Smart vehicles can cooperate in teams to perform crowdsensing tasks in smart cities. A critical challenge in this regard is to build a secure model for nondeterministic vehicle teams to achieve maximum social welfare. Although several crowdsensing models have been proposed, none of them has focused on real-time vehicle teamwork. In this article, to the best of our knowledge, we propose the first secure model, called blockchain-based nondeterministic teamwork cooperation (BNTC), for nondeterministic teamwork cooperation in a vehicular crowdsensing system. We model the system as a multiconditional NP-complete problem by explicitly considering the dynamic features of task issuers and workers. To solve the problem, we propose the winning teams selected (WTS) algorithm based on a reverse auction and utilize a knapsack-based method to solve the models. We consider the credit of teams for determining the payment. Thus, we propose a credit-based team payment (CTP) algorithm for BNTC to maximize the welfare of the system. We also propose a general blockchain-based framework to address trust issues and security challenges to make the method suitable for use in practical applications. Based on theoretical analyses and extensive simulations, we demonstrate that the proposed model performs better than the baselines and can achieve the maximum social welfare. Implementation with Ethereum suggests our model can operate within a reasonable cost.

中文翻译：

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基于区块链的车辆团队合作非确定性人群感知策略模型


智能车辆可以团队协作，在智慧城市中执行群智感知任务。这方面的一个关键挑战是为非确定性车辆团队建立一个安全模型，以实现最大的社会福利。尽管已经提出了几种群体感知模型，但没有一个专注于实时车辆团队合作。在本文中，据我们所知，我们提出了第一个安全模型，称为基于区块链的非确定性团队合作（BNTC），用于车辆众感知系统中的非确定性团队合作。我们通过明确考虑任务发布者和工作人员的动态特征，将系统建模为多条件 NP 完全问题。为了解决这个问题，我们提出了基于逆向拍卖的获胜团队选择（WTS）算法，并利用基于背包的方法来求解模型。我们在确定付款时会考虑团队的信用。因此，我们为 BTC 提出了一种基于信用的团队支付（CTP）算法，以最大化系统的福利。我们还提出了一个基于区块链的通用框架来解决信任问题和安全挑战，使该方法适合在实际应用中使用。基于理论分析和广泛的模拟，我们证明所提出的模型比基线表现更好，并且可以实现最大的社会福利。以太坊的实施表明我们的模型可以在合理的成本内运行。