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As the high-mobility nature of the vehicles results in frequent leaving and joining the transportation network, real-time data must be collected and shared in a timely manner. In such a transportation network, malicious vehicles can disrupt services and create serious issues, such as deadlocks and accidents. The blockchain is a technology that ensures traceability, consistency, and security in transportation networks. In this study, we integrated edge computing and blockchain technology to improve the optimal utilization of resources, especially in terms of computing, communication, security, and storage. We propose a novel, edge-integrated, blockchain-based vehicle platoon security scheme. For the vehicle platoon, we developed the security architecture, implemented smart contracts for practical network scenarios in network simulator version 3, and integrated them with the simulation urban mobility traffic control interface API. We exhaustively simulated all the scenarios and analyzed the communication performance metrics, such as throughput, delay, and jitter, and the security performance metrics, such as mean squared error, communication, and computational cost. The performance results demonstrate that the developed scheme can solve security-related issues more effectively and efficiently in smart cities.

Decentralized crowdsourcing removes the dependence on a trusted centralized platform based on blockchain and ensures system stability through the consensus of miners. The lack of centralized supervision requires all kinds of system nodes to voluntarily participate while their behaviors are profit-driven and unpredictable, thus introducing challenges to system performance. Moreover, the nodes in a decentralized crowdsourcing system inherently observe little information about the system status; therefore, it is difficult for them to discover and adopt theoretically optimal strategies. Current literature still lacks an effective mechanism to motivate the participation of all types of nodes. To this end, this paper employs a two-layer game model to simulate the interactions in the decentralized crowdsourcing system for investigating the participation willingness of different system nodes. Specifically, we apply a Stackelberg game to model the interactions of a crowdsourcing requester and other nodes, where the requester decides its reward policy and the others respond by selecting their roles to play. In addition, the interaction of the bounded rational other nodes is further represented as an evolutionary game. After analyzing the game model, we further design an incentive mechanism to maximize the requester utility while motivating other nodes to actively participate in the crowdsourcing. Through experimental simulations, we verify the effectiveness of the proposed incentive mechanism.

Blockchain has been applied for decentralized crowdsourcing management by deploying a number of miners to reach a consensus on crowdsourced task allocation and payment decision. In a blockchain-based crowdsourcing system (BCS), incentive becomes essential to motivate the participation and cooperation of all system entities. However, existing literature scarcely investigates how to motivate heterogeneous crowdsourcers, workers, and miners simultaneously toward satisfying multiple objectives without the support of centralized management. In this article, we propose GAIMMO, a novel grade-driven auction-based incentive mechanism for BCS with multiple objectives in mind: crowdsourcer utility maximization, social welfare maximization, social grade maximization, and social cost minimization. Concretely, we propose a grade-based task sorting (GTS) algorithm to determine the service priority of heterogeneous crowdsourcers in order to motivate their cooperative behaviors, which consequently maximizes crowdsourcer utility when combining with the carefully designed utility functions of other system entities. We propose a grade-based utility function of workers and employ a hierarchical premium-based task assignment (PTA) algorithm to realize social welfare maximization, social grade maximization, and social cost minimization. We further propose a fixed-grade-sum and grade-based reward-sharing (FGSGRS) method to encourage fast block generation and motivate high-grade miners without damaging the profits of the crowdsourcers. We conduct simulation-based experiments to show the effectiveness and advance of our proposed incentive mechanism in stimulating the participation willingness of high-grade system entities and achieving the multiple objectives.

Encrypted data deduplication, along with different preferences in data access control, brings the birth of hybrid encrypted cloud data deduplication (H-DEDU for short). However, whether H-DEDU can be successfully deployed in practice has not been seriously investigated. Obviously, the adoption of H-DEDU depends on whether it can bring economic benefits to all stakeholders. But existing economic models of cloud storage fail to support H-DEDU due to complicated interactions among stakeholders. In this article, we establish a formal economic model of H-DEDU by formulating the utilities of all involved stakeholders, i.e., data holders, data owners, and Cloud Storage Providers (CSPs). Then, we construct a multi-stage Stackelberg game, which consists of Holder Participation Game, Owner Online Game, and CSP Pricing Game, to capture the interactions among all system stakeholders. We further analyze the conditions of the existence of a sub-game perfect Nash Equilibrium and propose a gradient-based algorithm to help the stakeholders choose near-optimal strategies. Extensive experiments show the feasibility of the proposed algorithm in achieving the Nash Equilibrium of the Stackelberg game. Additionally, we investigate the effects of parameters related to CSP, data owners and data holders on H-DEDU adoption. Our study advises all stakeholders the best strategies to adopt H-DEDU.

A number of information and resource sharing systems arise and become popular with the rapid development of communication technologies and mobile smart devices. The interactions between humans and machines are intense and their synergistic reactions have attracted special attention for the reason of forming so called Human–Machine Networks (HMN). HMNs refer to these networks where humans and machines work together to provide synergistic effects on their payoffs. Game theory, which can capture the interactions among players dexterously, has been widely used in solving various problems in HMN systems from the view of economics. In this paper, we extensively review the literature about game theoretical methods in HMNs, in particular focusing on its typical systems such as crowdsourcing, an elemental HMN and Internet of Things (IoT), a hybrid HMN, as well as Bitcoin. We propose a series of requirements to evaluate existing work. For reviewing and analyzing each system, we specify application purposes, players, strategies, game models and equilibria based on our proposed requirements. In the sequel, we identify a number of common and distinct open issues in HMNs and point out future research directions.

Duplicated data storage wastes memory resources and brings extra data-management load and cost to cloud service providers (CSPs). Various feasible schemes to deduplicate encrypted cloud data have been reported. However, their successful deployment in practice depends on whether all system players or stakeholders are willing to accept and execute them in a cooperative way, which was scarcely investigated in the previous literature. In this paper, we employ a noncooperative game to model the interactions in a client-side server-controlled deduplication scheme (S-DEDU) and construct an incentive mechanism based on payment discount to motivate its final acceptance. The experimental results based on a real-world dataset demonstrate the individual rationality, incentive compatibility, profitability, and robustness of our incentive mechanism.