baselines like Simple Kriging (SK), Modified Shepard’s Method (MSM) and Nearest Neighbor (NN). Besides, in this paper, we attempt to perform visual analysis that consists of state-of-the-art visualization techniques to explore spatiotemporal variability at different granularities on the estimated pollution levels of MVDF.

Incorum: A Citizen-Centric Sensor Data Marketplace for Urban Participation

Abstract: Modern cities and their citizens rely on a vast availability of urban data to offer high living standards. In the past, various systems have been developed to address this problem by providing platforms for collecting, sharing and processing urban data. However, existing platforms are insufficient, as they do not put the needs of citizens first and do not offer solutions for connecting proprietary sensors as they are commonly found in households. We propose a Citizen-Centric Marketplace, in which citizens are first-class entities. With our Incorum approach, citizens will be able to easily collect, share and process data from their existing sensors while maintaining data sovereignty and privacy. Additionally, it is easily extensible with further services and applications which can be traded on the marketplace as well. This distributed marketplace offers incentives for active participation and improves the lives of citizens in a smart city.

Geo-tagged environmental noise measurement with smartphones: Accuracy and perspectives of crowdsourced mapping:

Abstract: Environmental noise affects life and health within urban environments through interfering with sleep, rest, study and personal communication. Noise mapping is an important issue of local authoritie...

SensiBlend: Sensing Blended Experiences in Professional and Social Contexts

Abstract: Unlike traditional workshops, SensiBlend is a living experiment about the future of remote, hybrid, and blended experiences within professional and other social contexts. The interplay of interpersonal relationships with tools and spaces—digital and physical—has been abruptly challenged and fundamentally altered as a result of the COVID-19 pandemic. With this meta-workshop, we seek to scrutinize and advance the role and impact of Ubiquitous Computing in the new “blended” social reality, and raise questions relating to the specific attributes of socio-technical experiences in the future organization of interpersonal relationships. How do we better equip people to deal with blended experiences? What dimensions of socio-technical experiences are at stake? To this end, we will utilize the occasion of a virtual UbiComp in combination with novel remote-working tools and participatory sensing with attendees to collectively examine, discuss, and elicit the potential routes of augmenting social practices in a discourse about the future of blended working, socializing, and living.

Mobile participatory sensing with strong privacy guarantees using secure probes

Abstract: Mobile participatory sensing (MPS) could benefit many application domains. A major domain is smart transportation, with applications such as vehicular traffic monitoring, vehicle routing, or driving behavior analysis. However, MPS’s success depends on finding a solution for querying large numbers of smart phones or vehicular systems, which protects user location privacy and works in real-time. This paper presents PAMPAS, a privacy-aware mobile distributed system for efficient data aggregation in MPS. In PAMPAS, mobile devices enhanced with secure hardware, called secure probes (SPs), perform distributed query processing, while preventing users from accessing other users’ data. A supporting server infrastructure (SSI) coordinates the inter-SP communication and the computation tasks executed on SPs. PAMPAS ensures that SSI cannot link the location reported by SPs to the user identities even if SSI has additional background information. Moreover, an enhanced version of the protocol, named PAMPAS+, makes the system robust even against advanced hardware attacks on the SPs. Hence, the risk of user location privacy leakage remains very low even for an attacker controlling the SSI and a few corrupted SPs. Our experimental results demonstrate that these protocols work efficiently on resource constrained SPs being able to collect the data, aggregate them, and share statistics or derive models in real-time.

Task allocation interface design and personalization in gamified participatory sensing for tourism.

Abstract: The collection of spatiotemporal tourism information is important in smart tourism and user-generated contents are perceived as reliable information. Participatory sensing is a useful method for collecting such data, and the active contribution of users is an important aspect for continuous and efficient data collection. This study has focused on the impact of task allocation interface design and individual personality on data collection efficiency and their contribution in gamified participatory sensing for tourism. We have designed two types of interfaces: a map-based with active selection and a chat-based with passive selection. Moreover, different levels of elaborateness and indirectness have been introduced into the chat-based interface. We have employed the Gamification User Types Hexad framework to identify the differences in the contributions and interface preferences of different user types. The results of our tourism experiment with 108 participants show that the map-based interface collects more data, while the chat-based interface collects data for spots with higher information demand. We also found that the contribution to sensing behavior and interface preference differed depending on the individual user type.

