ABSTRACT
The recruitment of trustworthy and high-quality workers is an important research issue for MCS. Previous studies either assume that the qualities of workers are known in advance, or assume that the platform knows the qualities of workers once it receives their collected data. In reality, to reduce costs and thus maximize revenue, many strategic workers do not perform their sensing tasks honestly and report fake data to the platform, which is called False data attacks. And it is very hard for the platform to evaluate the authenticity of the received data In this paper, an incentive mechanism named Semi-supervision based Combinatorial Multi-Armed Bandit reverse Auction (SCMABA) is proposed to solve the recruitment problem of multiple unknown and strategic workers in MCS. First, we model the worker recruitment as a multi-armed bandit reverse auction problem and design an UCB-based algorithm to separate the exploration and exploitation, regarding the Sensing Rates (SRs) of recruited workers as the gain of the bandit Next, a Semi-supervised Sensing Rate Learning (SSRL) approach is proposed to quickly and accurately obtain the workers' SRs, which consists of two phases, supervision and self-supervision. Last, SCMABA is designed organically combining the SRs acquisition mechanism with multi-armed bandit reverse auction, where supervised SR learning is used in the exploration, and the self-supervised one is used in the exploitation. We theoretically prove that our SCMABA achieves truthfulness and individual rationality and exhibits outstanding performances of the SCMABA mechanism through in-depth simulations of real-world data traces.
ABSTRACT
Our research deals with creating a suitable and sufficiently reliable methodology to use for evaluating the movement of passengers in public transport or pedestrians. The goal is to design crowds ensing-based methods that provide a quick alternative to large-scale evaluations. It is the settings, the number of installed elements, parameters, and possibly other attributes enabling an increase in quality and reliability in real city conditions. The methodology must comply with GDPR. We also deal with the possibility of evaluating the behavior of people in case of restrictions due to disease spreading (nowadays, for example, COVID19). Our goal is to find methods using wireless technology and their generally little-described features for evaluating the movement of people. The usability of these technologies extends to some branches of logistics or organizations of larger cultural or sporting events. © 2022 IEEE.
ABSTRACT
COVID-19 gave rise to discussions around designing for life during the pandemic, in particular related to health, leisure and education. In 2020, an online survey aimed at university students (N=225) pointed the authors to various challenges related to well-being in terms of studying, socializing, community, and safety during the COVID-19 pandemic. These results shaped the crowdsensing-enabled service design of a mobile application, Tecnico GO!, aimed at supporting students' well-being. Considering the constant changing context caused by the pandemic, we present a study conducted during the academic year 2021-2022 and if/how the App's features continue to respond to student's needs. The evaluation of the App focused on 12 semi-structured interviews and think-aloud protocols. Findings cluster around three themes: a) Supporting the study experience;b) Building a sense of community;c) Improving gamification for better participation. Discussion elaborates on the student's perceptions around well-being during pandemics. Students' insights of the App are overall positive and highlight that crowdsensing-enabled design does contribute to learning, community and safety, but the gamification as currently deployed does not. © 2022 ACM.
ABSTRACT
With the proliferation of smart devices and widespread Internet connectivity, social sensing is advancing as a pervasive sensing paradigm where experiences shared by individuals on social platforms (e.g., Twitter and Facebook) are analyzed to interpret the physical world. In this article, we introduce CovidTrak, a vision of social intelligence-empowered contact tracing that aims to scrutinize the knowledge derived using social sensing to track Coronavirus Disease 2019 (COVID-19) infections among the general public. Contact tracing is known to be an effective technique for detecting and monitoring persons who may have been exposed to individuals infected with any communicable disease. While a good number of contact tracing schemes are existent today (e.g., in-person and phone interviews, paper forms, email and web-based questionnaires, and smartphone apps), they often require active user participation and might miss certain cases of social interactions that go off-the-records but still lead to COVID-19 transmission. By contrast, social sensing provides an alternative avenue for spontaneously determining such contacts by harnessing the rich experiences and information conveyed by people on social data platforms (e.g., a group photograph tweeted from a house party with a potential contact). As such, CovidTrak can form a powerful basis to combat the COVID-19 pandemic. The vision of CovidTrak intends to answer the following questions: 1) how to bolster the privacy and security of the online users while determining their contacts? 2) how to collect relevant social signals that indicate in-person encounters among people? 3) how to reliably process the vast amount of noisy data from social platforms to identify chains of transmission? 4) how to handle the scarcity of location metadata in the incoming data? 5) how to effectively communicate crucial contact information to concerned individuals? and 6) how to model and handle the responses of the common people toward contact information? We envision unexplored opportunities to leverage multidisciplinary techniques to address the above questions and develop effective future CovidTrak schemes.
