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Register a new userHow Much Should I Pay for Privacy Concerns in Truthful Online Crowd Sensing?
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Added by
David Sun
Publication date
2013
fields
Informatics Engineering
and research's language is
English
Authors
Jiajun Sun
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Crowd sensing is a new paradigm which leverages the pervasive smartphones to efficiently collect sensing data, enabling numerous novel applications. To achieve good service quality for a crowd sensing application, incentive mechanisms are indispensable to attract more user participation. Most of existing mechanisms only apply for the offline scenario, where the system has full information about the users sensing profiles, i.e., a set of locations or mobility as well as the type of smartphones used, and their true costs. On the contrary, we focus on a more real scenario where users with their own privacy concerns arrive one by one online in a random order. We model the problem as a privacy-respecting online auction in which users are willing to negotiate access to certain private information and submit their sensing profiles satisfying privacy concerns to the platform (the provider of crowd sensing applications) over time, and the platform aims to the total total value of the services provided by selected users under a budget constraint. We then design two online mechanisms for a budgeted crowd sensing application, satisfying the computational efficiency, individual rationality, budget feasibility, truthfulness, consumer sovereignty, constant competitiveness and privacy concerns. Through extensive simulations, we evaluate the performance and validate the theoretical properties of our online mechanisms.
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Crowd sensing is a new paradigm which leverages the ubiquity of sensor-equipped mobile devices to collect data. To achieve good quality for crowd sensing, incentive mechanisms are indispensable to attract more participants. Most of existing mechanisms focus on the expected utility prior to sensing, ignoring the risk of low quality solution and privacy leakage. Traditional incentive mechanisms such as the Vickrey-Clarke-Groves (VCG) mechanism and its variants are not applicable here. In this paper, to address these challenges, we propose a behavior based incentive mechanism for crowd sensing applications with budget constraints by applying sequential all-pay auctions in mobile social networks (MSNs), not only to consider the effects of extensive user participation, but also to maximize high quality of the context based sensing content submission for crowd sensing platform under the budget constraints, where users arrive in a sequential order. Through an extensive simulation, results indicate that incentive mechanisms in our proposed framework outperform the best existing solution.
Crowd sensing is a new paradigm which leverages the pervasive smartphones to efficiently collect and upload sensing data, enabling numerous novel applications. To achieve good service quality for a crowd sensing application, incentive mechanisms are necessary for attracting more user participation. Most of existing mechanisms apply only for the budget-constraint scenario where the platform (the crowd sensing organizer) has a budget limit. On the contrary, we focus on a different scenario where the platform has a service limit. Based on the offline and online auction model, we consider a general problem: users submit their private profiles to the platform, and the platform aims at selecting a subset of users before a specified deadline for minimizing the total payment while a specific service can be completed. Specially, we design offline and online service-constraint incentive mechanisms for the case where the value function of selected users is monotone submodular. The mechanisms are individual rationality, task feasibility, computational efficiency, truthfulness, consumer sovereignty, constant frugality, and also performs well in practice. Finally, we use extensive simulations to demonstrate the theoretical properties of our mechanisms.
Vehicular mobile crowd sensing is a fast-emerging paradigm to collect data about the environment by mounting sensors on vehicles such as taxis. An important problem in vehicular crowd sensing is to design payment mechanisms to incentivize drivers (agents) to collect data, with the overall goal of obtaining the maximum amount of data (across multiple vehicles) for a given budget. Past works on this problem consider a setting where each agent operates in isolation---an assumption which is frequently violated in practice. In this paper, we design an incentive mechanism to incentivize agents who can engage in arbitrary collusions. We then show that in a homogeneous setting, our mechanism is optimal, and can do as well as any mechanism which knows the agents preferences a priori. Moreover, if the agents are non-colluding, then our mechanism automatically does as well as any other non-colluding mechanism. We also show that our proposed mechanism has strong (and asymptotically optimal) guarantees for a more general heterogeneous setting. Experiments based on synthesized data and real-world data reveal gains of over 30% attained by our mechanism compared to past literature.
We consider two-alternative elections where voters preferences depend on a state variable that is not directly observable. Each voter receives a private signal that is correlated to the state variable. Voters may be contingent with different preferences in different states; or predetermined with the same preference in every state. In this setting, even if every voter is a contingent voter, agents voting according to their private information need not result in the adoption of the universally preferred alternative, because the signals can be systematically biased. We present an easy-to-deploy mechanism that elicits and aggregates the private signals from the voters, and outputs the alternative that is favored by the majority. In particular, voters truthfully reporting their signals forms a strong Bayes Nash equilibrium (where no coalition of voters can deviate and receive a better outcome).
Agents (specially humans) with smart devices are stemming with astounding rapidity and that may play a big role in information and communication technology apart from being used only as a mere calling devices. Inculcating the power of smart devices carried by the agents in several different applications is commonly termed as participatory sensing (PS). In this paper, for the first time a truthful quality adaptive participatory sensing is presented in an online double auction environment. The proposed algorithm is simulated with a benchmark mechanism that adapts the existing McAfees Double Auction (MDA) directly in the online environment.
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