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Register a new userBehavior-Based online Incentive Mechanism for Crowd Sensing with Budget Constraints
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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 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.
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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.
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.
Mobile crowdsensing has shown a great potential to address large-scale data sensing problems by allocating sensing tasks to pervasive mobile users. The mobile users will participate in a crowdsensing platform if they can receive satisfactory reward. In this paper, to effectively and efficiently recruit sufficient number of mobile users, i.e., participants, we investigate an optimal incentive mechanism of a crowdsensing service provider. We apply a two-stage Stackelberg game to analyze the participation level of the mobile users and the optimal incentive mechanism of the crowdsensing service provider using backward induction. In order to motivate the participants, the incentive is designed by taking into account the social network effects from the underlying mobile social domain. For example, in a crowdsensing-based road traffic information sharing application, a user can get a better and accurate traffic report if more users join and share their road information. We derive the analytical expressions for the discriminatory incentive as well as the uniform incentive mechanisms. To fit into practical scenarios, we further formulate a Bayesian Stackelberg game with incomplete information to analyze the interaction between the crowdsensing service provider and mobile users, where the social structure information (the social network effects) is uncertain. The existence and uniqueness of the Bayesian Stackelberg equilibrium are validated by identifying the best response strategies of the mobile users. Numerical results corroborate the fact that the network effects tremendously stimulate higher mobile participation level and greater revenue of the crowdsensing service provider. In addition, the social structure information helps the crowdsensing service provider to achieve greater revenue gain.
Federated learning (FL) has shown great potential for addressing the challenge of isolated data islands while preserving data privacy. It allows artificial intelligence (AI) models to be trained on locally stored data in a distributed manner. In order to build an ecosystem for FL to operate in a sustainable manner, it has to be economically attractive to data owners. This gives rise to the problem of FL incentive mechanism design, which aims to find the optimal organizational and payment structure for the federation in order to achieve a series of economic objectives. In this paper, we present a VCG-based FL incentive mechanism, named FVCG, specifically designed for incentivizing data owners to contribute all their data and truthfully report their costs in FL settings. It maximizes the social surplus and minimizes unfairness of the federation. We provide an implementation of FVCG with neural networks and theoretic proofs on its performance bounds. Extensive numerical experiment results demonstrated the effectiveness and economic reasonableness of FVCG.
The rise of fast communication media both at the core and at the edge has resulted in unprecedented numbers of sophisticated and intelligent wireless IoT devices. Tactile Internet has enabled the interaction between humans and machines within their environment to achieve revolutionized solutions both on the move and in real-time. Many applications such as intelligent autonomous self-driving, smart agriculture and industrial solutions, and self-learning multimedia content filtering and sharing have become attainable through cooperative, distributed and decentralized systems, namely, volunteer computing. This article introduces a blockchain-enabled resource sharing and service composition solution through volunteer computing. Device resource, computing, and intelligence capabilities are advertised in the environment to be made discoverable and available for sharing with the aid of blockchain technology. Incentives in the form of on-demand service availability are given to resource and service providers to ensure fair and balanced cooperative resource usage. Blockchains are formed whenever a service request is initiated with the aid of fog and mobile edge computing (MEC) devices to ensure secure communication and service delivery for the participants. Using both volunteer computing techniques and tactile internet architectures, we devise a fast and reliable service provisioning framework that relies on a reinforcement learning technique. Simulation results show that the proposed solution can achieve high reward distribution, increased number of blockchain formations, reduced delays, and balanced resource usage among participants, under the premise of high IoT device availability.
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