ABSTRACT
Acquiring the spatial distribution of users in mobile crowdsensing (MCS) brings many benefits to users (e.g., avoiding crowded areas during the COVID-19 pandemic). Although the leakage of users’location privacy has received a lot of research attention, existing works still ignore the rationality of users, resulting that users may not obtain satisfactory spatial distribution even if they provide true location information. To solve the problem, we employ game theory with incomplete information to model the interactions among users and seek an equilibrium state through learning approaches of the game. Specifically, we first model the service as a game in the satisfaction form and define the equilibrium for this service. Then, we design a LEFS algorithm for the privacy strategy learning of users when their satisfaction expectations are fixed, and further design LSRE that allows users to have dynamic satisfaction expectations. We theoretically analyze the convergence conditions and characteristics of the proposed algorithms, along with the privacy protection level obtained by our solution. We conduct extensive experiments to show the superiority and various performances of our proposal, which illustrates that our proposal can get more than 85% advantage in terms of the sensing distribution availability compared to the well-known differential privacy based solutions. IEEE
ABSTRACT
BACKGROUND: In recent years, the monitoring of occupant presence patterns has become an imperative for building energy optimization. Very often, there is a significant discrepancy between the building energy performance predicted at the design stage and the actual performance rendered during the building operation. This stems from the difference in user occupancy. In spite of this, user interaction and feedback are rarely taken into account and evidence of the impact of occupant presence patterns on energy consumption is still scarce. Thus, the purpose of this study is to apply crowd-sensing techniques to understand how energy is consumed and how appropriate performance indicators should be defined to provide inputs for building operations regarding more efficient use of resources. METHODS: Monitoring strategies were implemented in an office lab with controlled variables to collect quantitative data on occupancy patterns, ambient factors and energy consumption. In addition, crowd-sensing techniques were applied to model user activity in different ambient conditions over time and to contrast their occupancy with energy consumption patterns in combination with new inquiry tools to identify how occupants perceive their comfort level. In addition, a set of energy efficiency indicators was used to compare energy performance over different periods. RESULTS: It was discovered that there is a strong relation between user occupancy patterns and energy consumption. However, more than 50% of energy was consumed when no user activity was registered. Energy performance indicators revealed that measuring energy efficiency in terms of kWh per surface area encourages a less efficient use of space and, therefore, including a coefficient of person hours is advisable. It was also discovered that users do not fully rely on feedback mechanisms and they prefer to take action to adapt the ambient conditions rather than simply expressing their opinion. Analysis of energy usage during the Covid-19 lock down revealed substantial use of energy contrary to what was expected. This was because home computers were used as terminals only, while the actual tasks were performed on the lab computers, using remote desktop connections, which were turned on 24/7. In addition, energy consumed by each employee at his/her home should be taken into account. Moreover, a set of practical recommendations was formulated.
ABSTRACT
Buses and Rapid transit are an integral part of the mass transit system, especially in big cities have become a lifeline for many people. It has made commuting much easier and faster. With the increase in population, there has been a massive surge in the number of passengers leading to crowding at platforms and bus stops. Since the frequency of buses and rapid transit is uneven and lacks proper real-Time tracking of buses, we see a vast discrepancy in the number of passengers traveling. Such discrepancies not only have a vast economic impact but also make travelling by buses difficult for regular commuters, also increasing the travelling time. Especially considering the COVID 19 situation, it can cause a lot of problems. Thus, there is a need for a system that can adjust itself according to the number of passengers and real-Time tracking of public transportation systems available for passengers.With this paper, we aim towards providing an intelligent transportation system using real-Time data to manage the frequency of mass transit systems by crowdsourcing people on bus stands in real-Time using CCTV, analyzing the data, and making decisions realtime on the frequency of these mass transit systems by analyzing data through the help of data science and machine learning which would help in automation of rapid transit systems. © 2021 IEEE.
