  Crowd-intelligence tries to gather, process, infer and ascertain massive
useful information by utilizing the intelligence of crowds or distributed
computers, which has great potential in Industrial Internet of Things (IIoT). A
crowd-intelligence ecosystem involves three stakeholders, namely the platform,
workers (e.g., individuals, sensors or processors), and task publisher. The
stakeholders have no mutual trust but interest conflict, which means bad
cooperation of them. Due to lack of trust, transferring raw data (e.g.,
pictures or video clips) between publisher and workers requires the remote
platform center to serve as a relay node, which implies network congestion.
First we use a reward-penalty model to align the incentives of stakeholders.
Then the predefined rules are implemented using blockchain smart contract on
many edge servers of the mobile edge computing network, which together function
as a trustless hybrid human-machine crowd-intelligence platform. As edge
servers are near to workers and publisher, network congestion can be
effectively improved. Further, we proved the existence of the only one strong
Nash equilibrium, which can maximize the interests of involved edge servers and
make the ecosystem bigger. Theoretical analysis and experiments validate the
proposed method respectively.
