Mobile crowd sensing (MCS) acts as a key component of Internet of Things (IoT), which has attracted much attention. In an MCS system, participants play an important role, since all the data are collected and provided by them. It is challenging but essential to recruit credible participants and motive them to contribute high-quality data. In this article, we propose a learning-based credible participant recruitment strategy (LC-PRS), which aims to maximize the platform and participants’ profits at the same time via MCS participation. Specifically, the LC-PRS consists of two mechanisms, that a learning-based reward allocation mechanism (L-RAM) first calculates the maximum offered reward for different locations based on the number of participants in each location. Under a budget constraint, the proposed L-RAM prefers to collect sensing data from locations in which relatively few data have so far been collected. Furthermore, for each location, we develop a credible participant recruitment mechanism (C-PRM), which employs semi-Markov model and game theory to predict the quality of data provided by each participant and to recruit participants based on the predictions and the maximum offered reward calculated by L-RAM. We formally show LC-PRS has the desirable properties of computational efficiency, selection efficiency, individual rationality, and truthfulness. We evaluate the proposed scheme via simulation using three real data sets. Extensive simulation results well justify the effectiveness of the proposed approach in comparison with the other two methods.

/pdf/a-learning-based-credible-participant-recruitment-strategy-1uy47c54xb.pdf

A Learning-Based Credible Participant Recruitment Strategy for Mobile Crowd Sensing

In real-world scenarios, in order to encourage one to report others crimes, judicial department usually rents independent cloud storage spaces to receive the precious evidences from whistleblowers. Since the uploaded data are not controlled by cloud users, remote data integrity is very important. Public cloud auditing enables an auditor to periodically check the integrity of outsourcing data on behalf of users, without retrieving the entire data file. However, most existing data auditing schemes have potential security vulnerabilities, and thus cannot defense many security attacks (e.g., the man-in-the-middle attack). Meanwhile, it is significant to protect whistleblower’s identity privacy, reward the real data uploader, and further trace the responsibility of slanders accurately. From the aforementioned requirements, we present an efficient blockchain-assisted conditional anonymity privacy-preserving public auditing (BA-CAPPPA) scheme with reward mechanism. The Ethereum blockchain is integrated into BA-CAPPPA to enhance the security level of the whole public auditing mechanism. Theoretical analysis results show that the BA-CAPPPA achieves man-in-the-middle attack resistance, storage correctness guarantee, data privacy-preservation, conditional identity anonymity, and reward mechanism. Performance evaluations and comparisons demonstrate that BA-CAPPPA could outperform some state-of-the-art data auditing schemes. 

Blockchain-Assisted Conditional Anonymity Privacy-Preserving Public Auditing Scheme With Reward Mechanism

Adversarial training, a special case of multi-objective optimization, is an increasingly useful tool in machine learning. For example, two-player zero-sum games are important for generative modeling (GANs) and for mastering games like Go or Poker via self-play. A classic result in Game Theory states that one must mix strategies, as pure equilibria may not exist. Surprisingly, machine learning practitioners typically train a \emph{single} pair of agents -- instead of a pair of mixtures -- going against Nash's principle. Our main contribution is a notion of limited-capacity-equilibrium for which, as capacity grows, optimal agents -- not mixtures -- can learn increasingly expressive and realistic behaviors. We define \emph{latent games}, a new class of game where agents are mappings that transform latent distributions. Examples include generators in GANs, which transform Gaussian noise into distributions on images, and StarCraft II agents, which transform sampled build orders into policies. We show that minimax equilibria in latent games can be approximated by a \emph{single} pair of dense neural networks. Finally, we apply our latent game approach to solve differentiable Blotto, a game with an infinite strategy space.

Minimax Theorem for Latent Games or: How I Learned to Stop Worrying about Mixed-Nash and Love Neural Nets.

Many real-world cooperative problems can be implemented using Multi-Agent Reinforcement Learning (MARL) techniques, such as urban traffic control or multi-role games. However, the policy learning of MARL algorithms contains features of long-trajectory training and partial observability, which leads to the sparsity of reward and the lack of decision information. To solve the above issues, this article studies hierarchical deep MARL and proposes a novel model named Hierarchical Spatio-Temporal Communication Network (HSTCN). HSTCN designs hierarchical policies with two-time granularities: high-level and low-level policies. All agents are jointly entered into a joint policy containing the above two policies, and each has its execution policy. Specifically, the high-level policy provides intrinsic goals and continuous reward samples for the low-level policy to alleviate reward sparsity. The Low-level policy absorbs the above information to improve the efficiency of the agents' execution policies and interact with the environment to optimize the next reward. What's more, the high-level policy designs a graph-like structural model with Spatio-Temporal abstract. The Spatio-Temporal model expands receptive fields to receive neighborhood information and facilitates learning more robust policies by capturing the underlying graph's spatial dependencies and temporal dynamics. Meanwhile, an evaluation network is added to increase the robustness. Empirically, we demonstrated the effectiveness of HSTCN in a long-trajectory training environment through Simulation of Urban MObility (SUMO), while StarCraft II maps are tested as abstract environment. The above experimental results prove that the performance of HSTCN is superior to other advanced algorithms and verify the rationality of HSTCN design.

