305 research outputs found
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
What’s in it for me? Incentive-compatible route coordination of crowdsourced resources
With the recent trend in crowdsourcing, i.e., using the power of crowds to assist in satisfying demand, the pool of resources suitable for GeoPresence-capable systems has expanded to include already roaming devices, such as mobile phones, and moving vehicles. We envision an environment, in which the motion of these crowdsourced mobile resources is coordinated, according to their preexisting schedules to satisfy geo-temporal demand on a mobility field. In this paper, we propose an incentive compatible route coordination mechanism for crowdsourced resources, in which participating mobile agents satisfy geo-temporal requests in return for monetary rewards. We define the Flexible Route Coordination (FRC) problem, in which an agent’s flexibility is exploited to maximize the coverage of a mobility field, with an objective to maximize the revenue collected from satisfied paying requests. Given that the FRC problem is NP-hard, we define an optimal algorithm to plan the route of a single agent on a graph with evolving labels, then we use that algorithm to define a 1/2-approximation algorithm to solve the problem in its general model, with multiple agents. Moreover, we define an incentive compatible, rational, and cash-positive payment mechanism, which guarantees that an agent’s truthfulness about its flexibility is an ex-post Nash equilibrium strategy. Finally, we analyze the proposed mechanisms theoretically, and evaluate their performance experimentally using real mobility traces from urban environments.Supported in part by NSF Grants, #1430145, #1414119, #1347522, #1239021, and #1012798
Incentive-compatible route coordination of crowdsourced resources
Technical ReportWith the recent trend in crowdsourcing, i.e., using the power of crowds to assist in satisfying demand, the pool of resources suitable for GeoPresen-ce-capable systems has expanded to include already roaming devices, such as mobile phones, and moving vehicles. We envision an environment, in
which the motion of these crowdsourced mobile resources is coordinated, according to their preexisting schedules to satisfy geo-temporal demand on a mobility field. In this paper, we propose an incentive compatible route coordination mechanism for crowdsourced resources, in which participating mobile agents satisfy geo-temporal requests in return for monetary rewards. We define the Flexible Route Coordination (FRC) problem, in which an agent’s flexibility is exploited to maximize the coverage of a
mobility field, with an objective to maximize the revenue collected from satisfied paying requests. Given that the FRC problem is NP-hard, we define an optimal algorithm to plan the route of a single agent on a graph with evolving labels, then we use that algorithm to define a 1-approximation algorithm to solve the 2 problem in its general model, with multiple agents. Moreover, we define an incentive compatible, rational, and cash-positive payment mechanism, which guarantees that an agent’s truthfulness about its flexibility is an ex-post Nash equilibrium strategy. Finally, we analyze the proposed mechanisms theoretically, and evaluate their performance experimentally using real mobility traces from urban environments
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
Incentive compatible route coordination of crowdsourced resources and its application to GeoPresence-as-a-Service
With the recent trend in crowdsourcing, i.e., using the power of crowds to assist in satisfying demand, the pool of resources suitable for GeoPresen- ce-capable systems has expanded to include already roaming devices, such as mobile phones, and moving vehicles. We envision an environment, in which the motion of these crowdsourced mobile resources is coordinated, according to their preexisting schedules to satisfy geo-temporal demand on a mobility field. In this paper, we propose an incentive compatible route coordination mechanism for crowdsourced resources, in which participating mobile agents satisfy geo-temporal requests in return for monetary rewards. We define the Flexible Route Coordination (FRC) problem, in which an agent's exibility is exploited to maximize the coverage of a mo- bility field, with an objective to maximize the revenue collected from sat- isfied paying requests. Given that the FRC problem is NP-hard, we define an optimal algorithm to plan the route of a single agent on a graph with evolving labels, then we use that algorithm to define a 1 2 -approximation algorithm to solve the problem in its general model, with multiple agents. Moreover, we define an incentive compatible, rational, and cash-positive payment mechanism, which guarantees that an agent's truthfulness about its exibility is an ex-post Nash equilibrium strategy. Finally, we analyze the proposed mechanisms theoretically, and evaluate their performance experimentally using real mobility traces from urban environments.Supported in part by NSF Grants, #1430145, #1414119, #1347522, #1239021, and #1012798
Social Welfare Maximization Auction in Edge Computing Resource Allocation for Mobile Blockchain
Blockchain, an emerging decentralized security system, has been applied in
many applications, such as bitcoin, smart grid, and Internet-of-Things.
However, running the mining process may cost too much energy consumption and
computing resource usage on handheld devices, which restricts the use of
blockchain in mobile environments. In this paper, we consider deploying edge
computing service to support the mobile blockchain. We propose an auction-based
edge computing resource market of the edge computing service provider. Since
there is competition among miners, the allocative externalities (positive and
negative) are taken into account in the model. In our auction mechanism, we
maximize the social welfare while guaranteeing the truthfulness, individual
rationality and computational efficiency. Based on blockchain mining experiment
results, we define a hash power function that characterizes the probability of
successfully mining a block. Through extensive simulations, we evaluate the
performance of our auction mechanism which shows that our edge computing
resources market model can efficiently solve the social welfare maximization
problem for the edge computing service provider
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