Human and Artificial Intelligence Driven Incentive-Operation Model and Algorithms for a Multi-Purpose Integrated Crowdsensing-Crowdsourcing Scalable System

Abstract

The future sensing systems seem to need more performant crowdsensing, using highest technologies as artificial intelligence, but being also more complex by volunteer participation and progressively changing from crowdsensing to crowdsourcing. Our work main idea is to use human/artificial intelligence in order to provide highest incentives arguments and commitments for participants and users, transforming data into information and eventually in knowledge. The human/artificial intelligence support is used first to find the most desired/used tasks/targets/questions/issues, then to control the crowdsensing/crowdsourcing operation with learned artificial intelligence rules, based on two algorithms, first for implementing an optimal efficiency tasks covering strategy as reference and second for attracting participants to enlarge/improve accuracy of service by extending crowdsensing-crowdsourcing with correlation incentive/operation added features.