29 research outputs found
Incentive Mechanisms for Participatory Sensing: Survey and Research Challenges
Participatory sensing is a powerful paradigm which takes advantage of
smartphones to collect and analyze data beyond the scale of what was previously
possible. Given that participatory sensing systems rely completely on the
users' willingness to submit up-to-date and accurate information, it is
paramount to effectively incentivize users' active and reliable participation.
In this paper, we survey existing literature on incentive mechanisms for
participatory sensing systems. In particular, we present a taxonomy of existing
incentive mechanisms for participatory sensing systems, which are subsequently
discussed in depth by comparing and contrasting different approaches. Finally,
we discuss an agenda of open research challenges in incentivizing users in
participatory sensing.Comment: Updated version, 4/25/201
Mechanisms for improving information quality in smartphone crowdsensing systems
Given its potential for a large variety of real-life applications, smartphone crowdsensing has recently gained tremendous attention from the research community. Smartphone crowdsensing is a paradigm that allows ordinary citizens to participate in large-scale sensing surveys by using user-friendly applications installed in their smartphones. In this way, fine-grained sensing information is obtained from smartphone users without employing fixed and expensive infrastructure, and with negligible maintenance costs.
Existing smartphone sensing systems depend completely on the participants\u27 willingness to submit up-to-date and accurate information regarding the events being monitored. Therefore, it becomes paramount to scalably and effectively determine, enforce, and optimize the information quality of the sensing reports submitted by the participants. To this end, mechanisms to improve information quality in smartphone crowdsensing systems were designed in this work. Firstly, the FIRST framework is presented, which is a reputation-based mechanism that leverages the concept of mobile trusted participants to determine and improve the information quality of collected data. Secondly, it is mathematically modeled and studied the problem of maximizing the likelihood of successful execution of sensing tasks when participants having uncertain mobility execute sensing tasks. Two incentive mechanisms based on game and auction theory are then proposed to efficiently and scalably solve such problem. Experimental results demonstrate that the mechanisms developed in this thesis outperform existing state of the art in improving information quality in smartphone crowdsensing systems --Abstract, page iii
Towards Incentive Management Mechanisms in the Context of Crowdsensing Technologies based on TrackYourTinnitus Insights
The increased use of mobile devices has led to an improvement in the public health care through participatory interventions. For example, patients were empowered to contribute in treatment processes with the help of mobile crowdsourcing and crowdsensing technologies. However, when using the latter technologies, one prominent challenge constitutes a continuous user engagement. Incentive management techniques can help to tackle this challenge by motivating users through rewards and recognition in exchange of task completion. For this purpose, we aim at developing a conceptual framework that can be integrated with existing mHealth mobile crowdsourcing and crowdsensing platforms. The development of this framework is based on insights we obtained from the TrackYourTinnitus (TYT) mobile crowdsensing platform. TYT, in turn, pursues the goal to reveal insights to the moment-to-moment variability of patients suffering from tinnitus. The work at hands presents evaluated data of TYT and illustrates how the results drive the idea of a conceptual framework for an incentive management in this context. Our results indicate that a proper incentive management should play an important role in the context of any mHealth platform that incorporates the idea of the crowd
Location Contact Tracing: Penetration, Privacy, Position and Performance
The recent COVID-19 pandemic changed radically the world and how people interact, move and behave. Following a lockdown that was imposed worldwide, although with different timing, Mobile Contact Tracing Apps (MCTA) were proposed to digitally trace contacts between individuals, while releasing gradually mobility constraints mandated to contain the disease spread. A general privacy concern on the use of GPS data shifted the efforts towards distributed applications, which use Bluetooth technology to trace proximity and potential infections. Nonetheless, GPS data would help more health operators to understand where hotbeds are, and to what extent the spread is progressing and at what pace. On top of these premises, in this work we take a closer look at the major pillars of MCTA, namely Penetration, Privacy, Position and Performance. We focus on (i) how the penetration rate affects the ability for a tracing applications to work, (ii) the proposal of a novel method of tracing, which build on the GPS technology, (iii) how the position of infections is beneficial to rapidly reduce the infection, and (iv) the discussion of the effects of such paradigm in different scenarios
GreenCrowd: Toward a Holistic Algorithmic Crowd Charging Framework
Crowd charging represents an alternative peer-to-peer energy replenishment option for mobile users to align with the circular economy paradigm. Following this option, users bound by finite resource capacity utilize the energy from external to the crowd wireless or wired energy sources (such as shared chargers), and internal to the crowd energy sources (such as mobile devices, via wireless power transfer). If designed carefully, such utilization can boost the energy availability of users and provide energy ubiquitously to their devices for making them functional for longer. This article proposes the GreenCrowd framework, introducing a privacy-by-design in the digital domain crowd charging process, the architecture of which incorporates multiple crowd-* components, such as online social information exploitation, algorithmic battery aging mitigation, user reward mechanisms, and advanced decision making. The primary aim of article is to present the technological and applicative requirements and constraints of GreenCrowd, and provide practical evidence on its feasibility
