Mobile CrowdSensing (MCS) is a novel sensing scenario of Cyber-Physical-Social Systems. MCS has been widely adopted in smart cities, personal health care, and environment monitor areas. MCS applications recruit participants to obtain sensory data from the target area by allocating reward to them. Reward mechanisms are crucial in stimulating participants to join and provide sensory data. However, while the MCS applications execute the reward mechanisms, sensory data and personal private information can be in great danger, because of malicious task initiators/participants and hackers. This work proposes a novel blockchain-based MCS framework that preserves privacy and which secures both the sensing process and the incentive mechanism by leveraging the emergent blockchain technology. Moreover, to provide a fair incentive mechanism, this paper has considered an MCS scenario as a sensory data market, where the market separates the participants into two categories: monthlypay participants and instant-pay participants. By analysing two different kinds of participants and the task initiator, this paper proposes an incentive mechanism aided by a three-stage Stackelberg game. Through theoretical analysis and simulation, the evaluation addresses two aspects: the reward mechanism and the performance of the blockchain-based MCS. The proposed reward mechanism achieves up to a ten percent improvement of the task initiator's utility compared with a traditional Stackelberg game. It can also maintain the required market share for monthly-pay participants whilst achieving sustainable sensory data provision. The evaluation of the blockchain-based MCS shows that the latency increases in a tolerable manner as the number of participants grows. Finally, the paper discusses the future challenges of blockchain-based MCS. Index Terms-Mobile crowdsensing, blockchain, reward mechanism, Stackelberg game, sensory data market I. INTRODUCTION T HE development of network technology, sensing devices, and social networks has increased the deployment of the next generation of Internet of Things (IoT)-Mobile CrowdSensing (MCS). MCS is a novel sensing framework, which is assisted by smartphone sensors and with the inclusion of human intelligence in the loop. MCS has become a typical application in Cyber-Physical-Social Systems (CPSS) ,  because it adopts the multidisciplinary approach where knowledge from communication, computer science, computer network, economic, psychology, and social research unite to provide a solution of a sensing task.
The Internet of Things (IoT) connected by Software Defined Networking (SDN) promises to bring great benefits to cyber-physical systems. However, the increased attack surface offered by the growing number of connected vulnerable devices and separation of SDN control and data planes could overturn the huge benefits of such a system. This paper addresses the vulnerability of the trust relationship between the control and data planes. To meet this aim, we propose an edge computing based blockchain-as-a-service (BaaS), enabled by an external BaaS provider. The proposed solution provides verification of inserted flows through an efficient, edge-distributed, blockchain solution. We study two scenarios for the blockchain reward purpose: (a) information symmetry, in which the SDN operator has direct knowledge of the real effort spent by the BaaS provider; and (b) information asymmetry, in which the BaaS provider controls the exposure of information regarding spent effort. The latter yields the so called "moral hazard", where the BaaS may claim higher than actual effort. We develop a novel mathematical model of the edge BaaS solution; and propose an innovative algorithm of a fair reward scheme based on game theory that takes into account moral hazard. We evaluate the viability of our solution through analytical simulations. The results demonstrate the ability of the proposed algorithm to maximize the joint profits of the BaaS and the SDN operator, i.e. maximizing the social welfare.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.