ABSTRACT
Intelligently responding to a pandemic like Covid-19 requires sophisticated models over accurate real-time data, which is typically lacking at the start, e.g., due to deficient population testing. In such times, crowdsensing of spatially tagged disease-related symptoms provides an alternative way of acquiring real-time insights about the pandemic. Existing crowdsensing systems aggregate and release data for pre-fixed regions, e.g., counties. However, the insights obtained from such aggregates do not provide useful information about smaller regions - e.g., neighborhoods where outbreaks typically occur - and the aggregate-and-release method is vulnerable to privacy attacks. Therefore, we propose a novel differentially private method to obtain accurate insights from crowdsensed data for any number of regions specified by the users (e.g., researchers and a policy makers) without compromising privacy of the data contributors. Our approach, which has been implemented and deployed, informs the development of the future privacy-preserving intelligent systems for longitudinal and spatial data analytics.
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Wearable sensing technologies are having a worldwide impact on the creation of novel business opportunities and application services that are benefiting the common citizen. By using these technologies, people have transformed the way they live, interact with each other and their surroundings, their daily routines, and how they monitor their health conditions. We review recent advances in the area of wearable sensing technologies, focusing on aspects such as sensor technologies, communication infrastructures, service infrastructures, security, and privacy. We also review the use of consumer wearables during the coronavirus disease 19 (COVID-19) pandemic caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), and we discuss open challenges that must be addressed to further improve the efficacy of wearable sensing systems in the future.
Subject(s)
COVID-19 , Wearable Electronic Devices , Humans , Monitoring, Physiologic , Pandemics , SARS-CoV-2ABSTRACT
The possibility of understanding the dynamics of human mobility and sociality creates the opportunity to re-design the way data are collected by exploiting the crowd. We survey the last decade of experimentation and research in the field of mobile CrowdSensing, a paradigm centred on users' devices as the primary source for collecting data from urban areas. To this purpose, we report the methodologies aimed at building information about users' mobility and sociality in the form of ties among users and communities of users. We present two methodologies to identify communities: spatial and co-location-based. We also discuss some perspectives about the future of mobile CrowdSensing and its impact on four investigation areas: contact tracing, edge-based MCS architectures, digitalization in Industry 5.0 and community detection algorithms.
Subject(s)
Algorithms , Social Behavior , HumansABSTRACT
Physical and mental well-being during the COVID-19 pandemic is typically assessed via surveys, which might make it difficult to conduct longitudinal studies and might lead to data suffering from recall bias. Ecological momentary assessment (EMA) driven smartphone apps can help alleviate such issues, allowing for in situ recordings. Implementing such an app is not trivial, necessitates strict regulatory and legal requirements, and requires short development cycles to appropriately react to abrupt changes in the pandemic. Based on an existing app framework, we developed Corona Health, an app that serves as a platform for deploying questionnaire-based studies in combination with recordings of mobile sensors. In this paper, we present the technical details of Corona Health and provide first insights into the collected data. Through collaborative efforts from experts from public health, medicine, psychology, and computer science, we released Corona Health publicly on Google Play and the Apple App Store (in July 2020) in eight languages and attracted 7290 installations so far. Currently, five studies related to physical and mental well-being are deployed and 17,241 questionnaires have been filled out. Corona Health proves to be a viable tool for conducting research related to the COVID-19 pandemic and can serve as a blueprint for future EMA-based studies. The data we collected will substantially improve our knowledge on mental and physical health states, traits and trajectories as well as its risk and protective factors over the course of the COVID-19 pandemic and its diverse prevention measures.