Anchor-Assisted and Vote-Based Trustworthiness Assurance in Smart City Crowdsensing
Smart city sensing calls for crowdsensing via mobile devices that are equipped with various built-in sensors. As incentivizing users to participate in distributed sensing is still an open research issue, the trustworthiness of crowdsensed data is expected to be a grand challenge if this cloud-inspired recruitment of sensing services is to be adopted. Recent research proposes reputation-based user recruitment models for crowdsensing; however, there is no standard way of identifying adversaries in smart city crowdsensing. This paper adopts previously proposed vote-based approaches, and presents a thorough performance study of vote-based trustworthiness with trusted entities that are basically a subset of the participating smartphone users. Those entities are called trustworthy anchors of the crowdsensing system. Thus, an anchor user is fully trustworthy and is fully capable of voting for the trustworthiness of other users, who participate in sensing of the same set of phenomena. Besides the anchors, the reputations of regular users are determined based on vote-based (distributed) reputation. We present a detailed performance study of the anchor-based trustworthiness assurance in smart city crowdsensing through simulations, and compare it with the purely vote-based trustworthiness approach without anchors, and a reputation-unaware crowdsensing approach, where user reputations are discarded. Through simulation findings, we aim at providing specifications regarding the impact of anchor and adversary populations on crowdsensing and user utilities under various environmental settings. We show that significant improvement can be achieved in terms of usefulness and trustworthiness of the crowdsensed data if the size of the anchor population is set properl
From Personalized Medicine to Population Health: A Survey of mHealth Sensing Techniques
Mobile Sensing Apps have been widely used as a practical approach to collect
behavioral and health-related information from individuals and provide timely
intervention to promote health and well-beings, such as mental health and
chronic cares. As the objectives of mobile sensing could be either \emph{(a)
personalized medicine for individuals} or \emph{(b) public health for
populations}, in this work we review the design of these mobile sensing apps,
and propose to categorize the design of these apps/systems in two paradigms --
\emph{(i) Personal Sensing} and \emph{(ii) Crowd Sensing} paradigms. While both
sensing paradigms might incorporate with common ubiquitous sensing
technologies, such as wearable sensors, mobility monitoring, mobile data
offloading, and/or cloud-based data analytics to collect and process sensing
data from individuals, we present a novel taxonomy system with two major
components that can specify and classify apps/systems from aspects of the
life-cycle of mHealth Sensing: \emph{(1) Sensing Task Creation \&
Participation}, \emph{(2) Health Surveillance \& Data Collection}, and
\emph{(3) Data Analysis \& Knowledge Discovery}. With respect to different
goals of the two paradigms, this work systematically reviews this field, and
summarizes the design of typical apps/systems in the view of the configurations
and interactions between these two components. In addition to summarization,
the proposed taxonomy system also helps figure out the potential directions of
mobile sensing for health from both personalized medicines and population
health perspectives.Comment: Submitted to a journal for revie
Quality of Information in Mobile Crowdsensing: Survey and Research Challenges
Smartphones have become the most pervasive devices in people's lives, and are
clearly transforming the way we live and perceive technology. Today's
smartphones benefit from almost ubiquitous Internet connectivity and come
equipped with a plethora of inexpensive yet powerful embedded sensors, such as
accelerometer, gyroscope, microphone, and camera. This unique combination has
enabled revolutionary applications based on the mobile crowdsensing paradigm,
such as real-time road traffic monitoring, air and noise pollution, crime
control, and wildlife monitoring, just to name a few. Differently from prior
sensing paradigms, humans are now the primary actors of the sensing process,
since they become fundamental in retrieving reliable and up-to-date information
about the event being monitored. As humans may behave unreliably or
maliciously, assessing and guaranteeing Quality of Information (QoI) becomes
more important than ever. In this paper, we provide a new framework for
defining and enforcing the QoI in mobile crowdsensing, and analyze in depth the
current state-of-the-art on the topic. We also outline novel research
challenges, along with possible directions of future work.Comment: To appear in ACM Transactions on Sensor Networks (TOSN
Crowd-Based Learning of Spatial Fields for the Internet of Things: From Harvesting of Data to Inference
open4siThe knowledge of spatial distributions of physical quantities, such as radio-frequency (RF) interference, pollution, geomagnetic field magnitude, temperature, humidity, audio, and light intensity, will foster the development of new context-aware applications. For example, knowing the distribution of RF interference might significantly improve cognitive radio systems [1], [2]. Similarly, knowing the spatial variations of the geomagnetic field could support autonomous navigation of robots (including drones) in factories and/or hazardous scenarios [3]. Other examples are related to the estimation of temperature gradients, detection of sources of RF signals, or percentages of certain chemical components. As a result, people could get personalized health-related information based on their exposure to sources of risks (e.g., chemical or pollution). We refer to these spatial distributions of physical quantities as spatial fields. All of the aforementioned examples have in common that learning the spatial fields requires a large number of sensors (agents) surveying the area [4], [5].embargoed_20190303Arias-De-Reyna, Eva; Closas, Pau; Dardari, Davide; Djuric, Petar M.Arias-De-Reyna, Eva; Closas, Pau; Dardari, Davide; Djuric, Petar M