3,152 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
Participatory sensing as an enabler for self-organisation in future cellular networks
In this short review paper we summarise the emerging challenges in the field of participatory sensing for the self-organisation of the next generation of wireless cellular networks. We identify the potential of participatory sensing in enabling the self-organisation, deployment optimisation and radio resource management of wireless cellular networks. We also highlight how this approach can meet the future goals for the next generation of cellular system in terms of infrastructure sharing, management of multiple radio access techniques, flexible usage of spectrum and efficient management of very small data cells
Promoting Truthful Behaviour in Participatory-Sensing Mechanisms
In this paper, the interplay between a class of nonlinear estimators and
strategic sensors is studied in several participatory-sensing scenarios. It is
shown that for the class of estimators, if the strategic sensors have access to
noiseless measurements of the to-be-estimated-variable, truth-telling is an
equilibrium of the game that models the interplay between the sensors and the
estimator. Furthermore, performance of the proposed estimators is examined in
the case that the strategic sensors form coalitions and in the presence of
noise.Comment: IEEE Signal Processing Letters, In Pres
Providing Long-Term Participation Incentive in Participatory Sensing
Providing an adequate long-term participation incentive is important for a
participatory sensing system to maintain enough number of active users
(sensors), so as to collect a sufficient number of data samples and support a
desired level of service quality. In this work, we consider the sensor
selection problem in a general time-dependent and location-aware participatory
sensing system, taking the long-term user participation incentive into explicit
consideration. We study the problem systematically under different information
scenarios, regarding both future information and current information
(realization). In particular, we propose a Lyapunov-based VCG auction policy
for the on-line sensor selection, which converges asymptotically to the optimal
off-line benchmark performance, even with no future information and under
(current) information asymmetry. Extensive numerical results show that our
proposed policy outperforms the state-of-art policies in the literature, in
terms of both user participation (e.g., reducing the user dropping probability
by 25% to 90%) and social performance (e.g., increasing the social welfare by
15% to 80%).Comment: This manuscript serves as the online technical report of the article
published in IEEE International Conference on Computer Communications
(INFOCOM), 201
Studying user behavior through a participatory sensing framework in an urban context
A thesis submitted in partial fulfillment of the requirements for the degree of Doctor in Information Management, specialization in Geographic Information SystemsThe widespread use of mobile devices has given birth to participatory sensing,
a data collection approach leveraging the sheer number of device users, their
mobility, intelligence and device’s increasingly powerful computing and sensing
capabilities. As a result, participatory sensing is able to collect various types of
information at a high spatial and temporal resolution and it has many applications
ranging from measuring cellular signal strength or road condition monitoring to
observing the distribution of birds. However, in order to achieve better results from
participatory sensing, some issues needed to be dealt with. On a high level, this
thesis addressed two issues: (1) the design and development of a participatory
sensing framework that allows users to flexibly create campaigns and at the same
time collect different types of data and (2) the study of different aspects of the user
behaviors in the context of participatory sensing.
In particular, the first contribution of the thesis is the design and development of
Citizense, a participatory sensing framework that facilitates flexible deployments
of participatory sensing campaigns while at the same time providing intuitive
interfaces for users to create sensing campaigns and collect a variety of data
types. During the real-world deployments of Citizense, it has shown its effectiveness
in collecting different types of urban information and subsequently received
appreciation from different stakeholders. The second contribution of the thesis
is the in-depth study of user behavior under the presence of different monetary
incentive mechanisms and the analysis of the spatial and temporal user behavior
when participants are simultaneously exposed to a large number of participatory
sensing campaigns. Concerning the monetary incentive, it is observed that participants
prefer fixed micro-payment to other mechanisms (i.e., lottery, variable
micro-payment); their participation was increased significantly when they were
given this incentive. When taking part in the participatory sensing process, participants exhibit certain spatial and temporal behaviors. They tend to primarily
contribute in their free time during the working week, although the decision to
respond and complete a particular participatory sensing campaign seems to be
correlated to the campaign’s geographical context and/or the recency of the participants’
activities. Participants can be divided into two groups according to their
behaviors: a smaller group of active participants who frequently perform participatory
sensing activities and a larger group of regular participants who exhibit more
intermittent behaviors
Protecting Participatory Sensing Using Cloud Based Trust Management System against Sybil Attack
[[abstract]]Participatory sensing is an innovative model in mobile sensing network which allows volunteers to collect and share information from their local environment by using mobile phones. Unlike other participatory sensing application challenges which consider user privacy and data trustworthiness, we consider the network trustworthiness problem, namely, Sybil attacks, in participatory sensing. A Sybil attack is defined as a malicious illegal presentation of multiple identities, called Sybil identities.These Sybil identities will intend to spread misinformation to reduce the effectiveness of sensing data in the participatory sensing network. To cope with this problem, a cloud based trust management scheme (CbTMS) framework was proposed to detect Sybil attacks in a participatory sensing network. The CbTMS was proffered for performing Sybil attack characteristic checks, in addition to a trustworthiness management system, to verify coverage nodes in participatory sensing. Simulation studies show that the proposed CbTMS can efficiently detect numerous defined malicious Sybil nodes with lower power consumption in the network.[[notice]]補æ£å®Œç•¢[[incitationindex]]SCI[[booktype]]ç´™
A Trust-based Recruitment Framework for Multi-hop Social Participatory Sensing
The idea of social participatory sensing provides a substrate to benefit from
friendship relations in recruiting a critical mass of participants willing to
attend in a sensing campaign. However, the selection of suitable participants
who are trustable and provide high quality contributions is challenging. In
this paper, we propose a recruitment framework for social participatory
sensing. Our framework leverages multi-hop friendship relations to identify and
select suitable and trustworthy participants among friends or friends of
friends, and finds the most trustable paths to them. The framework also
includes a suggestion component which provides a cluster of suggested friends
along with the path to them, which can be further used for recruitment or
friendship establishment. Simulation results demonstrate the efficacy of our
proposed recruitment framework in terms of selecting a large number of
well-suited participants and providing contributions with high overall trust,
in comparison with one-hop recruitment architecture.Comment: accepted in DCOSS 201
PEPSI: Privacy-Enhanced Participatory Sensing Infrastructure.
Participatory Sensing combines the ubiquity of mobile phones with sensing capabilities of Wireless Sensor Networks. It targets pervasive collection of information, e.g., temperature, traffic conditions, or health-related data. As users produce measurements from their mobile devices, voluntary participation becomes essential. However, a number of privacy concerns -- due to the personal information conveyed by data reports -- hinder large-scale deployment of participatory sensing applications. Prior work on privacy protection, for participatory sensing, has often relayed on unrealistic assumptions and with no provably-secure guarantees.
The goal of this project is to introduce PEPSI: a Privacy-Enhanced Participatory Sensing Infrastructure. We explore realistic architectural assumptions and a minimal set of (formal) privacy requirements, aiming at protecting privacy of both data producers and consumers. We design a solution that attains privacy guarantees with provable security at very low additional computational cost and almost no extra communication overhead
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