Indoor noise level measurements and subjective comfort: a smartphone-based participatory experiment

Abstract: We designed and performed a participatory sensing initiative to explore the reliability and effectiveness of a distributed network of citizen-operated smartphones in evaluating the impact of environmental noise in residential areas. We analyzed noise level measurement data collected by participants from home and correlated them with contextual information and subjective comfort ratings referring to different situations, times and environments. We show how to strengthen methods and procedures, particularly regarding the calibration of the devices, in order to make similar citizen-science efforts effective and useful at monitoring environmental noise as a contribution to planning long-term solutions to human well-being.

ParmoSense: A Scenario-based Participatory Mobile Urban Sensing Platform with User Motivation Engine

Abstract: Rapid proliferation of mobile devices with various sensors have enabled Participatory Mobile Sensing (PMS) Several PMS platforms provide multiple functions for various sensing purposes, but they are suffering from the open issues: limited use of their functions for a specific scenario/case and requiring technical knowledge for organizers In this paper, we propose a novel PMS platform named ParmoSense for easily and flexibly collecting urban environmental information To reduce the burden on both organizers and participants, in ParmoSense, we employ two novel features: modularization of functions and scenario-based PMS system description For modularization, we provide the essential PMS functions as modules which can be easily chosen and combined for sensing in different scenarios The scenario-based description feature allows organizers to easily and quickly set up a new participatory sensing instance and participants to easily install the corresponding scenario and participate in the sensing Moreover, ParmoSense provides GUI tools as well for creating and distributing PMS system easily, editing and visualizing collected data quickly It also provides multiple functions for encouraging participants' motivation for sustainable operation of the system Through performance comparison with existing PMS platforms, we confirmed ParmoSense shows the best cost-performance in the perspective of the workload for preparing PMS system and varieties of functions In addition, to evaluate the availability and usability of ParmoSense, we conducted 19 case studies, which have different locations, scales, and purposes, over 4 years with cooperation from ordinary citizens Through the case studies and the questionnaire survey for participants and organizers, we confirmed that ParmoSense can be easily operated and participated by ordinary citizens including non-technical persons

A QoI Assessment Framework for Participatory Crowdsourcing Systems

Abstract: Participatory Crowdsourcing Systems have the potential to improve services in our daily life, such as health care, transportation and to monitor even the urban landscape using participatory sensing strategies. Data are the core mechanism that enables Participatory Crowdsourcing Systems to operate. It is very important to understand the evolution and relevance of data in Participatory Crowdsourcing Systems. Thus, this paper proposes a Quality of Information assessment framework which all Participatory Crowdsourcing Systems should strive to achieve to ensure data quality. The framework operates in a matrix schema that consists of four independent classes (horizontally) and has various dimensions within each class (vertically). The proposed framework will be flexible as it can incorporate new quality classes in the case of emerging technologies or domain areas. On the other hand, the vertical layer will have two subsections namely mandatory and desired features contained within a class.

Anonymous Data Reporting Strategy with Dynamic Incentive Mechanism for Participatory Sensing

Abstract: 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.

A Crowdsensing Based Traffic Monitoring Approach

Abstract: This chapter presents a crowdsensing based urban traffic monitoring system. Different from existing works that heavily rely on intrusive sensing or full cooperation from probe vehicles, our system exploits the power of participatory sensing and crowdsources the traffic sensing tasks to bus riders’ mobile phones. The bus riders are information source providers and meanwhile major consumers of the final traffic output. The system takes public buses as dummy probes to detect road traffic conditions, and collects minimum set of cellular data together with some lightweight sensing hints from the bus riders’ mobile phones. Based on the crowdsourced data from participants, the system recovers the bus travel information and further derives the instant traffic conditions of roads covered by bus routes. The real-world experiments with a prototype implementation demonstrate the feasibility of our system, which achieves accurate and fine-grained traffic estimations with modest sensing and computation overhead at the crowd.

pmSensing: A Participatory Sensing Network for Predictive Monitoring of Particulate Matter

Abstract: This work presents a proposal for a wireless sensor network for participatory sensing, with IoT sensing devices developed especially for monitoring and predicting air quality, as alternatives of high cost meteorological stations. The system, called pmSensing, aims to measure particulate material. A validation is done by comparing the data collected by the prototype with data from stations. The comparison shows that the results are close, which can enable low-cost solutions to the problem. The system still presents a predictive analysis using recurrent neural networks, in this case the LSTM-RNN, where the predictions presented high accuracy in relation to the real data.