Hierarchical Coordination Multi-Agent Reinforcement Learning With Spatio-Temporal Abstraction

Adversarial training, a special case of multi-objective optimization, is an increasingly prevalent machine learning technique: some of its most notable applications include GAN-based generative modeling and self-play techniques in reinforcement learning which have been applied to complex games such as Go or Poker. In practice, a \emph{single} pair of networks is typically trained in order to find an approximate equilibrium of a highly nonconcave-nonconvex adversarial problem. However, while a classic result in game theory states such an equilibrium exists in concave-convex games, there is no analogous guarantee if the payoff is nonconcave-nonconvex. Our main contribution is to provide an approximate minimax theorem for a large class of games where the players pick neural networks including WGAN, StarCraft II, and Blotto Game. Our findings rely on the fact that despite being nonconcave-nonconvex with respect to the neural networks parameters, these games are concave-convex with respect to the actual models (e.g., functions or distributions) represented by these neural networks.

A Limited-Capacity Minimax Theorem for Non-Convex Games or: How I Learned to Stop Worrying about Mixed-Nash and Love Neural Nets.

Nash equilibrium has long been a desired solution concept in multi-player games, especially for those on continuous strategy spaces, which have attracted a rapidly growing amount of interests due to advances in research applications such as the generative adversarial networks. Despite the fact that several deep learning based approaches are designed to obtain pure strategy Nash equilibrium, it is rather luxurious to assume the existence of such an equilibrium. In this paper, we present a new method to approximate mixed strategy Nash equilibria in multi-player continuous games, which always exist and include the pure ones as a special case. We remedy the pure strategy weakness by adopting the pushforward measure technique to represent a mixed strategy in continuous spaces. That allows us to generalize the Gradient-based Nikaido-Isoda (GNI) function to measure the distance between the players' joint strategy profile and a Nash equilibrium. Applying the gradient descent algorithm, our approach is shown to converge to a stationary Nash equilibrium under the convexity assumption on payoff functions, the same popular setting as in previous studies. In numerical experiments, our method consistently and significantly outperforms recent works on approximating Nash equilibrium for quadratic games, general blotto games, and GAMUT games.

/pdf/finding-mixed-strategy-nash-equilibrium-for-continuous-games-4sklxff9su.pdf

Finding Mixed Strategy Nash Equilibrium for Continuous Games through Deep Learning

With mobile devices extensively used in daily life, there are ample opportunities to exchange sensing data through them, even without centralized management. In this paper, we design a peer based data exchanging model, where relay nodes move to certain locations to connect data providers and consumers to facilitate data delivery. Consumers are willing to pay for the data and these rewards are given to both relays and data providers. We first prove the NP-hardness of the problem on how to assign relay nodes to proper locations, and present a centralized optimal method with an approximation ratio. Then we define an autonomous compensation game for relays to make their individual decisions without any central authority. The sufficient and necessary condition for the existence of Nash equilibrium is derived, and an efficient reinforcement learning solver is designed to find the exact forms of equilibria. We analyze and compare this distributed game to the centralized social optimal solution, showing that the game incurs small bounded social costs, and is efficient under various network sizes, number of providers, number of consumers and device mobility.

Incentive Facilitation for Peer Data Exchange in Crowdsensing

ABSTRACT For cooperative mutliagent reinforcement learning tasks, we propose a novel value factorization framework in the popular centralized training with decentralized execution paradigm, called multiagent Q-learning with sub-team coordination (QSCAN). This frame- work could flexibly exploit local coordination within sub-teams for effective factorization while honoring the individual-global-max (IGM) condition. QSCAN encompasses the full spectrum of sub-team coordination according to sub-team size, ranging from the monotonic value function class to the entire IGM function class, with familiar methods such as QMIX and QPLEX located at the respective extremes of the spectrum. Empirical results show that QSCAN’s performance dominates state-of-the-art methods in predator-prey tasks and the Switch challenge in MA-Gym.

Multiagent Q-learning with Sub-Team Coordination

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Finding Mixed Strategy Nash Equilibrium for Continuous Games through Deep Learning

Incentive Facilitation for Peer Data Exchange in Crowdsensing

Multiagent Q-learning with Sub-Team Coordination